scholarly journals In Silico Mutagenesis Based Remodeling of SARs-CoV-1 Peptide (ATLQAIAS) to Inhibit SARs-CoV-2: Structural-dynamics and Free Energy Calculations

2020 ◽  
Author(s):  
Abbas Khan ◽  
Shaheena Umbreen ◽  
Asma Hameed ◽  
Rida Fatima ◽  
Ujala Zahoor ◽  
...  
Keyword(s):  
Free Energy ◽  
Binding Affinity ◽  
In Silico ◽  
Therapeutic Potential ◽  
Decomposition Analysis ◽  
Free Energy Calculations ◽  
Energy Decomposition Analysis ◽  
Energy Calculation ◽  
Binding Affinities ◽  
The World

Abstract Background:The prolific spread of COVID-19 caused by a novel coronavirus (SARS-CoV-2) from its epicenter in Wuhan, China, to every nook and cranny of the world after December 2019, jeopardize the prevailing health system in the world and has raised serious concerns about human safety. To date efforts are continuing to design small molecule inhibitor, vaccines and many other therapeutic options are practiced but their final therapeutic potential is still to be tested. Using the old drug or vaccine or peptides could aid this process to avoid such long experimental procedure. Results:Hence, here we have repurposed a small peptide (ATLQAIAS) from the previous study which reported the inhibitory effects of this peptide. We used in silico mutagenesis approach to design more peptides from the native wild peptide, which revealed that substitutions (T2W, T2Y, L3R and A5W) could increase the binding affinity of the peptide towards the 3CLpro. Furthermore, using MD simulation and free energy calculation confirmed its dynamics stability and stronger binding affinities. Per-residues energy decomposition analysis revealed that the specified substitution significantly increased the binding affinity at residue level. Conclusion:Our wide-ranging analyses of binding affinities disclosed that our designed peptide owns the potential to hinder the SARS-CoV-2 and will reduce the progression of SARs-CoV-2-borne pneumonia. Our analysis strongly suggests the experimental and clinical validation of these peptides to curtail the recent corona outbreak.

scholarly journals Binding Affinities of Sanggenon Derivatives as PTP1B Inhibitors; Using Molecular Dynamics and Free Energy Calculations

2021 ◽  
Author(s):  
Safak OZHAN KOCAKAYA
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Tyrosine Phosphatase ◽  
Decomposition Analysis ◽  
Md Simulations ◽  
Free Energy Calculations ◽  
Energy Decomposition Analysis ◽  
Free Energy Decomposition ◽  
Poisson Boltzmann ◽  
Ptp1b Inhibitors

Abstract Recently, protein tyrosine phosphatase 1B (PTP1B) inhibitors have become the frontier as possible targeting for anti-cancer and antidiabetic drugs. The contemporary observe represents a pc assisted version to investigate the importance of precise residues within the binding web site of PTP1B with numerous Sanggenon derivatives remoted from nature. Molecular dynamics (MD) simulations were performed to estimate the dynamics of the complexes, and absolute binding unfastened energies have been calculated with exclusive additives, and carried out through the usage of the Molecular Mechanics-Poisson-Boltzmann floor region (MM-PB/SA) and Generalized Born surface vicinity (MM-GB/SA) strategies. The effects show that the expected free energies of the complexes are normally constant with the available experimental statistics. MM/GBSA free energy decomposition analysis shows that the residues Asp29, Arg24, Met258, and , Arg254 in the second active site in PTP1B are crucial for the excessive selectivity of the inhibitors.

scholarly journals Large-scale binding affinity calculations on commodity compute clouds

2020 ◽  
Vol 10 (6) ◽  
pp. 20190133
Author(s):  
S. J. Zasada ◽  
D. W. Wright ◽  
P. V. Coveney
Keyword(s):  
Free Energy ◽  
Binding Affinity ◽  
Large Scale ◽  
Model Building ◽  
Structural Information ◽  
Md Simulations ◽  
Free Energy Calculations ◽  
Binding Affinities ◽  
Software Infrastructure ◽  
Computing Platforms

