SNPs, protein structure, and disease

By Zhen Wang, John Moult. 2001

767 citations recorded by the source corpus.

Inherited disease susceptibility in humans is most commonly associated with single nucleotide polymorphisms (SNPs). The mechanisms by which this occurs are still poorly understood. We have analyzed the effect of a set of disease‐causing missense mutations arising from SNPs, and a set of newly determined SNPs from the general population. Results of in vitro mutagenesis studies, together with the protein structural context of each mutation, are used to develop a model for assigning a mechanism of action of each mutation at the protein level. Ninety percent of the known disease‐causing missense mutations examined fit this model, with the vast majority affecting protein stability, through a variety of energy related factors. In sharp contrast, over 70% of the population set are found to be neutral. The remaining 30% are potentially involved in polygenic disease. Hum Mutat 17:263–270, 2001. © 2001 Wiley‐Liss, Inc.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Predicting multiple conformations via sequence clustering and AlphaFold2 (2023, positive): “Disease-causing point mutations are often due to population changes of protein substates 3,4 and there is therefore great interest for methods to predict the effects of point mutations on structural ensembles and free energy.”
Using bioinformatics to predict the functional impact of SNVs (2011, background): “Changes in volume, hydrophobicity, net charge, packing density and solvent accessibility have been shown to correlate with the functional impact of cSNVs (Wang and Moult, 2001).”
Disease-related mutations predicted to impact protein function (2012, background): “As opposed to other studies addressing this question [3-6,10-12], we used predictions of functional effect to determine the fraction of deleterious point mutations in Figure 2 Effect most prevalent in disease mutants For each set”
Functional annotations improve the predictive score of human disease‐related mutations in proteins (2009, background): “, 2001], (3) structural criteria [Chasman and Adams, 2001; Cheng et al., 2008; Wang and Moult, 2001; Worth et al., 2007; Yue and Moult, 2005, 2006], (4) artificial neural networks [Bromberg and Rost, 2007; Ferrer-Costa et al.”
The amino-acid mutational spectrum of human genetic disease (2003, background): “We note that the mutation rate we estimate (a fraction of newly created deleterious mutations) is different from the fraction of existing SNPs with deleterious effects on protein function (estimated previously [1,2,23]).”
Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information (2006, methodology): “Recently several databases, servers and tools have been developed in order to study the effects of SNPs in Homo sapiens (Wang and Moult, 2001; Ramensky et al., 2002; Riva and Kohane, 2002; Ng and Henikoff, 2003; Stenson et al., 2003; Conde et al., 2004; Reumers et al., 2005; Karchin et al., 2005; Yu”
Predicting protein stability changes from sequences using support vector machines (2005, unknown): “Evidence is accumulating that many disease-causing mutations exert their effects by altering protein folding (Wang and Moult, 2001, 2003; Dobson, 2003; Selkoe, 2003).”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, background): “Incorrectfoldinganddecreasedstabilityarethemajorconsequences of pathogenic amino acid substitutions [Wang and Moult, 2001; Ferrer-Costa et al., 2002; Steﬂ et al., 2013; Peng and Alexov, 2016].”
Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks (2020, background): “4 single-point mutations are a key mechanism underlying a wide spectrum of molecular disorders 76 (3-5).”
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).”
Human non-synonymous SNPs: server and survey. (2002, methodology): “As has been noted by Wang and Moult (9), most disease mutations and supposedly deleterious nsSNPs affect protein stability rather than functionality.”
Protein–protein interaction sites are hot spots for disease‐associated nonsynonymous SNPs (2012, background): “This result accords with previous studies [Burke et al., 2007; Wang and Moult, 2001; Yue and Moult, 2006] and is expected since residues in a protein core generally are subject to more constraints than those not in the core.”
Evaluation of structural and evolutionary contributions to deleterious mutation prediction. (2002, methodology): “The final feature included in Table 2 is our reproduction of four structural rules that form a component of the prediction method used by Sunyaev et al.2 Here, each rule is intended to identify a different class of deleterious mutation on the basis of expert knowledge, an approach similar to that ta”
Prediction of multiple conformational states by combining sequence clustering with AlphaFold2 (2022, background): “Disease-causing point mutations are often due to population changes of protein substates 48,49 , and thus there is significant need for methods better able to predict the effects on free energy and conformation of point mutations.”
Impact of selection, mutation rate and genetic drift on human genetic variation. (2003, background): “For example, it was shown that majority of deleterious amino acid substitutions destabilize proteins (16).”
Neutral and weakly nonneutral sequence variants may define individuality (2013, background): “Many disease-causing mutations affect protein structure or stability (17).”
Predict impact of single amino acid change upon protein structure (2012, background): “For instance, the disruption of hydrophobic interactions, or the introduction of charged residues into buried sites, or mutations that break beta-sheets often impact phenotype severely and raise the susceptibility for disease [7-9].”
Improving the prediction of disease-related variants using protein three-dimensional structure (2011, methodology): “These algorithms are able to predict the effect of the mutation on protein stability [13,14,19,23-26], protein functional activity [9,21], and insurgence of human pathologies [7,8,10-12,1518,20-22].”