scholarly journals Potential Drug Candidates for SARS-CoV-2 Using Computational Screening and Enhanced Sampling Methods

2020 ◽  
Author(s):  
Sadanandam Namsani ◽  
Debabrata Pramanik ◽  
Mohd Aamir Khan ◽  
Sudip Roy ◽  
Jayant Singh
Keyword(s):  
Free Energy ◽  
High Performance ◽  
Search Space ◽  
Computational Method ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Drug Candidates ◽  
Drug Molecules ◽  
Computational Screening ◽  
Graphical Processing

<div><div><div><p>Here we report new chemical entities that are highly specific in binding towards the 3-chymotrypsin- like cysteine protease (3CLpro) protein present in the novel SARS-CoV2 virus. The viral 3CLpro</p><p>protein controls coronavirus replication. Therefore, 3CLpro is identified as a target for drug molecules. We have implemented an enhanced sampling method in combination with molecular dynamics and docking to bring down the computational screening search space to four molecules that could be synthesised and tested against COVID-19. Our computational method is much more robust than any other method available for drug screening e.g., docking, because of sampling of the free energy surface of the binding site of the protein (including the ligand) and use of explicit solvent. We have considered all possible interactions between all the atoms present in the protein, ligands, and water. Using high performance computing with graphical processing units we are able to perform large number of simulations within a month's time and converge to 4 most strongly bound ligands (by free energy and other scores) from a set of 17 ligands with lower docking scores. Based on our results and analysis, we claim with high confidence, that we have identified four potential ligands. Out of those, one particular ligand is the most promising candidate, based on free energy data, for further synthesis and testing against SARS-CoV-2 and might be effective for the cure of COVID-19.</p></div></div></div>

scholarly journals Potential Drug Candidates for SARS-CoV-2 Using Computational Screening and Enhanced Sampling Methods

2020 ◽  
Author(s):  
Sadanandam Namsani ◽  
Debabrata Pramanik ◽  
Mohd Aamir Khan ◽  
Sudip Roy ◽  
Jayant Singh
Keyword(s):  
Free Energy ◽  
High Performance ◽  
Search Space ◽  
Computational Method ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Drug Candidates ◽  
Drug Molecules ◽  
Computational Screening ◽  
Graphical Processing

<div><div><div><p>Here we report new chemical entities that are highly specific in binding towards the 3-chymotrypsin- like cysteine protease (3CLpro) protein present in the novel SARS-CoV2 virus. The viral 3CLpro</p><p>protein controls coronavirus replication. Therefore, 3CLpro is identified as a target for drug molecules. We have implemented an enhanced sampling method in combination with molecular dynamics and docking to bring down the computational screening search space to four molecules that could be synthesised and tested against COVID-19. Our computational method is much more robust than any other method available for drug screening e.g., docking, because of sampling of the free energy surface of the binding site of the protein (including the ligand) and use of explicit solvent. We have considered all possible interactions between all the atoms present in the protein, ligands, and water. Using high performance computing with graphical processing units we are able to perform large number of simulations within a month's time and converge to 4 most strongly bound ligands (by free energy and other scores) from a set of 17 ligands with lower docking scores. Based on our results and analysis, we claim with high confidence, that we have identified four potential ligands. Out of those, one particular ligand is the most promising candidate, based on free energy data, for further synthesis and testing against SARS-CoV-2 and might be effective for the cure of COVID-19.</p></div></div></div>

Potential Drug Candidates for SARS-CoV-2 Using Computational Screening and Enhanced Sampling Methods

2020 ◽  
Author(s):  
Sadanandam Namsani ◽  
Debabrata Pramanik ◽  
Mohd Aamir Khan ◽  
Sudip Roy ◽  
Jayant Singh
Keyword(s):  
Free Energy ◽  
High Performance ◽  
Specific Binding ◽  
Search Space ◽  
Computational Method ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Drug Candidates ◽  
Computational Screening ◽  
Experimental Crystal

