The document discusses computer aided drug design (CADD) for the M2 protein in H1N1 influenza virus. It provides background on CADD, H1N1, and the M2 protein. The study aims to use computational docking methods to screen a library of 100 ligands to identify inhibitors that bind to the M2 protein, in order to inhibit the virus. The results demonstrate that high performance computing technologies can be useful for applied drug design.
COMPUTATIONAL TOOLS FOR PREDICTION OF NUCLEAR RECEPTOR MEDIATED EFFECTSEAJOA
Endocrine disrupting chemicals pose a significant threat to human health, society and the environment. Many of these chemicals elicit their toxicological effects through nuclear hormone receptors, like the estrogen receptor. Computational tools for predicting receptor mediated effects have been envisaged for their potential to be used for prioritization of chemicals for toxicological evaluation to reduce the amount of costly experimental testing and enable early alerts for newly designed compounds.
Suresh Gopalan Reapply Cross Regulation of Immune Responses_NIH Grant Applica...Suresh Gopalan
This grant application (reapplied wit clarification) hypothesized that RNA interference (RNAi) is an operative mechanism against viral pathogens in humans and other mammals. Later proven right in back-to-back articles in Science magazine 11 October 2013: Vol. 342 no. 6155. This application further proposed to understand cross-regulation of multiple immune mechanisms against viral and bacterial infections using model organisms. Such experiments cannot be conducted with such ease and clarity in mammals/humans - but will uncover novel ways to regulate them to our advantage. The reapplication clarified many questions on hypothesis synthesized with limited data available.
Dana Vanderwall, Associate Director of Cheminformatics at Bristol-Myers Squibb, presented at Drexel University for Jean-Claude Bradley's Chemical Information Retrieval class on December 2, 2010. This first part covers "Cheminformatics & The evolving relationship between data in the public domain & pharma" and includes a general discussion of modern drug discovery and the details of a malaria dataset recently released from the pharmaceutical industry to the public.
Collaborative Drug Discovery: A Platform For Transforming Neglected Disease R...Sean Ekins
The document discusses the Collaborative Drug Discovery (CDD) platform, which aims to facilitate data sharing and collaboration in drug discovery. Key points:
- CDD allows users to securely store private data while selectively sharing subsets with collaborators. It also hosts public datasets totaling over 3 million compounds.
- CDD has been used to facilitate collaboration in neglected disease research, particularly for tuberculosis and malaria. It hosts over 15 public TB datasets totaling over 300,000 compounds.
- Analysis of TB and malaria hit compounds on the platform shows generally higher molecular weights and logP values compared to approved drugs. Many compounds also fail filtering for undesirable reactivity.
This document discusses bioinformatics and its applications in vaccine discovery. It begins with an introduction to bioinformatics, describing it as an interdisciplinary field that develops tools to analyze biological data using computer science, mathematics, and statistics. It then discusses the objectives and need for bioinformatics, as well as important bioinformatics databases. Next, it provides an overview of the concept of bioinformatics and how it has expanded from analyzing sequence data to include modeling and other areas. Finally, it details the impact of bioinformatics on vaccine discovery through approaches like reverse vaccinology, immunoinformatics, and structural vaccinology that use bioinformatics to select antigens and design new generation vaccines.
This document discusses issues with combinatorial chemistry techniques used in drug discovery and alternatives to increase molecular diversity. Specifically, it addresses the problems of screening large libraries of compounds produced by combinatorial synthesis. It proposes using improved virtual docking software that incorporates flexibility of drug targets to more accurately model binding and identify potentially active compounds. The document also reviews literature on fixing problems associated with large combinatorial libraries, such as using analytical techniques and docking simulations to search libraries and determine compound diversity.
2013-Blomquist-Targeted RNA-sequencing with competitive multiplex-PCR amplico...Ji-Youn Yeo
This document describes a new targeted RNA sequencing method using competitive multiplex PCR to generate amplicon libraries. It aims to address limitations of existing targeted RNA sequencing approaches by 1) controlling for inter-library variation in measurement of transcript expression, and 2) reducing the large number of sequencing reads required to quantify transcripts across a wide range of expression. The method involves amplifying native RNA targets alongside known quantities of competitive internal standard templates. This normalization approach causes amplification products to converge toward equimolar concentrations, improving reproducibility and allowing accurate quantification of transcripts using fewer total sequencing reads. Validation studies demonstrated excellent reproducibility, concordance with other methods, and ability to quantify over 100 transcripts across a 107-fold expression range using only 1.46105 sequencing
Powering Scientific Discovery with the Semantic Web (VanBUG 2014)Michel Dumontier
The document discusses how the semantic web can help power scientific discovery. It proposes building a massive network of interconnected data and software using web standards to 1) generate and test hypotheses by discovering associations in the data, 2) gather evidence to support or dispute hypotheses, and 3) contribute new knowledge back to the global network. This network, called the semantic web, treats data as a web of facts that can be shared and queried using semantic web standards. The document provides examples of how linked open data in the life sciences is being created and used via semantic web technologies to integrate data from multiple sources and answer complex queries.
COMPUTATIONAL TOOLS FOR PREDICTION OF NUCLEAR RECEPTOR MEDIATED EFFECTSEAJOA
Endocrine disrupting chemicals pose a significant threat to human health, society and the environment. Many of these chemicals elicit their toxicological effects through nuclear hormone receptors, like the estrogen receptor. Computational tools for predicting receptor mediated effects have been envisaged for their potential to be used for prioritization of chemicals for toxicological evaluation to reduce the amount of costly experimental testing and enable early alerts for newly designed compounds.
Suresh Gopalan Reapply Cross Regulation of Immune Responses_NIH Grant Applica...Suresh Gopalan
This grant application (reapplied wit clarification) hypothesized that RNA interference (RNAi) is an operative mechanism against viral pathogens in humans and other mammals. Later proven right in back-to-back articles in Science magazine 11 October 2013: Vol. 342 no. 6155. This application further proposed to understand cross-regulation of multiple immune mechanisms against viral and bacterial infections using model organisms. Such experiments cannot be conducted with such ease and clarity in mammals/humans - but will uncover novel ways to regulate them to our advantage. The reapplication clarified many questions on hypothesis synthesized with limited data available.
Dana Vanderwall, Associate Director of Cheminformatics at Bristol-Myers Squibb, presented at Drexel University for Jean-Claude Bradley's Chemical Information Retrieval class on December 2, 2010. This first part covers "Cheminformatics & The evolving relationship between data in the public domain & pharma" and includes a general discussion of modern drug discovery and the details of a malaria dataset recently released from the pharmaceutical industry to the public.
Collaborative Drug Discovery: A Platform For Transforming Neglected Disease R...Sean Ekins
The document discusses the Collaborative Drug Discovery (CDD) platform, which aims to facilitate data sharing and collaboration in drug discovery. Key points:
- CDD allows users to securely store private data while selectively sharing subsets with collaborators. It also hosts public datasets totaling over 3 million compounds.
- CDD has been used to facilitate collaboration in neglected disease research, particularly for tuberculosis and malaria. It hosts over 15 public TB datasets totaling over 300,000 compounds.
- Analysis of TB and malaria hit compounds on the platform shows generally higher molecular weights and logP values compared to approved drugs. Many compounds also fail filtering for undesirable reactivity.
This document discusses bioinformatics and its applications in vaccine discovery. It begins with an introduction to bioinformatics, describing it as an interdisciplinary field that develops tools to analyze biological data using computer science, mathematics, and statistics. It then discusses the objectives and need for bioinformatics, as well as important bioinformatics databases. Next, it provides an overview of the concept of bioinformatics and how it has expanded from analyzing sequence data to include modeling and other areas. Finally, it details the impact of bioinformatics on vaccine discovery through approaches like reverse vaccinology, immunoinformatics, and structural vaccinology that use bioinformatics to select antigens and design new generation vaccines.
This document discusses issues with combinatorial chemistry techniques used in drug discovery and alternatives to increase molecular diversity. Specifically, it addresses the problems of screening large libraries of compounds produced by combinatorial synthesis. It proposes using improved virtual docking software that incorporates flexibility of drug targets to more accurately model binding and identify potentially active compounds. The document also reviews literature on fixing problems associated with large combinatorial libraries, such as using analytical techniques and docking simulations to search libraries and determine compound diversity.
