Integrative structure of a 10-megadalton eukaryotic pyruvate dehydrogenase complex from native cell extracts

1. Download : Download high-res image (252KB)
2. Download : Download full-size image

     

Quasi-symmetry in the cryo-EM structure of EmrE provides the key to modeling its transmembrane domain

Small multidrug resistance (SMR) transporters contribute to bacterial resistance by coupling the efflux of a wide range of toxic aromatic cations, some of which are commonly used as antibiotics and antiseptics, to proton influx. EmrE is a prototypical small multidrug resistance transporter comprising four transmembrane segments (M1-M4) that forms dimers. It was suggested recently that EmrE molecules in the dimer have different topologies, i.e. monomers have opposite orientations with respect to the membrane plane. A 3-D structure of EmrE acquired by electron cryo-microscopy (cryo-EM) at 7.5 Angstroms resolution in the membrane plane showed that parts of the structure are related by quasi-symmetry. We used this symmetry relationship, combined with sequence conservation data, to assign the transmembrane segments in EmrE to the densities seen in the cryo-EM structure. A C alpha model of the transmembrane region was constructed by considering the evolutionary conservation pattern of each helix. The model is validated by much of the biochemical data on EmrE with most of the positions that were identified as affecting substrate translocation being located around the substrate-binding cavity. A suggested mechanism for proton-coupled substrate translocation in small multidrug resistance antiporters provides a mechanistic rationale to the experimentally observed inverted topology.     

Evaluation of AlphaFold2 structures as docking targets

AlphaFold2 is a promising new tool for researchers to predict protein structures and generate high-quality models, with low backbone and global root-mean-square deviation (RMSD) when compared with experimental structures. However, it is unclear if the structures predicted by AlphaFold2 will be valuable targets of docking. To address this question, we redocked ligands in the PDBbind datasets against the experimental co-crystallized receptor structures and against the AlphaFold2 structures using AutoDock-GPU. We find that the quality measure provided during structure prediction is not a good predictor of docking performance, despite accurately reflecting the quality of the alpha carbon alignment with experimental structures. Removing low-confidence regions of the predicted structure and making side chains flexible improves the docking outcomes. Overall, despite high-quality prediction of backbone conformation, fine structural details limit the naive application of AlphaFold2 models as docking targets.     

Complementing machine learning‐based structure predictions with native mass spectrometry

The advent of machine learning‐based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein complexes are based on user‐provided input and may require experimental validation. Mass spectrometry (MS) is a versatile, time‐effective tool that provides information on post‐translational modifications, ligand interactions, conformational changes, and higher‐order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on a small and large scale.     

The accuracy of protein structures in solution determined by AlphaFold and NMR

1. Download : Download high-res image (139KB)
2. Download : Download full-size image

     

Metaproteomic data analysis at a glance: advances in computational microbial community proteomics

Introduction: Within the last decade, the study of microbial communities has gained increasing research interest also driven by the recognition of the important role of these consortia in human health and disease. Metaproteomics, the analysis of the entire set of proteins from all microorganisms present in one ecosystem, has become a prominent technique for studying the relation between taxonomic diversity and functional profile of microbial communities.

Areas covered: The aim of this review is to address opportunities and challenges of metaproteomics from a computational perspective. Appealing to an audience of microbial ecologists and proteomic researchers alike, we provide an overview on state-of-the-art software and databases by which metaproteome data can be readily analyzed.

Expert commentary: While tailored protein databases, combined search algorithms and iterative workflows are means to improve the identification yield, software tools for taxonomic and functional analysis are challenged by the vast amount of unannotated sequences in metaproteomics.     

Prediction of transition metal-binding sites from apo protein structures

Metal ions are crucial for protein function. They participate in enzyme catalysis, play regulatory roles, and help maintain protein structure. Current tools for predicting metal-protein interactions are based on proteins crystallized with their metal ions present (holo forms). However, a majority of resolved structures are free of metal ions (apo forms). Moreover, metal binding is a dynamic process, often involving conformational rearrangement of the binding pocket. Thus, effective predictions need to be based on the structure of the apo state. Here, we report an approach that identifies transition metal-binding sites in apo forms with a resulting selectivity >95%. Applying the approach to apo forms in the Protein Data Bank and structural genomics initiative identifies a large number of previously unknown, putative metal-binding sites, and their amino acid residues, in some cases providing a first clue to the function of the protein.     

