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.
A fusion gene, which is composed by fusing pieces of two distinct genes, gives rise to a protein. The body may naturally produce fusion genes by transferring DNA between chromosomes. For instance, the BCR-ABL gene, which is a fusion gene that creates the BCR-ABL fusion protein, is found in a few different types of leukemia. By combining genes or segments of genes from related or unrelated animals, fusion genes and proteins can be produced further. Real-time lab tests, however, are expensive and time-consuming for automated fusion protein functionality prediction. In order to address this issue, this research suggests a brand-new Fusion Protein Functionality Prediction (FPFP) approach based on the Genetic Algorithm (GA) technique. The results of the experiments showed that the FPFP method accurately predicts the functionality of fusion proteins.
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