Protein structure prediction guided by cross-linking restraints - A systematic evaluation of the impact of the cross-linking spacer length
| Autor: | Hofmann, Tommy, Fischer, Axel W., Meiler, Jens, Kalkhof, Stefan |
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| Rok vydání: | 2015 |
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| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/j.ymeth.2015.05.014 |
| Popis: | Recent development of high-resolution mass spectrometry (MS) instruments enables chemical cross-linking (XL) to become a high-throughput method for obtaining structural information about proteins. Restraints derived from XL-MS experiments have been used successfully for structure refinement and protein-protein docking. However, one formidable question is under which circumstances XL-MS data might be sufficient to determine a protein's tertiary structure de novo? Answering this question will not only include understanding the impact of XL-MS data on sampling and scoring within a de novo protein structure prediction algorithm, it must also determine an optimal cross-linker type and length for protein structure determination. While a longer cross-linker will yield more restraints, the value of each restraint for protein structure prediction decreases as the restraint is consistent with a larger conformational space. In this study, the number of cross-links and their discriminative power was systematically analyzed in silico on a set of 2,055 non-redundant protein folds considering Lys-Lys, Lys-Asp, Lys-Glu, Cys-Cys, and Arg-Arg reactive cross-linkers between 1 {\AA} and 60 {\AA}. Depending on the protein size a heuristic was developed that determines the optimal cross-linker length. Next, simulated restraints of variable length were used to de novo predict the tertiary structure of fifteen proteins using the BCL::Fold algorithm. The results demonstrate that a distinct cross-linker length exists for which information content for de novo protein structure prediction is maximized. The sampling accuracy improves on average by 1.0 {\AA} and up to 2.2 {\AA} in the most prominent example. XL-MS restraints enable consistently an improved selection of native-like models with an average enrichment of 2.1. Comment: 21 pages, 8 figures, 3 tables |
| Databáze: | arXiv |
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