2013-Blomquist-Targeted RNA-sequencing with competitive multiplex-PCR amplico...Ji-Youn Yeo
This document describes a new targeted RNA sequencing method using competitive multiplex PCR to generate amplicon libraries. It aims to address limitations of existing targeted RNA sequencing approaches by 1) controlling for inter-library variation in measurement of transcript expression, and 2) reducing the large number of sequencing reads required to quantify transcripts across a wide range of expression. The method involves amplifying native RNA targets alongside known quantities of competitive internal standard templates. This normalization approach causes amplification products to converge toward equimolar concentrations, improving reproducibility and allowing accurate quantification of transcripts using fewer total sequencing reads. Validation studies demonstrated excellent reproducibility, concordance with other methods, and ability to quantify over 100 transcripts across a 107-fold expression range using only 1.46105 sequencing
Powering Scientific Discovery with the Semantic Web (VanBUG 2014)Michel Dumontier
The document discusses how the semantic web can help power scientific discovery. It proposes building a massive network of interconnected data and software using web standards to 1) generate and test hypotheses by discovering associations in the data, 2) gather evidence to support or dispute hypotheses, and 3) contribute new knowledge back to the global network. This network, called the semantic web, treats data as a web of facts that can be shared and queried using semantic web standards. The document provides examples of how linked open data in the life sciences is being created and used via semantic web technologies to integrate data from multiple sources and answer complex queries.
This document provides information about the 4th Plant Genomics and Gene Editing Asia Congress to be held on April 10-11, 2017 in Hong Kong. Over the past 5 years, plant research has been transformed by breakthroughs in sequencing technologies and data analysis. The conference will bring together over 200 experts working in plant science to discuss the latest NGS, omics, and gene editing technologies and their applications in plant research. Presentations will focus on regional crops and cover topics like genome editing, phenomics, disease resistance, and bioinformatics. The goal is to facilitate knowledge sharing and networking between researchers using these techniques and those looking to adopt new technologies and analysis approaches.
Collaboraive sharing of molecules and data in the mobile ageSean Ekins
The document discusses collaborative drug discovery and the use of mobile applications in chemistry. It describes how the Collaborative Drug Discovery (CDD) platform allows researchers to securely share molecules and data. Examples are provided of collaborations between academic labs and pharmaceutical companies using the CDD vault to work on projects related to tuberculosis drug development. The rise of mobile devices is creating new opportunities for chemistry applications to enable collaborative workflows involving tasks like structure drawing, database searching, and data sharing from any location.
With advances in technology, enormous amounts of data have become available for bioscience researchers. While this high volume of information holds tremendous promise for expanding the science knowledge base, it must be organized for meaningful study. Bioinformatics is a discipline that devises methods for storing, distributing, and analyzing biological data used by diverse areas of research. Bioinformatics professionals develop software and tools that assist researchers in the analysis of data related to molecular biology and genome studies.
BOUNCER: A Privacy-aware Query Processing Over Federations of RDF DatasetsKemele M. Endris
BOUNCER is a privacy-aware query engine for federations of RDF datasets that respects individual privacy and access control policies. It describes data sources using Privacy-aware RDF Molecule Templates (PRDF-MTs) that specify classes, predicates, access control operations, and links between templates. BOUNCER decomposes queries into star-shaped subqueries aligned with PRDF-MTs, selects sources allowing required operations, and generates bushy query plans respecting source policies. An evaluation found BOUNCER incurs overhead enforcing policies but can identify more efficient valid plans than existing engines. It was concluded BOUNCER provides a means to describe source policies and guarantees generating valid privacy-respecting query plans.
Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). This project was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an external validation set collected from the literature. In order to overcome the limitations of single models, a consensus was built weighting models based on their prediction accuracy scores (including sensitivity and specificity against training and external sets). Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. The final consensus predicted 4001 chemicals as actives to be considered as high priority for further testing and 6742 as suspicious chemicals. This abstract does not necessarily reflect U.S. EPA policy
Nonadaptive mastermind algorithms for string and vector databases, with case ...Ecway Technologies
This paper studies nonadaptive Mastermind algorithms for attacking the privacy of string and vector databases like DNA strings, movie ratings, and social network data. The algorithms can take advantage of minimal privacy leaks, like whether two people share any genetic mutations or common friends. The attacks are analyzed theoretically and experimentally on genomic, recommendation, and social network data. By exploiting the sparse nature of real-world databases and modulating query sparsity, the paper shows relatively few nonadaptive queries are needed to recover a large portion of each database.
Nc state lecture v2 Computational ToxicologySean Ekins
The document discusses computational approaches to modeling various aspects of toxicology, including physicochemical properties, quantitative structure-activity relationships, and interactions with proteins and pathways involved in toxicity. It provides examples of modeling properties like solubility and lipophilicity, as well as targets like cytochrome P450 enzymes and the pregnane X receptor. Statistical methodologies for building predictive models are also reviewed. The future of crowdsourced drug discovery is briefly mentioned.
The document discusses Lundbeck's use of open source software in pharmaceutical research and informatics. It describes how Lundbeck uses open source tools like Linux, Ruby on Rails, and GNU Scientific Library to build its in-house research platform LSP. It also discusses Lundbeck's involvement in pre-competitive collaborations through initiatives like Pistoia Alliance and IMI, which aim to improve data sharing and interoperability through open source solutions. The document advocates for more use of open standards and semantic technologies to better integrate diverse data sources and enable collaborative research.
Cresset is a computational chemistry company that provides software and consulting services to pharmaceutical and biotech companies. It has over 15 years of experience, 270+ projects delivered globally, and customers in North America, Europe, India, China, Japan and Korea. Cresset's expert computational chemists use ligand-based and structure-based modeling workflows to help customers gain insights into protein-ligand binding and design new molecules. Case studies demonstrate how Cresset has helped discover new hits and develop patents for clients. Testimonials from CEOs and heads of research praise Cresset's knowledge, collaboration, and ability to advance drug discovery projects.
Bioinformatics plays a key role in drug discovery by enabling researchers to efficiently analyze large amounts of biological data and computationally simulate drug-target interactions. Some important applications of bioinformatics in drug discovery include virtual high-throughput screening of compound libraries against protein targets to identify potential drug leads, analyzing genetic and protein sequences to infer evolutionary relationships and identify drug targets, and using homology modeling to predict the 3D structures of targets to aid in drug design when experimental structures are unknown.
1. Bioinformatics uses computer science and information technology to analyze biological data and assist with drug discovery. It helps identify drug targets and design drug candidates.
2. The drug design process involves identifying a disease target, studying compounds of interest, detecting molecular disease bases, rational drug design, refinement, and testing. Bioinformatics tools assist with each step.
3. CADD uses computational methods to simulate drug-receptor interactions and is heavily dependent on bioinformatics tools and databases. It supports techniques like virtual screening, sequence analysis, homology modeling, and physicochemical modeling to aid drug development.
In spite of extensive effort by industry and academia to develop new drugs, there are still several diseases that are in need of therapeutic agents and have yet to be developed.
10 years the identification rate of disease-associated targets has been higher than the therapeutics identification rate.
Nevertheless, it is apparent that computational tools provide high hopes that many of the diseases under investigation can be brought under control.
Computer Added Drug Design is one of the latest technology of medicine world. This short slide will help you to know a little about CADD.If you want to know a vast plz go throw the reference book.
This document provides a summary of a student assignment on drug design and toxicology. It discusses several topics:
1) It outlines the drug design process and different types of drug design approaches, including ligand-based and structure-based design.
2) It discusses the importance of toxicology testing in drug development to evaluate safety. Topics covered include emerging safety biomarkers, establishing human first-dose levels, pathway analysis, and genomic biomarker usage.
3) It explores various dynamic QSAR techniques and their applications in drug design and toxicology, as well as examining ADME and toxicology relationships.
New Drug Design & Discovery discusses the process of drug discovery and design. It begins with an introduction to how drugs work in the human body to modulate functions. The drug discovery process is then described as a long, expensive endeavor involving chemical synthesis, clinical development, and formulation. Computer-aided drug design uses molecular modeling and structure-based approaches to predict ligand-receptor binding and identify biological targets in silico. Combinatorial chemistry and high-throughput screening allow for the rapid synthesis and testing of large libraries of compounds. The goal is to develop more potent and safer drugs through these computational and high-throughput methods.
Bioinformatics role in Pharmaceutical industriesMuzna Kashaf
Bioinformatics plays a key role in the pharmaceutical industry by enabling target identification of diseases, rational drug design, compound refinement, and other processes. It facilitates identifying target diseases and compounds, detecting molecular bases of diseases, designing drugs, refining compounds, and testing drug solubility and effects. Bioinformatics supports various stages of drug development including formulation, crystallization determination, polymer modeling, and testing before human use. Its integration into the pharmaceutical industry supports drug discovery, healthcare advances, and realizing the promises of projects like the Human Genome Project.
The document discusses the applications of bioinformatics in drug discovery. It describes how bioinformatics supports computer-aided drug design through computational methods to simulate drug-receptor interactions. It also discusses how virtual high-throughput screening can identify compounds that strongly bind to protein targets. The document outlines the key steps in drug design, including identifying the disease target, studying lead compounds, rational drug design techniques, and testing drugs. It emphasizes that bioinformatics can predict important drug characteristics like absorption and toxicity to save costs during development.
