Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations.

By Raphaël Guérois, Jens Erik Nielsen, Luis Serrano. 2002

1,685 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, background): “Many studies have focused on optimizing an energy function for a particular problem in macromolecular modeling, for instance the FoldX energy function was empirically parameterized for predicting changes to the free energy of a protein when it is mutated (Guerois et al. 2002).”
Prediction of protein stability changes for single‐site mutations using support vector machines (2005, background): “The first three methods are similar in that they all rely on energy functions.(9) Physical potential approaches directly simulate the atomic force fields present in a given structure and, as such, remain too computationally intensive to be applied to large datasets.”
Selection for Protein Stability Enriches for Epistatic Interactions (2018, methodology): “88 The models analyzed here are simpler, and less realistic, than other commonly used models for 89 protein evolution based on force fields [25], contact energies [26], or lattice proteins [27].”
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation (2013, background): “Thus, during the last few years, several methods have been developed to predict the impact of nsSNPs on protein stability [10-16], protein function [17,18] and to detect their pathologic effect [19-29].”
A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design (2015, background): “[15]; a set of 2156 mutants collected by Potapov et al.”
mCSM: predicting the effects of mutations in proteins using graph-based signatures (2013, background): “Received on April 18, 2013; revised on November 12, 2013; accepted on November 21, 2013”
Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations (2015, methodology): “HaeIII ( PDB id 1dct ) [ 103 ] was first optimized using the FoldX RepairPDB function .”
PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality (2011, methodology): “We may distinguish the approaches based on statistical potentials extracted from datasets of protein structures [15-19], from those based on empirical potentials built from optimised combinations of various physical energy terms [20-23].”
The PDB_REDO server for macromolecular structure model optimization (2014, methodology): “Additional validation is performed by pdb-care for carbohydrates (Lütteke & von der Lieth, 2004) and FoldX (Guerois et al., 2002) for calculation of the Gibbs folding energy.”