Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health

Jubb, Harry; Pandurangan, Arun Prasad; Turner, Meghan A; Ochoa‐Montaño, Bernardo; Blundell, Tom L.; Ascher, David B.

doi:10.1016/j.pbiomolbio.2016.10.002

Cited by 143 publications

(115 citation statements)

References 167 publications

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“…One should, however, not forget that some problems will still require more rigorous computational approaches such as free energy perturbation methods and related in order to obtain reliable predictions that also properly consider entropic effects . These remain, however, computationally expensive and will, for the time being, not be applicable in high throughput manner for the thousands of mutations currently available in databases or to predict the impact of single nucleotide polymorphism on PPIs . Community‐wide blind challenges like CAPRI and D3R, and hopefully new ones, will remain important catalysts for the development of new methods and continuous improvement of existing ones.…”

Section: Discussionmentioning

confidence: 99%

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng

Roel‐Touris

et al. 2019

WIREs Comput Mol Sci

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Predicting the structure and thermodynamics of protein–protein interactions (PPIs) are key to a proper understanding and modulation of their function. Since experimental methods might not be able to catch up with the fast growth of genomic data, computational alternatives are therefore required. We present here a review dealing with various aspects of predicting binding affinity changes upon mutations (ΔΔG). We focus on predictors that consider three‐dimensional structure information to estimate the impact of mutations on the binding affinity of a protein–protein complex, excluding the rigorous free energy perturbation methods. Training and evaluation, ΔΔG databases, data selection, and existing ΔΔG predictors are specially emphasized. We also establish the parallel with scoring functions used in docking since those share many similar PPI features with ΔΔG predictors. The field has seen a common evolution of ΔΔG predictors and scoring functions over time, transforming from purely energetic functions to statistical energy‐based and further to machine learning‐based functions. As machine learning has come to age, limitations in terms of quantity, quality and variety of the available data become the bottlenecks for the future development of these computational methods. This can be alleviated by building infrastructures for data generation, collection and sharing. Further developments can be catalyzed by conducting community‐wide blind challenges for method assessment. This article is categorized under: Structure and Mechanism > Molecular Structures Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions

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Section: Discussionmentioning

confidence: 99%

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng

Roel‐Touris

et al. 2019

WIREs Comput Mol Sci

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“…Among the sequence variants identified in our sequencing analyses, we found 11 synonymous SNPs in exonic regions in the HHR and 1 nonsynonymous SNP in the NHR. It is well established that nonsynonymous mutations may impact amino acid sequences and therefore human health (18). However, synonymous variants that had previously been regarded as "silent" mutations and thought to have no effect on disease phenotype have also been reported to cause changes in proteinprotein interactions.…”

Section: Aq: 11mentioning

confidence: 99%

Involvement of human monogenic cardiomyopathy genes in experimental polygenic cardiac hypertrophy

Prestes

Marques

López–Campos

et al. 2018

Physiological Genomics

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Hypertrophic cardiomyopathy thickens heart muscles, reducing functionality and increasing risk of cardiac disease and morbidity. Genetic factors are involved, but their contribution is poorly understood. We used the hypertrophic heart rat (HHR), a unique normotensive polygenic model of cardiac hypertrophy and heart failure, to investigate the role of genes associated with monogenic human cardiomyopathy. We selected 42 genes involved in monogenic human cardiomyopathies to study: 1) DNA variants, by sequencing the whole genome of 13-wk-old HHR and age-matched normal heart rat (NHR), its genetic control strain; 2) mRNA expression, by targeted RNA-sequencing in left ventricles of HHR and NHR at 5 ages (2 days old and 4, 13, 33, and 50 wk old) compared with human idiopathic dilated cardiomyopathy data; and 3) microRNA expression, with rat microRNA microarrays in left ventricles of 2-day-old HHR and age-matched NHR. We also investigated experimentally validated microRNA-mRNA interactions. Whole-genome sequencing revealed unique variants mostly located in noncoding regions of HHR and NHR. We found 29 genes differentially expressed in at least 1 age. Genes encoding desmoglein 2 ( Dsg2) and transthyretin ( Ttr) were significantly differentially expressed at all ages in the HHR, but only Ttr was also differentially expressed in human idiopathic cardiomyopathy. Lastly, only two microRNAs differentially expressed in the HHR were present in our comparison of validated microRNA-mRNA interactions. These two microRNAs interact with five of the genes studied. Our study shows that genes involved in monogenic forms of human cardiomyopathies may also influence polygenic forms of the disease.

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“…Missense mutations occurring in these RNA binding proteins that alter protein-RNA interactions may cause significant deviation from the normal function of these proteins, potentially leading to various disorders including cancer (7,8). Indeed, several comprehensive studies of the structural nature of mutations in cancer and rare Mendelian diseases have shown that mutations located on binding interfaces may induce macromolecular interaction perturbations and play "driver" or "damaging" roles in many cancers and Mendelian diseases (9)(10)(11)(12)(13)(14)(15)(16). Quantifying the effects of missense mutations on specific protein-RNA interactions requires assessing the binding affinity changes upon introducing mutations that can be accurately measured by traditional mutagenesis technologies.…”

Section: Introductionmentioning

confidence: 99%

PremPRI: Predicting the Effects of Single Mutations on Protein-RNA Interactions

Zhang

Chen

et al. 2020

Preprint

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Protein-RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein-RNA interaction may contribute to the pathogenesis of many diseases. Here we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein-RNA interaction by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of 11 sequence-and structure-based features, and is parameterized on 248 mutations from 50 protein-RNA complexes. Our model shows a good agreement between calculated and experimental values with Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal mol -1 , and outperforms three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms underlying the effects, and designing new protein-RNA interaction inhibitors. PremPRI is freely available at

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Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health

Cited by 143 publications

References 167 publications

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Involvement of human monogenic cardiomyopathy genes in experimental polygenic cardiac hypertrophy

PremPRI: Predicting the Effects of Single Mutations on Protein-RNA Interactions

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