Prediction of protein stability changes for single‐site mutations using support vector machines

By Jianlin Cheng, Arlo Z. Randall, Pierre Baldi. 2005

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In Molecular Biology

Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “A first class relies on structural information, more precisely on changes in the thermodynamic stability [25–30], which have been argued to play a key role in determining mutational effects [31–34].”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, neutral): “They use features derived from tertiary protein structure, evolutionary conservation, sequence environment, network topology, and properties of amino acid residues to describe each variation [Capriotti et al., 2005; Cheng et al., 2006; Capriotti et al., 2008; Teng et al., 2010; Chen et al., 2013; Ya”
CUPSAT: prediction of protein stability upon point mutations (2006, methodology): “Cheng et al. ( 4 ) also used SVMs and reported an accuracy of 84%.”
Development of Bioinformatics Infrastructure for Genomics Research in H3Africa (2017, positive): “…the predictions of Provean [Choi et al., 2012], PolyPhen-2 [Adzhubei et al., 2013], PhD-SNP [Capriotti et al., 2006], FATHMM [Shihab et al., 2013], AUTO-MUTE [Masso & Vaisman, 2010], MuPro [Cheng et al., 2006], and IMutant 2.0 to predict the effects of mutations on protein function and stability.”
Machine learning in protein engineering (2018, methodology): “…machine-learning models to predict the effects of mutations on stability (Tian et al., 2010; Li & Fang, 2012; Jia et al., 2015; Capriotti et al., 2005b;a; Cheng et al., 2006; Buske et al., 2009; Liu & Kang, 2012; Pires et al., 2013; Jokinen et al., 2018; Dehouck et al., 2009; Giollo et al., 2014).”
DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations (2020, methodology): “52, comparable with the best-performing methods (Table 2) and significantly better than MUpro.(50) Although not directly comparable, as there is a correlation between changes upon mutation in stability (ΔG) and thermal stability (Tm), 51 the performance of DynaMut2 on predicting changes in melting t”
SPROUTS: a database for the evaluation of protein stability upon point mutation (2008, methodology): “Stability is evaluated by five programs publicly available: DFIRE (14), two versions of I-Mutant (15), MUpro (16) and PoPMuSiC (17).”
PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality (2011, positive): “The total number of adjusted parameters is 52, which remains very reasonable with respect to the size of the training/validation dataset (2648 non-redundant entries), and with respect to other prediction methods based on machine learning techniques [24-28].”
Machine Learning Methods for Protein Structure Prediction (2008, methodology): “For example, machine learning methods are being used to predict protein solubility [115], protein stability [116], protein signal peptides [117], [118], protein cellular localization [117], protein post-translation modification sites, such as phosphorilation sites [119], and protein epitopes [120]–[”
A structural biology community assessment of AlphaFold2 applications (2021, methodology): “These mutations were analyzed by computational predictive tools, including the sequence-based predictors I-Mutant 67 , SAAFEC-SEQ 68 and MUpro 69 , and the structure-based predictors mCSM-stability 70 , DUET 71 , SDM 72 , DynaMut 73 , MAESTRO 74 , ENCoM 75 , DynaMut2 (ref.”
SDM: a server for predicting effects of mutations on protein stability (2017, neutral): “However, the prediction performance of the machine-learning approaches like MUpro (11) and I-Mutant2.”
Stability effects of mutations and protein evolvability. (2009, background): “The combination of both has also been explored [19,25,26 ,27].”