This document discusses how bioinformatics tools can be used in drug design. It describes several approaches: chemical modification of existing drugs, receptor-based design by determining receptor structures, and ligand-based design using known active ligands. It also discusses identifying disease targets, refining drug structures, detecting drug binding sites using protein modeling, and rational drug design techniques like virtual screening. QSAR methods relate compound structures to activities, while molecular modeling and docking simulate drug-receptor interactions to aid design. Informatics plays a key role in storing and analyzing the large amounts of data generated.
Computer-aided drug design (CADD) is a widely used technology using computational tools and resources for the storage, management, analysis and modeling of compounds. It relies on digital repositories for study of designing compounds with physicochemical characteristics, predicting whether a given molecule will be combined with the target, and if so how strongly. Computer based methods can help us to search new hits in drug discovery, screen many irrelevant compounds at the same time and study the structure-activity relationship of drug molecules.
Cadd and molecular modeling for M.PharmShikha Popali
THE CADD IS FOR THE DRUG DEVELOPMENT THE DIFFERENT STRATEGIES ARE MENTIONED LIKE QSAR MOLECULAR DOCKING, THE DIFFERENT DIMNSIONAL FORMS OF QSAR , THE ADVANCE SAR of it.
DRUG DESIGN BASED ON BIOINFORMATICS TOOLSNIPER MOHALI
Drug design is a very complex process it takes many more times but using the these specific tools we can reduce complex process and save the time and produce a effective new drug that will be helpful in heath environment.
Computer-aided drug design (CADD) is a widely used technology using computational tools and resources for the storage, management, analysis and modeling of compounds. It relies on digital repositories for study of designing compounds with physicochemical characteristics, predicting whether a given molecule will be combined with the target, and if so how strongly. Computer based methods can help us to search new hits in drug discovery, screen many irrelevant compounds at the same time and study the structure-activity relationship of drug molecules.
Cadd and molecular modeling for M.PharmShikha Popali
THE CADD IS FOR THE DRUG DEVELOPMENT THE DIFFERENT STRATEGIES ARE MENTIONED LIKE QSAR MOLECULAR DOCKING, THE DIFFERENT DIMNSIONAL FORMS OF QSAR , THE ADVANCE SAR of it.
DRUG DESIGN BASED ON BIOINFORMATICS TOOLSNIPER MOHALI
Drug design is a very complex process it takes many more times but using the these specific tools we can reduce complex process and save the time and produce a effective new drug that will be helpful in heath environment.
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.
The techniques of drug designing and in silico studies are well defines in this presentation. Mooreover, the various softwares which are used in new era for determining the drug targets inside the body are elaborated.
PRESENTED BY: HARSHPAL SINGH WAHI, SHIKHA D. POPALI
USEFUL FOR PHARMACY STUDENTS AND ACADEMICS, INDUSTRIALS FOR MOLECULE DEVELOPMENT, MODELING, DRUG DISCOVERY, COMPUTATIONAL TOOLS, MOLECULAR DOCKING ITS TYPES, FACTORS AFFECTING, DIFFERENT STAGES, QSAR ADVANTAGES, NEED
Structure based drug design- kiranmayiKiranmayiKnv
This presentation helps in detail learning about the structure based drug design. It includes types of structure based drug design and detailed study of docking, de novo drug design.
Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO csandit
Each and every biological function in living organism happens as a result of protein-protein interactions. The diseases are no exception to this. Identifying one or more proteins for a
particular disease and then designing a suitable chemical compound (known as drug) to destroy these proteins has been an interesting topic of research in bio-informatics. In previous methods,drugs were designed using only seven chemical components and were represented as a fixedlength
tree. But in reality, a drug contains many chemical groups collectively known as
pharmacophore. Moreover, the chemical length of the drug cannot be determined before
designing the drug.
In the present work, a Particle Swarm Optimization (PSO) based methodology has been
proposed to find out a suitable drug for a particular disease so that the drug-protein interaction
becomes stable. In the proposed algorithm, the drug is represented as a variable length tree and essential functional groups are arranged in different positions of that drug. Finally, the structure of the drug is obtained and its docking energy is minimized simultaneously. Also, the
orientation of chemical groups in the drug is tested so that it can bind to a particular active site of a target protein and the drug fits well inside the active site of target protein. Here, several inter-molecular forces have been considered for accuracy of the docking energy. Results showthat PSO performs better than the earlier methods.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
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Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
1. DRUG DESIGN BASED ON
BIOINFORMATICS TOOLS
N.K.SUJEETH
II- M.Sc.INDUSTRIAL BIOTECHNOLOGY
DEPT OF MICROBIAL BIOTECHNOLOGY
BHARATHIAR UNIVERSITY
2. Chemical Modification of Known Drugs:
Drug improvement by chemical modification
Pencillin G -> Methicillin; morphine->nalorphine
Receptor Based drug design:
Receptor is the target (usually a protein)
Drug molecule binds to cause biological effects
It is also called lock and key system
Structure determination of receptor is important
Ligand-based drug design:
Search a lead ocompound or active ligand
Structure of ligand guide the drug design process
3. Identify Target Disease:
Identify and study the lead compounds
Marginally useful and may have severe side effects
Refinement of the chemical structures:
Detect the Molecular Bases for Disease
Detection of drug binding site
Tailor drug to bind at that site
Protein modeling techniques
Traditional Method (brute force testing)
4. Detect the Molecular Bases for Disease:
Detection of drug binding site
Tailor drug to bind at that site
Protein modeling techniques
Traditional Method (brute force testing)
Rational drug design techniques:
Screen likely compounds built
Modeling large number of compounds (automated)
Application of Artificial intelligence
Limitation of known structures
5. Quantitative Structure Activity Relationships (QSAR):
Compute functional group in compound
QSAR compute every possible number
Enormous curve fitting to identify drug activity
chemical modifications for synthesis and testing.
