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Open AccessJournal ArticleDOI

The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling

TLDR
Given the precision and robustness of the calculations, it is believed that the VSGB 2.0 model is suitable to tackle “real” problems, such as biological function modeling and structure‐based drug discovery.
Abstract
A novel energy model (VSGB 2.0) for high resolution protein structure modeling is described, which features an optimized implicit solvent model as well as physics-based corrections for hydrogen bonding, π-π interactions, self-contact interactions and hydrophobic interactions. Parameters of the VSGB 2.0 model were fit to a crystallographic database of 2239 single side chain and 100 11–13 residue loop predictions. Combined with an advanced method of sampling and a robust algorithm for protonation state assignment, the VSGB 2.0 model was validated by predicting 115 super long loops up to 20 residues. Despite the dramatically increasing difficulty in reconstructing longer loops, a high accuracy was achieved: all of the lowest energy conformations have global backbone RMSDs better than 2.0 A from the native conformations. Average global backbone RMSDs of the predictions are 0.51, 0.63, 0.70, 0.62, 0.80, 1.41, and 1.59 A for 14, 15, 16, 17, 18, 19, and 20 residue loop predictions, respectively. When these results are corrected for possible statistical bias as explained in the text, the average global backbone RMSDs are 0.61, 0.71, 0.86, 0.62, 1.06, 1.67, and 1.59 A. Given the precision and robustness of the calculations, we believe that the VSGB 2.0 model is suitable to tackle “real” problems, such as biological function modeling and structure-based drug discovery.

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High-throughput virtual screening of small-molecule inhibitors targeting immune cell checkpoints to discover new immunotherapeutics for human diseases

TL;DR: Three potential inhibitors have shown the potential to activate human immune cells and thus may control the spread of human lifestyle or infectious diseases and warrant the in vitro and in vivo validation to develop it as an immunotherapeutic.
Posted ContentDOI

GemSpot: A Pipeline for Robust Modeling of Ligands into CryoEM Maps

TL;DR: GemSpot is developed, a pipeline of computational chemistry methods that take into account EM map potentials, quantum mechanics energy calculations, and water molecule site prediction to generate candidate poses and provide a measure of the degree of confidence.
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A physics-based energy function allows the computational redesign of a PDZ domain.

TL;DR: It is shown that a PDZ domain can be entirely redesigned using a “physics-based”energy for the folded state and a knowledge-based energy for the unfolded state, demonstrating the physical principles that govern folded proteins, with a dash of empirical post-filtering, can allow successful whole-protein redesign.
Journal ArticleDOI

Multi-conformation representation of Mpro identifies promising candidates for drug repurposing against COVID-19.

TL;DR: In this article, the authors used virtual screening to find a molecule which is approved as a drug for some other disease but could be repurposed to inhibit the COVID-19 main protease (Mpro).
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Structure-based virtual screening to identify inhibitors against Staphylococcus aureus MurD enzyme

TL;DR: In silico high-throughput virtual screening with small molecule library of 1.60 million compounds, it is evident that van der Waals and Coulomb energy terms are major favorable contributors while electrostatic solvation energy term strongly disfavors the binding of ligands to the S. aureus MurD enzyme.
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Journal ArticleDOI

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