Protein structure prediction assisted with sparse NMR data in CASP13

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.

Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2

“…There is potential to use predicted models not only to guide structure analysis, as was done here, but to provide a complete analysis of both resonance assignments and 3D structures. Accurate models provided by methods like AlphaFold2 10 and RosTTAFold 50 open the potential of complete structure determination of small, relatively rigid protein structures from a single NOESY spectrum; for example, from a single simultaneous 13 C, 15 N-resolved NOESY spectrum. However, care must be exercised in using prediction models to interpret such experimental data, as was observed for T1029 using a CS-Rosetta structure to guide the analysis of the original T1029 structure.…”

“…Accordingly, we (N. Z., A. L., Y. J. H., and G. T. M.) carried out a careful repicking of the 3D 15 N-and 13 C-edited NOESY data. Due to the relatively low quality of the processed NOESY spectra, automatic peak picking was challenging and resulted in far too many peaks, particularly for the 13 C-edited NOESY.…”

A Preliminary Investigation in the Molecular Basis of Host Shutoff Mechanism in SARS-CoV

“…Protein modeling software I-TASSER [7] was utilized for the structural modeling of the mutations to nsp1 to understand the mechanism of each mutation better. I-TASSER was selected due to its continued high performance during the CASP competitions [13].…”

Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets

“…CASP-NMR is a sub-category of CASP (CASP11 and CASP13) which provides sparse, ambiguous, and noisy unassigned NMR data along with the protein sequences and asks groups to solve the corresponding three-dimensional structures. In the 13th edition of CASP, despite the successes of this approach in the NMR category, the determined structures, in some cases, were of lower accuracy than those predicted in the absence of data ( Kryshtafovych et al, 2019 ; Sala et al, 2019 ). In this work, we take the previous success and develop new strategies for better integration between data and MELD.…”