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.

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

2021 ◽  
Author(s):  
Jinyoung Byun ◽  
Juyong Lee
Keyword(s):  
Md Simulation ◽  
Force Fields ◽  
Hot Spot ◽  
Md Simulations ◽  
Solvation Energy ◽  
Binding Energies ◽  
Binding Affinities ◽  
Main Protease ◽  
Single Force ◽  
Simulation Time

Abstract In this study, we investigated the binding affinities between the main protease of SARS-CoV-2 virus and its various ligand to identify the hot spot residues of the protease. To investigate the effect of various force fields, we performed MD simulations with three different force fields: GROMOS54a7, Amber99-SB, and CHARMM36. The total amount of MD simulation time was 1.1 µs. To investigate how known ligands interact with Mpro of SARS-CoV-2, the binding affinities were calculated by using the MMPBSA approach. It is identified that no single force field succeeded in predicting the relative rankings of experimental binding affinities. When compared between different force fields, Amber99-SB and GROMOS54a7 results are fairly correlated while CHARMM36 results show weak or almost no correlations with the others. Additionally, we identified specific residues of Mpro, which contribute more importantly to the binding energies with ligands. 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 develop novel inhibitors of SARS-CoV-2.

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.

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Zbigniew Dutkiewicz
Keyword(s):  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Development Project ◽  
Quantum Mechanical ◽  
Prediction Methods ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Quantum Mechanical Description ◽  
Optimization Stage

AbstractDrug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

scholarly journals High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling

2021 ◽  
Author(s):  
Théo Jaffrelot Inizan ◽  
Frédéric Célerse ◽  
Olivier Adjoua ◽  
Dina El Ahdab ◽  
Luc-Henri Jolly ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
High Resolution ◽  
Adaptive Sampling ◽  
Force Fields ◽  
Sampling Strategy ◽  
Md Simulations ◽  
Conformational Space ◽  
Many Body ◽  
Polarizable Force Fields ◽  
Main Protease

We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs).

scholarly journals Climbing up and down binding landscapes: a high-throughput study of mutational effects in homologous protein-protein complexes

2020 ◽  
Author(s):  
Michael Heyne ◽  
Jason Shirian ◽  
Itay Cohen ◽  
Yoav Peleg ◽  
Evette S. Radisky ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Protein Interactions ◽  
Enzyme Inhibitor ◽  
Binding Free Energy ◽  
Hot Spot ◽  
Cold Spot ◽  
Binding Affinities ◽  
High Affinity ◽  
Binding Interface ◽  
Biological Complexes

AbstractEach protein-protein interaction (PPI) has evolved to possess binding affinity that is compatible with its cellular function. As such, cognate enzyme/inhibitor interactions frequently exhibit very high binding affinities, while structurally similar non-cognate PPIs possess substantially weaker binding affinities. To understand how slight differences in sequence and structure could lead to drastic changes in PPI binding free energy (ΔΔGbind), we study three homologous PPIs that span nine orders of magnitude in binding affinity and involve a serine protease interacting with an inhibitor BPTI. Using state-of-the-art methodology that combines protein randomization and affinity sorting coupled to next-generation sequencing and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations in the BPTI binding interface. We demonstrate that the three homologous PPIs possess drastically different binding landscapes and lie at different points in respect to the landscape maximum. Furthermore, the three PPIs demonstrate distinct patterns of coupling energies between two simultaneous mutations that depend not only on positions involved but also on the nature of the mutation. Interestingly, we find that in all three PPIs positive epistasis is frequently observed at hot-spot positions where mutations lead to loss of high affinity, while conversely negative epistasis is observed at cold-spot positions, where mutations lead to affinity enhancement. The new insights on PPI evolution revealed in this study will be invaluable in understanding evolution of other biological complexes and can greatly facilitate design of novel high-affinity protein inhibitors.SignificanceProtein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and non-cognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we studied the effect of tens of thousands of single and double mutations on binding affinity of three homologous protease-inhibitor complexes. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to all biological complexes in the cell.

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.

Estimation of the ligand-binding free energy of checkpoint kinase 1 via non-equilibrium MD simulations

2020 ◽  
Vol 100 ◽  
pp. 107648 ◽  
Author(s):  
Nguyen Thi Mai ◽  
Ngo Thi Lan ◽  
Thien Y Vu ◽  
Phuong Thi Mai Duong ◽  
Nguyen Thanh Tung ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Md Simulations ◽  
Checkpoint Kinase ◽  
Checkpoint Kinase 1 ◽  
Non Equilibrium

scholarly journals Simulating Chalcogen Bonding Using Molecular Mechanics: A Pseudoatom Approach to Model Ebselen.

2020 ◽  
Author(s):  
Thomas Fellowes ◽  
JONATHAN WHITE
Keyword(s):  
Decomposition Analysis ◽  
Md Simulations ◽  
Accurate Method ◽  
Energy Decomposition Analysis ◽  
Selenium Atom ◽  
Biological Macromolecules ◽  
Energy Decomposition ◽  
Amber Force Field ◽  
Sigma Hole ◽  
Positively Charged

The organoselenium compound ebselen has recently been investigated as a treatment for COVID-19, however<br>efforts to model ebselen in silico have been hampered by the lack of a efficient and accurate method to assess<br>its binding to biological macromolecules. We present here a Generalized Amber Force Field modification which<br>incorporates classical parameters for the selenium atom in ebselen, as well as a positively charged pseudoatom to<br>simulate the sigma?-hole, a quantum mechanical phenomenon that dominates the chemistry of ebselen. Our approach<br>is justified using an energy decomposition analysis of a number DFT optimised structures, which shows that the<br>?sigma-hole interaction is primarily electrostatic in origin. Finally, our model is verified by conducting MD simulations<br>on a number of simple complexes, as well the clinically relevant SOD1, which is known to bind to ebselen.

scholarly journals Exploring the Binding Mechanism and Accessible Angle of SARS-CoV-2 Spike and ACE2 by Molecular Dynamics Simulation and Free Energy Calculation

2020 ◽  
Author(s):  
Cheng Peng ◽  
Zhengdan Zhu ◽  
Yulong Shi ◽  
Xiaoyu Wang ◽  
Kaijie Mu ◽  
...  
Keyword(s):  
Drug Target ◽  
Decomposition Analysis ◽  
Dynamics Simulation ◽  
Energy Decomposition Analysis ◽  
Computational Techniques ◽  
Energy Calculation ◽  
Health Crisis ◽  
S Protein ◽  
Public Health Crisis ◽  
Binding Mechanisms

<p></p><p>The SARS-CoV-2 has caused more than 2,000 deaths as of 20 February 2020 worldwide but there is no approved effective drug. The <a>SARS-CoV-2</a> spike (S) glycoprotein is a key drug target due to its indispensable function for viral infection and fusion with ACE2 as a receptor. To facilitate the drug discovery and development with S protein as drug target, various computational techniques were used in this study to evaluate the binding mechanisms between S protein and its acceptor ACE2. Impressively, SARS-CoV-2 S protein has higher affinity binding to ACE2 at two different “up” angles of RBD than SARS-CoV S protein to ACE2 at the same angles. The energy decomposition analysis showed that more interactions formed between SARS-CoV-2 S protein and ACE2, which may partially account for its higher infectiousness than SARS-CoV. In addition, we found that 52.2° is a starting accessible “up” angle of the BRD of SARS-CoV-2 S protein to bind ACE2, demonstrating that BRD is not necessary to be fully opened in order to bind ACE2. We hope that this work will be helpful for the design of effective SARS-CoV-2 S protein inhibitors to address the ongoing public health crisis.</p><p></p>

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