Determination of Protein-Protein Complexes from Sparse Experimental Data

• Grant number: 16/20366-8
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: February 15, 2017
• End date: February 14, 2018
• Field of knowledge: Biological Sciences - Biochemistry - Molecular Biology
• Principal Investigator: Glaucius Oliva
• Grantee: Samuel Reghim Silva
• Supervisor: Michele Vendruscolo
• Host Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Institution abroad: University of Cambridge, England
• Associated to the scholarship: 13/20929-4 - Determination of protein-protein complexes from sparse experimental data, BP.DR

Abstract

The understanding and prediction of protein-protein molecular complex formation are fundamental for modern biomedicine and biotechnology efforts with protein plasticity being one of the main challenges for such predictions. Recent advanceson the use of Nuclear Magnetic Resonance data as restrains in molecular dynamics simulations (NMR-MD) allow us to obtain protein conformational ensembles compatible with experimental data. In our project we are developingnew computational approaches for determining the structures of protein-protein complexes using sparse experimental information. This system extends the Chemical Shift based approach used by our previous code CamDock.We have identified chemical shifts from NMR spectroscopy, EPR Double Electron-Electron Resonance and Cryo-Electron Microscopy as the most promising types of experimental data for this purpose, since they are readily measurable, at least with respect to other observables, and comparatively rich in structural information. Our intention is to provide a new standard tool to the structural biology community to determine the structures of large complexes, which are thebasic functional units in the cell, rather than the structures of individual proteins or small complexes, which currently represent the vast majority of the structures in the Protein Data Bank.