The SAAPdb web resource: A large-scale structural analysis of mutant proteins

Hurst, Jacob; McMillan, Lisa E. M.; Porter, Craig T.; Allen, James; Fakorede, Adebola; Martin, Andrew C.R.

doi:10.1002/humu.20898

Cited by 53 publications

(65 citation statements)

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“…This may, in turn, help to shed light on the mechanisms of compensation, which are as yet poorly understood 1,2,3,5 . We analyzed local structural consequences of mutations on a large dataset of OMIM mutations, using methods of structural analysis previously developed in our group 11,12,13,14,15 .…”

Section: Structural Features Of Cpdsmentioning

confidence: 99%

Compensated Pathogenic Deviations: Analysis of Structural Effects

Barešić

Hopcroft

Rogers

et al. 2010

Journal of Molecular Biology

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Pathogenic deviations (PDs) in humans are disease-causing missense mutations. However, in some cases, these disease-associated residues occur as the wild-type residues in functionally equivalent proteins in other species and these cases are termed 'Compensated Pathogenic Deviations' (CPDs). The lack of pathogenicity in a non-human protein is presumed to be explained in most cases by the presence of compensatory mutations, most commonly within the same protein. Identifying structural features of CPDs, and detecting specific compensatory events, will help us to understand traversal along fitness landscape valleys in protein evolution.We divided mutations listed in the OMIM database into PD and CPD datasets and performed two independent analyses: (i) we searched for potential compensatory mutations spatially close to the CPDs and (ii) using our SAAPdb database, we examined likely structural effects to try to explain why mutations are pathogenic, comparing PDs and CPDs. Our datasets were obtained from a set of 245 human proteins of known structure and contained a total of 2328 mutations of which 453 (from 85 structures) were seen to be compensated in at least one functionally equivalent protein in another (non-human) species.Structural analysis results confirm previous findings that CPDs are, on average, 'milder' in their likely structural effects than uncompensated PDs and tend to be on the protein surface. We also showed that the residues surrounding the CPD residue in the folded protein are more often mutated than the residues surrounding an uncompensated mutation, supporting the hypothesis that compensation is largely a result of structurally local mutations.

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Section: Structural Features Of Cpdsmentioning

confidence: 99%

Compensated Pathogenic Deviations: Analysis of Structural Effects

Barešić

Hopcroft

Rogers

et al. 2010

Journal of Molecular Biology

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“…SAAPdap provides cutoffs for each of the analyses to suggest whether these are likely to be damaging (Hurst et al, 2009;Al-Numair and Martin, 2013). To predict pathogenicity, a total of 47 features are derived from these analyses (Table S1) and are used as input to SAAPpred, a machine learning method that uses Random Forests to predict whether a mutation is pathogenic (Al-Numair and Martin, 2013).…”

Section: Saapdap Structural Analysis and Saappredmentioning

confidence: 99%

“…Initially our own focus was on trying to understand the effects that mutations have on protein structure and then to use this information to compare the effects of non-pathogenic mutations and pathogenic deviations (Hurst et al, 2009). Our approach has been to map mutations onto protein structure and to perform a rulebased analysis of the likely structural effects of these mutations in order to 'explain' the known functional effect (if any) of the mutation.…”

Section: Introductionmentioning

confidence: 99%

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The structural effects of mutations can aid in differential phenotype prediction of beta-myosin heavy chain (Myosin-7) missense variants

Al‐Numair

Lopes²,

Syrris³

et al. 2016

Bioinformatics

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Motivation: High-throughput sequencing platforms are increasingly used to screen patients with genetic disease for pathogenic mutations, but prediction of the effects of mutations remains challenging. Previously we developed SAAPdap (Single Amino Acid Polymorphism Data Analysis Pipeline) and SAAPpred (Single Amino Acid Polymorphism Predictor) that use a combination of rule-based structural measures to predict whether a missense genetic variant is pathogenic. Here we investigate whether the same methodology can be used to develop a differential phenotype predictor, which, once a mutation has been predicted as pathogenic, is able to distinguish between phenotypes -in this case the two major clinical phenotypes (hypertrophic cardiomyopathy, HCM, and dilated cardiomyopathy, DCM) associated with mutations in the beta-myosin heavy chain (MYH7) gene product (Myosin-7). Results: A random forest predictor trained on rule-based structural analyses together with structural clustering data gave a Matthews' correlation coefficient (MCC) of 0.53 (accuracy, 75%). A post hoc removal of machine learning models that performed particularly badly, increased the performance (MCC=0.61, Acc=79%). This proof of concept suggests that methods used for pathogenicity prediction can be extended for use in differential phenotype prediction.

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“…We use the term as defined by Hurst et al (7) to apply both to mutations resulting from strictly-defined nsSNPs (i.e. those that occur in at least 1% of a normal population) and to deleterious muations (DAMs) as defined below.…”

Section: Terminologymentioning

confidence: 99%

Compensated pathogenic deviations

Barešić

Martin

2011

BioMolecular Concepts

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Deleterious or ‘disease-associated’ mutations are mutations that lead to disease with high phenotype penetrance: they are inherited in a simple Mendelian manner, or, in the case of cancer, accumulate in somatic cells leading directly to disease. However, in some cases, the amino acid that is substituted resulting in disease is the wild-type native residue in the functionally equivalent protein in another species. Such examples are known as ‘compensated pathogenic deviations’ (CPDs) because, somewhere in the second species, there must be compensatory mutations that allow the protein to function normally despite having a residue which would cause disease in the first species. Depending on the nature of the mutations, compensation can occur in the same protein, or in a different protein with which it interacts. In principle, compensation can be achieved by a single mutation (most probably structurally close to the CPD), or by the cumulative effect of several mutations. Although it is clear that these effects occur in proteins, compensatory mutations are also important in RNA potentially having an impact on disease. As a much simpler molecule, RNA provides an interesting model for understanding mechanisms of compensatory effects, both by looking at naturally occurring RNA molecules and as a means of computational simulation. This review surveys the rather limited literature that has explored these effects. Understanding the nature of CPDs is important in understanding traversal along fitness landscape valleys in evolution. It could also have applications in treating diseases that result from such mutations.

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The SAAPdb web resource: A large-scale structural analysis of mutant proteins

Cited by 53 publications

References 50 publications

Compensated Pathogenic Deviations: Analysis of Structural Effects

Compensated Pathogenic Deviations: Analysis of Structural Effects

The structural effects of mutations can aid in differential phenotype prediction of beta-myosin heavy chain (Myosin-7) missense variants

Compensated pathogenic deviations

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