Statistical potential for assessment and prediction of protein structures

By Min-Yi Shen, Andrej Sali. 2006

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Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

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Citations in the corpus

Outcome of a Workshop on Applications of Protein Models in Biomedical Research (2009, background): “probabilities, and solvation properties (Benkert et al., 2008; Bhattacharya et al., 2008; Pettitt et al., 2005; Shen and Sali, 2006; Sippl, 1993; Wallner and Elofsson, 2003).”
The Protein Model Portal—a comprehensive resource for protein structure and model information (2013, background): “ModEval ModEval (24) reports quality scores such as predicted RMSD and native overlap, along with scores based on statistical potentials (DOPE) (25), GA341 (26) assessing the reliability of a model.”
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”
Protein structure modeling with MODELLER. (2014, methodology): “The normalized DOPE (or z-DOPE) score, however, is a z score that relates the DOPE score of the model to the average observed DOPE score for “reference” protein structures of similar size(27).”
Large-scale model quality assessment for improving protein tertiary structure prediction (2015, methodology): “, 2009); a recalibrated SELECTpro energy (Randall and Baldi, 2008); Dope (Shen and Sali, 2006); DFIRE2 (Yang and Zhou, 2008); OPUS_PSP (Lu et al.”
Integrating model simulation tools and cryo‐electron microscopy (2022, methodology): “221 Other validation functions include statistical potentials of mean force by comparing a candidate protein to a reference dataset, including DOPE 237 and QMEAN.”
How well can the accuracy of comparative protein structure models be predicted? (2008, methodology): “…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) selecting the most native-like model in a set of decoys that does not contain the native structure (Shortle et al. 1998;…”
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 design”
Fast Protein Loop Sampling and Structure Prediction Using Distance-Guided Sequential Chain-Growth Monte Carlo Method (2014, neutral): “Potential function To evaluate the energy of loops, we develop a simple atombased distance-dependent empirical potential function, following well-established practices [46,52,60–66].”
Protein Homology Detection Through Alignment of Markov Random Fields (2015, methodology): “These SVM methods are reported to outperform the simple feature comparison methods [18, 19].”
Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13 (2019, methodology): “It also generated 3D quality scores for each model by using 9 single-model QA methods (ie, SBROD, 55 OPUS_PSP, 56 RF_CB_SRS_OD, 57 Rwplus, 58 DeepQA, 59 ProQ2, 60 ProQ3, 61 Dope, 62 and Voronota 63 ) as well as three multimodel QA methods (ie, APOLLO, 48 Pcons, 64 and ModFOLDclust2 65 ).”