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Oxford University Press (OUP), Bioinformatics, 17(35), p. 3005-3012, 2019

DOI: 10.1093/bioinformatics/btz013

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Statistical Force-Field for Structural Modeling Using Chemical Cross-Linking/mass Spectrometry Distance Constraints

Journal article published in 2018 by Allan J. R. Ferrari ORCID, Fabio C. Gozzo, Leandro Martinez
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Abstract Motivation Chemical cross-linking/mass spectrometry (XLMS) is an experimental method to obtain distance constraints between amino acid residues which can be applied to structural modeling of tertiary and quaternary biomolecular structures. These constraints provide, in principle, only upper limits to the distance between amino acid residues along the surface of the biomolecule. In practice, attempts to use of XLMS constraints for tertiary protein structure determination have not been widely successful. This indicates the need of specifically designed strategies for the representation of these constraints within modeling algorithms. Results A force-field designed to represent XLMS-derived constraints is proposed. The potential energy functions are obtained by computing, in the database of known protein structures, the probability of satisfaction of a topological cross-linking distance as a function of the Euclidean distance between amino acid residues. First, the strategy suggests that XL constraints should be set to shorter distances than usually assumed. Second, the complete statistical force-field improves the models obtained and can be easily incorporated into current modeling methods and software. The force-field was implemented and is distributed to be used within the Rosetta ab initio relax protocol. Availability and implementation Force-field parameters and usage instructions are freely available online (http://m3g.iqm.unicamp.br/topolink/xlff). Supplementary information Supplementary data are available at Bioinformatics online.