Structure-based drug design (SBDD) is a computational approach that uses the 3D structure of target proteins to guide the design of potential drug molecules. SBDD leverages knowledge of molecular interactions between drugs and proteins to design drugs more likely to bind to targets and exert therapeutic effects. Computational techniques like molecular docking, dynamics simulations, and virtual screening are used to model interactions and screen large libraries of compounds. Laboratory methods like X-ray crystallography and NMR spectroscopy provide protein structures to inform computational modeling. SBDD has potential to increase drug discovery efficiency and success rates by enabling rational drug design focused on target binding and properties.
Bioinformatics plays an important role in drug discovery and development by enabling target identification, rational drug design, compound refinement, and other processes. Key applications of bioinformatics include virtual screening of large compound libraries to identify potential drug leads, homology modeling of protein structures to inform drug design, and similarity searches to find analogs of existing drug molecules. The overall drug development process involves studying the disease, identifying drug targets, designing compounds, testing and refining candidates, and conducting clinical trials. Computational techniques expedite many steps but experimental validation is still needed.
Docking studies on synthesized quinazoline compoundssrirampharma
The document summarizes docking studies performed on ten synthesized quinazoline compounds against the androgen receptor. Two compounds, 1e and 1g, were found to have the most favorable binding energies of -8.45 kcal/mol and -8.21 kcal/mol, respectively. Both compounds docked at the methionine pockets of the receptor. The study suggests compounds 1e and 1g may have potent anti-cancer activity against prostate cancer by binding well to the androgen receptor.
This document discusses structure-based drug design. It begins by explaining that structure-based drug design relies on knowledge of the three-dimensional structure of biological targets, usually determined through methods like X-ray crystallography. The structure of the target is then used to design ligands that will bind to the target. The process involves identifying drug targets, determining the target's structure, performing computer-aided drug design to identify potential binding ligands, and building or modifying ligands to optimize binding to the target.
This document provides information about the 4th Plant Genomics and Gene Editing Asia Congress to be held on April 10-11, 2017 in Hong Kong. Over the past 5 years, plant research has been transformed by breakthroughs in sequencing technologies and data analysis. The conference will bring together over 200 experts working in plant science to discuss the latest NGS, omics, and gene editing technologies and their applications in plant research. Presentations will focus on regional crops and cover topics like genome editing, phenomics, disease resistance, and bioinformatics. The goal is to facilitate knowledge sharing and networking between researchers using these techniques and those looking to adopt new technologies and analysis approaches.
Collaboraive sharing of molecules and data in the mobile ageSean Ekins
The document discusses collaborative drug discovery and the use of mobile applications in chemistry. It describes how the Collaborative Drug Discovery (CDD) platform allows researchers to securely share molecules and data. Examples are provided of collaborations between academic labs and pharmaceutical companies using the CDD vault to work on projects related to tuberculosis drug development. The rise of mobile devices is creating new opportunities for chemistry applications to enable collaborative workflows involving tasks like structure drawing, database searching, and data sharing from any location.
With advances in technology, enormous amounts of data have become available for bioscience researchers. While this high volume of information holds tremendous promise for expanding the science knowledge base, it must be organized for meaningful study. Bioinformatics is a discipline that devises methods for storing, distributing, and analyzing biological data used by diverse areas of research. Bioinformatics professionals develop software and tools that assist researchers in the analysis of data related to molecular biology and genome studies.
BOUNCER: A Privacy-aware Query Processing Over Federations of RDF DatasetsKemele M. Endris
BOUNCER is a privacy-aware query engine for federations of RDF datasets that respects individual privacy and access control policies. It describes data sources using Privacy-aware RDF Molecule Templates (PRDF-MTs) that specify classes, predicates, access control operations, and links between templates. BOUNCER decomposes queries into star-shaped subqueries aligned with PRDF-MTs, selects sources allowing required operations, and generates bushy query plans respecting source policies. An evaluation found BOUNCER incurs overhead enforcing policies but can identify more efficient valid plans than existing engines. It was concluded BOUNCER provides a means to describe source policies and guarantees generating valid privacy-respecting query plans.
Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). This project was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an external validation set collected from the literature. In order to overcome the limitations of single models, a consensus was built weighting models based on their prediction accuracy scores (including sensitivity and specificity against training and external sets). Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. The final consensus predicted 4001 chemicals as actives to be considered as high priority for further testing and 6742 as suspicious chemicals. This abstract does not necessarily reflect U.S. EPA policy
Nonadaptive mastermind algorithms for string and vector databases, with case ...Ecway Technologies
This paper studies nonadaptive Mastermind algorithms for attacking the privacy of string and vector databases like DNA strings, movie ratings, and social network data. The algorithms can take advantage of minimal privacy leaks, like whether two people share any genetic mutations or common friends. The attacks are analyzed theoretically and experimentally on genomic, recommendation, and social network data. By exploiting the sparse nature of real-world databases and modulating query sparsity, the paper shows relatively few nonadaptive queries are needed to recover a large portion of each database.
Nc state lecture v2 Computational ToxicologySean Ekins
The document discusses computational approaches to modeling various aspects of toxicology, including physicochemical properties, quantitative structure-activity relationships, and interactions with proteins and pathways involved in toxicity. It provides examples of modeling properties like solubility and lipophilicity, as well as targets like cytochrome P450 enzymes and the pregnane X receptor. Statistical methodologies for building predictive models are also reviewed. The future of crowdsourced drug discovery is briefly mentioned.
The document discusses Lundbeck's use of open source software in pharmaceutical research and informatics. It describes how Lundbeck uses open source tools like Linux, Ruby on Rails, and GNU Scientific Library to build its in-house research platform LSP. It also discusses Lundbeck's involvement in pre-competitive collaborations through initiatives like Pistoia Alliance and IMI, which aim to improve data sharing and interoperability through open source solutions. The document advocates for more use of open standards and semantic technologies to better integrate diverse data sources and enable collaborative research.
Cresset is a computational chemistry company that provides software and consulting services to pharmaceutical and biotech companies. It has over 15 years of experience, 270+ projects delivered globally, and customers in North America, Europe, India, China, Japan and Korea. Cresset's expert computational chemists use ligand-based and structure-based modeling workflows to help customers gain insights into protein-ligand binding and design new molecules. Case studies demonstrate how Cresset has helped discover new hits and develop patents for clients. Testimonials from CEOs and heads of research praise Cresset's knowledge, collaboration, and ability to advance drug discovery projects.
Bioinformatics plays a key role in drug discovery by enabling researchers to efficiently analyze large amounts of biological data and computationally simulate drug-target interactions. Some important applications of bioinformatics in drug discovery include virtual high-throughput screening of compound libraries against protein targets to identify potential drug leads, analyzing genetic and protein sequences to infer evolutionary relationships and identify drug targets, and using homology modeling to predict the 3D structures of targets to aid in drug design when experimental structures are unknown.
1. Bioinformatics uses computer science and information technology to analyze biological data and assist with drug discovery. It helps identify drug targets and design drug candidates.
2. The drug design process involves identifying a disease target, studying compounds of interest, detecting molecular disease bases, rational drug design, refinement, and testing. Bioinformatics tools assist with each step.
3. CADD uses computational methods to simulate drug-receptor interactions and is heavily dependent on bioinformatics tools and databases. It supports techniques like virtual screening, sequence analysis, homology modeling, and physicochemical modeling to aid drug development.
In spite of extensive effort by industry and academia to develop new drugs, there are still several diseases that are in need of therapeutic agents and have yet to be developed.
10 years the identification rate of disease-associated targets has been higher than the therapeutics identification rate.
Nevertheless, it is apparent that computational tools provide high hopes that many of the diseases under investigation can be brought under control.
Computer Added Drug Design is one of the latest technology of medicine world. This short slide will help you to know a little about CADD.If you want to know a vast plz go throw the reference book.
This document provides a summary of a student assignment on drug design and toxicology. It discusses several topics:
1) It outlines the drug design process and different types of drug design approaches, including ligand-based and structure-based design.
2) It discusses the importance of toxicology testing in drug development to evaluate safety. Topics covered include emerging safety biomarkers, establishing human first-dose levels, pathway analysis, and genomic biomarker usage.
3) It explores various dynamic QSAR techniques and their applications in drug design and toxicology, as well as examining ADME and toxicology relationships.
New Drug Design & Discovery discusses the process of drug discovery and design. It begins with an introduction to how drugs work in the human body to modulate functions. The drug discovery process is then described as a long, expensive endeavor involving chemical synthesis, clinical development, and formulation. Computer-aided drug design uses molecular modeling and structure-based approaches to predict ligand-receptor binding and identify biological targets in silico. Combinatorial chemistry and high-throughput screening allow for the rapid synthesis and testing of large libraries of compounds. The goal is to develop more potent and safer drugs through these computational and high-throughput methods.
