Computational algorithm to determine amino acids that suit alternative start codon's environment for protein mutation
Abstract – Parkinson's Disease (P.D.), characterized by uncontrollable movements in the body, affects millions of people worldwide. A current belief on a potential cause of P.D. is the brain's buildup of an α-helical protein called α-synuclein. My research in the Newberry Lab at UT Austin under Dr. Robert Newberry sought to determine what facilitates the buildup of α-synuclein in the brain. I hypothesized that the re-initiation of the gene at an alternative start codon causes the buildup. To test this, I developed a manual framework to determine which amino acid suits the alternative start codon's environment since none exists currently and validated the hypothesis. To improve the manual process, as part of my current work, I hypothesized that a computational algorithm could be developed to produce the same results expeditiously. I subsequently built the algorithm that considered factors such as amino-acid configurations, hydrophobicity, torsion angles, Ramachandra's plot, and the Eisenberg and Weiss table. Using that algorithm, I determined leucine might replace the alternative start codon and maintain the protein's structure which matches the results from my prior research. Since I had tested the mutation using the Quikchange procedure to perform site-directed mutagenesis in Newberry Lab, my hypothesis was validated. In the future, the algorithm can be evolved to refine the recommended mutations and design primers based on the calculated mutation and the protein genome, leveraging machine learning techniques. The algorithm can speed up P.D. research and potentially help end years of suffering for P.D. patients and the economic burden on countries.