In recent years, it has become possible to calculate binding affinities of compounds bound to proteins via rapid, accurate, precise and reproducible free energy calculations. This is imperative in drug discovery as well as personalized medicine. This approach is based on molecular dynamics (MD) simulations and draws on sequence and structural information of the protein and compound concerned. Free energies are determined by ensemble averages of many MD replicas, each of which requires hundreds of cores and/or GPU accelerators, which are now available on commodity cloud computing platforms; there are also requirements for initial model building and subsequent data analysis stages. To automate the process, we have developed a workflow known as the binding affinity calculator. In this paper, we focus on the software infrastructure and interfaces that we have developed to automate the overall workflow and execute it on commodity cloud platforms, in order to reliably predict their binding affinities on time scales relevant to the domains of application, and illustrate its application to two free energy methods.

scholarly journals Identifying the Hot Spot Residues of the SARS-CoV-2 Main Protease Using MM-PBSA and Multiple Force Fields

Life ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 54
Author(s):  
Jinyoung Byun ◽  
Juyong Lee
Keyword(s):  
Ligand Binding ◽  
Force Fields ◽  
Binding Free Energy ◽  
Hot Spot ◽  
Decomposition Analysis ◽  
Md Simulations ◽  
Energy Decomposition Analysis ◽  
Energy Calculation ◽  
Binding Affinities ◽  
Main Protease

In this study, we investigated the binding affinities between the main protease of SARS-CoV-2 virus (Mpro) and its various ligands to identify the hot spot residues of the protease. To benchmark the influence of various force fields on hot spot residue identification and binding free energy calculation, we performed MD simulations followed by MM-PBSA analysis with three different force fields: CHARMM36, AMBER99SB, and GROMOS54a7. We performed MD simulations with 100 ns for 11 protein–ligand complexes. From the series of MD simulations and MM-PBSA calculations, it is identified that the MM-PBSA estimations using different force fields are weakly correlated to each other. From a comparison between the force fields, AMBER99SB and GROMOS54a7 results are fairly correlated while CHARMM36 results show weak or almost no correlations with the others. Our results suggest that MM-PBSA analysis results strongly depend on force fields and should be interpreted carefully. Additionally, we identified the hot spot residues of Mpro, which play critical roles in ligand binding through energy decomposition analysis. It is identified that the residues of the S4 subsite of the binding site, N142, M165, and R188, contribute strongly to ligand binding. In addition, the terminal residues, D295, R298, and Q299 are identified to have attractive interactions with ligands via electrostatic and solvation energy. We believe that our findings will help facilitate developing the novel inhibitors of SARS-CoV-2.

Computational study on the molecular mechanisms of drug resistance of Narlaprevir due to V36M, R155K, V36M+R155K, T54A, and A156T mutations of HCV NS3/4A protease

2014 ◽  
Vol 92 (5) ◽  
pp. 357-369 ◽  
Author(s):  
Huiqun Wang ◽  
Lingling Geng ◽  
Bo-Zhen Chen ◽  
Mingjuan Ji
Keyword(s):  
Drug Resistance ◽  
Free Energy ◽  
Molecular Mechanism ◽  
Molecular Mechanisms ◽  
Computational Study ◽  
Decomposition Analysis ◽  
Md Simulations ◽  
Free Energy Calculations ◽  
Energy Decomposition Analysis ◽  
Free Energy Decomposition

Narlaprevir is a novel NS3/4A protease inhibitor of hepatitis C virus (HCV), and it has been tested in a phase II clinical trial recently. However, distinct drug-resistance of Narlaprevir has been discovered. In our study, the molecular mechanisms of drug-resistance of Narlaprevir due to the mutations V36M, R155K, V36M+R155K, T54A, and A156T of NS3/4A protease have been investigated by molecular dynamics (MD) simulations, free energy calculations, and free energy decomposition analysis. The predicted binding free energies of Narlaprevir towards the wild-type and five mutants show that the mutations V36M, R155K, and T54A lead to low-level drug resistance and the mutations V36M+R155K and A156T lead to high-level drug resistance, which is consistent with the experimental data. The analysis of the individual energy terms indicates that the van der Waals contribution is important for distinguishing the binding affinities of these six complexes. These findings again show that the combination of different molecular modeling techniques is an efficient way to interpret the molecular mechanism of drug-resistance. Our work mainly elaborates the molecular mechanism of drug-resistance of Narlaprevir and further provides valuable information for developing novel, safer, and more potent HCV antiviral drugs in the near future.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