Bioinformatics role in Pharmaceutical industriesMuzna Kashaf
Bioinformatics plays a key role in the pharmaceutical industry by enabling target identification of diseases, rational drug design, compound refinement, and other processes. It facilitates identifying target diseases and compounds, detecting molecular bases of diseases, designing drugs, refining compounds, and testing drug solubility and effects. Bioinformatics supports various stages of drug development including formulation, crystallization determination, polymer modeling, and testing before human use. Its integration into the pharmaceutical industry supports drug discovery, healthcare advances, and realizing the promises of projects like the Human Genome Project.
The document discusses the applications of bioinformatics in drug discovery. It describes how bioinformatics supports computer-aided drug design through computational methods to simulate drug-receptor interactions. It also discusses how virtual high-throughput screening can identify compounds that strongly bind to protein targets. The document outlines the key steps in drug design, including identifying the disease target, studying lead compounds, rational drug design techniques, and testing drugs. It emphasizes that bioinformatics can predict important drug characteristics like absorption and toxicity to save costs during development.
Structure-based drug design (SBDD) is a computational approach that uses the 3D structure of target proteins to guide the design of potential drug molecules. SBDD leverages knowledge of molecular interactions between drugs and proteins to design drugs more likely to bind to targets and exert therapeutic effects. Computational techniques like molecular docking, dynamics simulations, and virtual screening are used to model interactions and screen large libraries of compounds. Laboratory methods like X-ray crystallography and NMR spectroscopy provide protein structures to inform computational modeling. SBDD has potential to increase drug discovery efficiency and success rates by enabling rational drug design focused on target binding and properties.
Bioinformatics plays an important role in drug discovery and development by enabling target identification, rational drug design, compound refinement, and other processes. Key applications of bioinformatics include virtual screening of large compound libraries to identify potential drug leads, homology modeling of protein structures to inform drug design, and similarity searches to find analogs of existing drug molecules. The overall drug development process involves studying the disease, identifying drug targets, designing compounds, testing and refining candidates, and conducting clinical trials. Computational techniques expedite many steps but experimental validation is still needed.
Docking studies on synthesized quinazoline compoundssrirampharma
The document summarizes docking studies performed on ten synthesized quinazoline compounds against the androgen receptor. Two compounds, 1e and 1g, were found to have the most favorable binding energies of -8.45 kcal/mol and -8.21 kcal/mol, respectively. Both compounds docked at the methionine pockets of the receptor. The study suggests compounds 1e and 1g may have potent anti-cancer activity against prostate cancer by binding well to the androgen receptor.
This document discusses structure-based drug design. It begins by explaining that structure-based drug design relies on knowledge of the three-dimensional structure of biological targets, usually determined through methods like X-ray crystallography. The structure of the target is then used to design ligands that will bind to the target. The process involves identifying drug targets, determining the target's structure, performing computer-aided drug design to identify potential binding ligands, and building or modifying ligands to optimize binding to the target.
Computer aided drug design uses computational approaches to aid in the drug discovery process. There are several key approaches including ligand based approaches which identify characteristics of known active ligands, target based approaches which use information about the biological target, and structure based drug design which utilizes 3D structural information. The main steps in drug design include target identification and validation, lead identification and optimization, and preclinical and clinical trials. Computational tools are used throughout the process for tasks like molecular docking, ADMET prediction, and structure activity relationship analysis.
Biotechnology And Chemical Weapons Controlguest971b1073
Biotechnology has the potential to both aid medical research and fuel chemical weapons proliferation. Advances like genomics, microarrays, proteomics, and combinatorial chemistry could be used to rationally design new drugs, but also new chemical weapons. This threatens the Chemical Weapons Convention by enabling covert development of novel agents from unscheduled precursors. Additionally, development of non-lethal chemical weapons could provide cover for lethal programs and erode norms against chemical weapons use. The Review Conference should address these challenges to strengthen the Convention.
This document discusses molecular docking and its role in modern drug discovery. It begins with an introduction to docking and abbreviations used. It then explains that docking attempts to find the best match between two molecules and discusses how molecular docking is used in structure-based drug design. Applications of molecular docking mentioned include virtual screening to identify potential drug candidates from large libraries and optimizing ligands through studying their binding geometries with target proteins. The document concludes that molecular docking makes promising contributions to drug discovery by aiding in lead identification and optimization.
Data Mining and Big Data Analytics in Pharma Ankur Khanna
The document proposes software solutions for drug research, including text mining, data warehousing, data mining, database development, and big data analytics. It discusses common challenges in drug research like the high costs and low success rates. It then describes various solutions like text mining patents and research to help identify new research opportunities and reduce duplication of efforts. It provides examples of how various pharmaceutical companies use data mining and warehousing techniques. Overall, the document pitches different IT solutions that can help pharmaceutical and life sciences companies address their research challenges and make their processes more efficient.
1) AI systems like Adam and Eve have discovered new scientific knowledge by autonomously generating and testing hypotheses about yeast genes using public databases and laboratory experiments.
2) AI is being applied throughout the drug development process, including target identification, compound design and synthesis, clinical trial optimization, and drug repurposing.
3) Partnerships between pharmaceutical companies and AI firms are exploring applications like generating new immuno-oncology treatments, metabolic disease therapies, and cancer treatments through large-scale data analysis.
Similar to 87560480 a-study-on-computer-aided-drug-design-for-m2-protein-in-h1 n1 (20)
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
2. CONTENTS
Abstract
Introduction
I. Proteins
II. Drug Designing
III. Active Site in Drug Designing
Review OF
Methodology
Result and dissuasion
Results
Conclusion
References
3. Today Information Technology is highly involved in all Biotechnology and
Pharmacology research and development sectors to understand biological
data, their properties, functions and individual role in the metabolites of a
living organism.
Computer Aided Drug Designing (CADD) is also an involvement of
information technology in pharmacology studies. This technology makes
easy to understand biomolecules and biochemical reactions. This method is
based on receptor and inhibitors interaction theory, in technical term it is
called Docking.
Virtual screening uses computer-based methods to discover new ligands on
the basis of biological structures. Large libraries are available, compounds
are docked into the structure of receptor targets by a docking computer
program.
Each compound is sampled in thousands to millions of possible
configurations and scored on the basis of its complementarity to the
receptor. Of the hundreds of thousands of molecules in the library, tens of
top-scoring predicted ligands (hits) are subsequently tested for activity in an
experimental assay.
This project aims to demonstrating Linux platform software based docking
dramatically into the process of discovery new drug that bind to biological
micro molecules with clear benefit for both the pharmaceutical industry and
whole social community .
Abstract
4. Initially I have focused on the M2protein of H1N1 which is an matrix
protein, enables hydrogen ions to enter the viral particle (virion) from the
endosome, thus lowering pH of the inside of the virus, which causes
dissociation of the viral partical and work on computational docking to study
the model ligands ‘amantadine and rimantadine’, and collected the library of
100 ligands; to inhibit the receptor “M2protein”, in the computational
methodology which is useful in Virtual Screening for finding the minimum
scored inhibitors from the ligands library.
However the results achieved demonstrate that high power computer
clustering software services technology (HPCCSN), networking technology
and Linux platform can be useful in applied drug design.
5.
6. Chapter-1
Computer aided drug design CADD):
Computer aided drug design may be defined as the use of computational
techniques to design new drugs or drug molecule /to discovery new drugs.
1. Computer-Aided Drug Design (CADD) is a specialized discipline that
uses computational methods to simulate drug-receptor interactions.
CADD methods are heavily dependent on bioinformatics tools,
applications and databases. As such, there is considerable overlap in
CADD research and bioinformatics.
2. http://www.b-eye-network.com/view/852
BENIFITESOF CADD:
CADD methods and bioinformatics tools offer significant benefits for drug
discovery programs which are as follow.
Costsaving
The tuff report suggests that the cost of drug discovery and development
has reached $800 million for each drug successfully brought to market.
Many biopharmaceutical companies now use computational methods and
bioinformatics tools to reduce this cost burden. Virtual screening, lead
optimization and predictions of bioavailability and bioactivity can help
guide experimental research. Only the most promising experimental lines
of inquiry can be followed and experimental dead-ends can be avoided
early based on the results of CADD simulations.
7. Time-to-Market:
The predictive power of CADD can help drug research programs choose
only the most promising drug candidates. By focusing drug research on
specific lead candidates and avoiding potential “dead-end” compounds,
biopharmaceutical companies can get drugs to market more quickly.
Insight:
One of the non-quantifiable benefits of CADD and the use of
bioinformatics tools is the deep insight that researchers acquire about
drug-receptor interactions. Molecular models of drug compounds can
reveal intricate, atomic scale binding properties that are difficult to
envision in any other way. When we show researchers new molecular
models of their putative drug compounds, their protein targets and how
the two bind together, they often come up with new ideas on how to
modify the drug compounds for improved fit. This is an intangible
benefit that can help design research programs. .
