|
|||||
|
|
Protein structure prediction assisted with sparse NMR data in CASP13 |
|
| Authors: | Davide Sala Yuanpeng Janet Huang Casey A Cole David A Snyder Gaohua Liu Yojiro Ishida GVT Swapna Kelly P Brock Chris Sander Krzysztof Fidelis Andriy Kryshtafovych Masayori Inouye Roberto Tejero Homayoun Valafar Antonio Rosato Gaetano T Montelione |
| Institution: | 1. Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy;2. Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey
Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York;3. Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina;4. Department of Chemistry, College of Science and Health, William Paterson University, Wayne, New Jersey;5. Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey Nexomics Biosciences, Bordentown, New Jersey;6. Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey;7. Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey;8. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts;9. Department of Cell Biology, Harvard Medical School, Boston, Massachusetts cBio Center, Dana-Farber Cancer Institute, Boston, Massachusetts;10. Genome Center, University of California, Davis, California;11. Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey;12. Departamento de Quimica Fisica, Universidad de Valencia, Valencia, Spain |
| Abstract: | CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15N-1H residual dipolar coupling data, typical of that obtained for 15N,13C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models. |
| Keywords: | CASP contact prediction protein modeling residual dipolar coupling simulated NMR spectra sparse NMR data structure prediction |