Predicting the effects of amino acid substitutions on protein function.

By Pauline Crystal Ng, Steven Henikoff. 2006

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Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “Evolution provides a multitude of informative ’experiments’ on mutational landscapes.”
Predicting the functional impact of protein mutations: application to cancer genomics (2011, background): “4) Mutations in one-time-mutated genes not annotated as TS or OG 2324 699 (30) 777 (33) 693 (29.”
Protein sectors: evolutionary units of three-dimensional structure (2009, background): “…the importance of protein residues is sequence conservation—the degree to which the frequency of amino acids at a given position deviates from random expectation in a wellsampled multiple sequence alignment of the protein family (Capra and Singh, 2007; Ng and Henikoff, 2006; Zvelebil et al., 1987).”
PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels (2015, background): “Since then many other tools have been developed until recently (Cooper and Shendure, 2011; Ng and Henikoff, 2006).”
Predicting the Functional Effect of Amino Acid Substitutions and Indels (2012, background): “E-value [10], Condel [11] and several others [12,13].”
Characterising and Predicting Haploinsufficiency in the Human Genome (2010, background): “predicting the phenotypic impact of mutations that change the amino acid sequence of an encoded protein [1].”
Prediction of Missense Mutation Functionality Depends on both the Algorithm and Sequence Alignment Employed (2011, background): “A review of available computational methods for assessing the functional effects of missense mutations has been performed [Jordan et al., 2010; Karchin, 2009; Ng and Henikoff, 2006; Thusberg and Vihinen, 2009].”
PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality (2011, background): “Indeed, they can be expected to lead to significant improvements in the understanding of the mechanisms of various diseases, to the development of enhanced diagnostics, new therapeutic approaches, and more personalized treatment options [3,4].”
Pathways of cellular proteostasis in aging and disease (2018, background): “Production of misfolded proteins is further increased by stochastic errors of protein biogenesis occurring at the level of transcription and mRNA maturation and translation (Sachidanandam et al., 2001; Ng and Henikoff, 2006; Drummond and Wilke, 2008).”
dbNSFP: A Lightweight Database of Human Non-synonymous SNPs and Their Functional Predictions (2011, methodology): “Functional prediction algorithms for NS have been developed in recent years with the advance of phylogenetics, structure biology, bioinformatics and population genetics (see reviews by [Horner et al., 2010; Karchin, 2009; Ng and Henikoff, 2006; Thusberg and Vihinen, 2009]).”
PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations (2014, background): “Variability of the training datasets utilized by individual prediction tools coupled with the public inaccessibility of these datasets represent a serious obstacle to unbiased comparison of predictive power of the tools [21].”
Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm (2009, methodology): “Alternative methodologies There are alternative prediction tools that use a combination of methods based on sequence homology, protein structure information and physicochemical properties of amino acids for predictio”
Evolution-Based Functional Decomposition of Proteins (2015, positive): “SCA adds an extra concept; by weighting correlations with a function of the evolutionary conservation of the underlying amino acids, this approach incorporates a measure of their functional relevance (53; 54).”
SNAP: predict effect of non-synonymous polymorphisms on function (2007, background): “While non-coding SNPs are trivially more prevalent, an estimated 24 000–60 000 coding SNPs are found in the human genome (4,7).”
Performance of mutation pathogenicity prediction methods on missense variants (2011, background): “…about the genotypes of individuals accumulate, understanding the molecular level effects of variations and elucidating their possible disease association is an important research challenge [Karchin, 2009; Mooney, 2005; Ng and Henikoff, 2006; Steward et al., 2003; Thusberg and Vihinen, 2009].”
28-way vertebrate alignment and conservation track in the UCSC Genome Browser. (2007, background): “Miller et al. oping computational tools to help predict which of these changes might have implications for human health, as summarized by Ng and Henikoff (2006).”
Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. (2015, methodology): “Multiple algorithms were developed for predicting such deleteriousness based on different information of the variant, such as its sequence homology (2), protein structure (3,4) and evolutionary conservation (5).”