PremPRI: Predicting the Effects of Missense Mutations on Protein–RNA Interactions

Zhang, Ning; Lu, Haoyu; Chen, Yuting; Zhu, Zefeng; Yang, Qing; Wang, Shuqin; Li, Minghui

doi:10.3390/ijms21155560

Cited by 16 publications

(12 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Protein stability is one of the most important factors that characterize protein function, activity, and regulation [1]. Missense mutations can lead to protein dysfunction by affecting their stabilities and interactions with other biological molecules [2][3][4][5][6][7][8][9]. Several studies have shown that the mutations are deleterious due to decreasing or enhancing the stability of the corresponding protein [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

PremPS: Predicting the impact of missense mutations on protein stability

et al. 2020

Self Cite

View full text Add to dashboard Cite

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.

show abstract

Section: Introductionmentioning

confidence: 99%

PremPS: Predicting the impact of missense mutations on protein stability

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The occurrence of various silent mutations can contribute to a change in protein structure and function, as well as their effect on protein folding, missense mutations can cause instability in protein function and the effect on interaction with other biological molecules (33). The occurrence of a number of mutations in more than one polymorphism may be due to the fact that these animals are of the same origin (34), but it is important to note that both the Italian and Brazilian polymorphisms share the same mutations with the polymorphisms obtained in the current study.…”

Section: Discussionmentioning

confidence: 99%

Molecular description of melatonin receptor 1A gene in Iraqi buffalo

Habib¹,

Al-Rishdy²,

AL-Hellou³

2022

IJVS

View full text Add to dashboard Cite

The water buffalo has a seasonal reproductive pattern with reduced sexual activity during the longer photoperiod. The goal of this study was to identify the single nucleotide polymorphism (SNP) of melatonin receptor 1A (MTNR1A) gene in Iraqi buffalo cows and 3D structure of its protein and phylogeny with other sequences around the world. The 824 bp fragment of exon II of the MTNR1 A gene was amplified from 190 buffalo cows (4-5 years old) genomic DNA belonging to local breeders in Al-Chibayish Marshes, Southern Iraq. Amplified PCR products underwent custom sequencing at the two ends (5′ and 3′ ends). Five separate polymorphism sites, the 1st included 52 animals with 19 mutations (12 missense), the 2nd included 39 animals with 18 mutations (11 missense), the 3rd included 35 animals with 18 mutations (12 missense), the 4th included 32 animals with 18 mutations (12 missense) and the 5th included 32 animals with 14 mutations (8 missense). These polymorphic sites with accession numbers LC565046, LC565047, LC565709, LC565710 and LC565711 respectively were registered in gene bank. The phylogenetic tree reveals that in some of the Iraqi buffalo, the sequences of MTNR1A gene has identical to the Italian buffalo (GU817415), and the Brazilian buffalo (JN689386). Data revealed a marked difference in the fifth polymorphism sites' 3D protein structure because of the mutations. In conclusion, as a result of mutations, the gene MTNR1A in Iraqi buffalo has polymorphisms; these polymorphisms may be linked to gene function, Therefore, further studies are needed to connect the polymorphisms of this gene with the productive and reproductive traits.

show abstract

“…Although mCSM-NA generated a higher PCC value, its RMSE measure was approximately 3.5 times greater than our result. Moreover, we submitted MPR79 to the latest PremPRI webserver that was an extension of PremPDI for MPRs [43]. Although its training set included 31 overlapping mutations, PremPRI achieved worse PCC and RMSE metrics than our method.…”

Section: Independent Testing and Comparison With Existing Methodsmentioning

confidence: 99%

Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions

Jiang

Liu

2021

PLoS Comput Biol

View full text Add to dashboard Cite

The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI.

show abstract

PremPRI: Predicting the Effects of Missense Mutations on Protein–RNA Interactions

Cited by 16 publications

References 60 publications

PremPS: Predicting the impact of missense mutations on protein stability

PremPS: Predicting the impact of missense mutations on protein stability

Molecular description of melatonin receptor 1A gene in Iraqi buffalo

Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions

Contact Info

Product

Resources

About