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


Vicky Kumar1, Ashita Sood2, Anjana Munshi3, Tarkeshwar Gautam4 & Mahesh Kulharia2,*



1Centre for Computational Sciences, Central University of Punjab, Bathinda, Punjab, India; 2Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamsala, Himachal Pradesh, India;3Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda, India; 4Department of Zoology, Kalindi College, University of Delhi, Delhi, India; *Corresponding author



Vicky Kumar - E-mail: vickysibbal@gmail.com
Ashita Sood -E-mail: ashitasood01@gmail.com

Anjana Munshi - E-mail: anjanadurani@yahoo.co.in

Tarkeshwar Gautam - E-mail: tarukmc@gmail.com

Mahesh Kulharia - E-mail: kulharia@gmail.com; Tel: +91-99884 28856


Article Type

Research Article



Received June 2, 2022; Revised July 31, 2022; Accepted July 31, 2022, Published July 31, 2022



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.



YAPPIS-Finder, protein-protein interaction site, predictions



Kumar et al. Bioinformation 18(7): 604-612 (2022)


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.