Application of machine learning techniques for prospecting protein exosites as modulators of protein-protein interaction and their interfaces with identified hot spots

• Grant number: 23/13399-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: March 01, 2024
• End date: December 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Geraldo Francisco Donegá Zafalon
• Grantee: Bruno Rodrigues da Silveira
• Host Institution: Instituto de Biociências, Letras e Ciências Exatas (IBILCE). Universidade Estadual Paulista (UNESP). Campus de São José do Rio Preto. São José do Rio Preto , SP, Brazil
• Associated research grant: 20/08615-8 - Protein exosites, cryptic sites and moonlighting: identification, functional mapping and effects of changes in structure, AP.TEM

Abstract

Exosites are structures found on protein surfaces that allow protein-protein and protein-peptide interactions and can be identified by analyzing its physicochemical and structural descriptors. By knowing these exosites we can define new possibilities of interactions even if it hasn't been found in nature yet, however discovering these new exosites is a difficult and costly work when done manually, which often makes their identification and analysis unfeasible. Therefore, this project proposes the use of machine learning techniques to develop a classifier capable of identify, along the physicochemical and structural descriptors, which of them represents new exosites as well as an analysis of specific proteins aided by the classifier itself and sequence alignment tools in order to find new possibilities for interactions that may support designing new drugs and vaccines.