Visualisation techniques are used in the area of small molecules, drug molecules, protein and to understand complex functions and interaction points to infer the mechanisms
Molecular Dynamics for Beginners : Detailed OverviewGirinath Pillai
Detailed presentation of what is molecular dynamics, how it is performed, why it is performed, applications, limitations and software resources on how to perform calculations are discussed.
How to implement cheminformatics methods and computational approaches in medicinal chemistry for a drug candidate selection.
Many images and charts are adapted from research articles and webpages cited in the original slide deck.
Molecular dynamics (MD) simulations allow atoms and molecules to interact over time, representing a virtual experiment. MD was used to give dynamics to SUMO proteins in solution. The SUMO protein was divided into fragments which were given random conformations using CYANA. These conformations were then converted to GROMACS format and molecular dynamics simulations were performed using GROMACS. The simulations involved energy minimization to relieve strain, followed by production runs. Various analysis tools were then used to analyze the results.
Molecular dynamics (MD) is a computer simulation technique that uses Newton's laws of motion to model molecular systems. MD allows studying kinetics and thermodynamic properties by simulating molecular motions over time. The key components of an MD simulation include force fields, integration algorithms, boundary conditions like periodic boundary conditions, and ensembles like NVE, NVT, NPT. Limitations include the approximations in force fields and sampling limitations. Enhanced sampling techniques help address some limitations. MD has many applications and continues to provide insights into molecular behavior.
Machine Learning in Chemistry and Drug Candidate SelectionGirinath Pillai
Application of machine learning and its importance in chemistry, drug discovery, materials science and requirement of the right dataset of chemical structures and activities. Drug Candidate selection criteria is important to avoid failures
Computer Aided Drug Design and Discovery : An Overview (2006)Girinath Pillai
The document discusses computer aided drug design and virtual screening. It describes how virtual screening can be used to discover new inhibitors for drug development by simulating the binding of compounds to protein targets. The document outlines the drug discovery process and different types of virtual screening techniques, such as ligand-based and structure-based approaches. It also discusses molecular docking methods and tools that are commonly used to simulate compound binding as part of virtual screening.
This document discusses docking scoring functions, which are mathematical functions used to predict the binding affinity between molecules after docking. There are three main applications of scoring functions: determining the binding mode of a ligand on a protein, predicting absolute binding affinity, and identifying potential drug hits through virtual screening. The document outlines different classes of scoring functions, including force field-based, empirical, knowledge-based, consensus, and shape/chemical complementary scores. It provides examples of popular docking programs that utilize different scoring function approaches.
This document discusses structure-based and ligand-based drug design approaches. Structure-based design uses the 3D structure of biological targets to dock potential drug molecules. Ligand-based design analyzes similar molecules that bind to the target to derive pharmacophore models or quantitative structure-activity relationships (QSAR) to predict new candidates. Specific structure-based methods covered include docking tools like AutoDock and CDOCKER, and accounting for protein and complex flexibility. Ligand-based methods discussed are QSAR techniques like Comparative Molecular Field Analysis (CoMSIA) and Field Analysis (CoMFA). In conclusion, computational approaches like these are valuable for drug discovery by facilitating the identification and testing of new ligand
Molecular Dynamics for Beginners : Detailed OverviewGirinath Pillai
Detailed presentation of what is molecular dynamics, how it is performed, why it is performed, applications, limitations and software resources on how to perform calculations are discussed.
How to implement cheminformatics methods and computational approaches in medicinal chemistry for a drug candidate selection.
Many images and charts are adapted from research articles and webpages cited in the original slide deck.
Molecular dynamics (MD) simulations allow atoms and molecules to interact over time, representing a virtual experiment. MD was used to give dynamics to SUMO proteins in solution. The SUMO protein was divided into fragments which were given random conformations using CYANA. These conformations were then converted to GROMACS format and molecular dynamics simulations were performed using GROMACS. The simulations involved energy minimization to relieve strain, followed by production runs. Various analysis tools were then used to analyze the results.
Molecular dynamics (MD) is a computer simulation technique that uses Newton's laws of motion to model molecular systems. MD allows studying kinetics and thermodynamic properties by simulating molecular motions over time. The key components of an MD simulation include force fields, integration algorithms, boundary conditions like periodic boundary conditions, and ensembles like NVE, NVT, NPT. Limitations include the approximations in force fields and sampling limitations. Enhanced sampling techniques help address some limitations. MD has many applications and continues to provide insights into molecular behavior.
Machine Learning in Chemistry and Drug Candidate SelectionGirinath Pillai
Application of machine learning and its importance in chemistry, drug discovery, materials science and requirement of the right dataset of chemical structures and activities. Drug Candidate selection criteria is important to avoid failures
Computer Aided Drug Design and Discovery : An Overview (2006)Girinath Pillai
The document discusses computer aided drug design and virtual screening. It describes how virtual screening can be used to discover new inhibitors for drug development by simulating the binding of compounds to protein targets. The document outlines the drug discovery process and different types of virtual screening techniques, such as ligand-based and structure-based approaches. It also discusses molecular docking methods and tools that are commonly used to simulate compound binding as part of virtual screening.
This document discusses docking scoring functions, which are mathematical functions used to predict the binding affinity between molecules after docking. There are three main applications of scoring functions: determining the binding mode of a ligand on a protein, predicting absolute binding affinity, and identifying potential drug hits through virtual screening. The document outlines different classes of scoring functions, including force field-based, empirical, knowledge-based, consensus, and shape/chemical complementary scores. It provides examples of popular docking programs that utilize different scoring function approaches.
