Automated Protein Structure Determination Research Articles

NMR-based automated protein structure determination

NMR spectra analysis for protein structure determination can now in many cases be performed by automated computational methods. This overview of the computational methods for NMR protein structure analysis presents recent automated methods for signal identification in multidimensional NMR spectra, sequence-specific resonance assignment, collection of conformational restraints, and structure calculation, as implemented in the CYANA software package. These algorithms are sufficiently reliable and integrated into one software package to enable the fully automated structure determination of proteins starting from NMR spectra without manual interventions or corrections at intermediate steps, with an accuracy of 1–2 Å backbone RMSD in comparison with manually solved reference structures.

The J-UNIO protocol for automated protein structure determination by NMR in solution

The J-UNIO (JCSG protocol using the software UNIO) procedure for automated protein structure determination by NMR in solution is introduced. In the present implementation, J-UNIO makes use of APSY-NMR spectroscopy, 3D heteronuclear-resolved [(1)H,(1)H]-NOESY experiments, and the software UNIO. Applications with proteins from the JCSG target list with sizes up to 150 residues showed that the procedure is highly robust and efficient. In all instances the correct polypeptide fold was obtained in the first round of automated data analysis and structure calculation. After interactive validation of the data obtained from the automated routine, the quality of the final structures was comparable to results from interactive structure determination. Special advantages are that the NMR data have been recorded with 6-10 days of instrument time per protein, that there is only a single step of chemical shift adjustments to relate the backbone signals in the APSY-NMR spectra with the corresponding backbone signals in the NOESY spectra, and that the NOE-based amino acid side chain chemical shift assignments are automatically focused on those residues that are heavily weighted in the structure calculation. The individual working steps of J-UNIO are illustrated with the structure determination of the protein YP_926445.1 from Shewanella amazonensis, and the results obtained with 17 JCSG targets are critically evaluated.

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.

Blind Testing of Routine, Fully Automated Determination of Protein Structures from NMR Data

The protocols currently used for protein structure determination by nuclear magnetic resonance (NMR) depend on the determination of a large number of upper distance limits for proton-proton pairs. Typically, this task is performed manually by an experienced researcher rather than automatically by using a specific computer program. To assess whether it is indeed possible to generate in a fully automated manner NMR structures adequate for deposition in the Protein Data Bank, we gathered 10 experimental data sets with unassigned nuclear Overhauser effect spectroscopy (NOESY) peak lists for various proteins of unknown structure, computed structures for each of them using different, fully automatic programs, and compared the results to each other and to the manually solved reference structures that were not available at the time the data were provided. This constitutes a stringent "blind" assessment similar to the CASP and CAPRI initiatives. This study demonstrates the feasibility of routine, fully automated protein structure determination by NMR.

Is protein structure prediction still an enigma

Proteins are large molecules indispensable for the existence and proper functioning of biological organisms. They perform a wide array of functions including catalysis, structure formation, transport, body defense, etc. Understanding the functions of proteins is a fundamental problem in the discovery of drugs to treat various diseases. The structure of a protein can be determined by physical methods which are slow and expensive but owing to the dramatic increase in the numbers of proteins sent to the public data bank during the last few years, it is highly desirable to develop some rapid and effective computational methods to predict the structure of new proteins so as to expedite the process of deducing their function. All the structure prediction methods basically rely on the idea that there is a correlation between residue sequence and structure. The primary structure is unique for each protein and it is generally accepted that a protein’s primary structure is enough to determine its folding process to secondary, tertiary and quaternary structure. Despite recent efforts to develop automated protein structure determination protocols, structural genomic projects are slow in generating fold assignments for complete proteomes, and spatial structures remain unknown for many protein families. Alternative cheap and fast methods to assign folds using prediction algorithms continue to provide valuable structural information for many proteins. Protein structure determination and prediction has been a focal research subject in life sciences due to the importance of protein structure in understanding the biological and chemical activities of organisms/cell. This review comprehends the various recent advanced methods for protein structure predictions such as a two-stage method for assigning residues one of the three secondary structure states, prediction of homo-oligomeric proteins based on nearest neighbour algorithm, sequence–based hidden markov model, practical ab initio methods aimed at finding the native structure of the protein by simulating the biological process of protein folding, and metapredictors based on consensus form multiple methods.

Sequence specific resonance assignment via Multicanonical Monte Carlo search using an ABACUS approach

ABACUS [Grishaev et al. (2005) Proteins 61:36-43] is a novel protocol for automated protein structure determination via NMR. ABACUS starts from molecular fragments defined by unassigned J-coupled spin-systems and involves a Monte Carlo stochastic search in assignment space, probabilistic sequence selection, and assembly of fragments into structures that are used to guide the stochastic search. Here, we report further development of the two main algorithms that increase the flexibility and robustness of the method. Performance of the BACUS [Grishaev and Llinás (2004) J Biomol NMR 28:1-101] algorithm was significantly improved through use of sequential connectivities available from through-bond correlated 3D-NMR experiments, and a new set of likelihood probabilities derived from a database of 56 ultra high resolution X-ray structures. A Multicanonical Monte Carlo procedure, Fragment Monte Carlo (FMC), was developed for sequence-specific assignment of spin-systems. It relies on an enhanced assignment sampling and provides the uncertainty of assignments in a quantitative manner. The efficiency of the protocol was validated on data from four proteins of between 68-116 residues, yielding 100% accuracy in sequence specific assignment of backbone and side chain resonances.

