Robust, Integrated Computational Control of NMR Experiments to Achieve Optimal Assignment by ADAPT-NMR

ADAPT-NMR (Assignment-directed Data collection Algorithm utilizing a Probabilistic Toolkit in NMR) represents a groundbreaking prototype for automated protein structure determination by nuclear magnetic resonance (NMR) spectroscopy. With a [13C,15N]-labeled protein sample loaded into the NMR spectrometer, ADAPT-NMR delivers complete backbone resonance assignments and secondary structure in an optimal fashion without human intervention. ADAPT-NMR achieves this by implementing a strategy in which the goal of optimal assignment in each step determines the subsequent step by analyzing the current sum of available data. ADAPT-NMR is the first iterative and fully automated approach designed specifically for the optimal assignment of proteins with fast data collection as a byproduct of this goal. ADAPT-NMR evaluates the current spectral information, and uses a goal-directed objective function to select the optimal next data collection step(s) and then directs the NMR spectrometer to collect the selected data set. ADAPT-NMR extracts peak positions from the newly collected data and uses this information in updating the analysis resonance assignments and secondary structure. The goal-directed objective function then defines the next data collection step. The procedure continues until the collected data support comprehensive peak identification, resonance assignments at the desired level of completeness, and protein secondary structure. We present test cases in which ADAPT-NMR achieved results in two days or less that would have taken two months or more by manual approaches

The Center for Eukaryotic Structural Genomics

The Center for Eukaryotic Structural Genomics (CESG) is a “specialized” or “technology development” center supported by the Protein Structure Initiative (PSI). CESG’s mission is to develop improved methods for the high-throughput solution of structures from eukaryotic proteins, with a very strong weighting toward human proteins of biomedical relevance. During the first three years of PSI-2, CESG selected targets representing 601 proteins from Homo sapiens, 33 from mouse, 10 from rat, 139 from Galdieria sulphuraria, 35 from Arabidopsis thaliana, 96 from Cyanidioschyzon merolae, 80 from Plasmodium falciparum, 24 from yeast, and about 25 from other eukaryotes. Notably, 30% of all structures of human proteins solved by the PSI Centers were determined at CESG. Whereas eukaryotic proteins generally are considered to be much more challenging targets than prokaryotic proteins, the technology now in place at CESG yields success rates that are comparable to those of the large production centers that work primarily on prokaryotic proteins. We describe here the technological innovations that underlie CESG’s platforms for bioinformatics and laboratory information management, target selection, protein production, and structure determination by X-ray crystallography or NMR spectroscopy

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

Automated protein resonance assignments of magic angle spinning solid-state NMR spectra of β1 immunoglobulin binding domain of protein G (GB1)

Magic-angle spinning solid-state NMR (MAS SSNMR) represents a fast developing experimental technique with great potential to provide structural and dynamics information for proteins not amenable to other methods. However, few automated analysis tools are currently available for MAS SSNMR. We present a methodology for automating protein resonance assignments of MAS SSNMR spectral data and its application to experimental peak lists of the β1 immunoglobulin binding domain of protein G (GB1) derived from a uniformly 13C- and 15N-labeled sample. This application to the 56 amino acid GB1 produced an overall 84.1% assignment of the N, CO, CA, and CB resonances with no errors using peak lists from NCACX 3D, CANcoCA 3D, and CANCOCX 4D experiments. This proof of concept demonstrates the tractability of this problem

NMReDATA, a standard to report the NMR assignment and parameters of organic compounds

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Open access articleEven though NMR has found countless applications in the field of small molecule characterization, there is no standard file for the NMR data relevant to structure characterization of small molecules. A file format is introduced to associate the NMR parameters extracted from 1D and 2D spectra of organic compounds to the assigned chemical structure. These NMR parameters, which we shall call NMReDATA, include chemical shift values, signal integrals, intensities, multiplicities, scalar coupling constants, lists of 2D correlations, relaxation times and diffusion rates. The file format is an extension of the existing SDF (Structure Data Format), which is compatible with the commonly used MOL format. The association of an NMReDATA file with the raw and spectral data from which it originates constitutes an NMR record. This format is easily readable by humans and computers and provides a simple and efficient way for disseminating results of structural chemistry investigations, automating the verification of published result, and for assisting the constitution of highly needed open-source structural databases