This document discusses structure-based and ligand-based drug design approaches. Structure-based design uses the 3D structure of biological targets to dock potential drug molecules. Ligand-based design analyzes similar molecules that bind to the target to derive pharmacophore models or quantitative structure-activity relationships (QSAR) to predict new candidates. Specific structure-based methods covered include docking tools like AutoDock and CDOCKER, and accounting for protein and complex flexibility. Ligand-based methods discussed are QSAR techniques like Comparative Molecular Field Analysis (CoMSIA) and Field Analysis (CoMFA). In conclusion, computational approaches like these are valuable for drug discovery by facilitating the identification and testing of new ligand
PubChem and Its Applications for Drug DiscoverySunghwan Kim
PubChem is a public repository maintained by the NIH that contains over 243 million substance descriptions, 97 million unique chemical structures, and over 264 million biological activity test results. It serves as both a large data archive and knowledgebase. Programmatic interfaces allow for automated retrieval and integration of PubChem data into virtual screening pipelines. PubChemRDF encodes the data as RDF triples, enabling local storage and integration with other datasets using semantic web technologies.
1) De novo drug design involves generating new drug molecules from scratch based on the 3D structure of the target receptor.
2) It uses molecular modeling tools to modify lead compounds to better interact with the receptor's binding site.
3) The process involves defining interaction sites on the receptor, generating potential drug molecules, scoring them based on their fit with the receptor, and using search algorithms to refine candidates.
The document discusses structure-based drug design (SBDD). It first provides background on drug design and SBDD. It then describes some key aspects of SBDD, including using the 3D structure of the biological target obtained from techniques like X-ray crystallography and NMR spectroscopy. It also discusses ligand-based and receptor-based drug design approaches. The document then outlines the typical steps involved in SBDD, including target selection, ligand selection, target preparation, docking, evaluating results, and discusses some molecular docking techniques and scoring functions used to predict binding.
Cheminformatics is the application of computer science to solve chemical problems. It involves acquiring chemical data through experiments or simulations, managing the information in databases, and analyzing the data. Key aspects of cheminformatics include computer-assisted synthesis design, representing chemical structures digitally, and using mathematical models to analyze chemical data. Cheminformatics plays an important role in drug discovery by aiding processes like target identification, lead discovery, and molecular modeling.
Molecular dynamics (MD) simulations follow the motions and interactions of atoms and molecules over time. MD allows investigation of complex systems like fluids, polymers, and proteins. It is commonly used to study phenomena in liquids, solids, soft matter, and astrophysics. Popular MD software packages include GROMACS, CHARMM, AMBER, NAMD, and LAMMPS. While powerful, MD has limitations such as small system sizes and neglecting quantum mechanical effects.
Homology modeling is a technique used to predict the 3D structure of a protein from its amino acid sequence by comparing it to proteins with similar sequences whose structures are already known. It involves searching a database for template structures, aligning the target sequence to the template, building a model by transferring 3D coordinates from the template to the target sequence, and validating the resulting model. Homology modeling works best when the sequence identity between the target and template is over 30% since protein structure is more conserved than sequence over evolution.
1. Structure-based drug design relies on knowledge of the three-dimensional structure of the biological target obtained through methods such as x-ray crystallography. Candidate drugs that are predicted to bind with high affinity and selectivity to the target can be designed.
2. Structure-based drug design approaches include receptor-based drug design, which involves "building" ligands within the constraints of the binding pocket, and ligand-based drug design.
3. De novo drug design is a receptor-based approach that uses the target's 3D structure to design new molecules without existing leads. It involves building ligands that complement the active site properties through manual or automated methods.
Finding PDB files of molecules, locating binding sites, positioning ligand to a macromolecule, building grid and grid parameter file, performing molecular docking, and analysis of docking results by looking over various energy parameters and uses in drug discovery technology.
The document discusses several key concepts in pharmacophore modeling:
1) A pharmacophore defines the important chemical features shared among active molecules, such as hydrogen bond donors/acceptors and hydrophobic regions.
2) Bioisosteres are atoms or groups with similar physical/chemical properties that produce similar biological effects.
3) 3D pharmacophores specify the spatial relationships between features as distance ranges and angles.
4) Constrained systematic searching and ensemble distance geometry are used to identify pharmacophores from a set of molecules while considering multiple conformations.
5) Clique detection identifies all possible combinations of pharmacophoric groups in molecules by finding "maximal completely connected subgraphs".
PyMol is a molecular graphics program that allows visualization and manipulation of protein structures. It takes protein data files in PDB format as input and outputs the 3D protein structure that can be visualized, animated, exported, and analyzed through various features and commands. PyMol is open-source, cross-platform software that is effective for protein structure analysis and commonly used in research.
This document provides an overview of the field of chemoinformatics. It defines chemoinformatics as the combination of chemistry and information technology used to process and analyze chemical data. The document discusses common representations of molecules including 1D, 2D, and 3D formats. It also outlines several common file formats used in chemoinformatics like Mol, SDF, and SMILES. Finally, it describes several important chemical databases, including PubChem, ChemBank, ChEMBL, and DrugBank, and gives examples of chemoinformatics applications such as virtual screening and QSAR modeling.
Energy minimization methods - Molecular ModelingChandni Pathak
Methods to minimize the energy of molecules during drug designing - Computational chemistry. According to the PCI syllabus, B.Pharm 8th Sem - Computer-Aided Drug Design (CADD).
