Efficient Hybrid String Matching Algorithm for Protein Sequence Analysis

Protein sequence examination stands crucial in bioinformatics research because it discovers motifs and predicts functions and enables evolutionary analysis. Default stringmatching algorithms Berry-Ravindran and Index-Based Search (IBS) experience operational limitations in their computational efficiency. A new hybrid Minimum number of Attempts and Character Comparisons (MAC) algorithm presents itself as a solution to overcome the existing limitations by combining the advantages of Berry-Ravindran and IBS methods. The newly proposed algorithm detects redundant sequence comparisons to improve speed when searching sequences. The experimental data demonstrates that MAC enhances execution speed together with scalability which leads to more efficient analysis of large protein sequences. This innovative technique finds uses in genomic studies as well as drug identification processes and protein annotation experiments.