Evaluation of structural and evolutionary contributions to deleterious mutation prediction.

By Christopher T. Saunders, David Baker. 2002

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Citations in the corpus

Quantitative missense variant effect prediction using large-scale mutagenesis data (2017, positive): “…Importance and Future Improvements Features known to be predictive of variant effects, including solvent accessibility and evolutionary conservation, were the most highly represented in the Envision decision tree ensemble (Figure 4A and Table S5) (Kumar et al., 2009; Saunders and Baker, 2002).”
HHsenser: exhaustive transitive profile search using HMM–HMM comparison (2006, background): “Examples are the prediction of secondary structure (1), solvent accessebility (2), disorder (3), transmembrane helices (4), intraprotein contacts (5), protein–protein interactions (6), subcellular localization (7), internal repeats (8), deleterious mutations (9) and conserved or subtype-specific fun”
Performance of mutation pathogenicity prediction methods on missense variants (2011, background): “Studies have shown that combining information obtained from the multiple sequence alignment and three-dimensional protein structure can increase prediction performance [Bromberg and Rost, 2007; Saunders and Baker, 2002].”
Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm (2009, background): “However, addition of a protein structure marginally improves the performance of AAS prediction algorithm”
SNAP: predict effect of non-synonymous polymorphisms on function (2007, methodology): “A range of approaches to classification was considered by these studies including use of machine-learning [SVM (17,21), decision trees (21), neural networks (22), random forests (23), Bayesian models (20), statistical approaches (11,12, 16 )], and rule-based systems (15).”
High-resolution Structural and Thermodynamic Analysis of Extreme Stabilization of Human Procarboxypeptidase by Computational Protein Design (2007, unknown): “Weights for the various energy terms were determined as described.(51) Briefly, all rotamers from a backbone-dependent library are placed at each position in a set of proteins.”
PMUT: a web-based tool for the annotation of pathological mutations on proteins (2005, background): “Others are centered on the study of non-synonymous mutations mapped on proteins; for discussions see (Chasman and Adams, 2001; Ferrer-Costa et al., 2002, 2004, 2005; Ng and Henikoff, 2002; Saunders and Baker, 2002; Sunyaev et al., 2001; Wang and Moult, 2001).”
Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles (2015, background): “Based on their success in the prediction of ΔΔG values for protein stability [17, 18], sequence based features have been suggested as predictors of protein-protein interaction ΔΔG values [19].”
Assessment of computational methods for predicting the effects of missense mutations in human cancers (2013, background): “Combining sequence and structure information can increase prediction accuracy to a certain degree [25].”
Automated inference of molecular mechanisms of disease from amino acid substitutions (2009, methodology): “Features from comparative protein structural models have been incorporated via the LS-SNP approach (Karchin et al., 2005), while the usefulness of ab initio structural models was evaluated by Saunders and Baker (2002).”
SIFT: predicting amino acid changes that affect protein function (2003, methodology): “The SIFT algorithm relies solely on sequence for prediction, yet performs similarly to tools that use structure (3,6–8).”