Building native protein conformation from NMR backbone chemical shifts using Monte Carlo fragment assembly

We have been analyzing the extent to which protein secondary structure determines protein tertiary structure in simple protein folds. An earlier paper demonstrated that three-dimensional structure can be obtained successfully using only highly approximate backbone torsion angles for every residue. Here, the initial information is further diluted by introducing a realistic degree of experimental uncertainty into this process. In particular, we tackle the practical problem of determining three-dimensional structure solely from backbone chemical shifts, which can be measured directly by NMR and are known to be correlated with a protein's backbone torsion angles. Extending our previous algorithm to incorporate these experimentally determined data, clusters of structures compatible with the experimentally determined chemical shifts were generated by fragment assembly Monte Carlo. The cluster that corresponds to the native conformation was then identified based on four energy terms: steric clash, solvent-squeezing, hydrogen-bonding, and hydrophobic contact. Currently, the method has been applied successfully to five small proteins with simple topology. Although still under development, this approach offers promise for high-throughput NMR structure determination.     

Distinguishing enzyme structures from non-enzymes without alignments

The ability to predict protein function from structure is becoming increasingly important as the number of structures resolved is growing more rapidly than our capacity to study function. Current methods for predicting protein function are mostly reliant on identifying a similar protein of known function. For proteins that are highly dissimilar or are only similar to proteins also lacking functional annotations, these methods fail. Here, we show that protein function can be predicted as enzymatic or not without resorting to alignments. We describe 1178 high-resolution proteins in a structurally non-redundant subset of the Protein Data Bank using simple features such as secondary-structure content, amino acid propensities, surface properties and ligands. The subset is split into two functional groupings, enzymes and non-enzymes. We use the support vector machine-learning algorithm to develop models that are capable of assigning the protein class. Validation of the method shows that the function can be predicted to an accuracy of 77% using 52 features to describe each protein. An adaptive search of possible subsets of features produces a simplified model based on 36 features that predicts at an accuracy of 80%. We compare the method to sequence-based methods that also avoid calculating alignments and predict a recently released set of unrelated proteins. The most useful features for distinguishing enzymes from non-enzymes are secondary-structure content, amino acid frequencies, number of disulphide bonds and size of the largest cleft. This method is applicable to any structure as it does not require the identification of sequence or structural similarity to a protein of known function.     

Atomically detailed potentials to recognize native and approximate protein structures

Atomically detailed potentials for recognition of protein folds are presented. The potentials consist of pair interactions between atoms. One or three distance steps are used to describe the range of interactions between a pair. Training is carried out with the mathematical programming approach on the decoy sets of Baker, Levitt, and some of our own design. Recognition is required not only for decoy-native structural pairs but also for pairs of decoy and homologous structures. Performance is tested on the targets of CASP5 using templates from the Protein Data Bank, on two test ab initio decoy sets from Skolnick's laboratory, and on decoy sets from Moult's laboratory. We conclude that the newly derived potentials have significant recognition capacity, comparable to the best models derived from other techniques. The new potentials require a significantly smaller number of parameters. The enhanced recognition capacity extends primarily to the identification of structures generated by ab initio simulation and less to the recognition of approximate shapes created by homology.     

Models of S/pi interactions in protein structures: comparison of the H2S benzene complex with PDB data

S/pi interactions are prevalent in biochemistry and play an important role in protein folding and stabilization. Geometries of cysteine/aromatic interactions found in crystal structures from the Brookhaven Protein Data Bank (PDB) are analyzed and compared with the equilibrium configurations predicted by high-level quantum mechanical results for the H(2)S-benzene complex. A correlation is observed between the energetically favorable configurations on the quantum mechanical potential energy surface of the H(2)S-benzene model and the cysteine/aromatic configurations most frequently found in crystal structures of the PDB. In contrast to some previous PDB analyses, configurations with the sulfur over the aromatic ring are found to be the most important. Our results suggest that accurate quantum computations on models of noncovalent interactions may be helpful in understanding the structures of proteins and other complex systems.     