STRUCTURE BASED DRUG DESIGN - MOLECULAR MODELLING AND DRUG DISCOVERYTHILAKAR MANI
This document discusses molecular modeling and structure-based drug design. It begins by outlining the general process of drug development from basic studies to clinical trials. It then discusses how structural bioinformatics can facilitate drug discovery through molecular design. Structure-based drug design starts with target identification and verification, then determining the 3D structure of the target protein. Key approaches discussed include structure-based ligand generation methods like docking and de novo design, as well as virtual library design and computer-aided drug design more broadly.
PubChem is a key chemical information resource at the National Center for Biotechnology Information that contains 247.3 million substance descriptions, 96.5 million unique chemical structures, and 237 million bioactivity test results. It organizes data into the Substance, Compound, and BioAssay databases. PubChem provides search and analysis tools for its extensive and growing collection of chemical and biological data.
This document provides an overview of molecular dynamics (MD) simulations and their analysis. MD simulations calculate the time-dependent behavior of molecules and can be used to study conformational changes in proteins and nucleic acids. The document outlines various analyses that can be done on MD simulations including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration, hydrogen bonding, secondary structure analysis using Ramachandran plots, free energy surfaces, and principal component analysis. It also provides examples of running MD simulations using VMD and applications of MD simulations such as understanding allostery and molecular docking.
Molecular modelling techniques help scientists visualize molecules and discover new drug compounds. They use computational methods to mimic molecular behavior without physical experiments. Molecular modelling includes molecular mechanics, which calculates molecular energies and motions using parameters like potential energy surfaces and force fields, and quantum mechanics, which provides nuclear positions and distributions based on electron and nuclear interactions using equations like the Schrodinger equation. Key steps in molecular modelling for drug design include generating lead molecules, minimizing molecular energies, analyzing conformations, and developing pharmacophore models of receptor sites.
This document discusses chemoinformatics and its role in distance education. It defines chemoinformatics as the application of informatics methods to solve chemical problems, involving the design, creation, organization, management, analysis and use of chemical information. The document then outlines proposed courses for a distance learning program in chemoinformatics, including introductory courses covering fundamental concepts as well as more advanced topics involving programming, databases, and molecular modeling. It concludes by discussing how chemoinformatics can help improve distance education programs in chemistry fields.
Bilibili video website has grown into a giant video platform. With the anime culture that can attract the younger
generation, Bilibili has built a large-scale user creation platform. To stimulate users’ creative inspiration, Bilibili
issued several plans to provide corresponding rewards for the content produced by video creators, attracting
more and more people to participate in the creative party. In this context, many excellent works were born, but at
the same time, there are also works with mixed qualities in the video, i.e., “low-innovation” works. "Low-innovation"
works hinder personal development and have a bad impact on the production climate of the platform. First, this
paper uses the principal component analysis algorithm to pre-process the user data of Bilibili to improve the
efficiency of the algorithm. Based on the K-means clustering algorithm, it analyzes and identifies "low-innovation"
users. According to the analysis results, it sets different incentive plans for different types of user groups and plays
a positive role in the video quality of Bilibili.
PubChem and Its Applications for Drug DiscoverySunghwan Kim
PubChem is a public repository maintained by the NIH that contains over 243 million substance descriptions, 97 million unique chemical structures, and over 264 million biological activity test results. It serves as both a large data archive and knowledgebase. Programmatic interfaces allow for automated retrieval and integration of PubChem data into virtual screening pipelines. PubChemRDF encodes the data as RDF triples, enabling local storage and integration with other datasets using semantic web technologies.
1) De novo drug design involves generating new drug molecules from scratch based on the 3D structure of the target receptor.
2) It uses molecular modeling tools to modify lead compounds to better interact with the receptor's binding site.
3) The process involves defining interaction sites on the receptor, generating potential drug molecules, scoring them based on their fit with the receptor, and using search algorithms to refine candidates.
The document discusses structure-based drug design (SBDD). It first provides background on drug design and SBDD. It then describes some key aspects of SBDD, including using the 3D structure of the biological target obtained from techniques like X-ray crystallography and NMR spectroscopy. It also discusses ligand-based and receptor-based drug design approaches. The document then outlines the typical steps involved in SBDD, including target selection, ligand selection, target preparation, docking, evaluating results, and discusses some molecular docking techniques and scoring functions used to predict binding.
Cheminformatics is the application of computer science to solve chemical problems. It involves acquiring chemical data through experiments or simulations, managing the information in databases, and analyzing the data. Key aspects of cheminformatics include computer-assisted synthesis design, representing chemical structures digitally, and using mathematical models to analyze chemical data. Cheminformatics plays an important role in drug discovery by aiding processes like target identification, lead discovery, and molecular modeling.
Molecular dynamics (MD) simulations follow the motions and interactions of atoms and molecules over time. MD allows investigation of complex systems like fluids, polymers, and proteins. It is commonly used to study phenomena in liquids, solids, soft matter, and astrophysics. Popular MD software packages include GROMACS, CHARMM, AMBER, NAMD, and LAMMPS. While powerful, MD has limitations such as small system sizes and neglecting quantum mechanical effects.
Homology modeling is a technique used to predict the 3D structure of a protein from its amino acid sequence by comparing it to proteins with similar sequences whose structures are already known. It involves searching a database for template structures, aligning the target sequence to the template, building a model by transferring 3D coordinates from the template to the target sequence, and validating the resulting model. Homology modeling works best when the sequence identity between the target and template is over 30% since protein structure is more conserved than sequence over evolution.
