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.
Drug discovery take years to decade for discovering a new drug and very costly
Effort to cut down the research timeline and cost by reducing wet-lab experiment use computer modeling
Others have done the work. Some have used the work. I have spoken only on behalf of their behalf.
HERE IN THIS PRESENTATION HY HOMOLOGY MODELING IS EXPLAIN , WITH EXAMPLES OF PROTEIN PRIMARY AND SECONDARY, SHOWING THE IMAGES FORM WHICH MAKES EASY TO UNDERSTAND
A lecture on molecular docking that I give for master students at University Paris Diderot.
Warning: this presentation has numerous animations which are not included in the slideshare document.
https://florentbarbault.wordpress.com/
Prediction of the three dimensional structure of a given protein sequence i.e. target protein from the amino acid sequence of a homologous (template) protein for which an X-ray or NMR structure is available based on an alignment to one or more known protein structures
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.
Drug discovery take years to decade for discovering a new drug and very costly
Effort to cut down the research timeline and cost by reducing wet-lab experiment use computer modeling
Others have done the work. Some have used the work. I have spoken only on behalf of their behalf.
HERE IN THIS PRESENTATION HY HOMOLOGY MODELING IS EXPLAIN , WITH EXAMPLES OF PROTEIN PRIMARY AND SECONDARY, SHOWING THE IMAGES FORM WHICH MAKES EASY TO UNDERSTAND
A lecture on molecular docking that I give for master students at University Paris Diderot.
Warning: this presentation has numerous animations which are not included in the slideshare document.
https://florentbarbault.wordpress.com/
Prediction of the three dimensional structure of a given protein sequence i.e. target protein from the amino acid sequence of a homologous (template) protein for which an X-ray or NMR structure is available based on an alignment to one or more known protein structures
Proteins facilitates most biological processes in a cell, including gene expression, cell growth, proliferation, nutrient uptake, morphology, motility, intercellular communication and apoptosis.
Protein–protein interactions (PPIs) refer to physical contacts established between two or more proteins as a result of biochemical events.
These interactions are very important in our lives as any disorder in them can lead to fatal diseases such as Alzheimer’s and Creutzfeld- Jacob Disease.
The most well known example of Protein-Protein Interaction is between Actin and Myosin while regulating Muscular contraction in our body.
The protein –protein interaction have commonly been termed the ‘INTERACTOME’ by scientists.
Homo-Oligomers: Complexes having one type of protein subunits.
E.g. : PPIs in Muscle Contraction
Hetero-Oligomers: Complexes having multiple types protein subunits.
E.g. : PPI between Cytochrome Oxidase and TRPC3 (Transient receptor potential cation channels
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).
molecular docking its types and de novo drug design and application and softw...GAUTAM KHUNE
This ppt deals with all the aspects related to molecular docking ,its types(rigid ,flexible and manual) and screening based on it and also deals with de novo drug design , various softwares available for docking methodologies and applications for molecular docking in new drug design
Proteins facilitates most biological processes in a cell, including gene expression, cell growth, proliferation, nutrient uptake, morphology, motility, intercellular communication and apoptosis.
Protein–protein interactions (PPIs) refer to physical contacts established between two or more proteins as a result of biochemical events.
These interactions are very important in our lives as any disorder in them can lead to fatal diseases such as Alzheimer’s and Creutzfeld- Jacob Disease.
The most well known example of Protein-Protein Interaction is between Actin and Myosin while regulating Muscular contraction in our body.
The protein –protein interaction have commonly been termed the ‘INTERACTOME’ by scientists.
Homo-Oligomers: Complexes having one type of protein subunits.
E.g. : PPIs in Muscle Contraction
Hetero-Oligomers: Complexes having multiple types protein subunits.
E.g. : PPI between Cytochrome Oxidase and TRPC3 (Transient receptor potential cation channels
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).
molecular docking its types and de novo drug design and application and softw...GAUTAM KHUNE
This ppt deals with all the aspects related to molecular docking ,its types(rigid ,flexible and manual) and screening based on it and also deals with de novo drug design , various softwares available for docking methodologies and applications for molecular docking in new drug design
Pharmacy students to you will learn principles of drug discovery molecular docking, systematic and simple approach.
Molecular recognition plays a key role in drug receptor interactions, drug activity depends on the molecular binding of the ligand to the receptor binding site.
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.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
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.
1. Computational protein–ligand
docking and virtual drug screening
with the AutoDock suite
Authors: Stafano Forli, Ruth Huey,
Micheal E. Pique, Michel F Sanner,
David Goodsell and Arthur J Olson
A REVIEW BY JUSTICE AKWENSI
2. OUTLINE
• Introduction to Autodock suites
• Comparism
• Autodock
• Autodock vina
• Raccoon2
• AutoLigand
• Docking of a drug molecule with an anticancer target
• Virtual screening with a small ligand library
• Docking with selective receptor flexibility
• Active site prediction
• Docking with explicit hydration
3. Introduction
• Docking is widely used for the study of
biomolecular interactions and mechanisms.
