PREDICTION OF HOTSPOT IN PROTEIN-PROTEIN/PROTEIN-SUBSTRATE INTERACTION: A NOVEL COMPUTATIONAL APPROACH

Protein-protein, as well as protein-substrate interactions, play an important role in regulating specific biological functions like signal transduction, apoptosis, gene regulation, and immune response. These play a major role in drug discovery applications and can be used as molecular targets to stop disease development at the molecular level. Thus, understanding the biological mechanism of protein is an integral part of any computational study. Moreover, these interactions can be manipulated through protein engineering to address industrial applications. As part of the protein engineering strategy, the first step is the determination of hotspots. Hotspots are defined as positions in the amino acid sequence that can be targeted for mutagenesis to improve the catalytic activity or stability of an enzyme. The commonly-used method for determining hotspot residues is computational alanine scanning (CAS) mutagenesis experiments, which are computationally expensive and time-consuming. In the current study, we aim to develop a protocol to predict hotspots using a computationally less costly method. We have used two published datasets to cross-validate our method. Both the datasets belong to a different enzyme class. The key element here is that the substrate is present in the active site of the enzyme. The nearby residues present within a distance of 5 Å from the substrate were also considered during prediction. This helped in determining the exact residues responsible for the majority of performance on protein’s characteristic frequency. The objective was to predict hotspots with more precision and sensitivity while demanding limited computing resources.