Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As HT, solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution. This article is protected by copyright. All rights reserved.
|Original language||English (US)|
|Number of pages||13|
|Journal||Proteins: Structure, Function, and Bioinformatics|
|State||Published - Feb 7 2018|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: For financial support, we thank NIH (PO1CA92584 to J.A.T., R01GM110387 to S.T., and R01GM100482 to KF); and KAUST for CRG3 funding to J.A.T. J.A.T. acknowledges support by a Robert A. Welch Chemistry Chair, the Cancer Prevention and Research Institute of Texas, and the University of Texas System Science and Technology Acquisition and Retention. This research used resources of the Advanced Light Source, which are DOE Office of Science User Facilities under contract no. DE-AC02-05CH11231. The SIBYLS beamline 12.3.1 is supported by the IDAT program from the DOE BER and KAUST.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.