scholarly journals Molecular Docking and Virtual Screening of an Influenza Virus Inhibitor That Disrupts Protein–Protein Interactions

Viruses ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2229
Author(s):  
Yixin Ren ◽  
Sihui Long ◽  
Shuang Cao
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Influenza Virus ◽  
Protein Interactions ◽  
Acute Respiratory Infection ◽  
Infection Process ◽  
Protein Protein Interactions ◽  
Rna Dependent Rna Polymerase ◽  
Virus Inhibitor ◽  
Free Energy Of Binding

Influenza is an acute respiratory infection caused by the influenza virus, but few drugs are available for its treatment. Consequently, researchers have been engaged in efforts to discover new antiviral mechanisms that can lay the foundation for novel anti-influenza drugs. The viral RNA-dependent RNA polymerase (RdRp) is an enzyme that plays an indispensable role in the viral infection process, which is directly linked to the survival of the virus. Methods of inhibiting PB1–PB2 (basic polymerase 1–basic polymerase 2) interactions, which are a key part of RdRp enzyme activity, are integral in the design of novel antiviral drugs, a specific PB1–PB2 interactions inhibitor has not been reported. We have screened Enamine’s database and conducted a parallel screening of multiple docking schemes, followed by simulations of molecular dynamics to determine the structure of a stable ligand—PB1 complex. We also calculated the free energy of binding between the screened compounds and PB1 protein. Ultimately, we screened and identified a potential PB1–PB2 inhibitor using the ADMET prediction model.

scholarly journals Analyzing Machupo virus-receptor binding by molecular dynamics simulations

2014 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Protein Interactions ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Virus Protein ◽  
Test Case ◽  
Protein Protein Interactions ◽  
Distance Curve ◽  
Machupo Virus

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-­protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non­alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-­virus protein-­protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-­versus-­distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-­crystal structure shows two large hydrogen-­bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-­resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.

scholarly journals Analyzing Machupo virus-receptor binding by molecular dynamics simulations

2014 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Protein Interactions ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Virus Protein ◽  
Test Case ◽  
Protein Protein Interactions ◽  
Distance Curve ◽  
Machupo Virus

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-­protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non­alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-­virus protein-­protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-­versus-­distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-­crystal structure shows two large hydrogen-­bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-­resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.

scholarly journals Molecular dynamics shows complex interplay and long-range effects of post-translational modifications in yeast protein interactions

2021 ◽  
Vol 17 (5) ◽  
pp. e1008988
Author(s):  
Nikolina ŠoŠtarić ◽  
Vera van Noort
Keyword(s):  
Molecular Dynamics ◽  
Conformational Changes ◽  
Protein Interactions ◽  
Protein Structures ◽  
Cellular Protein ◽  
Free Energy Calculations ◽  
Protein Protein Interactions ◽  
Post Translational Modifications ◽  
Protein Properties ◽  
Dynamics Simulations

Post-translational modifications (PTMs) play a vital, yet often overlooked role in the living cells through modulation of protein properties, such as localization and affinity towards their interactors, thereby enabling quick adaptation to changing environmental conditions. We have previously benchmarked a computational framework for the prediction of PTMs’ effects on the stability of protein-protein interactions, which has molecular dynamics simulations followed by free energy calculations at its core. In the present work, we apply this framework to publicly available data on Saccharomyces cerevisiae protein structures and PTM sites, identified in both normal and stress conditions. We predict proteome-wide effects of acetylations and phosphorylations on protein-protein interactions and find that acetylations more frequently have locally stabilizing roles in protein interactions, while the opposite is true for phosphorylations. However, the overall impact of PTMs on protein-protein interactions is more complex than a simple sum of local changes caused by the introduction of PTMs and adds to our understanding of PTM cross-talk. We further use the obtained data to calculate the conformational changes brought about by PTMs. Finally, conservation of the analyzed PTM residues in orthologues shows that some predictions for yeast proteins will be mirrored to other organisms, including human. This work, therefore, contributes to our overall understanding of the modulation of the cellular protein interaction networks in yeast and beyond.

scholarly journals Accurate calculation of the absolute free energy of binding for drug molecules

2016 ◽  
Vol 7 (1) ◽  
pp. 207-218 ◽  
Author(s):  
Matteo Aldeghi ◽  
Alexander Heifetz ◽  
Michael J. Bodkin ◽  
Stefan Knapp ◽  
Philip C. Biggin
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Free Energy Calculations ◽  
Accurate Calculation ◽  
Energy Calculations ◽  
Thermodynamic Cycles ◽  
Drug Molecules ◽  
The Absolute ◽  
Free Energy Of Binding

Free energy calculations based on molecular dynamics and thermodynamic cycles accurately reproduce experimental affinities of diverse bromodomain inhibitors.

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