| Grant number: | 18/14274-9 |
| Support Opportunities: | Regular Research Grants |
| Start date: | November 01, 2018 |
| End date: | October 31, 2020 |
| Field of knowledge: | Physical Sciences and Mathematics - Chemistry - Physical-Chemistry |
| Principal Investigator: | Leandro Martinez |
| Grantee: | Leandro Martinez |
| Host Institution: | Instituto de Química (IQ). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| City of the host institution: | Campinas |
Abstract
Molecular modeling methods are an integral part of strategies for the determination of protein structures, with or without the use of experimental data. The extent of the role of modeling methods depends on the structural resolution of the experimental data. If the experimental data provides very precise information on the structure, the modeling strategies are used only to finely adjust the structure or model minor fragments. This is common, for example, in protein structure determination by protein X-ray crystallography. On the other side, when the structural information is of lower resolution, the modeling strategies assume a central role. For example, they are intrinsic parts of the process of determining protein structures from NMR, cryoelectron microscopy or low resolution data as small angle X-ray scattering or circular dichroism. More recently, chemical crosslinking experiments, analyzed by mass spectrometry, have been developed to obtain distance restraints that can be, in principle, used to to aid the determination of protein tridimensional structures. These data has been shown to be useful for the determination of the relative orientation of structures in protein complexes, but their use for tertiary structure resolution is not yet successful. In this project, we propose the development of a series of computational strategies to address the limitations of the information provided by chemical crosslinking/mass spectrometry for the determination of protein tertiary structures. Protocols for modeling, force-fields, and statistical methods for the classification and analysis of experimental data are proposed, which take into consideration the structural nature and the resolution of these experimental restraints. (AU)
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