Automated technologies and novel techniques to accelerate protein crystallography for structural genomics

The sequence infrastructure that has arisen through large-scale genomic projects dedicated to protein analysis, has provided a wealth of information and brought together scientists and institutions from all over the world. As a consequence, the development of novel technologies and methodologies in proteomics research is helping to unravel the biochemical and physiological mechanisms of complex multivariate diseases at both a functional and molecular level. In the late sixties, when X-ray crystallography had just been established, the idea of determining protein structure on an almost universal basis was akin to an impossible dream or a miracle. Yet only forty years after, automated protein structure determination platforms have been established. The widespread use of robotics in protein crystallography has had a huge impact at every stage of the pipeline from protein cloning, over-expression, purification, crystallization, data collection, structure solution, refinement, validation and data management- all of which have become more or less automated with minimal human intervention necessary. Here, recent advances in protein crystal structure analysis in the context of structural genomics will be discussed. In addition, this review aims to give an overview of recent developments in high throughput instrumentation, and technologies and strategies to accelerate protein structure/function analysis.

Automated Protein Structure Determination from NMR Spectra

Fully automated structure determination of proteins in solution (FLYA) yields, without human intervention, three-dimensional protein structures starting from a set of multidimensional NMR spectra. Integrating existing and new software, automated peak picking over all spectra is followed by peak list filtering, the generation of an ensemble of initial chemical shift assignments, the determination of consensus chemical shift assignments for all (1)H, (13)C, and (15)N nuclei, the assignment of NOESY cross-peaks, the generation of distance restraints, and the calculation of the three-dimensional structure by torsion angle dynamics. The resulting, preliminary structure serves as additional input to the second stage of the procedure, in which a new ensemble of chemical shift assignments and a refined structure are calculated. The three-dimensional structures of three 12-16 kDa proteins computed with the FLYA algorithm coincided closely with the conventionally determined structures. Deviations were below 0.95 A for the backbone atom positions, excluding the flexible chain termini. 96-97% of all backbone and side-chain chemical shifts in the structured regions were assigned to the correct residues. The purely computational FLYA method is suitable for substituting all manual spectra analysis and thus overcomes a main efficiency limitation of the NMR method for protein structure determination.

UV laser-excited fluorescence as a tool for the visualization of protein crystals mounted in loops

Structural proteomics has promoted the rapid development of automated protein structure determination using X-ray crystallography. Robotics are now routinely used along the pipeline from genes to protein structures. However, a bottleneck still remains. At synchrotron beamlines, the success rate of automated sample alignment along the X-ray beam is limited by difficulties in visualization of protein crystals, especially when they are small and embedded in mother liquor. Despite considerable improvement in optical microscopes, the use of visible light transmitted or reflected by the sample may result in poor or misleading contrast. Here, the endogenous fluorescence from aromatic amino acids has been used to identify even tiny or weakly fluorescent crystals with a high success rate. The use of a compact laser at 266 nm in combination with non-fluorescent sample holders provides an efficient solution to collect high-contrast fluorescence images in a few milliseconds and using standard camera optics. The best image quality was obtained with direct illumination through a viewing system coaxial with the UV beam. Crystallographic data suggest that the employed UV exposures do not generate detectable structural damage.

Automated protein structure determination withBnP

Automated protein structure determination with<i>BnP</i>

Practical lessons from protein structure prediction

Despite recent efforts to develop automated protein structure determination protocols, structural genomics projects are slow in generating fold assignments for complete proteomes, and spatial structures remain unknown for many protein families. Alternative cheap and fast methods to assign folds using prediction algorithms continue to provide valuable structural information for many proteins. The development of high-quality prediction methods has been boosted in the last years by objective community-wide assessment experiments. This paper gives an overview of the currently available practical approaches to protein structure prediction capable of generating accurate fold assignment. Recent advances in assessment of the prediction quality are also discussed.

Towards automated protein structure determination: BnP, the SnB-PHASES interface

Abstract The direct-methods program SnB provides an efficient means for solving protein substructures containing many heavy-atom sites (current record: 160). In order to meet the high-throughput requirements of structural genomics projects, substructure determination needs to be tightly integrated with other aspects of the protein-phasing process. This has been accomplished through the design of a common Java interface, BnP, for SnB and components of PHASES, a popular and proven program suite that provides all the tools necessary to proceed from substructure refinement to the computation of an unambiguous protein electron-density map. Therefore, BnP will facilitate a high degree of automation and enable rapid structure determination by both experienced and novice crystallographers.

Automated combined assignment of NOESY spectra and three-dimensional protein structure determination.

A procedure for automated protein structure determination is presented that is based on an iterative procedure during which the NOESY peak list assignment and the structure calculation are performed simultaneously. The input consists of a list of NOESY peak positions and a list of chemical shifts as obtained from sequence-specific resonance assignment. For the present applications of this approach the previously introduced NOAH routine was implemented in the distance geometry program DIANA. As an illustration, experimental 2D and 3D NOESY cross-peak lists of six proteins have been analyzed, for which complete sequence-specific 1H assignments are available for the polypeptide backbone and the amino acid side chains. The automated method assigned 70-90% of all NOESY cross peaks, which is on average 10% less than with the interactive approach, and only between 0.8% and 2.4% of the automatically assigned peaks had a different assignment than in the corresponding manually assigned peak lists. The structures obtained with NOAH/DIANA are in close agreement with those from manually assigned peak lists, and with both approaches the residual constraint violations correspond to high-quality NMR structure determinations. Systematic comparisons of the bundles of conformers that represent corresponding automatically and interactively determined structures document the absence of significant bias in either approach, indicating that an important step has been made towards automation of structure determination from NMR spectra.