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NMR structure and dynamics of monomeric neutrophil-activating peptide 2.

ABSTRACT: Neutrophil-activating peptide 2 (NAP-2), which demonstrates a range of proinflammatory activities, is a 72-residue protein belonging to the alpha-chemokine family. Although NAP-2, like other alpha-chemokines, is known to self-associate into dimers and tetramers, it has been shown that the monomeric form is physiologically active. Here we investigate the solution structure of monomeric NAP-2 by multi-dimensional 1H-NMR and 15N-NMR spectroscopy and computational modelling. The NAP-2 monomer consists of an amphipathic, triple-stranded, anti-parallel beta-sheet on which is folded a C-terminal alpha-helix and an aperiodic N-terminal segment. The backbone fold is essentially the same as that found in other alpha-chemokines. 15N T1, T2 and nuclear Overhauser effects (NOEs) have been measured for backbone NH groups and used in a model free approach to calculate order parameters and conformational exchange terms that map out motions of the backbone. N-terminal residues 1 to 17 and the C-terminus are relatively highly flexible, whereas the beta-sheet domain forms the most motionally restricted part of the fold. Conformational exchange occurring on the millisecond time scale is noted at the top of the C-terminal helix and at proximal residues from beta-strands 1 and 2 and the connecting loop. Dissociation to the monomeric state is apparently responsible for increased internal mobility in NAP-2 compared with dimeric and tetrameric states in other alpha-chemokines.

SUBMITTER: Young H

PROVIDER: S-EPMC1220091 | biostudies-other | 1999 Mar

REPOSITORIES: biostudies-other

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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.

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NMR structure and dynamics of monomeric neutrophil-activating peptide 2.

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