Understanding the binding affinities between SFRP1CRD, SFRP1Netrin, Wnt5B and frizzled receptors 2, 3 and 7 using MD simulations

2021 ◽  
pp. 1-14
Author(s):  
Raghava R. Sunkara ◽  
Shruti Koulgi ◽  
Vinod Jani ◽  
Nikhil Gadewal ◽  
Uddhavesh Sonavane ◽  
...  
Keyword(s):  
Md Simulations ◽  
Binding Affinities ◽  
Frizzled Receptors

scholarly journals Increasing the Affinity of an O-Antigen Polysaccharide Binding Site in Shigella Flexneri Bacteriophage Sf6 Tailspike Protein

2019 ◽  
Author(s):  
R. Sonja Kunstmann ◽  
Olof Engström ◽  
Marko Wehle ◽  
Göran Widmalm ◽  
Mark Santer ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Surface Plasmon Resonance ◽  
Binding Site ◽  
Shigella Flexneri ◽  
Md Simulations ◽  
Conformational Space ◽  
Binding Affinities ◽  
The Impact ◽  
Tailspike Protein ◽  
Good Agreement

We analysed the tailspike from bacteriophage Sf6 in complex with the O-polysaccharide of the pathogen Shigella flexneri. The conformational space populated by the polyrhamnose backbone of the S. flexneri O-polysaccharide as studied by an octasaccharide in complex with Sf6TSP could be well described with 2D 1H,1H-trNOESY NMR, utilizing a combination of methine-methine and methine-methyl correlations. The results are in good agreement with the conformations obtained from molecular dynamics (MD) simulations. To examine the impact of amino acid exchanges in the glycan binding site of Sf6TSP, MD simulations were used to predict increased O-polysaccharide binding affinities. We used surface plasmon resonance on S. flexneri O-polysaccharide surfaces to measure affinity increases in the obtained mutants. <br>

scholarly journals Increasing the Affinity of an O-Antigen Polysaccharide Binding Site in Shigella Flexneri Bacteriophage Sf6 Tailspike Protein

2019 ◽  
Author(s):  
R. Sonja Kunstmann ◽  
Olof Engström ◽  
Marko Wehle ◽  
Göran Widmalm ◽  
Mark Santer ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Surface Plasmon Resonance ◽  
Binding Site ◽  
Shigella Flexneri ◽  
Md Simulations ◽  
Conformational Space ◽  
Binding Affinities ◽  
The Impact ◽  
Tailspike Protein ◽  
Good Agreement

We analysed the tailspike from bacteriophage Sf6 in complex with the O-polysaccharide of the pathogen Shigella flexneri. The conformational space populated by the polyrhamnose backbone of the S. flexneri O-polysaccharide as studied by an octasaccharide in complex with Sf6TSP could be well described with 2D 1H,1H-trNOESY NMR, utilizing a combination of methine-methine and methine-methyl correlations. The results are in good agreement with the conformations obtained from molecular dynamics (MD) simulations. To examine the impact of amino acid exchanges in the glycan binding site of Sf6TSP, MD simulations were used to predict increased O-polysaccharide binding affinities. We used surface plasmon resonance on S. flexneri O-polysaccharide surfaces to measure affinity increases in the obtained mutants. <br>

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.

Ligand Binding Affinities from MD Simulations

2002 ◽  
Vol 35 (6) ◽  
pp. 358-365 ◽  
Author(s):  
Johan Åqvist ◽  
Victor B. Luzhkov ◽  
Bjørn O. Brandsdal
Keyword(s):  
Ligand Binding ◽  
Md Simulations ◽  
Binding Affinities

scholarly journals Evaluating the Performance of a Non-Bonded Cu2+ Model Including Jahn−Teller Effect into the Binding of Tyrosinase Inhibitors

2020 ◽  
Vol 21 (13) ◽  
pp. 4783
Author(s):  
Lucas Sousa Martins ◽  
Jerônimo Lameira ◽  
Hendrik G. Kruger ◽  
Cláudio Nahum Alves ◽  
José Rogério A. Silva
Keyword(s):  
Active Site ◽  
Binding Free Energy ◽  
Classical Model ◽  
Md Simulations ◽  
Teller Effect ◽  
Binding Affinities ◽  
Linear Interaction ◽  
Jahn Teller ◽  
Jahn Teller Effect ◽  
Type 3

Tyrosinase (TYR) is a metalloenzyme classified as a type-3 copper protein, which is involved in the synthesis of melanin through a catalytic process beginning with the conversion of the amino acid l-Tyrosine (l-Tyr) to l-3,4-dihydroxyphenylalanine (l-DOPA). It plays an important role in the mechanism of melanogenesis in various organisms including mammals, plants, and fungi. Herein, we used a combination of computational molecular modeling techniques including molecular dynamic (MD) simulations and the linear interaction energy (LIE) model to evaluate the binding free energy of a set of analogs of kojic acid (KA) in complex with TYR. For the MD simulations, we used a dummy model including the description of the Jahn–Teller effect for Cu2+ ions in the active site of this enzyme. Our results show that the LIE model predicts the TYR binding affinities of the inhibitor in close agreement to experimental results. Overall, we demonstrate that the classical model provides a suitable description of the main interactions between analogs of KA and Cu2+ ions in the active site of TYR.

scholarly journals Relative Affinities of Protein-Cholesterol Interactions from Equilibrium Molecular Dynamics Simulations

2021 ◽  
Author(s):  
T. Bertie Ansell ◽  
Luke Curran ◽  
Michael R Horrell ◽  
Tanadet Pipatpolkai ◽  
Suzanne C Letham ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Membrane Proteins ◽  
Membrane Protein ◽  
Specific Interaction ◽  
Protein Structures ◽  
Md Simulations ◽  
Lipid Binding ◽  
Coarse Grained ◽  
Binding Affinities ◽  
Interaction Sites

Specific interactions of lipids with membrane proteins contribute to protein stability and function. Multiple lipid interactions surrounding a membrane protein are often identified in molecular dynamics (MD) simulations and are, increasingly, resolved in cryo-EM densities. Determining the relative importance of specific interaction sites is aided by determination of lipid binding affinities by experimental or simulation methods. Here, we develop a method for determining protein-lipid binding affinities from equilibrium coarse-grained MD simulations using binding saturation curves, designed to mimic experimental protocols. We apply this method to directly obtain affinities for cholesterol binding to multiple sites on a range of membrane proteins and compare our results with free energies obtained from density-based equilibrium methods and with potential of mean force calculations, getting good agreement with respect to the ranking of affinities for different sites. Thus, our binding saturation method provides a robust, high-throughput alternative for determining the relative consequence of individual sites seen in e.g. cryo-EM derived membrane protein structures surrounded by a plethora of ancillary lipid densities.

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.

ChemInform Abstract: Ligand Binding Affinities from MD Simulations

ChemInform ◽  
2010 ◽  
Vol 33 (36) ◽  
pp. no-no
Author(s):  
Johan Aaqvist ◽  
Victor B. Luzhkov ◽  
Bjoern O. Brandsdal
Keyword(s):  
Ligand Binding ◽  
Md Simulations ◽  
Binding Affinities
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