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SAXSDom: Modeling multidomain protein structures using small‐angle X‐ray scattering data
Author(s) -
Hou Jie,
Adhikari Badri,
Tanner John J.,
Cheng Jianlin
Publication year - 2020
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25865
Subject(s) - small angle x ray scattering , domain (mathematical analysis) , homology modeling , computer science , probabilistic logic , protein structure , scattering , computational biology , protein domain , crystallography , small angle scattering , sequence homology , chemistry , artificial intelligence , physics , biology , peptide sequence , biochemistry , gene , mathematics , mathematical analysis , optics , enzyme
Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small-angle X-ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input-Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS-based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at https://github.com/jianlin-cheng/SAXSDom.