• Applied to structure-based drug design.
• Fast virtual screening of ligand libraries
Computational docking can be used to predict bound conformations and
free energies of binding for small-molecule ligands to macromolecular
targes.
4. Introduction Con’t
• Widely used for the study of protein– ligand interactions and for
drug
• The process starts with a target of known structure of an enzyme of
medicinal interest.
• Predict the bound conformation and binding free energy of small
molecules to the target.
• Single docking experiments
• Virtual screening used to identify new inhibitors for drug
development.
AutoDock is a suite of free open-source software for the computational docking and virtual screening of small
molecules to macromolecular receptors.
5. Autodock suites
• AutoDock Vina: a turnkey docking program that is based on a
simple scoring function and rapid conformational search.
• AutoDock: a docking program based on an free-energy force field
and rapid Lamarckian genetic algorithm search method.
• Raccoon2: an interactive tool for virtual screening and analysis
• AutoDockTools (ADT): an interactive GUI for coordinate
preparation, docking and analysis.
• AutoLigand: a program for predicting optimal sites of ligand
binding on receptors.
The AutoDock suite including source code is free.
7. Assumptions and sophistications
1. The conformational space is reduced due to a rigid receptor
and fixed bond angles and lengths in the ligand. No induced fit
binding
2. A simplified scoring function based on empirical free energies
of binding is used to score poses for each conformation search.
• Ordered water molecules mediate interactions between ligands
and receptors
More Advanced conformational search:
Molecular dynamics or free-energy perturbation. Accuracy is very Good
10. Types of Docking
Manual docking
The user manually moves, rotates or
translates the compound inside the protein
cavity.
New association energy are recorded
manually.
11. Types of Docking
Automatic docking
Ligand is automatically placed onto the
macromolecule.
More exhaustiveness requires long CPU
time.
12. Experimental design
• Starts with receptor coordinates
• Because of the stochastic nature of the search, the method cannot ensure
that a global minimum has been found.
For this reason, it is important to use re-
docking experiments with known
complexes of similar conformational
complexity to evaluate the docking
protocol being used.
13. Experimental design Con’t
• For receptors with significant motion the
following methods may be used:
• Using receptor structures taken from
receptor–ligand complexes, in which there is
some expectation that the receptor is in the
relevant conformation.
• Docking to a collection of different receptor
structures that cover the expected range of
flexibility in the receptor
Use of explicit receptor side-chain flexibility during docking, if information is
available on relevant side chains.
14. Raccoon2 and virtual Screening
Graphical User Interface (GUI)
• Automated server connection manager and installation of docking services
(such as AutoDock Vina).
• Ligand library for upload and management of large ligand collections.
• Receptor management from multiple targets and flexible residues.
• Graphical interface for docking parameter setup.
• Graphical management of jobs on computational resources.
• Automated retrieval and preprocessing of results to extract features of
interest.
• User-friendly filtering of virtual screening results based on properties and
interactions.
• Export of filtered results.
15. Coordinate preparation with ADT.
Property Autodock Autodock Vina
PDB PDBQT YES YES
United atom representation(Polar H) YES YES
Atom typing YES YES
Atomic charges: Gasteiger–Marsili atomic charges No atomic charges
Ligand flexibility (specify the torsional
degrees of freedom in ligand)
YES YES
Search space (Grid box) YES YES
16. Limitations of the protocol
• AutoDock suite is designed to solve a specific problem: the docking
of small, drug-like molecules to biological macromolecules of
known structure.
• Systems that deviate from these design parameters will give
variable results, and they should be approached with caution.
1. Docking of very large ligands eg decapeptides with too many
degrees of freedom. Most often, the best way to solve this problem is to
break it down into smaller pieces.
2. The protein targets often show significant conformational
flexibility, which is not modeled in the AutoDock suite.
18. About the protocol; Drug Design Process
Target is the proto-oncogene tyrosine protein kinase c-Abl with
Gleevec (imatinib).
The protocol covers the following:
• Re-docking of c-Abl using AutoDock Vina and AutoDock.
• Virtual screening with Raccoon2
• Cross-docking of imatinib with c-Abl coordinates
• Prediction of optimal ligands for c-Abl using AutoLigand.
