mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

Rodrigues, Carlos H M; Myung, Yoochan; Pires, Douglas E V; Ascher, David B.

doi:10.1093/nar/gkz383

Cited by 299 publications

(343 citation statements)

References 59 publications

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“…Column legend -Distance to Interface: The minimum distance of the wild-type residue to the SARS-CoV-2 S-protein as resolved in PDB 6vw1 9 . mCSM-PPI2 ΔΔG: The predicted ΔΔG in kcal/mol for the missense mutation calculated by mCSM-PPI2 12 with PDB 6vw1 as the model structure. Prediction: Our assessment of the effect of the variant on ACE2 binding to SARS-CoV-2 S-protein on the basis of whether predicted ΔΔG exceeds our calibration thresholds (see Methods) and our structural assessment of the modelled variant.…”

Section: Gnomad Variants In Ace2 Can Significantly Modify Its Affinitmentioning

confidence: 99%

“…This interpretation hinges on mCSM-PPI2 providing predicted ΔΔG that is unbiased. It is conceivable that mCSM-PPI2 could be biased towards negative ΔΔG predictions because the training datasets are skewed towards these observations 12,23 . Fortunately, the mCSM-PPI2 training data were augmented to mitigate this possibility 12 and our own analyses of this bias in the ACE2 SARS-CoV S complex suggests that although the magnitudes of positive predictions can be depressed, overall the predictions are well-balanced (see Methods).…”

Section: More Mutations In Ace2 Would Reduce Ace2-s Affinity Than Incmentioning

confidence: 99%

“…It is conceivable that mCSM-PPI2 could be biased towards negative ΔΔG predictions because the training datasets are skewed towards these observations 12,23 . Fortunately, the mCSM-PPI2 training data were augmented to mitigate this possibility 12 and our own analyses of this bias in the ACE2 SARS-CoV S complex suggests that although the magnitudes of positive predictions can be depressed, overall the predictions are well-balanced (see Methods). In addition, some further evidence that mCSM-PPI2 ΔΔG is unbiased comes from the equivalent distribution of predicted ΔΔG for all possible mutations in SARS-CoV-2 S at sites that interact directly with ACE2 ( Figure 7C).…”

Section: More Mutations In Ace2 Would Reduce Ace2-s Affinity Than Incmentioning

confidence: 99%

See 2 more Smart Citations

Missense variants in ACE2 are predicted to encourage and inhibit interaction with SARS-CoV-2 Spike and contribute to genetic risk in COVID-19

MacGowan

Barton

2020

Preprint

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Key results• 1 ACE2 gnomAD missense variant (p.Gly326Glu) and one unobserved missense mutation (Thr27Arg) are predicted to enhance ACE2 binding with SARS-CoV-2 Spike protein, which could result in increased susceptibility and severity of COVID-19 • 3 ACE2 missense variants in gnomAD plus another 38 unobserved missense mutations are predicted to inhibit Spike binding, these are expected to confer a high degree of resistance to infection • The prevalence of the strongly protective variants is estimated between 12-70 per 100,000 population but higher prevalence may exist in local populations or those underrepresented in gnomAD • A strategy to design a recombinant ACE2 with tailored affinity towards Spike and its potential therapeutic value is presented • The predictions were extensively validated against published ACE2 mutant binding assays for SARS-CoV Spike protein Abstract SARS-CoV-2 invades host cells via an endocytic pathway that begins with the interaction of the SARS-CoV-2 Spike glycoprotein (S-protein) and human Angiotensin-converting enzyme 2 (ACE2). Genetic variability in ACE2 may be one factor that mediates the broad-spectrum severity of SARS-CoV-2 infection and COVID-19 outcomes. We investigated the capacity of ACE2 variation to influence SARS-CoV-2 infection with a focus on predicting the effect of missense variants on the ACE2 SARS-CoV-2 S-protein interaction. We validated the mCSM-PPI2 variant effect prediction algorithm with 26 published ACE2 mutant SARS-CoV S-protein binding assays and found it performed well in this closely related system (True Positive Rate = 0.7, True Negative Rate = 1). Application of mCSM-PPI2 to ACE2 missense variants from the Genome Aggregation Consortium Database (gnomAD) identified three that are predicted to strongly inhibit or abolish the S-protein ACE2 interaction altogether

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Section: Gnomad Variants In Ace2 Can Significantly Modify Its Affinitmentioning

confidence: 99%

Section: More Mutations In Ace2 Would Reduce Ace2-s Affinity Than Incmentioning

confidence: 99%

Section: More Mutations In Ace2 Would Reduce Ace2-s Affinity Than Incmentioning

confidence: 99%

See 1 more Smart Citation

Missense variants in ACE2 are predicted to encourage and inhibit interaction with SARS-CoV-2 Spike and contribute to genetic risk in COVID-19

MacGowan

Barton

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Many efforts to computationally predict and model the effects of missense mutations on protein stability and protein-protein interactions have been made that help uncover the relationships between genotype and phenotype (19)(20)(21)(22)(23)(24)(25). However, predicting the impacts of mutations on protein-nucleic acid interactions has been more intractable and very few methods have been proposed that can do this (26)(27)(28)(29)(30).…”

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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“…Furthermore, instead of analyzing each hotspot individually as is commonly done in cancer driver mutation prediction [26][27][28][29] , we connect the effects of all hotspots to construct a hotspot-affected interactome network at the full proteome scale. Such network has not been possible to construct in any previous studies since interactome-based approaches suffered from either low coverage (i.e., focusing on only interactions with cocrystal structures) [30][31][32][33] or low resolution (i.e., examining (sub-)network properties at the protein level, not the residue level) [34][35][36][37] . We show utilities of our innovative hotspot-affected interactome network in uncovering novel relationships among different hotspots, generating hypotheses of how hotspot mutations function at the molecular level, and identifying novel oncogenic proteins that do not harbor hotspot mutations themselves.…”

Section: Introductionmentioning

confidence: 99%

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

Zhang

et al. 2019

Preprint

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Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically-informed computational framework that characterizes the functional relevance of all 1,107 published mutational hotspots identified in ~25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole human interactome, we uncover genomewide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we demonstrate that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

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mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

Cited by 299 publications

References 59 publications

Missense variants in ACE2 are predicted to encourage and inhibit interaction with SARS-CoV-2 Spike and contribute to genetic risk in COVID-19

Missense variants in ACE2 are predicted to encourage and inhibit interaction with SARS-CoV-2 Spike and contribute to genetic risk in COVID-19

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

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

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