1. Structure-based drug design relies on knowledge of the three-dimensional structure of the biological target obtained through methods such as x-ray crystallography. Candidate drugs that are predicted to bind with high affinity and selectivity to the target can be designed.
2. Structure-based drug design approaches include receptor-based drug design, which involves "building" ligands within the constraints of the binding pocket, and ligand-based drug design.
3. De novo drug design is a receptor-based approach that uses the target's 3D structure to design new molecules without existing leads. It involves building ligands that complement the active site properties through manual or automated methods.
Finding PDB files of molecules, locating binding sites, positioning ligand to a macromolecule, building grid and grid parameter file, performing molecular docking, and analysis of docking results by looking over various energy parameters and uses in drug discovery technology.
The document discusses several key concepts in pharmacophore modeling:
1) A pharmacophore defines the important chemical features shared among active molecules, such as hydrogen bond donors/acceptors and hydrophobic regions.
2) Bioisosteres are atoms or groups with similar physical/chemical properties that produce similar biological effects.
3) 3D pharmacophores specify the spatial relationships between features as distance ranges and angles.
4) Constrained systematic searching and ensemble distance geometry are used to identify pharmacophores from a set of molecules while considering multiple conformations.
5) Clique detection identifies all possible combinations of pharmacophoric groups in molecules by finding "maximal completely connected subgraphs".
PyMol is a molecular graphics program that allows visualization and manipulation of protein structures. It takes protein data files in PDB format as input and outputs the 3D protein structure that can be visualized, animated, exported, and analyzed through various features and commands. PyMol is open-source, cross-platform software that is effective for protein structure analysis and commonly used in research.
This document provides an overview of the field of chemoinformatics. It defines chemoinformatics as the combination of chemistry and information technology used to process and analyze chemical data. The document discusses common representations of molecules including 1D, 2D, and 3D formats. It also outlines several common file formats used in chemoinformatics like Mol, SDF, and SMILES. Finally, it describes several important chemical databases, including PubChem, ChemBank, ChEMBL, and DrugBank, and gives examples of chemoinformatics applications such as virtual screening and QSAR modeling.
Energy minimization methods - Molecular ModelingChandni Pathak
Methods to minimize the energy of molecules during drug designing - Computational chemistry. According to the PCI syllabus, B.Pharm 8th Sem - Computer-Aided Drug Design (CADD).
STRUCTURE BASED DRUG DESIGN - MOLECULAR MODELLING AND DRUG DISCOVERYTHILAKAR MANI
This document discusses molecular modeling and structure-based drug design. It begins by outlining the general process of drug development from basic studies to clinical trials. It then discusses how structural bioinformatics can facilitate drug discovery through molecular design. Structure-based drug design starts with target identification and verification, then determining the 3D structure of the target protein. Key approaches discussed include structure-based ligand generation methods like docking and de novo design, as well as virtual library design and computer-aided drug design more broadly.
PubChem is a key chemical information resource at the National Center for Biotechnology Information that contains 247.3 million substance descriptions, 96.5 million unique chemical structures, and 237 million bioactivity test results. It organizes data into the Substance, Compound, and BioAssay databases. PubChem provides search and analysis tools for its extensive and growing collection of chemical and biological data.
This document provides an overview of molecular dynamics (MD) simulations and their analysis. MD simulations calculate the time-dependent behavior of molecules and can be used to study conformational changes in proteins and nucleic acids. The document outlines various analyses that can be done on MD simulations including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration, hydrogen bonding, secondary structure analysis using Ramachandran plots, free energy surfaces, and principal component analysis. It also provides examples of running MD simulations using VMD and applications of MD simulations such as understanding allostery and molecular docking.
Molecular modelling techniques help scientists visualize molecules and discover new drug compounds. They use computational methods to mimic molecular behavior without physical experiments. Molecular modelling includes molecular mechanics, which calculates molecular energies and motions using parameters like potential energy surfaces and force fields, and quantum mechanics, which provides nuclear positions and distributions based on electron and nuclear interactions using equations like the Schrodinger equation. Key steps in molecular modelling for drug design include generating lead molecules, minimizing molecular energies, analyzing conformations, and developing pharmacophore models of receptor sites.
This document discusses chemoinformatics and its role in distance education. It defines chemoinformatics as the application of informatics methods to solve chemical problems, involving the design, creation, organization, management, analysis and use of chemical information. The document then outlines proposed courses for a distance learning program in chemoinformatics, including introductory courses covering fundamental concepts as well as more advanced topics involving programming, databases, and molecular modeling. It concludes by discussing how chemoinformatics can help improve distance education programs in chemistry fields.
Bilibili video website has grown into a giant video platform. With the anime culture that can attract the younger
generation, Bilibili has built a large-scale user creation platform. To stimulate users’ creative inspiration, Bilibili
issued several plans to provide corresponding rewards for the content produced by video creators, attracting
more and more people to participate in the creative party. In this context, many excellent works were born, but at
the same time, there are also works with mixed qualities in the video, i.e., “low-innovation” works. "Low-innovation"
works hinder personal development and have a bad impact on the production climate of the platform. First, this
paper uses the principal component analysis algorithm to pre-process the user data of Bilibili to improve the
efficiency of the algorithm. Based on the K-means clustering algorithm, it analyzes and identifies "low-innovation"
users. According to the analysis results, it sets different incentive plans for different types of user groups and plays
a positive role in the video quality of Bilibili.
