Computational identification of binding energy hot spots in protein–RNA complexes using an ensemble approach

Pan, Yuliang; Wang, Zixiang; Zhan, Wenfeng; Deng, Lei

doi:10.1093/bioinformatics/btx822

Cited by 88 publications

(86 citation statements)

References 61 publications

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“…Moreover, 140 additional mutations obtained from the PrabHot benchmark and independent datasets were added into our training dataset, and they satisfy all of above criteria (29). PrabHot is a computational method for identifying hot spots at the protein-RNA binding interfaces.…”

Section: Experimental Datasets Used For Trainingmentioning

confidence: 99%

“…Thus, the neutral pH was chosen at which the default charged states are assigned to the ionizable residues. We also compared our training dataset of S248 with them used for developing mCSM-NA (26) and PrabHot methods (29), and the details are shown in the Table S1.…”

Section: Experimental Datasets Used For Trainingmentioning

confidence: 99%

“…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). One of the reasons that has hindered the development of methods is due to the complexity of nucleic acid chemistry and binding that has limited the availability of high-quality data.…”

Section: Introductionmentioning

confidence: 99%

“…We have previously used molecular mechanics force fields and statistical potentials to model the impacts of single mutations on protein-protein (22) (26). Moreover, several computational approaches have been proposed for predicting binding hot spots at protein-RNA binding interfaces (29)(30)(31). Although these advancements have been made, the issue of estimating the effects of mutations on protein-RNA interactions is still at the initial stage.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Experimental Datasets Used For Trainingmentioning

confidence: 99%

Section: Experimental Datasets Used For Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Chen

et al. 2020

Preprint

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“…Since solvent accessible area (ASA) has been widely used in many bioinformatics fields, including hot spots identification [51], catalytic sites prediction [52], and glycosylation sites prediction [38]…”

Section: Solvent Accessible Area (Asa) Featuresmentioning

confidence: 99%

PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact

Fan

Marquez‐Lago

et al. 2019

Preprint

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Using machine‐learning‐driven approaches to boost hot‐spot's knowledge

Rosário‐Ferreira

Bonvin

Moreira

2022

WIREs Comput Mol Sci

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Understanding protein-protein interactions (PPIs) is fundamental to describe and to characterize the formation of biomolecular assemblies, and to establish the energetic principles underlying biological networks. One key aspect of these interfaces is the existence and prevalence of hot-spots (HS) residues that, upon mutation to alanine, negatively impact the formation of such proteinprotein complexes. HS have been widely considered in research, both in case studies and in a few large-scale predictive approaches. This review aims to pre-

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Computational identification of binding energy hot spots in protein–RNA complexes using an ensemble approach

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 88 publications

References 61 publications

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

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

PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact

Using machine‐learning‐driven approaches to boost hot‐spot's knowledge

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