Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction

By Hongyi Zhou, Yaoqi Zhou. 2002

953 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Chemistry & Drug Discovery

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Citations in the corpus

Toward the estimation of the absolute quality of individual protein structure models (2010, background): “The performance of QMEAN with respect to other stateof-the-art methods such as ProSA (Sippl, 1993) and DFIRE (Zhou and Zhou, 2002) has also been recently assessed in an independent study (Rykunov and Fiser, 2010).”
Computational Protein Design with Deep Learning Neural Networks (2018, methodology): “58 SPIN was trained on 1532 non-redundant proteins and reaches a sequence identity of 30.3% on a test set containing 500 proteins.”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, methodology): “, 2005; Skolnick, 2006) indicate that knowledge-based statistical potentials (Lu et al., 2008; Miyazawa and Jernigan, 1985; Shen and Sali, 2006; Simons et al., 1997; Sippl, 1990; Tanaka and Scheraga, 1976; Zhang and Zhang, 2010; Zhou and Zhou, 2002) compare favorably to physics-based potentials (Bro”
Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019, neutral): “Examples are the VoroMQA score developed by the Venclovas group, 64 the combined use of three scoring functions, GOAP, 65 Dfire, 66 on the most frequent interface residue contacts observed in these models.”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, methodology): “, 2008], Dmutant [Zhou and Zhou, 2002], and FoldX [Schymkowitz et al.”
The ModFOLD4 server for the quality assessment of 3D protein models (2013, methodology): “Traditionally, statistically derived energy functions, such as ProSA (5) and DFIRE (6), have been used along with the alternative knowledge-based approaches, such as VERIFY3D (7), to provide single scores that relate to the global quality of protein models.”
Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. (2011, background): “Edfire is the pairwise statistical potential from DFIRE (21), and ELJ is the pairwise physicsbased Lennard-Jones 12-6 potential (22).”
How well can the accuracy of comparative protein structure models be predicted? (2008, background): “2002; McGuffin and Jones 2003; Melo and Sali 2007), (2) discriminating between the native and near-native states (Sippl 1993; Melo and Feytmans 1997, 1998; Park et al. 1997; Fiser et al. 2000; Lazaridis and Karplus 2000; Zhou and Zhou 2002; Seok et al. 2003; Tsai et al. 2003; Shen and Sali 2006), (3”
Prediction of protein stability changes for single‐site mutations using support vector machines (2005, methodology): “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.”
New statistical potential for quality assessment of protein models and a survey of energy functions (2010, background): “…(i) atom-based, i.e. all-heavy-atom or reduced set of atom types, namely “ QMEAN-all_atom ” [46], “ OPUS_PSP ” [47], “ DOPE ” [22], “ dFIRE ” [17], “ Shortle2006 ” [35], VSCORE-pair [38], ANOLEA-like ("Melo-ANOLEA ” , “ Melo-NL ” ) [56], our “ RF_HA ” , “ RF_HA_SRS ” [21], and “…”
Protein Homology Detection Through Alignment of Markov Random Fields (2015, methodology): “See [21] for a review of these methods.”
Comparative Protein Structure Modeling Using Modeller (2006, background): “…(Luthy et al., 1992), Prosa2003 (Sippl, 1993; Wiederstein and Sippl, 2007), HARMONY (Topham et al., 1994), ANOLEA (Melo and Feytmans, 1998), DFIRE (Zhou and Zhou, 2002), DOPE (Shen and Sali, 2006), SOAP (Dong et al., 2013), QMEAN local (Benkert et al., 2011), and TSVMod (Eramian et al., 2008).”
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].”