Arash Bahrami

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.

PINE-SPARKY

Summary: PINE-SPARKY supports the rapid, user-friendly and efficient visualization of probabilistic assignments of NMR chemical shifts to specific atoms in the covalent structure of a protein in the context of experimental NMR spectra. PINE-SPARKY is based on the very popular SPARKY package for visualizing multidimensional NMR spectra (T. D. Goddard and D. G. Kneller, SPARKY 3, University of California, San Francisco). PINE-SPARKY consists of a converter (PINE2SPARKY), which takes the output from an automated PINE-NMR analysis and transforms it into SPARKY input, plus a number of SPARKY extensions. Assignments and their probabilities obtained in the PINE-NMR step are visualized as labels in SPARKYs spectrum view. Three SPARKY extensions (PINE Assigner, PINE Graph Assigner, and Assign the Best by PINE) serve to manipulate the labels that signify the assignments and their probabilities. PINE Assigner lists all possible assignments for a peak selected in the dialog box and enables the user to choose among these. A window in PINE Graph Assigner shows all atoms in a selected residue along with all atoms in its adjacent residues; in addition, it displays a ranked list of PINE-derived connectivity assignments to any selected atom. Assign the Best-by-PINE allows the user to choose a probability threshold and to automatically accept as “fixed” all assignments above that threshold; following this operation, only the less certain assignments need to be examined visually. Once assignments are fixed, the output files generated by PINE-SPARKY can be used as input to PINE-NMR for further refinements. Availability: The program, in the form of source code and binary code along with tutorials and reference manuals, is available at http://pine.nmrfam.wisc.edu/PINE-SPARKY. Contact: whlee@nmrfam.wisc.edu; markley@nmrfam.wisc.edu

Three-dimensional structure of the AAH26994.1 protein from Mus musculus, a putative eukaryotic Urm1.

We have used NMR spectroscopy to determine the solution structure of protein AAH26994.1 from Mus musculus and propose that it represents the first three‐dimensional structure of a ubiquitin‐related modifier 1 (Urm1) protein. Amino acid sequence comparisons indicate that AAH26994.1 belongs to the Urm1 family of ubiquitin‐like modifier proteins. The best characterized member of this family has been shown to be involved in nutrient sensing, invasive growth, and budding in yeast. Proteins in this family have only a weak sequence similarity to ubiquitin, and the structure of AAH26994.1 showed a much closer resemblance to MoaD subunits of molybdopterin synthases (known structures are of three bacterial MoaD proteins with 14%–26% sequence identity to AAH26994.1). The structures of AAH26994.1 and the MoaD proteins each contain the signature ubiquitin secondary structure fold, but all differ from ubiquitin largely in regions outside of this fold. This structural similarity bolsters the hypothesis that ubiquitin and ubiquitin‐related proteins evolved from a protein‐based sulfide donor system of the molybdopterin synthase type.

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.

Fast automated protein NMR data collection and assignment by ADAPT-NMR on Bruker spectrometers.

ADAPT-NMR (Assignment-directed Data collection Algorithm utilizing a Probabilistic Toolkit in NMR) supports automated NMR data collection and backbone and side chain assignment for [U-(13)C, U-(15)N]-labeled proteins. Given the sequence of the protein and data for the orthogonal 2D (1)H-(15)N and (1)H-(13)C planes, the algorithm automatically directs the collection of tilted plane data from a variety of triple-resonance experiments so as to follow an efficient pathway toward the probabilistic assignment of (1)H, (13)C, and (15)N signals to specific atoms in the covalent structure of the protein. Data collection and assignment calculations continue until the addition of new data no longer improves the assignment score. ADAPT-NMR was first implemented on Varian (Agilent) spectrometers [A. Bahrami, M. Tonelli, S.C. Sahu, K.K. Singarapu, H.R. Eghbalnia, J.L. Markley, PLoS One 7 (2012) e33173]. Because of broader interest in the approach, we present here a version of ADAPT-NMR for Bruker spectrometers. We have developed two AU console programs (ADAPT_ORTHO_run and ADAPT_NMR_run) that run under TOPSPIN Versions 3.0 and higher. To illustrate the performance of the algorithm on a Bruker spectrometer, we tested one protein, chlorella ubiquitin (76 amino acid residues), that had been used with the Varian version: the Bruker and Varian versions achieved the same level of assignment completeness (98% in 20 h). As a more rigorous evaluation of the Bruker version, we tested a larger protein, BRPF1 bromodomain (114 amino acid residues), which yielded an automated assignment completeness of 86% in 55 h. Both experiments were carried out on a 500 MHz Bruker AVANCE III spectrometer equipped with a z-gradient 5 mm TCI probe. ADAPT-NMR is available at http://pine.nmrfam.wisc.edu/ADAPT-NMR in the form of pulse programs, the two AU programs, and instructions for installation and use.