• Docking with explicit water molecules
19. EQUIPMENT and REQUIREMENT
• Coordinate file for receptor (in a variety of formats, including pdb,
mol2, cif and sdf)
• Coordinate file for ligand (in a variety of formats, including pdb,
mol2, cif and sdf)
•Computer: Linux, Macintosh or Windows PC; Internet access
• For virtual screening with Raccoon, a Linux cluster/HPC with either
a PBS or SGE scheduler
• Text editor
20. PROCEDURE
Coordinate preparation with ADT
1| Generate the ligand coordinate file. Start ADT and set the working
directory
2| Read the atomic coordinates.
3| Prepare a PDBQT file
4| Generate the receptor coordinate file.
5| Methods for docking simulation
21. Single docking experiment with
autoDock Vina
(i) Generate a configuration file and Restart
ADT
(ii) Docking
(iii) Run AutoDock Vina.
(iv) To visualize the results from AutoDock Vina
22. Single docking experiment with autoDock
(ii) Run AutoGrid
(iii) (Optional) Visualize AutoGrid maps in
ADT.
(iv) Generate the docking parameter file
(v) Run AutoDock. ‘Run → RunAutoDock’.
(vi) Visualize AutoDock results.
(i) Start ADT and set the default working directory.
23. Virtual screening with raccoon2 and autoDock Vina
(i) Start Raccoon2 and configure the server. Launch
Raccoon2
(ii) Set up the Ligand library.
(iii) Set up the receptor coordinates.
(iv) Configure AutoDock Vina docking parameters.
(v) Perform the virtual screening calculation.
(vi) Filter and analyze the results.
(vii) Export results.
24. AutoDock Vina with flexible side chains
(i) Generate receptor coordinate files.
(ii)Start ADT.
(iii) Generate parameter files for AutoDock
Vina.
(iv) Perform flexible side-chain docking in
AutoDock Vina at the command line
(v) Analyze the flexible docking results
using ADT.
25. Active site prediction with autoligand
• (i) Start ADT.
• (ii) AutoLigand
• (iii) AutoLigand may be run in two modes.
• (iv) Start the AutoLigand
26. Docking with explicit waters
(i) To add water to the ligand, type at the command line
(ii) Calculate the default atomic grid maps. Start ADT. Run
AutoGrid
(iii) Generate the ‘W’ map.
(iv) Create a modified docking parameter file.
(v) Run AutoDock ‘Run → RunAutoDock’
(vi) Extract and score the results at the command line
27. ANTICIPATED RESULTS
• In our tests of the docking of imatinib with c-Abl, we found that the default
docking parameters are sufficient to give a consistent solution in most cases.
• The conformational flexibility of this system is at the limit of the default docking
protocol.
• For ligands with 1–4 torsional degrees of freedom, short (250,000) or medium
(2,500,000 is fine.
• The clustering analysis is the best way to determine whether the simulation has
adequately searched the available conformation space.
• Virtual screening with Raccoon2 allows the docking and ranking of tens of
thousands of compounds to a macromolecular target.
• AutoDock and AutoDock Vina may be configured to dock ligands with selected
receptor residues treated explicitly as flexible.
28. ANTICIPATED RESULTS
• For best results, AutoDock Vina needs to be run with a more
exhaustive search to find the proper pose.
•For systems with larger motion of loops or domains,
separate docking simulations may be run for different
conformations of the protein.
•Explicit hydration is particularly useful for the docking of
small ligands and fragment molecules
• AutoLigand analyzes the atomic affinity maps to predict the
optimal locations for substrate binding.
• AutoLigand is used to identify the regions of a given ligand
that are providing the most affinity.
29. General Conclusions
• However, molecular docking has a weakness
for the determination of the interaction energy
(scoring function).
• Generally, molecular docking calculations and
their applications don't give an unique solution
but rather several solutions. Human has the
last word.
• Molecular docking is mainly applied for the
drug-design and get many success.
Molecular docking is an efficient method to predict the structural interaction of
an organic molecule inside a biomacromolecule binding site.
Editor's Notes
Docking methods generally search a larger conformational space, but more advanced methods can predict conformation and energy more accurately.
They all perform similarly
Highly clustered results are an indication that the conformational search procedure is exhaustive enough to ensure coverage of the accessible conformational space.
Virtual screening is rapidly becoming the primary application of computational docking methods, with many successes in the discovery of new lead compounds for pharmaceutical development
Atom typing: both methods require a simplified typing of atoms, including identification of aromatic and aliphatic carbon atoms and identification of the hydrogen bonding state of heteroatoms.
The protein is an important target for cancer chemotherapy—in particular, the treatment of chronic myelogenous leukemia
A coordinate set that includes hydrogen atoms is required.
A coordinate set that includes hydrogen atoms is required.
‘Choose’ is used when coordinates have already been read into ADT, and ‘Open’ is used to read coordinates from a file.
‘Choose’ is used when coordinates have already been read into ADT, and ‘Open’ is used to read coordinates from a file.