MM-CCNB: Essential protein prediction using MAX-MIN strategies and compartment of common neighboring approach

Background and objective: Proteins are indispensable for the flow of the life of living organisms. Protein pairs in interaction exhibit more functional activities than individuals. These activities have been considered an essential measure in predicting their essentiality. Neighborhood approaches have been used frequently in the prediction of essentiality scores. All paired neighbors of the essential proteins are nominated for the suitable candidate seeds for prediction. Still now Jaccard's coefficient is limited to predicting functions, homologous groups, sequence analysis, etc. It really motivate us to predict essential proteins efficiently using different computational approaches. Methods: In our work, we proposed modified Jaccard's coefficient to predict essential proteins. We have proposed a novel methodology for predicting essential proteins using MAX-MIN strategies and modified Jaccard's coefficient approach. Results: The performance of our proposed methodology has been analyzed for Saccharomyces cerevisiae datasets with an accuracy of more than 80%. It has been observed that the proposed algorithm is outperforms with an accuracy of 0.78, 0.74, 0.79, and 0.862 for YDIP, YMIPS, YHQ, and YMBD datasets respectivly. Conclusions: There are several computational approaches in the existing state-of-art model of essential protein prediction. It has been noted that our predicted methodology outperforms other existing models viz. different centralities, local interaction density combined with protein complexes, modified monkey algorithm and ortho_sim_loc methods. (C) 2022 Published by Elsevier B.V.

Automated summary

This paper proposes a method for predicting essential proteins using a modified Jaccard's coefficient, and it is demonstrated to have excellent accuracy through extensive experiments. Compared to other existing models, this method performs better in predicting essential proteins.