Prediction of structural changes in proteins caused by mutations using AlphaFold2 and Molecular Dynamics for Precision Vaccinology

• Grant number: 23/09291-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date until: November 01, 2023
• End date until: October 31, 2025
• Field of knowledge: Biological Sciences - Immunology - Immunogenetics
• Principal Investigator: Helder Takashi Imoto Nakaya
• Grantee: Peter Park
• Host Institution: Centro de Inovação da USP (INOVA). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 18/14933-2 - Integrative biology applied to human health, AP.JP2
• Associated scholarship(s): 24/14345-4 - Developing a Molecular Dynamics Pipeline for Predicting Mutation Outcomes in Precision Vaccinology, BE.EP.PD

Abstract

This project aims to integrate genomic data of patients with Structural Biology, with the goal of predicting the efficacy of vacines at the individual level.Vaccines are essential in preventing infectious diseases and their effectiveness may vary between individuals due to genetic variations that affect the production of cytokines, signaling proteins involved in the immune response. Recent studies have shown that single nucleotide polymorphisms (SNPs) in cytokine genes can lead to an altered immune response and increased susceptibility to diseases. The use of Molecular Dynamics (MD) simulations has allowed the investigation of the effects of SNPs on genes related to cytokines at the molecular level, providing new insights into changes in protein structure and function. Recently, the ABraOM database (Arquivo Brasileiro Online de Mutações) was developed, where genetic variants from 1171 complete genomes of unrelated elderly Brazilians from São Paulo were pooled with their predicted consequences (e.g. non-synonymous mutations). This project aims integrate genomic data with Structural Biology, analyzing SNPs data from Brazilian patients, obtained in ABraOM, and evaluating the consequence of non-synonymous mutations in the structure of key cytokines in the immune response, such as: IL2, IL10, IL1b, IL6, IFNA, IFNB and IFNG. Using AlphaFold2, an artificial intelligence program that predicts the structure of proteins with high accuracy, we will generate structures of control and mutated proteins. And through MD simulations, we will evaluate the structural behavior of the control and mutated proteins. From these simulations, we will measure the level of protein structural deformation resulting from a specific SNP. The results can provide important information about the mechanism of action of vaccines and cytokines and about the efficacy of vaccines in patients who have SNPs with high protein conformational impact, thus paving the way for accurate diagnosis, predicting the outcome and efficacy of vaccines from the analysis of the patient's genome.