Technology acceptance of augmented reality and wearable technologies ilrn 201...Mikhail Fominykh
"Technology Acceptance of Augmented Reality and Wearable Technologies" #TAM at #iLRN2017
by Fridolin Wild, Roland Klemke, Paul Lefrere, Mikhail Fominykh and Timo Kuula
Paper presented at the 3rd Immersive Learning Research Network Conference in Coimbra, Portugal on 28 June 2017
Publication: https://link.springer.com/chapter/10.1007/978-3-319-60633-0_11
KNIME in Life Science, Cheminformatics and Computational ChemistryGirinath Pillai
This document discusses using KNIME for life sciences applications like drug discovery. It provides an overview of KNIME's life science extensions and nodes, capabilities for data management and analysis, and examples of building predictive models. Specific topics covered include data visualization, cheminformatics nodes, generating molecular properties, similarity searching, and developing QSAR models using data from sources like ChEMBL. The presenter aims to demonstrate how KNIME can be used to generate and analyze chemistry data for machine learning applications in drug discovery.
This document summarizes Yves Sucaet's presentation on whole slide imaging and digital pathology. It discusses the history of digital pathology, how digital pathology can improve biobanks by allowing remote querying and analysis of virtual slides, and the future of intelligent querying of biobanks using digital pathology and bioinformatics tools. The presentation concludes by encouraging attendees to implement digital pathology workflows and continue the conversation around computational pathology.
The report documents findings from a pilot study exploring the potential of blockchain technology (BCT) for the agrifood sector. Key findings include:
- BCT could increase transparency and trust in agrifood supply chains by providing a shared record of transactions.
- A proof of concept was developed tracking certificates for table grapes from South Africa using a blockchain demonstrator.
- Opportunities exist for traceability and quality assurance, but challenges relate to technical immaturity, lack of standards, and high costs of implementation.
- Further research is needed to understand governance models, develop use cases, address technical barriers, and engage stakeholders to realize potential benefits of BCT for agrifood.
The BondMachine: a fully reconfigurable computing ecosystemMirko Mariotti
The aim of the BondMachine (BM) project is to implement a computing system to enable a real and full exploitation of the underlying hardware. This is a key to the success in the era of hybrid computing. In order to achieve this objective the BM has been designed to create a heterogeneous and flexible architecture on top of FPGAs. Moreover the overall vision is based on a reduction of the number of hardware/software layers which by product guarantees a simpler software development. As such, the BM project has been thought as a complete reconfigurable computing ecosystem that, starting from a high-level description, creates both the hardware and the software that runs on it.
The two architectural pillars are computing elements (processors) and non-computing elements (for example memories, channels, barriers). The latter are meant to be shared among processors. Finally, thanks to a custom network protocol, many BondMachines can be interconnected together, therefore building heterogeneous multi-core systems or even clusters of multi-cores.
The flexibility of the BM makes possible the implementation of any computing system ranging from networks of small agents, like IoT (Internet of Things), to high-performance devices for ML (Machine Learning) or real time data analysis, and even systems that mix all this different characteristics together. The BM can interact with standard Linux workstations both as a special purpose hardware accelerator or as part of a computer/BM hybrid clusters.
This document discusses the Micro:bit Educational Foundation and the micro:bit device. It provides an overview of the micro:bit's origins in 2015 as a learning tool for UK students aged 11-12. It describes the various programming languages and curricula available to teach coding concepts using the micro:bit. Examples of lessons and projects are shown. Accessories, books, and ways to purchase micro:bits in the US are also outlined. The presentation concludes with a demonstration of sample micro:bit projects.
This document provides an overview of the Computational Modeling in Biology Network (COMBINE) which coordinates the standardization of data and models in computational biology. It describes COMBINE's role in developing standards for encoding models (SBML), visualizing models (SBGN), and simulating models (SED-ML). The document also discusses COMBINE's guidance on publishing models according to FAIR principles, developing software tools and libraries to support the standards, and establishing best practices through documentation and training resources.
A global integrative ecosystem for digital pathology: how can we get there?Yves Sucaet
Digital pathology has many faces. Its stakeholders can roughly be classified into four categories: education, research, clinical, and clinical research. We come together at events like Pathology Informatics or Pathology Visions, and discuss the evolution of the field.
While progression is being made, it sometimes appears that around every corner are more challenges and forks in the road. New applications and scenarios emerge at a rapid pace, and it is clear that a single one-size-fits-all type of software is unlikely to satisfy most participants in this space, if any.
At the institutional level, ecosystems of digital pathology have already been established. At a national level, attempts are being made. At a global level, this is still a wide open question, but one very much worth exploring.
Digital pathology comes with some unique properties, like the data it generates and the pace at which this happens. This guest lecture then will examine the solutions that already exist, and what an inclusive global scalable digital pathology ecosystem may look like in the future.
[Video recording available at https://www.youtube.com/playlist?list=PLewjn-vrZ7d3x0M4Uu_57oaJPRXkiS221]
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, hiring, sales, and lending. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
This document outlines a course on strategic management taught by Prof. Prasad Kulkarni at the Gogte Institute of Technology in Belgaum, India. The course objectives include understanding strategic management, strategy formulation, external environment assessment, resource analysis, and applying strategies. It also discusses key concepts like developing a strategic vision and mission, analyzing company values, and linking values to strategic vision. Example strategies of companies like AMUL, JSW, and Reliance are provided.
