Automatic structure-based NMR methyl resonance assignment in large proteins.
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ABSTRACT:
SUBMITTER: Pritisanac I
PROVIDER: S-EPMC6820720 | biostudies-literature | 2019 Oct
REPOSITORIES: biostudies-literature
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Project description:Selective methyl labeling is an extremely powerful approach to study the structure, dynamics and function of biomolecules by NMR. Despite spectacular progress in the field, such studies remain rather limited in number. One of the main obstacles remains the assignment of the methyl resonances, which is labor intensive and error prone. Typically, NOESY crosspeak patterns are manually correlated to the available crystal structure or an in silico template model of the protein. Here, we propose methyl assignment by graphing inference construct, an exhaustive search algorithm with no peak network definition requirement. In order to overcome the combinatorial problem, the exhaustive search is performed locally, i.e. for a small number of methyls connected through-space according to experimental 3D methyl NOESY data. The local network approach drastically reduces the search space. Only the best local assignments are combined to provide the final output. Assignments that match the data with comparable scores are made available to the user for cross-validation by additional experiments such as methyl-amide NOEs. Several NMR datasets for proteins in the 25-50 kDa range were used during development and for performance evaluation against the manually assigned data. We show that the algorithm is robust, reliable and greatly speeds up the methyl assignment task.
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Project description:High-throughput functional protein NMR studies, like protein interactions or dynamics, require an automated approach for the assignment of the protein backbone. With the availability of a growing number of protein 3D structures, a new class of automated approaches, called structure-based assignment, has been developed quite recently. Structure-based approaches use primarily NMR input data that are not based on J-coupling and for which connections between residues are not limited by through bonds magnetization transfer efficiency. We present here a robust structure-based assignment approach using mainly H(N)-H(N) NOEs networks, as well as (1)H-(15) N residual dipolar couplings and chemical shifts. The NOEnet complete search algorithm is robust against assignment errors, even for sparse input data. Instead of a unique and partly erroneous assignment solution, an optimal assignment ensemble with an accuracy equal or near to 100% is given by NOEnet. We show that even low precision assignment ensembles give enough information for functional studies, like modeling of protein-complexes. Finally, the combination of NOEnet with a low number of ambiguous J-coupling sequential connectivities yields a high precision assignment ensemble. NOEnet will be available under: http://www.icsn.cnrs-gif.fr/download/nmr.
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Project description:Methyl-specific isotope labeling is a powerful tool to study the structure, dynamics and interactions of large proteins and protein complexes by solution-state NMR. However, widespread applications of this methodology have been limited by challenges in obtaining confident resonance assignments. Here, we present Methyl Assignments Using Satisfiability (MAUS), leveraging Nuclear Overhauser Effect cross-peak data, peak residue type classification and a known 3D structure or structural model to provide robust resonance assignments consistent with all the experimental inputs. Using data recorded for targets with known assignments in the 10-45?kDa size range, MAUS outperforms existing methods by up to 25,000 times in speed while maintaining 100% accuracy. We derive de novo assignments for multiple Cas9 nuclease domains, demonstrating that the methyl resonances of multi-domain proteins can be assigned accurately in a matter of days, while reducing biases introduced by manual pre-processing of the raw NOE data. MAUS is available through an online web-server.
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Project description:Characterization of proteins using NMR methods begins with assignment of resonances to specific residues. This is usually accomplished using sequential connectivities between nuclear pairs in proteins uniformly labeled with NMR active isotopes. This becomes impractical for larger proteins, and especially for proteins that are best expressed in mammalian cells, including glycoproteins. Here an alternate protocol for the assignment of NMR resonances of sparsely labeled proteins, namely, the ones labeled with a single amino acid type, or a limited subset of types, isotopically enriched with 15N or 13C, is described. The protocol is based on comparison of data collected using extensions of simple two-dimensional NMR experiments (correlated chemical shifts, nuclear Overhauser effects, residual dipolar couplings) to predictions from molecular dynamics trajectories that begin with known protein structures. Optimal pairing of predicted and experimental values is facilitated by a software package that employs a genetic algorithm, ASSIGN_SLP_MD. The approach is applied to the 36-kDa luminal domain of the sialyltransferase, rST6Gal1, in which all phenylalanines are labeled with 15N, and the results are validated by elimination of resonances via single-point mutations of selected phenylalanines to tyrosines. Assignment allows the use of previously published paramagnetic relaxation enhancements to evaluate placement of a substrate analog in the active site of this protein. The protocol will open the way to structural characterization of the many glycosylated and other proteins that are best expressed in mammalian cells.
