Abstract
Many proteins are composed of several domains that pack together into a complex tertiary structure. Some multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for the domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the assembled protein. Small-angle X-ray scattering (SAXS) reports on the solution structural properties of proteins and has the potential for guiding homology modeling of multidomain proteins. In this work, we describe a novel multi-domain protein assembly modeling method, SAXSDom, that integrates experimental knowledge from SAXS profiles with probabilistic Input-Output Hidden Markov model (IOHMM). Four scoring functions to account for the energetic contribution of SAXS restraints for domain assembly were developed and tested. The method was evaluated on multi-domain proteins from two public datasets. Based on the results, the accuracy of domain assembly was improved for 40 out of 46 CASP multi-domain proteins in terms of RMSD and TM-score when SAXS information was used. Our method also achieved higher accuracy for at least 45 out of 73 multi-domain proteins according to RMSD and TM-score metrics in the AIDA 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 http://github.com/multicom-toolbox/SAXSDom.
Original language | American English |
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Journal | bioRxiv |
DOIs | |
State | Published - Feb 24 2019 |
Keywords
- CASP
- SAXS
- Small-angle X-ray scattering
- domain assembly
- machine learning
- probabilistic model
- protein structure
Disciplines
- Biology
- Genetics
- Systems Biology