China
Africa
Explore chapters and articles related to this topic
AI and Immunology Considerations in Pandemics and SARS-CoV-2 COVID-19
Published in Louis J. Catania, AI for Immunology, 2021
AI and immunoinformatics are being used to better understand the structure of proteins involved in SARS-Cov-2 infection in search for potential treatments and vaccines. Proteins have a three-dimensional structure, which is determined by their genetically encoded amino acid sequence (next-gen sequencing of genetic code), and this structure influences the role and function of the protein. An AI Google DeepMind system called AlphaFold70 uses amino acid sequencing and protein structure to make predictions to construct a “potential of mean force” which can be used to characterize the protein’s shape. This system has been applied to predict the structures of six proteins related to SARS-CoV-2.71
Computational representations of protein–ligand interfaces for structure-based virtual screening
Published in Expert Opinion on Drug Discovery, 2021
Tong Qin, Zihao Zhu, Xiang Simon Wang, Jie Xia, Song Wu
The descriptors for protein–ligand interfaces proposed by the Zheng group were potentials of mean force (PMF) decomposed on each amino acid residue, which were originally used to develop the BRD4-specific scoring model [83]. By analyzing the available crystallographic data for BRD4–ligand complexes and critical residues, key residues of the binding interfaces were identified. For each key residue, the PMF score Si was the sum of all PMFs between each atom of the residue and each atom of the ligand, which was further decomposed into three contribution terms, polar, nonpolar, and HB based on the type of protein–ligand atom pair. For example, given a residue i and a ligand j, the value of the nonpolar term of xi,np(j) is computed as the sum of all PMFs between the nonpolar atoms of residue i and the nonpolar atoms of the ligand, and then divided by the number of nonpolar atoms of ligand j (Nj):
A molecular dynamics approach towards evaluating osmotic and thermal stress in the extracellular environment
Published in International Journal of Hyperthermia, 2018
David Fuentes, Nina M. Muñoz, Chunxiao Guo, Urzsula Polak, Adeeb A. Minhaj, William J. Allen, Michael C. Gustin, Erik N. K. Cressman
The binding energy, ΔGbind of the fibronectin–integrin structure, 4MMX, was evaluated as a function of temperature and salt concentration. Binding energy was derived from a series of umbrella sampling simulations [36]. Umbrella sampling simulations follow the same workflow for solvation, energy minimization, and equilibrium simulations as the discussed previously. However, during the production simulation, the fibronectin is pulled from the integrin. A series of fibronectin–integrin configurations along a reaction coordinate representing the distance between the fibronectin and integrin is thus generated. Here the reaction coordinate represents the distance between the centre of mass (COM) of the fibronectin (Chain C) and the integrin (Chains A and B). The simulation domain was increased in the direction of the reaction coordinate to satisfy the minimum image convention. The total system for the structure, solvent, and salt consists of >800 K atoms. The reaction coordinate was increased using a pull rate of 0.0137]. The Weighted Histogram Analysis Method (WHAM) [38] was used to extract the potential of mean force (PMF) relationship and calculate the free energy change representing the binding energy, ΔGbind. The force constant and pulling rate were empirically chosen such that the umbrella windows overlap for proper reconstruction of the PMF curve.
Strategies for targeting the cardiac sarcomere: avenues for novel drug discovery
Published in Expert Opinion on Drug Discovery, 2020
Joshua B. Holmes, Chang Yoon Doh, Ranganath Mamidi, Jiayang Li, Julian E. Stelzer
After the screening process identifies promising chemical compounds, additional computational tools can optimize the ligand-receptor binding free energies to augment ligand selectivity/potency to the desired target. Some of these methods include force-field based Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA), cheminformatics-based QSAR, quantum mechanics-based Coupled Cluster calculations, or enhanced molecular dynamics sampling methods including weighted histogram analysis method (WHAM), the potential of mean force (PMF), metadynamics, and convex-PL scoring function [39]. A more comprehensive and detailed review of these and other computational methods in drug discovery and cardiology can be found elsewhere [40,41].