RECEPETOR
such binding occurs, the receptor goes into a conformational change which
ordinarily initiates a cellular response. However, some ligands merely block
receptors without inducing any response (e.g. antagonists). Ligand-induced
activity of the ligands changes in receptors result in physiological changes
which constitute the biological. In biochemistry, a receptor is a protein
molecule, embedded in either the plasma membrane or cytoplasm of a cell,
to which a mobile signaling (or "signal") molecule may attach. A molecule
8. which binds to a receptor is called a "ligand," and may be a peptide (such as
a neurotransmitter), a hormone, a pharmaceutical drug, or a toxin, and when
http://en.wikipedia.org/wiki/Receptor_(biochemistry)
http://upload.wikimedia.org/wikipedia/commons/0/07/Transmembrane_
receptor.png
DRUG:
Drugs are chemical subestances thet specifically interact with specific
biological recepeter and increase or decrease its activity.
A drug, broadly speaking, is any substance that, when absorbed into the
body of a living organism, alters normal bodily function.
Pharmacology,defines a drug as "a chemical substance used in the
treatment, cure, prevention, or diagnosis of disease or used to otherwise
enhance physical or mental well-being.
9. Drugs are usually distinguished from endogenous biochemicals by being
introduced from outside the organism. For example, insulin is a hormone
that is synthesized in the body; it is called a hormone when it is synthesized
by the pancreas inside the body, but if it is introduced into the body from
outside, it is called a drug.
http://en.wikipedia.org/wiki/Drug
http://www.scleroderma.org/images/medicalimages/research_advances/
Figure-3_Receptor-Ligand.jpg
Virtual Screening:
Virtual screening (VS) is a computational technique used in drug discovery
research. It involves the rapid in silico assessment of large libraries of
chemical structures in order to identify those structures most likely to bind to
a drug target, typically a protein receptor or enzyme.
Virtual screening has become an integral part of the drug discovery process.
Related to the more general and long pursued concept of database searching,
the term "virtual screening" is relatively new. Walters, et al. define virtual
10. screening as "automatically evaluating very large libraries of compounds"
using computer programs.[
The purpose of virtual screening to come up with hits of novel chemical
structure that bind to the macromolecular target of interest. Thus, success of
a virtual screen is defined in terms of finding interesting new scaffolds rather
than many hits. Interpretations of VS accuracy should therefore be
considered with caution. Low hit rates of interesting scaffolds are clearly
preferable over high hit rates of already known scaffolds.
Method:
There are two broad categories of screening techniques: ligand-based and
structure-based.
1. Ligand-based method
Given a set of structurally diverse ligands that binds to a receptor, a model of
the receptor can be built based on what binds to it. These are known as
pharmacophore models. A candidate ligand can then be compared to the
pharmacophore model to determine whether it is compatible with it and
therefore likely to bin
Another approach to ligand-based virtual screening is to use chemical
similarity analysis methods to scan a database of molecules against one
active ligand structure.
11. 2. Structure-based
Structure-based virtual screening involves docking of candidate ligands into
a protein target followed by applying a scoring function to estimate the
likelihood that the ligand will bind to the protein with high affinity.
Applications:
Virtuale screening useful in folloing situations.
The number avilable compounds in a library greatly exceeds the
experimentally based "wet" screen capacity to evaluate these
compounds. Virtual screening can then be used to prioritize
compounds for screening thereby identifying a greater number of hits
than could be identified by screening a random subset of compounds
selected from the same library.
The number of compounds that could be synthesized using
combinatorial chemistry methods greatly exceeds the synthetic
capacity. Virtual screening can be used to screen a virtual library of
compounds that could be synthesized to identify those most likely to
bind. Then synthetic capacity can be focused on those compounds.
http://en.wikipedia.org/wiki/Virtual_screening
12. Chapter-2
H1N1:
Influenza A(H1N1) virus is a subtype of influenzavirus A and the most
common cause of influenza (flu) in humans. Some strains of H1N1 are
endemic in humans and cause a small fraction of all influenza-like illness
and a large fraction of all seasonal influenza. H1N1 strains caused roughly
half of all human flu infections in 2006. Other strains of H1N1 are endemic
in pigs (swine influenza) and in birds (avian influenza).
In June 2009, WHO declared that flu due to a new strain of swine-origin
H1N1 was responsible for the 2009 flu pandemic. This strain is commonly
called "swine flu" by the public media.
http://en.wikipedia.org/wiki/Influenza_A_virus_subtype_H1N1
14. NOMENCULTURE:
Influenza A virus strains are categorized according to two proteins found on
the surface of the virus: hemagglutinin (H) and neuraminidase (N). All
influenza A viruses contain hemagglutinin and neuraminidase, but the
structures of these proteins differ from strain to strain, due to rapid genetic
mutation in the viral genome.
Influenza A virus strains are assigned an H number and an N number based
on which forms of these two proteins the strain contains. There are 16 H and
9 N subtypes known in birds, but only H 1, 2 and 3, and N 1 and 2 are
commonly found in humans.
Classification
Of the three genera of influenza viruses that cause human flu, two also cause
influenza in pigs, with influenza A being common in pigs and influenza C
being rare. Influenza B has not been reported in pigs. Within influenza A
and influenza C, the strains found in pigs and humans are largely distinct,
although due to reassortment there have been transfers of genes among
strains crossing swine, avian, and human species boundaries.
Influenza C
Influenza C viruses infect both humans and pigs, but do not infect birds.
Transmission between pigs and humans have occurred in the past.For
example, influenza C caused small outbreaks of a mild form of influenza
amongst children in Japan and California. Due to its limited host range and
the lack of genetic diversity in influenza C, this form of influenza does not
cause pandemics in humans.
15. Influenza A
Swine influenza is known to be caused by influenza A subtypes H1N1,
H1N2, H3N1, H3N2, and H2N3. In pigs, three influenza A virus subtypes
(H1N1, H3N2, and H1N2) are the most common strains worldwide. In the
United States, the H1N1 subtype was exclusively prevalent among swine
populations before 1998; however, since late August 1998, H3N2 subtypes
have been isolated from pigs. As of 2004, H3N2 virus isolates in US swine
and turkey stocks were triple reassortants, containing genes from human
(HA, NA, and PB1), swine (NS, NP, and M), and avian (PB2 and PA)
lineages.
http://en.wikipedia.org/wiki/Influenza_A_virus_subtype_H1N1
(H1N1) pandemic:
In the 2009 flu pandemic, the virus isolated from patients in the United
States was found to be made up of genetic elements from four different flu
viruses – North American Mexican influenza, North American avian
influenza, human influenza, and swine influenza virus typically found in
Asia and Europe – "an unusually mongrelised mix of genetic sequences”.
This new strain appears to be a result of reassortment of human influenza
and swine influenza viruses, in all four different strains of subtype H1N1.
Preliminary genetic characterization found that the hemagglutinin (HA) gene
was similar to that of swine flu viruses present in U.S. pigs since 1999, but
the neuraminidase (NA) and matrix protein (M) genes resembled versions
present in European swine flu isolates. The six genes from American swine
flu are themselves mixtures of swine flu, bird flu, and human flu viruses.
16. While viruses with this genetic makeup had not previously been found to be
circulating in humans or pigs, there is no formal national surveillance system
to determine what viruses are circulating in pigs in the U.S. On June 11,
2009, the WHO declared an H1N1 pandemic, moving the alert level to phase
6, marking the first global pandemic since the 1968 Hong Kong flu.
http://upload.wikimedia.org/wikipedia/commons/d/d0/AntigenicShift_H
iRes.png
17. How H1N1 causes flu(Mechanismof pathogenesis):
Flu is caused by a virus called Influenza virus and mainly affects the
respiratory tract.
Mode of transmission - Droplet borne, or air borne i.e., when a person
sneezes or coughs, large number of infectious droplets gets dispersed in air
and when these are inhaled they infect others. Hence it spreads very fast
from person to person and has a potential to cause pandemic
Mechanism or Pathogenesis - the virus has 2 important antigens
1)Neuraminidase(N)
2)Hemaglutinin(H)
Depending on these only H1N1 is classified.. It has the tendency to change
its serotype, like previously it was caused by H5N1 so on.
When person inhales a virus partical, it gets to the respiratory epithelial cells
with the help of hem agglutinin and enters the cells with the help of
neuraminidase. It replicates inside the cells and spreads the neighbouring
cells. Remember it does not enter blood usually its infection is only confine
to respiratory epithelium.
The epithelium gets damaged, which invites secondary bacterial infections.
