Evidence map›Paper›PMID 37313049›Full record

ArticleBioinformation2022

YAPPIS-Finder: A novel method for protein-protein interaction site predictions.

Vicky Kumar, Ashita Sood, Anjana Munshi, Tarkeshwar Gautam, Mahesh Kulharia

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In one paragraph

Article in Bioinformation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Vicky KumarCentre for Computational Sciences, Central University of Punjab, Bathinda, Punjab, India.
Ashita SoodCentre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamsala, Himachal Pradesh, India.
Anjana MunshiDepartment of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda, India.
Tarkeshwar GautamDepartment of Zoology, Kalindi College, University of Delhi, Delhi, India.
Mahesh KulhariaCentre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamsala, Himachal Pradesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We describe a multi parametric-approach, YAPPIS-Finder, for predicting the PPI sites on protein surface. A non-redundant database of comprised of 2,265 protein-protein interaction interfaces (PPIIs) involving 4,530 protein-protein interacting partners (PPIPs) and depicting the interaction between protein-chains of experimentally determined PPCs was used in designing the YAPPIS-Finder. Parametric score obtained on analyzing these 4,530 PPIPs with respect to their residue interface propensity, their hydrophobic content, and amount of solvation free energy associated with them provided the basis of YAPPIS-Finder. By applying YAPPIS-Finder on another dataset 4,290 PPIPs from 2,145 PPIIs, the optimal range of the parametric scores and protein-probe van der Waals energy of interaction was determined. Subsequently, taking the optimal range of PPIP parametric scores and threshold for protein-probe van der Waals energy of interaction into the consideration, the YAPPIS-Finder was tested on a blind dataset of 554 protein-chains and it was found predicting 69.67% sites correctly. On predicting only one PPI site on each protein-chain, the YAPPIS-Finder found covering 22.91% of actually sites in the predicted site. Contrary to this, the sites predicted by SPPIDER covered 22.7% of actual sites. However, on predicting two PPI sites for each protein-chain, the percentage coverage of actual sites in the predicted sites by YAPPIS-Finder exceeded two-fold (i.e. 41.81%), thus making the YAPPIS-Finder a better method.

Indexed as

predictionsprotein-protein interaction siteYAPPIS-Finder

Identifiers

PMID37313049
PMCPMC10259227

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