Ortho_Sim_Loc: Essential protein prediction using orthology and priority-based similarity approach

Proteins are the essential macro-molecules of living organism. But all proteins cannot be considered as essential in different relevant studies. Essentiality of a protein is thus computed by computation methods rather than biological experiments which in turn save both time and effort. Different computational approaches are already predicted to select essential proteins successfully with different biological significances by researchers. Most of the experimental approaches return higher false negative outcomes with respect to others. In order to retain the prediction accuracy level, a novel methodology Ortho_Sim_Lochas been proposed which is a combined approach of Orthology, Similarity (using clustering and priority based GO-Annotation) and Subcellular localization. Ortho_Sim_Loc can predict enriched functional set essential proteins. The predicted results are validated with other existing methods like different centrality measures, LIDC. The validation results exhibits better performance of Ortho Sim Loc in compare to other existing computational approaches.

Automated summary

Proteins are essential macro-molecules of living organism that are computed for essentiality by computational methods rather than biological experiments, saving time and effort. Different computational approaches successfully select essential proteins with different biological significances, outperforming experimental methods with higher false negative outcomes. The novel methodology Ortho_Sim_Loc predicts enriched functional set essential proteins by combining Orthology, Similarity, and Subcellular localization, demonstrating better performance compared to other existing computational approaches.