The person suffers from a bacterial pneumonia. Person dies of secondary
bacterial infection. Not because of flu as such. Many a times or most of the
times the flu is just confined to respiratory system without any secondary
infection and hence most of the people just suffer from a just a short
duration of cold or cough. But they transmit the infection.
Once infected, the person starts infecting others with the release of the
infectious particles into the air.
18. http://in.answers.yahoo.com/question/index?qid=20090625044628AAa7nD
u
M2 protein:
Introduction
The M2 protein is a proton-selective ion channel protein, integral in the viral
envelope of the influenza A virus. The channel itself is a homotetramer
(consists of four identical M2 units), where the units are helices stabilized by
two disulfide bonds. It is activated by low pH.
Structure
Fig: M2 Protein of H1N1
The M2 protein unit consists of three protein domains: the 24 amino acids on
the N-terminal end, exposed to the outside environment, the 19 hydrophobic
amino acids on the transmembrane region, and the 54 amino acids on the C-
terminal end, oriented towards the inside of the viral particle. Two different
high-resolution structures of truncated forms of M2 have been reported: the
structure of a mutated form of the M2 transmembrane region by itself , as
well as a longer version of the protein containing only naturally-occurring
sequence in the transmembrane region. The two structures also suggest
different binding sites for the adamantane class of anti-influenza drugs.
19. Function
The M2 protein has an important role in the life cycle of the influenza A
virus. It is located in the viral envelope. It enables hydrogen ions to enter the
viral particle (virion) from the endosome, thus lowering pH of the inside of
the virus, which causes dissociation of the viral matrix protein M1 from the
ribonucleoprotein RNP. This is a crucial step in uncoating of the virus and
exposing its content to the cytoplasm of the host cell.
Inhibition and resistance
The function of the M2 channel can be inhibited by antiviral drugs
amantadine and rimantadine, which then blocks the virus from taking over
the host cell. The molecule of the drug binds to the transmembrane region,
sterically blocking the channel. This stops the protons from entering the
virion, which then does not disintegrate.
These drugs are sometimes effective against influenza A if given early in
the infection but are always ineffective against influenza B because B
viruses do not possess M2 molecules
However, the M2 gene is susceptible to mutations. When one of five amino
acids in the transmembrane region gets suitably substituted, the virus gains
resistance to the existing M2 inhibitors. As the mutations are relatively
frequent, presence of the selection factors (eg. using amantadine for
treatment of sick poultry) can lead to emergence of a resistant strain ontent
to the cytoplasm of the host cell.
20. http://en.wikipedia.org/wiki/M2_protein
TREATMENTOF SWINE FLU (PIGS AND HUMAN):
In swine
As swine influenza is rarely fatal to pigs, little treatment beyond rest and
supportive care is required. Instead veterinary efforts are focused on
preventing the spread of the virus throughout the farm, or to other
farms.Vaccination and animal management techniques are most important in
these efforts. Antibiotics are also used to treat this disease, which although
they have no effect against the influenza virus, do help prevent bacterial
pneumonia and other secondary infections in influenza-weakened herds.
In humans
If a person becomes sick with swine flu, antiviral drugs can make the illness
milder and make the patient feel better faster. They may also prevent serious
flu complications. For treatment, antiviral drugs work best if started soon
after getting sick (within 2 days of symptoms). Beside antivirals, supportive
care at home or in hospital, focuses on controlling fevers, relieving pain and
maintaining fluid balance, as well as identifying and treating any secondary
infections or other medical problems. The U.S. Centers for Disease Control
and Prevention recommends the use of Tamiflu (oseltamivir) or Relenza
(zanamivir) for the treatment and/or prevention of infection with swine
influenza viruses; however, the majority of people infected with the virus
make a full recovery without requiring medical attention or antiviral drugs.
The virus isolates in the 2009 outbreak have been found resistant to
amantadine and rimantadine.
21. http://en.wikipedia.org/wiki/Swine_influenza#Treatment
INHABITOR:
Antiviral drugs Amantadine and Rimantadine are the principle inhibitor of
M2 protein of H1N1.
These antiviral medicines prevent the spread of type influenza A by
interfering with the production of the virus inside the body. They do not treat
or protect you against influenza B. These antiviral drugs inhibits the M2
protein by blocking its ion chanel.
These antiviral medicines reduce the severity of influenza (flu) symptoms
and shorten the course of the illness of influenza A. They need to be started
within 48 hours of the first symptoms and continued, usually, for 7 days.
For the past few years, the U.S. Centers for Disease Control and Prevention
(CDC) have advised doctors not to use amantadine (Symadine or
Symmetrel) or rimantadine (Flumadine) to treat or prevent the flu. These
medicines have not worked against most types of the flu virus.
When used to protect people during a flu outbreak, antiviral medicines
usually are used for 7 days but may be continued for 5 to 7 weeks.
In healthy young adults and children, antiviral medicines can be very
effective in preventing influenza A during an outbreak. But these antiviral
medicines do not always treat or prevent the flu.
22. When given within 48 hours after symptoms begin, they may reduce
symptoms, shorten the length of influenza A illness by 1 or 2 days, and
allow for a faster return to usual activities.
Side Effects:
Side effects have been reported with both amantadine and rimantadine:
Amantadine can cause sleeplessness (insomnia), hallucinations, and
agitation in a small number of people (2%).
Rimantadine often causes side effects that affect the digestive system,
such as an upset stomach, nausea, and loss of appetite.
More serious but less frequent side effects (seizures, confusion) have been
reported in older adults and, most commonly, in adults who have seizure
disorders. Lowering the dose reduces these side effects without reducing the
effectiveness of the medication.
Side effects decrease after about 1 week of use and reverse as soon as
treatment stops.
23.
24.
25. Mark S.P. Sansomand Ian D. Kerr . : a molecularmodelling study of
the ion channel , Received September3, 1992; revised October 22, 1992;
accepted October 28, 1991.
The influenza A M2 protein forms cation-selective ion channels which
are blocked by the anti-influenza drug amantadine. A molecular model
of the M2 channel is presented in which a bundle of four parallel M2
transbilayer helices surrounds a central ion-permeable pore. Analysis
of helix amphipathicity was used to aid determination of the
orientation of the helices about their long axes. The helices are tilted
such that the N-terminal mouth of the pore is wider than the C-
terminal mouth. The channel is lined by residues V27, S31 and I42.
Residues D24 and D44 are located at opposite mouths of the pore,
which is narrowest in the vicinity of I42. Energy profiles for
interaction of the channel with Na+, amantadine-H+ and
cyclopentylamine-H+ are evaluated. The interaction profile for Na+
exhibits three minima, one at each mouth of the pore, and one in the
region of residue S31. The amantadine-H+ profile exhibits a minimum
close to S31 and a barrier near residue I42. This provides a molecular
model for amantadine-H+ block of M2 channels. The profile for
cyclopentylamine-H+ does not exhibit such a barrier. It is predicted
that cyclopentyl-amine-H+ will not act as an M2 channel blocker
(Mark S.P. Sansom et.al 1992).
26. Takeda M, Pekosz A, Shuck K, Pinto LH, Lamb RA. Influenza a virus
M2 ion channel activity is essential for efficient replication in tissue
culture. J Virol. 2002 Feb;76(3):1391-9.
The amantadine-sensitive ion channel activity of influenza A virus
M2 protein was discovered through understanding the two steps in the
virus life cycle that are inhibited by the antiviral drug amantadine:
virus uncoating in endosomes and M2 protein-mediated equilibration
of the intralumenal pH of the trans Golgi network. Recently it was
reported that influenza virus can undergo multiple cycles of
replication without M2 ion channel activity (T. Watanabe, S.
Watanabe, H. Ito, H. Kida, and Y. Kawaoka, J. Virol. 75:5656-5662,
2001). An M2 protein containing a deletion in the transmembrane
(TM) domain (M2-del(29-31)) has no detectable ion channel activity,
yet a mutant virus was obtained containing this deletion. Watanabe
and colleagues reported that the M2-del(29-31) virus replicated as
efficiently as wild-type (wt) virus. We have investigated the effect of
amantadine on the growth of four influenza viruses: A/WSN/33;
N31S-M2WSN, a mutant in which an asparagine residue at position
31 in the M2 TM domain was replaced with a serine residue;
MUd/WSN, which possesses seven RNA segments from WSN plus
the RNA segment 7 derived from A/Udorn/72; and A/Udorn/72.
