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Implementation, evaluation and application of mass spectrometric data in protein structure modeling

Grant number: 19/07961-2
Support type:Scholarships in Brazil - Master
Effective date (Start): August 01, 2019
Effective date (End): February 28, 2021
Field of knowledge:Biological Sciences - Biochemistry - Chemistry of Macromolecules
Principal Investigator:Fabio Cesar Gozzo
Grantee:Amanda Miquilini Cordibello Marques
Home Institution: Instituto de Química (IQ). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:14/17264-3 - New frontiers in structural proteomics: characterizing protein and protein complex structures by mass spectrometry, AP.TEM

Abstract

The use of experimental mass spectrometric (MS) data in protein structure characterization is a very attractive approach due to the intrinsic characteristics of MS, such as high sensitivity, fast analysis, and, most importantly, its universality. Although many protein-protein complexes have been solved by cross-linking/mass spectrometry (XLMS) technique, there is no effective protocol to use XLMS data to model protein tertiary structures. Recently, our group published the first effective methodology to incorporate distance constraints from XLMS experiments into protein modeling, called cross-linking force field (XLFF). We have also stablished a cooperation with Prof. Frank Dimaio to implement XLFF in a new modeling protocol in Rosetta software, called Hybridize, which is based on RosettaCM (Comparative Modeling), presenting excellent results. This project, therefore, intends to create an even more effective modeling protocol using Hybridize methodology along with XLFF, establishing a general protocol for using XLMS data in protein modeling. This project is part of the thematic project "New frontiers in structural proteomics: protein and protein-protein-complex structures characterization by mass spectrometry" and corresponds to one of its main goals which is "development of modeling methodologies based on mass spectrometric data"