Statistical potentials for fold assessment

By Francisco Melo, R. Sánchez, Andrej Sali. Protein Science, 2002

378 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a3cac53750231e7a58b415e30de8a4b64806ca12

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Citations in the corpus

Tools for comparative protein structure modeling and analysis (2003, methodology): “In particular, we use a composite GA341 model score that combines a Z-score calculated with a statistical potential function (25), target– Nucleic Acids Research, 2003, Vol.”
The Victor/FRST Function for Model Quality Estimation (2005, neutral): “It is, however, in apparent contrast to other reports testing the discriminatory power for model quality assessment, where it was found to be of little use (Melo et al. , 2002; Wallner et al. , 2003).”
Optimal contact definition for reconstruction of Contact Maps (2010, background): “[5,6], in structural modelling by the extraction of contactbased empirical potentials [7-10] or for the identification of residues critical for folding [11], stability [12] and function [13].”
How well can the accuracy of comparative protein structure models be predicted? (2008, methodology): “MODPIPE also calculated five model-based assessment scores: a C a - and C b -based distance-dependent statistical potential score (PAIR) (Melo et al. 2002), a C b -based accessible surface statistical potential score (SURFACE) (Melo et al. 2002), a combined distance and surface potential score…”
Comparative protein structure modeling by iterative alignment, model building and model assessment. (2003, methodology): “The statistical potential Z-score, Zs (step 5), was used to assess the overall accuracy of the molecular models for each of the representative alignments from the current iteration (16).”
Can correct protein models be identified? (2003, methodology): “A recent study by Melo et al. (2002) used a similar approach to study different statistical potentials for fold assessment.”
ModBase, a database of annotated comparative protein structure models and associated resources (2013, methodology): “The resulting models are then evaluated by a composite model quality criterion that depends on the compactness of a model, the sequence identity of the sequence‐structure match and statistical energy Z-scores ( 25 ).”
MODBASE: a database of annotated comparative protein structure models and associated resources (2005, methodology): “The resulting models are then evaluated by a composite model quality criterion that depends on the compactness of a model, the sequence identity of the sequence±structure match and statistical energy Z-scores (25).”
Comparative Protein Structure Modeling Using MODELLER (2016, methodology): “…mutations and cross-overs; (2) comparative models corresponding to these alignments are built by satisfaction of spatial restraints, as implemented in the program MODELLER; and (3) the models are assessed by a composite score, partly depending on an atomic statistical potential (Melo et al., 2002).”
UCSF Chimera, MODELLER, and IMP: an Integrated Modeling System (2011, methodology): “The table lists the GA341 (Melo et al., 2002), zDOPE and DOPE scores (Pieper et al., 2011; Shen and Sali, 2006).”
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.”
Comparative Protein Structure Modeling Using Modeller (2006, methodology): “D profiles and statistical potentials (Sippl, 1990; Luthy et al., 1992; Melo et al., 2002) to assess the compatibility between the sequence and modeled structure by evaluating the environment of each residue in a model with respect to the expected environment as found in native high-resolution…”
Comparative Protein Structure Modeling Using MODELLER (2007, methodology): “D proﬁles and statistical potentials (Sippl, 1990; Luthy et al., 1992; Melo et al., 2002) to assess the compatibility between the sequence and modeled structure by evaluating the environment of each residue in a model with respect to the expected environment as found in native high-resolution…”
QMEAN: A comprehensive scoring function for model quality assessment (2008, methodology): “The potential has been implemented in analogy to Melo et al ., 2002.55”
Statistical potential for assessment and prediction of protein structures (2006, background): “…negative function determined and computationally predicted protein structures (Sippl 1993b; DeBolt and Skolnick 1996; Gatchell et al. 2000; Melo et al. 2002; John and Sali 2003; Wang et al. 2004; Topf and Sali 2005; Topf et al. 2006), ab initio protein structure prediction and Crippen…”
Comparative Protein Structure Modeling Using MODELLER (2016, background): “Even identical decapeptides in different proteins do not always have the same conformation (Kabsch and Sander, 1984; Mezei, 1998).”
Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction (2002, background): “1997; Samudrala and Moult 1998; Lu and Skolnick 2001) potentials were developed and applied to fold recognition and assessment (Hendlich et al. 1990; Sippl 1990; Casari and Sippl 1992; Jones et al. 1992; Bryant and Lawrence 1993; Samudrala and Moult 1998; Miyazawa and Jernigan 1999; Lu and Skolnick ”