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Project description:Membrane proteins in detergent micelles are large and dynamic complexes that present challenges for solution NMR investigations such as spectral overlap and line broadening. In this study, multiple methods are introduced to facilitate resonance assignment of ?-barrel membrane proteins using Opa60 from Neisseria gonorrhoeae as a model system. Opa60 is an eight-stranded ?-barrel with long extracellular loops (?63% of the protein) that engage host receptors and induce engulfment of the bacterium. The NMR spectra of Opa60 in detergent micelles exhibits significant spectral overlap and resonances corresponding to the loop regions had variable line widths, which interfered with a complete assignment of the protein. To assign the ?-barrel residues, trypsin cleavage was used to remove much of the extracellular loops while preserving the detergent solubilized ?-barrel. The removal of the loop resonances significantly improved the assignment of the Opa60 ?-barrel region (97% of the resonances corresponding to the ?-barrel and periplasmic turns were assigned). For the loop resonance assignments, two strategies were implemented; modulating temperature and synthetic peptides. Lowering the temperature broadened many peaks beyond detection and simplified the spectra to only the most dynamic regions of the loops facilitating 27 loop resonances to be assigned. To further assign functionally important and unstructured regions of the extracellular loops, a synthetic 20 amino acid peptide was synthesized and had nearly complete spectral overlap with the full-length protein allowing 17 loop resonances to be assigned. Collectively, these strategies are effective tools that may accelerate solution NMR structure determination of ?-barrel membrane proteins.
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Project description:MOTIVATION: A prerequisite for any protein study by NMR is the assignment of the resonances from the (15)N-(1)H HSQC spectrum to their corresponding atoms of the protein backbone. Usually, this assignment is obtained by analyzing triple resonance NMR experiments. An alternative assignment strategy exploits the information given by an already available 3D structure of the same or a homologous protein. Up to now, the algorithms that have been developed around the structure-based assignment strategy have the important drawbacks that they cannot guarantee a high assignment accuracy near to 100%. RESULTS: We propose here a new program, called NOEnet, implementing an efficient complete search algorithm that ensures the correctness of the assignment results. NOEnet exploits the network character of unambiguous NOE constraints to realize an exhaustive search of all matching possibilities of the NOE network onto the structural one. NOEnet has been successfully tested on EIN, a large protein of 28 kDa, using only NOE data. The complete search of NOEnet finds all possible assignments compatible with experimental data that can be defined as an assignment ensemble. We show that multiple assignment possibilities of large NOE networks are restricted to a small spatial assignment range (SAR), so that assignment ensembles, obtained from accessible experimental data, are precise enough to be used for functional proteins studies, like protein-ligand interaction or protein dynamics studies. We believe that NOEnet can become a major tool for the structure-based backbone resonance assignment strategy in NMR. AVAILABILITY: The NOEnet program will be available under: http://www.icsn.cnrs-gif.fr/download/nmr.
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Project description:Resonance assignment is a prerequisite for almost any NMR-based study of proteins. It can be very challenging in some cases, however, due to the nature of the protein under investigation. This is the case with intrinsically disordered proteins, for example, whose NMR spectra suffer from low chemical shifts dispersion and generally low resolution. For these systems, sequence specific assignment is highly time-consuming, so the prospect of using automatic strategies for their assignment is very attractive. In this article we present a new version of the automatic assignment program TSAR dedicated to intrinsically disordered proteins. In particular, we demonstrate how the automatic procedure can be improved by incorporating methods for amino acid recognition and information on chemical shifts in selected amino acids. The approach was tested in silico on 16 disordered proteins and experimentally on ?-synuclein, with remarkably good results.
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Project description:The quality of protein structures determined by nuclear magnetic resonance (NMR) spectroscopy is contingent on the number and quality of experimentally-derived resonance assignments, distance and angular restraints. Two key features of protein NMR data have posed challenges for the routine and automated structure determination of small to medium sized proteins; (1) spectral resolution - especially of crowded nuclear Overhauser effect spectroscopy (NOESY) spectra, and (2) the reliance on a continuous network of weak scalar couplings as part of most common assignment protocols. In order to facilitate NMR structure determination, we developed a semi-automated strategy that utilizes non-uniform sampling (NUS) and multidimensional decomposition (MDD) for optimal data collection and processing of selected, high resolution multidimensional NMR experiments, combined it with an ABACUS protocol for sequential and side chain resonance assignments, and streamlined this procedure to execute structure and refinement calculations in CYANA and CNS, respectively. Two graphical user interfaces (GUIs) were developed to facilitate efficient analysis and compilation of the data and to guide automated structure determination. This integrated method was implemented and refined on over 30 high quality structures of proteins ranging from 5.5 to 16.5 kDa in size.
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Project description:Intrinsically disordered proteins (IDPs) have recently attracted much interest, due to their role in many biological processes, including signaling and regulation mechanisms. High-dimensional (13)C direct-detected NMR experiments have proven exceptionally useful in case of IDPs, providing spectra with superior peak dispersion. Here, two such novel experiments recorded with non-uniform sampling are introduced, these are 5D HabCabCO(CA)NCO and 5D HNCO(CA)NCO. Together with the 4D (HACA)CON(CA)NCO, an extension of the previously published 3D experiments (Pantoja-Uceda and Santoro in J Biomol NMR 59:43-50, 2014. doi: 10.1007/s10858-014-9827-1), they form a set allowing for complete and reliable resonance assignment of difficult IDPs. The processing is performed with sparse multidimensional Fourier transform based on the concept of restricting (fixing) some of spectral dimensions to a priori known resonance frequencies. In our study, a multiple-fixing method was developed, that allows easy access to spectral data. The experiments were tested on a resolution-demanding alpha-synuclein sample. Due to superior peak dispersion in high-dimensional spectrum and availability of the sequential connectivities between four consecutive residues, the overwhelming majority of resonances could be assigned automatically using the TSAR program.
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