Protein NMR structure determination with automated NOE-identification in the NOESY spectra using the new software ATNOS

Novel algorithms are presented for automated NOESY peak picking and NOE signal identification in homonuclear 2D and heteronuclear-resolved 3D [1H,1H]-NOESY spectra during denovoprotein structure determination by NMR, which have been implemented in the new software ATNOS (automated NOESY peak picking). The input for ATNOS consists of the amino acid sequence of the protein, chemical shift lists from the sequence-specific resonance assignment, and one or several 2D or 3D NOESY spectra. In the present implementation, ATNOS performs multiple cycles of NOE peak identification in concert with automated NOE assignment with the software CANDID and protein structure calculation with the program DYANA. In the second and subsequent cycles, the intermediate protein structures are used as an additional guide for the interpretation of the NOESY spectra. By incorporating the analysis of the raw NMR data into the process of automated denovoprotein NMR structure determination, ATNOS enables direct feedback between the protein structure, the NOE assignments and the experimental NOESY spectra. The main elements of the algorithms for NOESY spectral analysis are techniques for local baseline correction and evaluation of local noise level amplitudes, automated determination of spectrum-specific threshold parameters, the use of symmetry relations, and the inclusion of the chemical shift information and the intermediate protein structures in the process of distinguishing between NOE peaks and artifacts. The ATNOS procedure has been validated with experimental NMR data sets of three proteins, for which high-quality NMR structures had previously been obtained by interactive interpretation of the NOESY spectra. The ATNOS-based structures coincide closely with those obtained with interactive peak picking. Overall, we present the algorithms used in this paper as a further important step towards objective and efficient de novoprotein structure determination by NM

Automated protein structure calculation from NMR data

Current software is almost at the stage to permit completely automatic structure determination of small proteins of < 15 kDa, from NMR spectra to structure validation with minimal user interaction. This goal is welcome, as it makes structure calculation more objective and therefore more easily validated, without any loss in the quality of the structures generated. Moreover, it releases expert spectroscopists to carry out research that cannot be automated. It should not take much further effort to extend automation to ca 20 kDa. However, there are technological barriers to further automation, of which the biggest are identified as: routines for peak picking; adoption and sharing of a common framework for structure calculation, including the assembly of an automated and trusted package for structure validation; and sample preparation, particularly for larger proteins. These barriers should be the main target for development of methodology for protein structure determination, particularly by structural genomics consortia

Structural characterization of intrinsically disordered proteins by NMR spectroscopy.

Recent advances in NMR methodology and techniques allow the structural investigation of biomolecules of increasing size with atomic resolution. NMR spectroscopy is especially well-suited for the study of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) which are in general highly flexible and do not have a well-defined secondary or tertiary structure under functional conditions. In the last decade, the important role of IDPs in many essential cellular processes has become more evident as the lack of a stable tertiary structure of many protagonists in signal transduction, transcription regulation and cell-cycle regulation has been discovered. The growing demand for structural data of IDPs required the development and adaption of methods such as 13C-direct detected experiments, paramagnetic relaxation enhancements (PREs) or residual dipolar couplings (RDCs) for the study of 'unstructured' molecules in vitro and in-cell. The information obtained by NMR can be processed with novel computational tools to generate conformational ensembles that visualize the conformations IDPs sample under functional conditions. Here, we address NMR experiments and strategies that enable the generation of detailed structural models of IDPs

The NOESY Jigsaw: Automated Protein Secondary Structure and Main-Chain Assignment from Sparse, Unassigned NMR Data

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the Jigsaw algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). Jigsaw consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. Jigsaw\u27s deferment of assignment until after secondary structure identification differs greatly from traditional approaches, which begin by correlating peaks among dozens of experiments. By deferring assignment, Jigsaw not only eliminates this bottleneck, it also allows the number of experiments to be reduced from dozens to four, none of which requires 13C-labeled protein. This in turn dramatically reduces the amount and expense of wet lab molecular biology for protein expression and purification, as well as the total spectrometer time to collect data. Our results for three test proteins demonstrate that we are able to identify and align approximately 80 percent of alpha-helical and 60 percent of beta-sheet structure. Jigsaw is extremely fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations, utilizing a suite of graph analysis algorithms to compensate for the data sparseness. Jigsaw could be used for quick structural assays to speed data to the biologist early in the process of investigation, and could in principle be applied in an automation-like fashion to a large fraction of the proteome

Algorithms for automated assignment of solution-state and solid-state protein NMR spectra.

Protein nuclear magnetic resonance spectroscopy (Protein NMR) is an invaluable analytical technique for studying protein structure, function, and dynamics. There are two major types of NMR spectroscopy that are used for investigation of protein structure – solution-state and solid-state NMR. Solution-based NMR spectroscopy is typically applied to proteins of small and medium size that are soluble in water. Solid-state NMR spectroscopy is amenable for proteins that are insoluble in water. In the vast majority NMR-based protein studies, the first step after experiment optimization is the assignment of protein resonances via the association of chemical shift values to specific atoms in a protein macromolecule. Depending on the quality of the spectra, a manual protein resonance assignment process often requires a considerable amount of time, from weeks to months-worth of effort even, by an experienced NMR spectroscopist . The resonance assignment processes for solution-state and solid-state protein NMR studies are conceptually similar, but have distinct differences due to the utilization of different NMR experiments and to the use of different resonances for grouping peaks into spin systems. Currently, there is a shortage of robust, effective software tools that can perform solid-state protein resonance assignment and there is no general software that can perform both solution-state and solid-state protein resonance assignment in a reliable, automated fashion. Hence, the motivation of this research is to design and implement algorithms and software tools that will automate the resonance assignment problem. As a result of this research, several algorithms and software packages that aid several important steps in the protein resonance assignment process were developed. For example, the nmrstarlib software package can access and utilize data deposited in the NMR-STAR format; the core of this library is the lexical analyzer for NMR-STAR syntax that acts as a generator-based state-machine for token processing. The jpredapi software package provides an easy-to-use API to submit and retrieve results from secondary structure prediction server. The single peak list and pairwise peak list registration algorithms address the problem of multiple sources of variance within single peak list and between different peak lists and is capable of calculating the match tolerance values necessary for spin system grouping. The single peak list and pairwise peak list grouping algorithms are based on the well-known DBSCAN clustering algorithm and are designed to group peaks into spin systems within single peak list as well as between different peak lists

Advances in Nuclear Magnetic Resonance for Drug Discovery

Background—Drug discovery is a complex and unpredictable endeavor with a high failure rate. Current trends in the pharmaceutical industry have exasperated these challenges and are contributing to the dramatic decline in productivity observed over the last decade. The industrialization of science by forcing the drug discovery process to adhere to assembly-line protocols is imposing unnecessary restrictions, such as short project time-lines. Recent advances in nuclear magnetic resonance are responding to these self-imposed limitations and are providing opportunities to increase the success rate of drug discovery. Objective/Method—A review of recent advancements in NMR technology that have the potential of significantly impacting and benefiting the drug discovery process will be presented. These include fast NMR data collection protocols and high-throughput protein structure determination, rapid protein-ligand co-structure determination, lead discovery using fragment-based NMR affinity screens, NMR metabolomics to monitor in vivo efficacy and toxicity for lead compounds, and the identification of new therapeutic targets through the functional annotation of proteins by FASTNMR. Conclusion—NMR is a critical component of the drug discovery process, where the versatility of the technique enables it to continually expand and evolve its role. NMR is expected to maintain this growth over the next decade with advancements in automation, speed of structure calculation, incell imaging techniques, and the expansion of NMR amenable targets

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