Predicting protein stability changes upon mutation using database-derived potentials: solvent accessibility determines the importance of local versus non-local interactions along the sequence.

By Dimitri Gilis, Marianne Rooman. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, neutral): “…et al., 1999), and predicting amino acid sidechain conformations (Bower, Cohen, & Dunbrack, 1997; Jacobson, Kaminski, Friesner, & Rapp, 2002) and free energy changes associated with protein mutations (Gilis & Rooman, 1997; Guerois, Nielsen, & Serrano, 2002; Potapov, Cohen, & Schreiber, 2009).”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, background): “…et al., 2007a; Zhao et al., 2008, 2010), docking (Zhang et al., 1997), binding (Kortemme and Baker, 2002; Laurie and Jackson, 2005), mutation study (Gilis and Rooman, 1996, 1997), decoy ranking (Bauer and Beyer, 1994; Casari and Sippl, 1992; Gatchell et al., 2000; Hendlich et al., 1990; Samudrala…”
PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality (2011, background): “Indeed, it was shown previously that the relative weights of the different types of interactions vary according to whether they concern residues at the surface or in the core [38].”
Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized (2010, background): “Nonetheless, PMFs are widely used with considerable success; not only for protein structure prediction [3, 18, 19], but also for quality assessment and identification of errors [20–22], fold recognition and threading [23,24], molecular dynamics [24], protein-ligand interactions [16,25], protein desi”
Predicting protein stability changes from sequences using support vector machines (2005, unknown): “…the stability free energy changes in protein structures when mutating one residue at a time in the sequence (Prevost et al., 1991; Topham et al., 1997; Pitera and Kollman, 2000; Gilis and Rooman, 1997; Kwasigroch et al., 2002; Funahashi et al., 2001; Guerois et al., 2002; Zhou and Zhou, 2002).”
Prediction of protein stability changes for single‐site mutations using support vector machines (2005, background): “Statistical potential approaches [16, 18, 19, 20, 21, 22, 23, 24, 25] derive potential functions using statistical analysis of the environmental propensities, substitution frequencies, and correlations of contacting residues in solved tertiary structures.”
Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins (2016, background): “Measuring exposure of amino acid residues of proteins to solvent is important for understanding and predicting protein structure, function and interactions (Gilis and Rooman, 1997; Lee and Richards, 1971; Rost and Sander, 1994; Tuncbag et al., 2009).”
Role of conformational sampling in computing mutation-induced changes in protein structure and stability (2010, background): “3,5,6,15 Although the correlation within the category of exposed residues is poor (r = 0.47), the stability-classification accuracy is very similar (0.71 for exposed mutations and 0.78 for buried residues, Table II).”
Can correct protein models be identified? (2003, methodology): “Also, interaction between different atom types has been used (Bryant and Amzel 1987; Colovos and Yeates 1993; Melo and Feytmans 1997, 1998; Samudrala and Moult 1998; Rojnuckarin and Subramaniam 1999; Lu and Skolnick 2001), as have buried and exposed surfaces (Bowie et al. 1991; Jones et al. 1992; Lu”
Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0 (2009, methodology): “These trends are in agreement with a previous study showing that the influence of local (non-local) interactions increases (decreases) from the core to the surface (Gilis and Rooman, 1997).”
Enhancing the stability and solubility of TEV protease using in silico design (2007, background): “Correlation coefficients between computed and measured DDGs are equal to 0.87 on surface residues and 0.80 on buried residues of a test set (Gilis and Rooman 1996, 1997).”