A Hybrid Genetic Particle Swarm Optimization Algorithm Based Fusion Protein Functionality Prediction

Abstract

A fusion protein is a protein with at least two domains that are each encoded by a different gene and are combined into a single polypeptide by transcription and translation. For example, chromosomal rearrangement could result in the in vivo production of fusion proteins. One such fusion protein is the one responsible for chronic myelogenous leukaemia, the BCR-ABL protein. Recombinant DNA techniques could be used to create fusion proteins in vitro. By combining genes or portion of genes from similar or dissimilar organisms, fusion genes and proteins may be produced. But, real-time lab experiments for automated fusion protein functionality prediction are expensive and time-consuming. This paper proposes a novel Fusion Protein Functionality Prediction based on a Hybrid Genetic Particle Swarm Optimization (HybGPSO) algorithm to deal with this problem. The cellular component, biological process, and molecular function of an unannotated fusion protein by the GO consortium are the three functionalities predicted by this algorithm. The results of the experiments demonstrate that the proposed HybGPSO algorithm accurately predicts the function of fusion proteins.