N31S-M2WSN was amantadine sensitive, whereas A/WSN/33 was
amantadine resistant, indicating that the M2 residue N31 is the sole
determinant of resistance of A/WSN/33 to amantadine. The growth of
influenza viruses inhibited by amantadine was compared to the
growth of an M2-del (29-31) virus. We found that the M2-del (29-31)
27. virus was debilitated in growth to an extent similar to that of influenza
virus grown in the presence of amantadine. Furthermore, in a test of
biological fitness, it was found that wt virus almost completely
outgrew M2-del(29-31) virus in 4 days after co-cultivation of a 100:1
ratio of M2-del (29-31) virus to wt virus, respectively. We conclude
that the M2 ion channel protein, which is conserved in all known
strains of influenza virus, evolved its function because it contributes
to the efficient replication of the virus in a single cycle (Takeda M
et.al 2002)
Thanyada Rungrotmongkol, Pathumwadee Intharathep a, Maturos
Malaisree a, Nadtanet Nunthaboot c, Nopphorn Kaiyawet a, Pornthep
Sompornpisut a, Sanchai Payungporn d, Yong Poovorawan d, Supot
Hannongbua a,Susceptibility of antiviral drugs against2009 influenza A
(H1N1) virus Biochemical and Biophysical Research Communications
Due to antigenic differences amongst influenza A strains, the current
seasonal influenza vaccines cannot provide protection against this new
strain of A (H1N1) influenza virus. Up to date, there are two classes
of anti-influenza agents:
(i) NA inhibitors, oseltamivir and zanamivir, protecting the release and
spread of progeny virions
(ii) (ii) adamantane derivatives, amantadine and rimantadine, preventing
the proton transfer in the M2 ion-channel. The A (H1N1) viruses
isolated from patients in USA and Mexicoare sensitive to NA
inhibitors but show resistance to adamantane derivatives. To gain
28. the fundamental knowledge on the structure and the drug–target
interactions of the new strain of influenza A (H1N1) virus,
homology modeling and molecular dynamics (MD) simulations
were carried out on the three inhibitor–enzyme complexes: OTV-
NA, AMT-M2 and RMT-M2. The present study is an extension
from, and is compared to, our previous works on avian
influenzaH5N1 virus were focused to understand the structural
properties, intermolecular interactions and predictive inhibitory
potencies of both wild- and mutant-type viruses at the NA and HA
(Thanyada Rungrotmongkol et.al 2009).
Kanta Subbarao & Tomy Joseph. Scientific barriers to developing
vaccines againstavianinfluenza viruses. Nature Reviews Immunology 7,
267-278 (April 2007)
The influenza A virus particle has a lipid envelope that is derived
from the host cell membrane. Three envelope proteins haemagglutinin
(HA), neuraminidase (NA) and an ion channel protein (matrix protein
2, M2) are embedded in the lipid bilayer of the viral envelope. HA
(rod shaped) and NA (mushroom shaped) are the main surface
glycoproteins of influenza A viruses. The ratio of HA to NA
molecules in the viral envelope usually ranges from 4:1 to 5:1. b | The
HA glycoprotein is synthesized as an HA0 molecule that is post-
translationally cleaved into HA1 and HA2 subunits; this cleavage is
essential for virus infectivity. The HA glycoprotein is responsible for
binding of the virus to sialic-acid residues on the host cell surface and
29. for fusion of the viral envelope with the endosomal membrane during
virus uncoating. The NA glycoprotein cleaves sialic-acid receptors
from the cell membrane and thereby releases new virions from the cell
surface. M2 functions as a pH-activated ion channel that enables
acidification of the interior of the virion, leading to uncoating of the
virion. Matrix protein 1 (M1), which is the most abundant protein in
the virion, underlies the viral envelope and associates with the
ribonucleoprotein (RNP) complex. Inside the M1 inner layer are eight
single-stranded RNA molecules of negative sense that are
encapsidated with nucleoprotein (NP) and associated with three RNA
polymerase proteins , polymerase basic protein 1 (PB1), PB2 and
polymerase acidic protein (PA) , to form the RNP complex. The PB1,
PB2 and PA proteins are responsible for the transcription and
replication of viral RNA. The virus also encodes a non-structural
protein (NS) that is expressed in infected cells and a nuclear export
protein (NEP). The location of NEP in the virion is not known (Kanta
Subbarao et. al. 2007).
.
.
30.
31.
32. Tools and techniques:
I used a variety of tools and a large number of techniques in completion of
this project. These all tools and techniques were unknown to me before and I
have used these for the first time.
HARDWARE CONFIGURATION:
Processor - Intel® Pentium® P4 2.8GHz D2
RAM - 1 GB
Hard disk – 160 GB
Server computer platform:
LINUX:
We did our search for the best operating system for our life science and we
use LINUX operating system. Nowadays, Linux is one of the most flexible
and popular operating systems for biological purposes. Linux is used
because of the following reasons:
Cost: For desktop or home use, Linux is very cheap or free,
Windows is expensive: Forserver use, Linux is very cheap compared
to Windows.
Running from CD: Linux can run from a CD. But for Windows, it
has to first be installed to hard disk.
Viruses: Compared to Windows, Linux is virus-free.
Security: You have to log on to Linux with a user id and password.
This is not true of Windows.
Bugs: Linux has a reputation for fewer bugs than Windows,
Hardware the OS runs on: Linux runs on many different hardware
platforms, not so with Windows.
33. Multiple Users: Linux is a multi- user system; Windows is not.
Traditional comparative genomics process is a time consuming as well as
money. The introduction of HighPerformanceComputing andNetworking
(HPCN) techniques in this process would decrease the costs and the time
necessary to compare the genomes. The current project focuses on the
Computer Assisted motifs finding using High Performance Computing &
Networking (HPCN) as a tool to improve the process of comparing
genomes. Computational power is now available in the form of Linux
Cluster Technologies.
Client computer platform:
WINDOW’S Operating System
OPERATING SYSTEM: PCQ LINUX version – 2004, WINDOWS -XP
and LIVE CDs.
TOOLS AND SOFTWARES:
ON LINE SOFTWARES: PDB, KEGG, NCBI, and
UNIPROT.
OFFLINE SOFTWARES: PYMOL, GHEMICAL, OPEN
BABEL, FRED, VIDA.
SOFTWARES:
Supercomputing in Linux:
A step-by-step guide on how to set up a cluster of PCQ Linux machines for
supercomputing .To keep it simple, we start with a cluster of three machines.
One will be the server and the other two will be the nodes. However,
plugging in additional nodes is easy and will tell the modification to
34. accommodate additional nodes. Instead of two nodes, we can have a single
node. So, even if we have two PCs, we can build a cluster.
Set up server hardware:
We should have at least a 2 GB or bigger hard disk on the server. It should
have a graphics card that is supported by PCQ Linux 7.1 and a floppy drive.
We also need to plug in two network cards, preferably the faster PCI cards
instead of ISA, supported by PCQ Linux. Why two network cards? Adhering
to the standards for cluster setups, if the server node needs to be connected
to the outside (external) network, Internet or your private network the nodes
in the cluster must be on a separate network. This is needed if we want to
remotely execute programs on the server. If not, we can do away with a
second network card for the external network. . Hence, on the server, one
network card (called external interface) will be connected to the Labs
network and the other network card (internal interface) will be connected to
a switch. We used a 100/10 Mbps switch. A 100 Mbps switch is
recommended because the faster the speed of the network, the faster is the
message passing. All cluster nodes will also be connected to the switch.
PYMOL:
PYMOL is a molecular graphics system with an embedded Python
interpreter designed for real-time visualization and rapid generation of high-
quality molecular graphics images and animations. It can also perform many
other valuable tasks (such as editing PDB files) to assist you in your
research. The extensible core PyMOL module (hosted here at Source Forge)
is available free to everyone via the "Python" license (a simple BSD-like
permission statement), but we ask all users to purchase a license and
maintenance agreement in order to cover our development and supportcosts.
35. In order to motivate such sponsorship, we offer support and other incentives
to PyMOL’s licensees with current maintenance subscriptions. In this way,
we seek to insure the viability of the Open-Source project by providing a
specific incentive (or reward) for outside support. However, our hope is that
only a small subset of PyMOL's total value will need to be restricted to
Incentive packages just enough to justify regular contributions and keep the
project self-sustaining.
Fig: PYMOL software home page.
36. FRED RECEPTOR
Fred receptor is a wizard like graphical utility that prepares an active site for
docking with FRED, Open- Eye’s docking program. fred receptor was
created to make preparing an active site a more intuitive process by allowing
the user to visualize the active site and how it is setup, however FRED does
not require that the active site be prepared with fred receptor. Input to fred
receptor is the structure of the target protein, generally from an X-ray
crystallography experiment. Output is a receptor file, which is a specialized
OEB (Open Eye’s molecule format) file, used by FRED.
Fig: FRED RECEPTOR home page.
37. F.R.E.D
F.R.E.D. (Fast Rigid Exhaustive Docking) is a protein-ligand docking
program, which takes a multiconformer library/database and receptor file as
input and outputs molecules of the input database most likely to bind to the
receptor. FRED is a command line program, although a GUI is available to
setup and create receptor files prior to docking. Typical docking time for
FRED is a few seconds per ligand. FRED jobs can also be easily distributed
over multiple computers/processors using PVM to further reduce docking
time.