Ph.D. Thesis: A Methodology for the Development of Autonomic and Cognitive In...Universita della Calabria,
Doctoral Defence in ICT (Università della Calabria, Italy). Ph.D. candidate Claudio Savaglio. Thesis title: A Methodology for the Development of Autonomic and Cognitive Internet of Things Ecosystems.
Big Data and Analytics Across the Interdisciplinary DividePhilip Bourne
4th International Conference on Big Data and Information Analytics, Theories, Algorithms and Applications in Data Science, December 17-19, 2018, Houston Texas. https://sph.uth.edu/divisions/biostatistics/bigdia/
SigOpt Research Engineer Michael McCourt and DarwinAI CTO Alexander Wong explain how they used SigOpt and hyperparameter optimization to successfully improve accuracy of detecting COVID-19 cases from chest X-Rays, using the COVID-Net model and the COVIDx open dataset.
This document discusses computational methods for identifying metabolites from tandem mass spectrometry data. It begins with background on metabolites and challenges in identification. Common approaches are described, including mass spectra libraries, in silico fragmentation using rules or machine learning, and machine learning methods. Recent machine learning works are summarized, such as using kernels to model peak interactions, unsupervised methods to group metabolites by shared substructures, and automatically recommending substructures from mass spectra. The document concludes that metabolite identification is important for metabolomics and machine learning is key to recent advances.
This document discusses the Micro:bit educational foundation and the Micro:bit device. Some key points:
- The Micro:bit is a small programmable device designed for educational use by students aged 11-12. Over 1 million were distributed in the UK in 2015.
- Studies found 90% of students said the Micro:bit helped show anyone can code, and 70% more girls said they would choose computing.
- The Micro:bit educational foundation was formed in 2016 to make the Micro:bit available globally. It can be programmed through block coding in MakeCode or text coding in JavaScript, Python, and more.
- Many lessons and curricula have been developed to teach concepts like
This document discusses academia-industry collaboration in Asia. It outlines different modes of collaboration such as open innovation, competitive research funding, and IP commercialization. It notes that the primary mission of academia is academic research while industry focuses on providing skills for local and global markets. Enabling collaboration requires addressing challenges like IPR issues and developing an ecosystem where ideas and solutions can thrive. Successful collaboration depends on factors like reputation, alumni networks, and aligning with academia's goals of employment, education, innovation and R&D. The document advocates developing innovative solutions to meet the unmet needs of persons with disabilities in Asia through a participatory process involving organizations, academia, SMEs and individuals.
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Basics of Quantum and Computational ChemistryGirinath Pillai
Basic fundamentals of theoretical, quantum and computational chemistry. The methods and approaches helps in predicting the electronic structure properties as well as other spectral data.
Details on several key chemical, natural products and commercial databases well-curated for Drug Discovery studies. Importance of pharmacokinetics and ADME in drug candidate selection in the hit-to-lead process of optimisation.
Autodock Made Easy with MGL Tools - Molecular DockingGirinath Pillai
Restructured tutorial for AutoDock and AutoGrid with MGL Tools. Prepared during 2011 adapted from original AutoDock MGL Tools Tutorial
and a video tutorial with the latest enhancements and options are uploaded to Youtube: https://www.youtube.com/watch?v=n53gJE8SHOM
This document provides an overview and installation instructions for machine learning basics using various tools and libraries. It discusses installing and setting up Orange, KNIME, Anaconda, and related Python libraries. Key steps include downloading installers, setting paths, defining workspaces, installing extensions, and creating workflows in Orange and KNIME. Popular cheminformatics and deep learning libraries supported include RDKit, DeepChem, numpy, and scikit-learn.
How 3D structures to be considered for 3D QSAR?Girinath Pillai
3D molecular descriptors for 3D QSAR.
Physical significance and meaning of descriptors are important.
QSAR should be reliable and reproducible
Drug Discovery
Target Identification - Gene Disease and Protein Target PredictionGirinath Pillai
Target Identification with relationship between Genes, Interacting Partners and Disease Associations. Protein Target Prediction and Binding Site predictions
Drug and Chemical Databases 2018 - Drug DiscoveryGirinath Pillai
Latest collection of Chemical and Drug Databases for Biological Research as well as Drug Design studies. Databases statistics, links and overview data with CADD introduction.
Why Drug Design and Computational Methods are important?Girinath Pillai
The document discusses in silico drug design and computer-aided drug discovery (CADD). It describes drug design as an iterative process involving chemistry, biophysics, and other fields. The workflow involves algorithms, programming, data mining, and other techniques. CADD techniques include modeling disease, drug inhibition, and drug interactions in 3D. Specific CADD techniques discussed include docking, similarity analysis, motif identification, subcellular location prediction, stability and solubility indices, half-life prediction, protein surface scanning, and secondary structure analysis for target identification and modeling.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
1. Visualization Tools : An Overview
Girinath G. Pillai, PhD
Coordinator DDH2020, MHRD Govt of India.
@giribio
2. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
Note
Slides contains contents/pictures/videos taken from web, articles, lectures, tutorials
and its respective authors own their copyrights.