SAMPL7 TrimerTrip Host-Guest Binding Poses and Binding Affinities from Spherical-Coordinates-Biased Simulations

2020 ◽  
Author(s):  
Zhaoxi Sun
Keyword(s):  
Free Energy ◽  
Computational Modelling ◽  
Free Energy Calculations ◽  
Energy Estimates ◽  
Binding Affinities ◽  
Spherical Coordinates ◽  
Collective Variables ◽  
Guest Binding ◽  
Starting Point ◽  
Sampl Challenge

Host-guest binding remains a major challenge in modern computational modelling. The newest 7<sup>th</sup> statistical assessment of the modeling of proteins and ligands (SAMPL) challenge contains a new series of host-guest systems. The TrimerTrip host binds to 16 structurally diverse guests. Previously, we have successfully employed the spherical coordinates as the collective variables coupled with the enhanced sampling technique metadynamics to enhance the sampling of the binding/unbinding event, search for possible binding poses and predict the binding affinities in all three host-guest binding cases of the 6<sup>th</sup> SAMPL challenge. In this work, we employed the same protocol to investigate the TrimerTrip host in the SAMPL7 challenge. As no binding pose is provided by the SAMPL7 host, our simulations initiate from randomly selected configurations and are proceeded long enough to obtain converged free energy estimates and search for possible binding poses. The predicted binding affinities are in good agreement with the experimental reference, and the obtained binding poses serve as a nice starting point for end-point or alchemical free energy calculations.

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Combinatorial Libraries ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Efficient Estimation ◽  
Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

scholarly journals Predicting gas selectivity in organic ionic plastic crystals by free energy calculations

RSC Advances ◽  
2021 ◽  
Vol 11 (32) ◽  
pp. 19623-19629
Author(s):  
Vinay S. Kandagal ◽  
Jennifer M. Pringle ◽  
Maria Forsyth ◽  
Fangfang Chen
Keyword(s):  
Free Energy ◽  
Gas Separation ◽  
Free Energy Calculations ◽  
Free Energy Calculation ◽  
Energy Calculation ◽  
Plastic Crystal ◽  
Plastic Crystals ◽  
New Type ◽  
Gas Molecules ◽  
Gas Selectivity

The free energy calculation shows the different free energy changes of the adsorption and absorption of gas molecules into an organic ionic plastic crystal, successfully predicting the gas selectivity of this new type of gas separation material.

scholarly journals Screening of Chloroquine, Hydroxychloroquine and Its Derivatives for Their Binding Affinity to Multiple SARS-CoV-2 Protein Drug Targets

2020 ◽  
Author(s):  
Mallikarjuna Nimgampalle ◽  
Vasudharani Devanthan ◽  
Ambrish Saxena
Keyword(s):  
Binding Affinity ◽  
In Silico ◽  
Drug Targets ◽  
Therapeutic Potential ◽  
Viral Proteins ◽  
Protein Drug ◽  
Potential Treatment ◽  
Health Authorities ◽  
Derivatives Of

Recently Chloroquine and its derivative Hydroxychloroquine have garnered enormous interest amongst the clinicians and health authorities’ world over as a potential treatment to contain COVID-19 pandemic. The present research aims at investigating the therapeutic potential of Chloroquine and its potent derivative Hydroxychloroquine against SARS-CoV-2 viral proteins. At the same time we have screened some chemically synthesized derivatives of Chloroquine and compared their binding efficacy with chemically synthesized Chloroquine derivatives through <i>in silico</i>approaches. For the purpose of the study, we have selected some essential viral proteins and enzymes implicated in SARS-CoV-2 replication and multiplication as putative drug targets.<br>

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