The NMR restraints grid at BMRB for 5,266 protein and nucleic acid PDB entries

Several pilot experiments have indicated that improvements in older NMR structures can be expected by applying modern software and new protocols (Nabuurs et al. in Proteins 55:483–186, 2004; Nederveen et al. in Proteins 59:662–672, 2005; Saccenti and Rosato in J Biomol NMR 40:251–261, 2008). A recent large scale X-ray study also has shown that modern software can significantly improve the quality of X-ray structures that were deposited more than a few years ago (Joosten et al. in J. Appl Crystallogr 42:376–384, 2009; Sanderson in Nature 459:1038–1039, 2009). Recalculation of three-dimensional coordinates requires that the original experimental data are available and complete, and are semantically and syntactically correct, or are at least correct enough to be reconstructed. For multiple reasons, including a lack of standards, the heterogeneity of the experimental data and the many NMR experiment types, it has not been practical to parse a large proportion of the originally deposited NMR experimental data files related to protein NMR structures. This has made impractical the automatic recalculation, and thus improvement, of the three dimensional coordinates of these structures. We here describe a large-scale international collaborative effort to make all deposited experimental NMR data semantically and syntactically homogeneous, and thus useful for further research. A total of 4,014 out of 5,266 entries were ‘cleaned’ in this process. For 1,387 entries, human intervention was needed. Continuous efforts in automating the parsing of both old, and newly deposited files is steadily decreasing this fraction. The cleaned data files are available from the NMR restraints grid at http://restraintsgrid.bmrb.wisc.edu

Modular Architecture and Unique Teichoic Acid Recognition Features of Choline-Binding Protein L (CbpL) Contributing to Pneumococcal Pathogenesis

The human pathogen Streptococcus pneumoniae is decorated with a special class of surface-proteins known as choline-binding proteins (CBPs) attached to phosphorylcholine (PCho) moieties from cell-wall teichoic acids. By a combination of X-ray crystallography, NMR, molecular dynamics techniques and in vivo virulence and phagocytosis studies, we provide structural information of choline-binding protein L (CbpL) and demonstrate its impact on pneumococcal pathogenesis and immune evasion. CbpL is a very elongated three-module protein composed of (i) an Excalibur Ca 2+ -binding domain -reported in this work for the very first time-, (ii) an unprecedented anchorage module showing alternate disposition of canonical and non-canonical choline-binding sites that allows vine-like binding of fully-PCho-substituted teichoic acids (with two choline moieties per unit), and (iii) a Ltp-Lipoprotein domain. Our structural and infection assays indicate an important role of the whole multimodular protein allowing both to locate CbpL at specific places on the cell wall and to interact with host components in order to facilitate pneumococcal lung infection and transmigration from nasopharynx to the lungs and blood. CbpL implication in both resistance against killing by phagocytes and pneumococcal pathogenesis further postulate this surface-protein as relevant among the pathogenic arsenal of the pneumococcus.We gratefully acknowledge Karsta Barnekow and Kristine Sievert-Giermann, for technical assistance and Lothar Petruschka for in silico analysis (all Dept. of Genetics, University of Greifswald). We are further grateful to the staff from SLS synchrotron beamline for help in data collection. This work was supported by grants from the Deutsche Forschungsgemeinschaft DFG GRK 1870 (to SH) and the Spanish Ministry of Economy and Competitiveness (BFU2014-59389-P to JAH, CTQ2014-52633-P to MB and SAF2012-39760-C02-02 to FG) and S2010/BMD- 2457 (Community of Madrid to JAH and FG).Peer Reviewe

Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy

The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (http://pine.nmrfam.wisc.edu). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination