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Determination of Protein-Protein Complexes from Sparse Experimental Data

Grant number: 16/20366-8
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): February 15, 2017
Effective date (End): February 14, 2018
Field of knowledge:Biological Sciences - Biochemistry - Molecular Biology
Principal researcher:Glaucius Oliva
Grantee:Samuel Reghim Silva
Supervisor abroad: Michele Vendruscolo
Home Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: 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.

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