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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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Aromatic Stacking Facilitated Self-Assembly of Ultrashort Ionic Complementary Peptide Sequence: β‑Sheet Nanofibers with Remarkable Gelation and Interfacial Properties

TL;DR: Phg4 is the shortest ionic self-complementary peptide rationally designed to self-assemble into stable β-sheet nanofibres capable of gelation and emulsification and suggest that Ultra-short Ionic-complementationary Constrained Peptides or UICPs have significant potential for the development of cost-effective, sustainable and multifunctional soft bionanomaterials.
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Automated De Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn?

TL;DR: Different chemical spaces were used as training sets for reinforcement learning (RL) in combination with different reward functions, and 3D-shape or QSAR (quantitative structure-activity relationship) models produced significantly different proposals by sampling a broader region of the chemical space, thus supporting lead generation.
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Molecular dynamics and MM/GBSA-integrated protocol probing the correlation between biological activities and binding free energies of HIV-1 TAR RNA inhibitors.

TL;DR: It is clear that in case of neamine derivatives as simulation time increased the correlation between binding free energy and experimental pIC50 values increased correspondingly, and the binding energies which can be interpreted at longer simulation times can be used to predict the bioactivity of new neamines derivatives.
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Structural analysis reveals TLR7 dynamics underlying antagonism

TL;DR: This work identifies the open conformation of the receptor and reveals the structural equilibrium between open and closed conformations that underlies TLR7 antagonism, as well as the detailed mechanism by which TLR 7-specific antagonists bind to their binding pocket inTLR7.
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Docking of Covalent Ligands: Challenges and Approaches.

TL;DR: The question arises how to approach covalent ligands and which methods to choose for their docking and design, and the need for a broader exploration of binding modes in docking calculations.
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Journal ArticleDOI

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