The following structure-based scoring functions are available in FRED.
These scoring functions also have MASC variant.
1. SHAPEGAUSS: A shape-based scoring function that uses smooth
Gaussian functions to represent the shapes of molecules.
2. PLP: or Piecewise Linear Potential
3. CHEMGAUSS2: Version 2 of the Chemgauss scoring function, which
uses smooth Gaussian functions to represent the shape and chemistry of
molecules.
4. CHEMGAUSS3: Version 3 of the Chemgauss scoring function, which
uses smooth Gaussian functions to represent the shape and chemistry of
molecules.
Fred receptor is a wizard like graphical utility that prepares an active site for
docking with FRED, Open-Eye’s docking program. fred receptor was
created to make preparing an active site a more intuitive process by allowing
the user to visualize the active site and how it is setup, however FRED does
not require that the active site be prepared with fred receptor. Input to Fred
receptor is the structure of the target protein, generally from an X-ray
38. crystallography experiment. Output is a receptor file, which is a specialized
OEB (Open Eye’s molecule format) file, used by FRED.
PROTOTYPESOFTWARE:
The Fred receptor program is prototype software, which means the overall
design of this program may be changed in future versions. This is the first
time a GUI has been created to assist Fred, or any OpenEye computational
program, and as such is somewhat experimental. We expect and hope to get
feedback from users regarding the Fred receptor program. Based on that
feedback and other considerations future versions of Fred receptor may have
a substantially different look and feel as well as somewhat modified
functionality. For example the next version of Fred may have a completely
graphical interface that the Fred receptor programs functionality is merged
into.
Prototype software does not mean beta software. The Fred receptor is a
complete stable utility to assist Fred users in preparing their active site for
docking.
OPEN BABEL:
Open babel is free software, a chemical expert system mainly used for
converting chemical file formats. Due to the strong relationship to
informatics this program belongs more to the category cheminformatics than
to molecular modeling. It is available for Windows, UNIX, and Mac OS. It
is distributed under the GNU GPL. The project's stated goal is: "Open Babel
is a community-driven scientific project assisting both users and developers
as a cross-platform program and library designed to support molecular
modeling, chemistry, and many related areas, including interconversion of
39. file formats and data. Open Babel is a full chemical software toolbox. In
addition to converting file formats, it offers a complete programming library
for developing chemistry software. The library is written primarily in C++
and also offers interfaces to other languages (e.g., Perl, Python, Ruby, and
Java) using essentially the same API. This documentation outlines the Open
Babel programming interface, providing information on all public classes,
methods, and data. In particular, strives to provide as much (or as little)
detail as needed. More information can also be found on the main website
and through the Open babel-discuss mailing list. Open babel is a
community-driven scientific project including both cross-platform programs
and a developer library designed to support molecular modeling, chemistry,
and many related areas, including interconversion of file formats and data.
OPEN BABEL GUI:
A graphical user interface to babel's functionality. You can start Open Babel
GUI using the shortcut in the Start Menu. You can copy this onto your
desktop to make it easier to access it. This graphical interface is an
alternative to a command line and has the same capabilities. It is written
using wxWidgets and has the capability to be compiled on most platforms.
Currently it is available only on Windows and is available in the compiled
download. At present the interface is entirely text with no graphical display
of molecular structure. It does however provide an environment likely to be
familiar to Windows users and displays the options available rather than the
user having to remember them.
41. OMEGA:
Omega builds initial models of structures by assembling fragment templates
along sigma bonds. Input molecules graphs are fragmented at exocyclic
sigma and carbon to heteroatom acyclic (but not exocyclic) sigma bonds.
Fig: OMEGA software
42. VIDA:
VIDA is Open Eye’s main visualization application designed to intuitively
and effortlessly handle very large data sets while still generating extremely
high quality images. VIDA provides multiple modes of display including
1D, 2D, and 3D displays. Furthermore, VIDA is an excellent interface for
data analysis as it contains a chemically-aware spreadsheet and a powerful
list-based architecture.
Fig: VIDA home page.
43. List of online and off line tools:
S.No.
Tools / Servers /
Databases Used
Used for
1. NCBI Sequence Database
2. PDB Protein structure
3. PYMOL Protein structure visualization
4. FRED RECEPTOR Active site finding
5. PUBCHEM Ligand libraroy
6. OPEN BABEL Converting chemical files
7. OMEGA Fragment of Chemical Compounds
9. FRED Docking
10 VIDA Docking visualization
44.
45. Proteinsearching:
In first step I retrieved target protein (M2 Protein) sequence by
usingNCBI,provides scientific community.
Fig: This image showing some information on NCBI.
47. Fig : Protein sequence of M2 protein by using FASTA format.
48. Protein structure:
After taking the M2 protein sequence, I download the struture from PDB.
Fig: This image showing some information of various M2 protein on PDB.
50. Protein Study by Pymol:
After downloading the M2 protein sequence we use the Pymol visualization
software to study the 3-D structure of M2protein.
Fig: Image showing 3-D structure of M2 protein on Pymol.
51. Protein study: For further study (Helix, loops, chains etc) of M2 protein
we use the Pymol visualization software.
1. Load the PDB file
File -> Open -> 1w2i.pdb
2. Hide everything and then show protein cartton
PyMOL> hide everything, all
PyMOL> show cartoon, all
Fig :Visualization of target M2 protein structure on pymol.In this image
pymol shows four chains.
52. 3. Color the helix, sheet, and loop
PyMOL> color purple, ss h
PyMOL> color yellow, ss s
PyMOL> color green, ss ""
Fig: Image showing the helix in purpel color of M2 protein on Pymol.
53. Fig : This image showing helix of M2 protein on Pymol.
54. 4. Color chain A and B
PyMOL> color red, chain A
PyMOL> color blue, chain B
Fig : Image showing the four chains of M2 protein by four different colour
on Pymol.
55. Fig: This image showing the surface structure of M2 protein on PYMOL.
The four colours showing the four chains of M2 protein.
56. Active site finding :
In next step to find active site of target protein we used the software FRED
RECEPTOR.
Fig : This image showing active site of M2 protein whitch is kept in box.
57. Ligand libraroy:
The next step is to creat ligand liberary based on model inhabitor
amantadine and rimantadine.
Fig : This image showing some information for modal ligand on
PUBCHEM.
58. Fig : This image showing some information about amantadine ligand on
PUBAHEM .
59. Fig : This image showing some information about rimantadine ligand on
PUBCHEM.
60. After that I collectedthe 100 inhibitors library for next docking step.
Fig : This image showing the ligand library.
61. Running process of docking:
1) Prepare the receptor and find active sites via FRED receptor and save
file in “.oeb” format.
2) Library preparation: collect SDF file from PUBCHEM, NCBI.
a) Open the “OPEN BABEL” software and convert all SDF files
into MOL2 format.
3) Merge all the converted SDF files into a single file by the below given
command in linux operating system.
4) For the preparation of fragments of all chemical compounds as a
merged file by OMEGA.
C) Save the target protein file and omega fragment file in the
directory of FRED.
5) For the final step of docking proceed with the FRED software
a) Run the FRED software in LINUX and give the following
command:
64. Fig: This image is showing the files formed during docking process.
65.
66. RESULTS & DISCUSSION
CADD is a technique to perform the initial results of any pharmaceutical
products. This is fully based on computer and computing power and I have
used this in my project and entitled as “A STUDY ON COMPUTER
AIDED DRUG DESIGN FOR M2 PROTEIN OF H1N1 “which deals with a
real life problem for human health.
M2 protein (matrix protein) is involved in causing swine flu disease. It
enables hydrogen ions to enter the viral particl from the endosome, thus
lowering pH inside the viral partical, which causes dissociation of the viral
partical. This is a crucial step in uncoating of the virus and exposing its
content to the cytoplasm of the host cell.
I have used M2 protein as target and collected the possible 100 ligands
library from the PUBCHEM and through virtual screening I found out the
minimum energy inhibitor CID_44918, IUPAC name-: 1-(1-aminopentyl)
adamantane hydrochloride.
67. Fig: This image is showing shapegauss file having the shapegauss scores of
all the ligands used in the docking.
68. Fig: This image is showing some information of finally selected ligand on
PUBCHEM.
69. Fig: This image is showing some information of finally selected adamantane
hydrochloride adamantane hydrochloride ligand on PUBCHEM.
70. Docking visualization:
After the docking process we study the result by using docking visualization
software, VIDA.
Fig: This image is showing the binding of selected ligand with target protein
on VIDA.
71. Fig: This image is showing the binding of selected ligand with target protein
with different angle.
72. Fig: This image is showing the binding of selected ligand with target protein,
kept in net.
73. Fig: This image is showing the binding of selected ligand with target protein
helix.