8. Drug Discovery & Development
Identify disease
Isolate protein
involved in
disease
Find a drug effective
against disease protein
Preclinical testing
Formulation
Human clinical trials
Scale-up
FDA approval
9. Techology is impacting this process
Identify disease
Isolate protein
Find drug
Preclinical testing
GENOMICS, PROTEOMICS & BIOPHARM.
HIGH THROUGHPUT SCREENING
MOLECULAR MODELING
VIRTUAL SCREENING
COMBINATORIAL CHEMISTRY
IN VITRO & IN SILICO ADME MODELS
Potentially producing many more targets
and “personalized” targets
Screening up to 100,000 compounds a
day for activity against a target protein
Using a computer to
predict activity
Rapidly producing vast numbers
of compounds
Computer graphics & models help improve activity
Tissue and computer models begin to replace animal testing
10. Informatics Implications
Need to be able to store chemical structure and biological data for millions
of data points
Computational representation of 2D structure
Need to be able to organize thousands of active compounds into
meaningful groups
Group similar structures together and relate to activity
Need to learn as much information as possible from the data (data mining)
Apply statistical methods to the structures and related information
11. Computational Models of Activity
Machine Learning Methods
E.g. Neural nets, Bayesian nets, SVMs, Kahonen nets
Train with compounds of known activity
Predict activity of “unknown” compounds
Scoring methods
Profile compounds based on properties related to target
Fast Docking
Rapidly “dock” 3D representations of molecules into 3D
representations of proteins, and score according to how well
they bind
12. Molecular Modeling
• 3D Visualization of interactions between compounds and proteins
• “Docking” compounds into proteins computationally
13. 3D Visualization
X-ray crystallography and NMR Spectroscopy can
reveal 3D structure of protein and bound
compounds
Visualization of these “complexes” of proteins and
potential drugs can help scientists understand the
mechanism of action of the drug and to improve
the design of a drug
Visualization uses computational “ball and stick”
model of atoms and bonds, as well as surfaces
Stereoscopic visualization available
15. In Vitro & In Silico ADME models
Traditionally, animals were used for pre-human testing.
However, animal tests are expensive, time consuming and
ethically undesirable
ADME (Absorbtion, Distribution, Metabolism, Excretion)
techniques help model how the drug will likely act in the
body
These methods can be experemental (in vitro) using
cellular tissue, or in silico, using computational models
16. In Silico ADME Models
Computational methods can predict compound
properties important to ADME, e.g.
LogP, a liphophilicity measure
Solubility
Permeability
Cytochrome p450 metabolism
Means estimates can be made for millions of
compouds, helping reduce “atrittion” – the failure
rate of compounds in late stage
17. GRID Based Docking Methods
• Grid Based methods
– GRID (Goodford, 1985, J. Med. Chem. 28:849)
– GREEN (Tomioka & Itai, 1994, J. Comp.
Aided. Mol. Des. 8:347)
– MCSS (Mirankar & Karplus, 1991, Proteins,
11:29).
• Functional groups are placed at regularly spaced
(0.3-0.5A) lattice points in the active site and their
interaction energies are evaluated.
18. Automated Docking Methods
• Basic Idea is to fill the active site of the
Target protein with a set of spheres.
• Match the centre of these spheres as good as
possible with the atoms in the database of
small molecules with known 3-D structures.
• Examples:
– DOCK, CAVEAT, AUTODOCK, LEGEND,
ADAM, LINKOR, LUDI.
19. Commercial Structural Genomics
Initiatives
IBM (Blue Gene project: 2000)
Computational protein folding
Geneformatics (1999)
Modeling for identifying active sites
Prospect Genomics (1999)
Homology modeling
Protein Pathways (1999)
Phylogenetic profiling, domain analysis, expression
profiling
Structural Bioinformatics Inc (1996)
Homology modeling, docking
20. STRUCTURE PREDICTION:
Homology modelling:
NAME METHOD DESCRIPTION
CPHModel Fragment assembly Automated web server
ESyPred3D Template
detection,Alignment, 3D
modeling
Automated web server
HHpred Template
detection,Alignment, 3D
modelling
Interactive web server
with help facility
RaptorX Remmote homology
detection,protein 3D
modelling, binding site
prediction.
Automated web server
and downloadable
program
21. Description of binding site:
GRID
De novo ligand design:
LIGBUILDER
Docking of compounds:
AUTODOCK
3D database scanning:
CATALYST
22. REFERENCE:
BOOK REFERENCE:
ESSENTIAL BIOINFORMATIC
-Jin xiong
-Texas A&M University
-Cambridge University press
Lin, Jung-Hsin, et al. "Computational drug design
accommodating receptor flexibility: the relaxed complex
scheme." Journal of the American Chemical
Society 124.20 (2002): 5632-5633.
Azuaje, Francisco. "Computational models for predicting
drug responses in cancer research." Briefings in
bioinformatics 18.5 (2016): 820-829.