<p>Here, we report new chemical entities that exhibit highly specific binding to the 3-chymotrypsin-like cysteine protease (3CLpro) present in the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Because the viral 3CLpro protein controls coronavirus replication, 3CLpro is identified as a target for drug molecules. We implemented an enhanced sampling method in combination with molecular dynamics and docking to reduce the computational screening search space to four molecules that could be synthesized and tested against SARS-CoV-2. Our computational method is much more robust than any other method available for drug screening (e.g., docking) because of sampling of the free energy surface of the binding site of the protein (including the ligand) and use of explicit solvent. We have considered all possible interactions between all the atoms present in the protein, ligands, and water. Using high-performance computing with graphical processing units, we were able to perform a large number of simulations within a month and converge the results to the four most strongly bound ligands (based on free energy and other scores) from a set of 17 ligands with lower docking scores. Additionally, we have considered N3 and 13b α-ketoamide inhibitors as controls for which experimental crystal structures are available. Out of the top four ligands, PI-06 was found to have a higher screening score compared to the controls. Based on our results and analysis, we confidently claim that we have identified four potential ligands, out of which one ligand is the best choice based on free energy and the most promising candidate for further synthesis and testing against SARS-CoV-2.<br></p>

scholarly journals Potential Drug Candidates for SARS-CoV-2 Using Computational Screening and Enhanced Sampling Methods

2020 ◽  
Author(s):  
Sadanandam Namsani ◽  
Debabrata Pramanik ◽  
Mohd Aamir Khan ◽  
Sudip Roy ◽  
Jayant Singh
Keyword(s):  
Free Energy ◽  
High Performance ◽  
Specific Binding ◽  
Search Space ◽  
Computational Method ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Drug Candidates ◽  
Computational Screening ◽  
Experimental Crystal

<p>Here, we report new chemical entities that exhibit highly specific binding to the 3-chymotrypsin-like cysteine protease (3CLpro) present in the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Because the viral 3CLpro protein controls coronavirus replication, 3CLpro is identified as a target for drug molecules. We implemented an enhanced sampling method in combination with molecular dynamics and docking to reduce the computational screening search space to four molecules that could be synthesized and tested against SARS-CoV-2. Our computational method is much more robust than any other method available for drug screening (e.g., docking) because of sampling of the free energy surface of the binding site of the protein (including the ligand) and use of explicit solvent. We have considered all possible interactions between all the atoms present in the protein, ligands, and water. Using high-performance computing with graphical processing units, we were able to perform a large number of simulations within a month and converge the results to the four most strongly bound ligands (based on free energy and other scores) from a set of 17 ligands with lower docking scores. Additionally, we have considered N3 and 13b α-ketoamide inhibitors as controls for which experimental crystal structures are available. Out of the top four ligands, PI-06 was found to have a higher screening score compared to the controls. Based on our results and analysis, we confidently claim that we have identified four potential ligands, out of which one ligand is the best choice based on free energy and the most promising candidate for further synthesis and testing against SARS-CoV-2.<br></p>

scholarly journals Discover, Sample and Refine: Exploring Chemistry with Enhanced Sampling Techniques

2021 ◽  
Author(s):  
Umberto Raucci ◽  
Valerio Rizzi ◽  
Michele Parrinello
Keyword(s):  
Free Energy ◽  
Ad Hoc ◽  
Spectral Graph Theory ◽  
Ab Initio Molecular Dynamics ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Collective Variables ◽  
User Input ◽  
Specific Reactions

Over the last few decades enhanced sampling methods have made great strides. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and characterization of reaction paths. Central to our strategy is the use of the recently developed explore variant of the on-the-fly probability enhanced sampling method. Like metadynamics, this method is based on the identification of appropriate collective variables. Our first step is the discovery of new chemical reactions and it is performed biasing a one dimensional collective variable derived from spectral graph theory. Once new reaction pathways are detected, we construct ad-hoc tailored neural-network based collective variables to improve sampling of specific reactions and finally we refine the results using free energy perturbation theory. Our workflow has been successfully applied to both intramolecular and intermolecular reactions. Without any chemical hypothesis, we discovered several possible products, computed the free energy surface at semiempirical level, and finally refined it with a more accurate Hamiltonian. Our workflow requires minimal user input, and thanks to its modularity and flexibility, can extend the scope of ab initio molecular dynamics for the exploration and characterization of reaction space.

scholarly journals Thermodynamics and free energy landscape of BAR-domain dimerization from molecular simulations

2020 ◽  
Author(s):  
Adip Jhaveri ◽  
Dhruw Maisuria ◽  
Matthew Varga ◽  
Dariush Mohammadyani ◽  
Margaret E Johnson
Keyword(s):  
Free Energy ◽  
Energy Surface ◽  
Md Simulations ◽  
Coarse Grained ◽  
Enhanced Sampling ◽  
Free Energies ◽  
Free Energy Surface ◽  
Bar Domain ◽  
Bar Domains ◽  
Protein Models