Technical Slides : slideshare.net/giribio
Handson Videos : youtube.com/giribio (Autodock, Modeller, PaDel, QSAR, MD, etc)
Workflows & Notebooks : github.com/giribio
2
3. What to expect?
● What is Visualization
● Role of Viz in Drug Discovery
● Image Processing
● Viz Chemical Structures
● Viz Cheminformatics Data
● Viz Chemical Space
● Viz Proteins/Interactions/MD
● Case Studies
3
4. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
Why Visualization is Important?
4
● What ideas these visualizations trying to communicate?
● Learning objectives
● Graphical or schematics
● 2D or 3D representations
● When experimental data is not available, we choose data-driven-structures
● Structure determines functions
8. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
OSRA
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● OSRA: Optical Structure Recognition Application
● Convert graphical representations of chemical
structures
● From journal articles, patent documents, textbooks,
trade magazines etc.
● Read documents like GIF, JPEG, PNG, TIFF, PDF, PS etc.
● Generates SMILES or SD files
● Curation by a human knowledgeable in chemical
structures is highly recommended.
● Free and Open Source Downloadable Linux Software
● Web Interface
https://cactus.nci.nih.gov/osra/
J Chem Inf Model. 2009 Mar; 49(3): 740–743.
9. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
Marvin Sketch
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● A featured chemical editor for making science
accessible on all platforms
● Drawing chemical compounds
● Import Export multiple file formats
● Structure display and Journal formats
● MarvinView
● Molconvert
● Freemium - check with providers!
https://chemaxon.com/products/marvin
10. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
ChemSketch
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● Comprehensive Chemical Drawing Package
● Drawing chemical compounds
● Identify Tautomers
● 3D View etc
● Freemium - Free Edu/Academic license
https://www.acdlabs.com/resources/freeware/chemsketch/
11. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
BIOVIA Draw
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● To draw and edit complex molecules
● Capabilities for managing complex biological
entities including the ability to register and retrieve
peptides, oligonucleotides, and oligosaccharides
● Add-ins include tools for molecular property
calculation/prediction, enumeration, bioavailability,
isotopomer distribution, and stoichiometry
calculations, and many more.
● Freemium - Free Edu/Academic license
https://discover.3ds.com/biovia-draw-academic
13. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
DataWarrior
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● Open-Source Program for Data Visualization and
Analysis with Chemical Intelligence
● Interactive data visualization and analysis
● Built-in chemical intelligence
● Real Time data filtering on alphanumerical and
chemical criteria
● Prediction of molecular properties from the
chemical structure
● Dedicated cheminformatics modules support drug
discovery
● Free to use without commercialization
http://www.openmolecules.org/datawarrior/
14. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
KNIME
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● Analytics platform - an open source software for
creating data science
● Access, merge and transform all of your data
● Make sense of your data with the tools you
choose - Model and Visualise
● Leverage insights gained from your data -
Optimise
● Several extensions / nodes for cheminformatics,
bioinformatics, QSAR, machine learning,
statistical modelling, etc
● Free - Nodes/Extensions could be proprietary
https://www.knime.com/downloads/download-knime
16. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
Chimera
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● Interactive visualization and analysis of
molecular structures and related data.
● High-quality images and animations
● Chimera Interface to Modeller
● CASTp Pocket Data
● Structure-Based Sequence Alignment
● Superimposing structures
● UCSF ChimeraX (or simply ChimeraX) is the
next-generation molecular visualization
program with VR and ambient occlusions
● Free for all
https://www.cgl.ucsf.edu/chimera/download.ht
ml
17. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
VMD
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● A molecular visualization program for displaying,
animating, and analyzing large biomolecular
systems
● Interface to NAMD
● MD data analysis
● Collective Variables Interface
● Freemium - Free Edu/Academic license
https://www.ks.uiuc.edu/Research/vmd/
18. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
MGL Tools - PMV
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A molecular visualization program for displaying,
animating, and analyzing biomolecular systems
Interface to AutoDock, AutoDock Vina
Preparation of data
Freemium - Free Edu/Academic license
http://mgltools.scripps.edu/downloads
19. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
PyMol
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● A stand-alone molecular visualization program
that is very popular with protein
crystallographers because of the high quality of
its rendering, its speed and versatility
● Labeling, editing and representations
● Open Source & Free to Education only
https://pymol.org/edu/
20. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
Avogadro
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● An advanced molecule editor and visualizer
designed for cross-platform use in
computational chemistry, molecular modeling,
bioinformatics, materials science, and related
areas.
● Flexible high quality rendering and a powerful
plugin architecture.
● Conformer generation, optimization, etc
● Free, Open Source
https://pymol.org/edu/
21. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
NGLView
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● NGL Viewer is a collection of tools for web-
based molecular graphics.
● WebGL is employed to display molecules like
proteins and DNA/RNA with a variety of
representations.
● Jupyter/IPython widget
● MDsrv is a web-based tool
● MDtraj
● Pytraj
http://nglviewer.org/
22. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
Additional Visualizers
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● Jmol, Rasmol, SwissPDB Viewer, etc
● Maestro - Free Visualizer for academics
● Discovery Studio Visualizer - Free for academics
● ICM Browser - Free for academics
● Flare Viewer - Free for academics
23. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
PoseView
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https://proteins.plus/
24. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
LigPlot+
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https://www.ebi.ac.uk/thornton-srv/software/LigPlus/
26. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
1: Molecules move through random collisions
27
Molecules move around through collisions resulting in random
Brownian motion. In this eg, A, an Arp2/3 complex moves
smoothly and linearly before binding to an actin filament, whereas
in B, the motion is complex and chaotic.
Consider depicting random walks.