RNA-PAIRS: RNA probabilistic assignment of imino resonance shifts

The significant biological role of RNA has further highlighted the need for improving the accuracy, efficiency and the reach of methods for investigating RNA structure and function. Nuclear magnetic resonance (NMR) spectroscopy is vital to furthering the goals of RNA structural biology because of its distinctive capabilities. However, the dispersion pattern in the NMR spectra of RNA makes automated resonance assignment, a key step in NMR investigation of biomolecules, remarkably challenging. Herein we present RNA Probabilistic Assignment of Imino Resonance Shifts (RNA-PAIRS), a method for the automated assignment of RNA imino resonances with synchronized verification and correction of predicted secondary structure. RNA-PAIRS represents an advance in modeling the assignment paradigm because it seeds the probabilistic network for assignment with experimental NMR data, and predicted RNA secondary structure, simultaneously and from the start. Subsequently, RNA-PAIRS sets in motion a dynamic network that reverberates between predictions and experimental evidence in order to reconcile and rectify resonance assignments and secondary structure information. The procedure is halted when assignments and base-parings are deemed to be most consistent with observed crosspeaks. The current implementation of RNA-PAIRS uses an initial peak list derived from proton-nitrogen heteronuclear multiple quantum correlation (1H–15N 2D HMQC) and proton–proton nuclear Overhauser enhancement spectroscopy (1H–1H 2D NOESY) experiments. We have evaluated the performance of RNA-PAIRS by using it to analyze NMR datasets from 26 previously studied RNAs, including a 111-nucleotide complex. For moderately sized RNA molecules, and over a range of comparatively complex structural motifs, the average assignment accuracy exceeds 90%, while the average base pair prediction accuracy exceeded 93%. RNA-PAIRS yielded accurate assignments and base pairings consistent with imino resonances for a majority of the NMR resonances, even when the initial predictions are only modestly accurate. RNA-PAIRS is available as a public web-server at http://pine.nmrfam.wisc.edu/RNA/.

ADAPT-NMR Enhancer

Summary: ADAPT-nuclear magnetic resonance (ADAPT-NMR) offers an automated approach to the concurrent acquisition and processing of protein NMR data with the goal of complete backbone and side chain assignments. What the approach lacks is a useful graphical interface for reviewing results and for searching for missing peaks that may have prevented assignments or led to incorrect assignments. Because most of the data ADAPT-NMR collects are 2D tilted planes used to find peaks in 3D spectra, it would be helpful to have a tool that reconstructs the 3D spectra. The software package reported here, ADAPT-NMR Enhancer, supports the visualization of both 2D tilted planes and reconstructed 3D peaks on each tilted plane. ADAPT-NMR Enhancer can be used interactively with ADAPT-NMR to automatically assign selected peaks, or it can be used to produce PINE-SPARKY-like graphical dialogs that support atom-by-atom and peak-by-peak assignment strategies. Results can be exported in various formats, including XEASY proton file (.prot), PINE pre-assignment file (.str), PINE probabilistic output file, SPARKY peak list file (.list) and TALOS+ input file (.tab). As an example, we show how ADAPT-NMR Enhancer was used to extend the automated data collection and assignment results for the protein Aedes aegypti sterol carrier protein 2. Availability: The program, in the form of binary code along with tutorials and reference manuals, is available at http://pine.nmrfam.wisc.edu/adapt-nmr-enhancer. Contact: whlee@nmrfam.wisc.edu or markley@nmrfam.wisc.edu