AbstractNearly all proteins interact specifically with other proteins, often forming reversible bound structures whose stability is critical to function. Proteins with BAR domains function to bind to, bend, and remodel biological membranes, where the dimerization of BAR domains is a key step in this function. Here we characterize the binding thermodynamics of homodimerization between the LSP1 BAR domain proteins in solution, using Molecular Dynamics (MD) simulations. By combining the MARTINI coarse-grained protein models with enhanced sampling through metadynamics, we construct a two-dimensional free energy surface quantifying the bound versus unbound ensembles as a function of two distance variables. Our simulations portray a heterogeneous and extraordinarily stable bound ensemble for these modeled LSP1 proteins. The proper crystal structure dimer has a large hydrophobic interface that is part of a stable minima on the free energy surface, which is enthalpically the minima of all bound structures. However, we also find several other stable nonspecific dimers with comparable free energies to the specific dimer. Through structure-based clustering of these bound structures, we find that some of these ‘nonspecific’ contacts involve extended tail regions that help stabilize the higher-order oligomers formed by BAR-domains, contacts that are separated from the homodimer interface. We find that the known membrane-binding residues of the LSP1 proteins rarely participate in any of the bound interfaces, but that both patches of residues are aligned to interact with the membrane in the specific dimer. Hence, we would expect a strong selection of the specific dimer in binding to the membrane. The effect of a 100mM NaCl buffer reduces the relative stability of nonspecific dimers compared to the specific dimer, indicating that it would help prevent aggregation of the proteins. With these results, we provide the first free energy characterization of interaction pathways in this important class of membrane sculpting domains, revealing a variety of interfacial contacts outside of the specific dimer that may help stabilize its oligomeric assemblies.

scholarly journals Free Energy Surfaces and Barriers for Vacancy Diffusion on Al(100), Al(110), Al(111) Reconstructed Surfaces

Nanomaterials ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 76
Author(s):  
Junais Habeeb Mokkath ◽  
Mufasila Mumthaz Muhammed ◽  
Ali J. Chamkha
Keyword(s):  
Free Energy ◽  
Energy Barrier ◽  
Computational Study ◽  
Metastable States ◽  
Vacancy Diffusion ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Collective Variables ◽  
Surface Vacancy ◽  
Energy Surfaces

Metadynamics is a popular enhanced sampling method based on the recurrent application of a history-dependent adaptive bias potential that is a function of a selected number of appropriately chosen collective variables. In this work, using metadynamics simulations, we performed a computational study for the diffusion of vacancies on three different Al surfaces [reconstructed Al(100), Al(110), and Al(111) surfaces]. We explored the free energy landscape of diffusion and estimated the barriers associated with this process on each surface. It is found that the surfaces are unique regarding vacancy diffusion. More specically, the reconstructed Al(110) surface presents four metastable states on the free energy surface having sizable and connected passage-ways with an energy barrier of height 0.55 eV. On the other hand, the reconstructed Al(100)/Al(111) surfaces exhibit two/three metastable states, respectively, with an energy barrier of height 0.33 eV. The findings in this study can help to understand surface vacancy diffusion in technologically relevant Al surfaces.

Study of Transcrystallization in Polymer Composites

1989 ◽  
Vol 170 ◽  
Author(s):  
Benjamin S. Hsiao ◽  
J. H. Eric
Keyword(s):  
Thermal Conductivity ◽  
Free Energy ◽  
Carbon Fiber ◽  
Plasma Treatment ◽  
Polymer Composites ◽  
High Performance ◽  
Interfacial Strength ◽  
Fiber Surface ◽  
Semicrystalline Polymers ◽  
First Case

AbstractTranscrystallization of semicrystalline polymers, such as PEEK, PEKK and PPS, in high performance composites has been investigated. It is found that PPDT aramid fiber and pitch-based carbon fiber induce a transcrystalline interphase in all three polymers, whereas in PAN-based carbon fiber and glass fiber systems, transcrystallization occurs only under specific circumstances. Epitaxy is used to explain the surface-induced transcrystalline interphase in the first case. In the latter case, transcrystallization is probably not due to epitaxy, but may be attributed to the thermal conductivity mismatch. Plasma treatment on the fiber surface showed a negligible effect on inducing transcrystallization, implying that surface-free energy was not important. A microdebonding test was adopted to evaluate the interfacial strength between the fiber and matrix. Our preliminary results did not reveal any effect on the fiber/matrix interfacial strength of transcrystallinity.

scholarly journals Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Germano Heinzelmann ◽  
Michael K. Gilson
Keyword(s):  
Free Energy ◽  
Early Stage ◽  
Binding Free Energy ◽  
Low Cost ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Drug Candidates ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

AbstractAbsolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

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