Topological analysis and predictive modeling of amino acid structures with implications for bioinformatics and structural biology.

Amino acids, as the fundamental constituents of proteins and enzymes, play a vital role in various biological processes. Amino acids such as histidine, cysteine, and methionine are known to coordinate with metal ions in proteins and enzymes, playing critical roles in their structure and function. In metalloproteins, metal ions are often coordinated by specific amino acid residues, contributing to the protein's stability and catalytic activity. Investigating the structural properties of amino acids is paramount to understanding the intricacies of protein function and interactions. The molecular structure of amino acid structures are examined using topological indices that are based on both distance and degree. These indices capture unique structural features of amino acids in their molecular graphs. We have developed linear, quadratic, and logarithmic regression models to estimate the five physical/chemical properties of twenty-two amino acids molecules. The findings reveal novel insights into the structural determinants of amino acid properties and present efficient predictive models for various attributes. This research contributes towards better understanding amino acid structures and offers practical applications in bioinformatics, drug design, and structural biology, enhancing the ability to manipulate and comprehend the molecular world.