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Experimental Protein Structure Verification by Scoring with a Single, Unassigned NMR Spectrum.

ABSTRACT: Standard methods for de novo protein structure determination by nuclear magnetic resonance (NMR) require time-consuming data collection and interpretation efforts. Here we present a qualitatively distinct and novel approach, called Comparative, Objective Measurement of Protein Architectures by Scoring Shifts (COMPASS), which identifies the best structures from a set of structural models by numerical comparison with a single, unassigned 2D (13)C-(13)C NMR spectrum containing backbone and side-chain aliphatic signals. COMPASS does not require resonance assignments. It is particularly well suited for interpretation of magic-angle spinning solid-state NMR spectra, but also applicable to solution NMR spectra. We demonstrate COMPASS with experimental data from four proteins--GB1, ubiquitin, DsbA, and the extracellular domain of human tissue factor--and with reconstructed spectra from 11 additional proteins. For all these proteins, with molecular mass up to 25 kDa, COMPASS distinguished the correct fold, most often within 1.5 Å root-mean-square deviation of the reference structure.

SUBMITTER: Courtney JM

PROVIDER: S-EPMC4786943 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Experimental Protein Structure Verification by Scoring with a Single, Unassigned NMR Spectrum.

Courtney Joseph M JM Ye Qing Q Nesbitt Anna E AE Tang Ming M Tuttle Marcus D MD Watt Eric D ED Nuzzio Kristin M KM Sperling Lindsay J LJ Comellas Gemma G Peterson Joseph R JR Morrissey James H JH Rienstra Chad M CM

Structure (London, England : 1993) 20150910 10

Standard methods for de novo protein structure determination by nuclear magnetic resonance (NMR) require time-consuming data collection and interpretation efforts. Here we present a qualitatively distinct and novel approach, called Comparative, Objective Measurement of Protein Architectures by Scoring Shifts (COMPASS), which identifies the best structures from a set of structural models by numerical comparison with a single, unassigned 2D (13)C-(13)C NMR spectrum containing backbone and side-cha ...[more]

PMID: 26365800

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Project description:Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15 N- 1 H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.HighlightsAF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.

Dataset Information

Experimental Protein Structure Verification by Scoring with a Single, Unassigned NMR Spectrum.

Publications

Experimental Protein Structure Verification by Scoring with a Single, Unassigned NMR Spectrum.

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