A B
27. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
2: Molecules are in constant motion
28
Newton’s first law states that objects remain in motion without external forces.
While molecules are subjected to constant forces from all sides, the result is
they are in constant motion and do not start and stop spontaneously. In this eg,
in A, CRAF, MEK, and ERK, components in a signalling cascade are not moving
unless they are involved in binding, whereas in B, they continue moving, even
when not immediately involved in the process.
Consider keeping molecules moving.
A B
28. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
3: Intermolecular attractions are local forces
29
Relative motion between two binding partners. In this eg, in A, Na ions flow
towards and traverse a voltage-gated ion channel as though attracted by a
magnetic force, whereas in B, they move through the channel through local
collisions and interactions with the transmembrane channel.
Consider ensuring that distant molecules are unaffected by pulling forces.
A B
29. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
4: Unproductive collisions occurs more
30
Not every encounter between complementary molecules results in binding;
in fact statistically there are likely to be many more unproductive collisions
than productive ones. In this eg, in A, Sos and Ras come together and bind
on first encounter, whereas in B, they collide a number of times before
their orientations result in tight binding.
Consider including non-binding collisions.
A B
30. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
5: Many instances of molecules and events exist
31
Typically many instances of molecules and events present in a given
environment; repetition can also reinforce the process being depicted.
In this eg, in A, only one copy each of siderocalin and enterobactin are
shown, which bind together, whereas in B, there are several copies of
each, and multiple binding events.
Consider presenting multiple copies.
A B
31. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
6: Not all instances of a molecule change states
32
Not every molecule is used in a process or
changes its state. More monomers are
present than will be incorporated into a
polymer, and typically more substrates are
present than will be converted into a product.
Likewise, not all molecules will cross a barrier
or will bind to a chelator. In this eg, in A, all
subunits form a single viral capsid, whereas in
B, once the capsid is complete, there are still
subunits that remain.
Consider leaving some molecules behind.
32. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
7: Molecular landscapes are crowded and diverse
33
Cellular environments are busy and crowded, with very little empty space,
particularly if molecular water is included. Even without the depiction of
molecular water, macromolecules take up a sizeable % of the volume. In this
eg, in A, a PTPS molecule moves in an intracellular environment consisting
of actin filaments and a nucleus, whereas in B, a diverse suite of proteins,
RNA, metabolites and more fill the surrounding cytoplasmic space.
Consider populating molecular environments with macromolecules.
33. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
8: Molecules are physical entities with defined boundaries
34
Molecules are physical entities with definable boundaries. Intersecting surface
meshes provide conflicting or obscured information about interaction and binding
sites. In this eg, in A, a PCNA DNA clamp slides along a DNA strand with coarse
meshing that overlaps with itself and with the DNA, whereas in B, the meshes are
tightly defined and show space between the clamp and DNA, indicating intermolecular
forces that keep the molecules from occupying the same space.
Consider respecting atomic boundaries with surface meshes.
A B
34. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
9: Proteins exhibit a range of flexibility
35
Proteins have internal freedom of motion that allows for specific
functionality. Some parts of a protein are more flexible than others and
some proteins are more rigid than others. In this eg, in A, the membrane
protein cadherin moves in the membrane, but is internally rigid, whereas in
B, the five extracellular domains are linked by short flexible regions.
Consider showing protein flexibility.
A B
35. June 18, 2021 Girinath G. Pillai, PhD | Workshop on Visualization
@giribio
10: Many binding reactions are reversible
36
Molecules do not bind permanently and many reactions are reversible at
the individual molecule level. In this eg, in A, the mTORC1 complex
phosphorylates its substrate S6K1 the first time that it binds in every
case, whereas in B, the substrate dissociates and binds again before
being phosphorylated.
Consider including some unproductive binding and dissociation.
A B
36. Thank you!
Dr. Abhay Jere
Prof. Narahari Sastry, Dr. Nagamani, Lijo & Team
Dr. Kunal Roy
Dr. Amit Prasad
Dr. Srinivas
Mr. Rajeev Gangal
37
Do you have any questions?
gpillai@zastrain.com
+91 94483 67493
www.zastrain.com
@giribio
You could request for
evaluation for Softwares
We do accept research interns
Editor's Notes
Newton’s first law states that objects remain in motion without external forces. While molecules are subjected to constant forces from all sides, the result is they are in constant motion and do not start and stop spontaneously. In this eg, in A, CRAF, MEK, and ERK, components in a signalling cascade are not moving unless they are involved in binding, whereas in B, they continue moving, even when not immediately involved in the process.
To use this concept, consider keeping molecules moving.
At this scale, showing negative pressure or distant molecules flooding toward a target invokes agency. The same applies to the relative motion between two binding partners. In this example, in Treatment A, sodium ions flow towards and traverse a voltage-gated ion channel as though attracted by a magnetic force, whereas in Treatment B, they move through the channel through local collisions and interactions with the transmembrane channel.
To use this concept, consider ensuring that distant molecules are unaffected by pulling forces.
Molecules are physical entities with definable boundaries. Intersecting surface meshes provide conflicting or obscured information about interaction and binding sites. In this example, in Treatment A, a PCNA DNA clamp slides along a DNA strand with coarse meshing that overlaps with itself and with the DNA, whereas in Treatment B, the meshes are tightly defined and show space between the clamp and DNA, indicating intermolecular forces that keep the molecules from occupying the same space.
To use this concept, consider respecting atomic boundaries with surface meshes.