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Protein Structure Determination from Distance Constraints Derived from Chemical Cross-linking: Computational Methods and Applications

Grant number: 18/14274-9
Support Opportunities:Regular Research Grants
Duration: November 01, 2018 - 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


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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Scientific publications (6)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SONORA, MARTIN; MARTINEZ, LEANDRO; PANTANO, SERGIO; MACHADO, MATIAS R.. Wrapping Up Viruses at Multiscale Resolution: Optimizing PACKMOL and SIRAH Execution for Simulating the Zika Virus. JOURNAL OF CHEMICAL INFORMATION AND MODELING, v. 61, n. 1, p. 408-422, . (10/16947-9, 13/08293-7, 18/14274-9, 18/24293-0)
BOTTINO, GUILHERME F.; FERRARI, ALLAN J. R.; GOZZO, FABIO C.; MARTINEZ, LEANDRO. Structural discrimination analysis for constraint selection in protein modeling. Bioinformatics, v. 37, n. 21, p. 3766-3773, . (13/08293-7, 16/13195-2, 19/17007-4, 10/16947-9, 18/14274-9)
DOS SANTOS, RICARDO N.; BOTTINO, GUILHERME F.; GOZZO, FABIO C.; MORCOS, FARUCK; MARTINEZ, LEANDRO. Structural complementarity of distance constraints obtained from chemical cross-linking and amino acid coevolution. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, v. 88, n. 4, . (18/14274-9, 10/16947-9, 15/13667-9, 18/24293-0, 13/08293-7, 14/17264-3)
FERRARI, ALLAN J. R.; GOZZO, FABIO C.; MARTINEZ, LEANDRO. Statistical force-field for structural modeling using chemical cross-linking/mass spectrometry distance constraints. Bioinformatics, v. 35, n. 17, p. 3005-3012, . (13/08293-7, 13/05475-7, 18/14274-9, 16/13195-2, 14/17264-3, 13/23814-3, 10/16947-9)
FERRARI, ALLAN J. R.; CLASEN, MILAN A.; KURT, LOUISE; CARVALHO, PAULO C.; GOZZO, FABIO C.; MARTINEZ, LEANDRO. TopoLink: evaluation of structural models using chemical crosslinking distance constraints. Bioinformatics, v. 35, n. 17, p. 3169-3170, . (13/08293-7, 13/05475-7, 18/14274-9, 16/13195-2, 10/16947-9)

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