Refinement of protein structures by iterative comparative modeling and CryoEM density fitting

We developed a method for structure characterization of assembly components by iterative comparative protein structure modeling and fitting into cryo-electron microscopy (cryoEM) density maps. Specifically, we calculate a comparative model of a given component by considering many alternative alignments between the target sequence and a related template structure while optimizing the fit of a model into the corresponding density map. The method relies on the previously developed Moulder protocol that iterates over alignment, model building, and model assessment. The protocol was benchmarked using 20 varied target-template pairs of known structures with less than 30% sequence identity and corresponding simulated density maps at resolutions from 5A to 25A. Relative to the models based on the best existing sequence profile alignment methods, the percentage of C(alpha) atoms that are within 5A of the corresponding C(alpha) atoms in the superposed native structure increases on average from 52% to 66%, which is half-way between the starting models and the models from the best possible alignments (82%). The test also reveals that despite the improvements in the accuracy of the fitness function, this function is still the bottleneck in reducing the remaining errors. To demonstrate the usefulness of the protocol, we applied it to the upper domain of the P8 capsid protein of rice dwarf virus that has been studied by cryoEM at 6.8A. The C(alpha) root-mean-square deviation of the model based on the remotely related template, bluetongue virus VP7, improved from 8.7A to 6.0A, while the best possible model has a C(alpha) RMSD value of 5.3A. Moreover, the resulting model fits better into the cryoEM density map than the initial template structure. The method is being implemented in our program MODELLER for protein structure modeling by satisfaction of spatial restraints and will be applicable to the rapidly increasing number of cryoEM density maps of macromolecular assemblies.     

Protein structure prediction constrained by solution X-ray scattering data and structural homology identification

Here we perform a systematic exploration of the use of distance constraints derived from small angle X-ray scattering (SAXS) measurements to filter candidate protein structures for the purpose of protein structure prediction. This is an intrinsically more complex task than that of applying distance constraints derived from NMR data where the identity of the pair of amino acid residues subject to a given distance constraint is known. SAXS, on the other hand, yields a histogram of pair distances (pair distribution function), but the identities of the pairs contributing to a given bin of the histogram are not known. Our study is based on an extension of the Levitt-Hinds coarse grained approach to ab initio protein structure prediction to generate a candidate set of C(alpha) backbones. In spite of the lack of specific residue information inherent in the SAXS data, our study shows that the implementation of a SAXS filter is capable of effectively purifying the set of native structure candidates and thus provides a substantial improvement in the reliability of protein structure prediction. We test the quality of our predicted C(alpha) backbones by doing structural homology searches against the Dali domain library, and find that the results are very encouraging. In spite of the lack of local structural details and limited modeling accuracy at the C(alpha) backbone level, we find that useful information about fold classification can be extracted from this procedure. This approach thus provides a way to use a SAXS data based structure prediction algorithm to generate potential structural homologies in cases where lack of sequence homology prevents identification of candidate folds for a given protein. Thus our approach has the potential to help in determination of the biological function of a protein based on structural homology instead of sequence homology.     

Protein Information and Knowledge Extractor: Discovering biological information from proteomics data

One of the main goals in proteomics is to solve biological and molecular questions regarding a set of identified proteins. In order to achieve this goal, one has to extract and collect the existing biological data from public repositories for every protein and afterward, analyze and organize the collected data. Due to the complexity of this task and the huge amount of data available, it is not possible to gather this information by hand, making it necessary to find automatic methods of data collection. Within a proteomic context, we have developed Protein Information and Knowledge Extractor (PIKE) which solves this problem by automatically accessing several public information systems and databases across the Internet. PIKE bioinformatics tool starts with a set of identified proteins, listed as the most common protein databases accession codes, and retrieves all relevant and updated information from the most relevant databases. Once the search is complete, PIKE summarizes the information for every single protein using several file formats that share and exchange the information with other software tools. It is our opinion that PIKE represents a great step forward for information procurement and drastically reduces manual database validation for large proteomic studies. It is available at http://proteo.cnb.csic.es/pike .     

A protein-RNA docking benchmark (II): extended set from experimental and homology modeling data

We present here an extended protein-RNA docking benchmark composed of 71 test cases in which the coordinates of the interacting protein and RNA molecules are available from experimental structures, plus an additional set of 35 cases in which at least one of the interacting subunits is modeled by homology. All cases in the experimental set have available unbound protein structure, and include five cases with available unbound RNA structure, four cases with a pseudo-unbound RNA structure, and 62 cases with the bound RNA form. The additional set of modeling cases comprises five unbound-model, eight model-unbound, 19 model-bound, and three model-model protein-RNA cases. The benchmark covers all major functional categories and contains cases with different degrees of difficulty for docking, as far as protein and RNA flexibility is concerned. The main objective of this benchmark is to foster the development of protein-RNA docking algorithms and to contribute to the better understanding and prediction of protein-RNA interactions. The benchmark is freely available at http://life.bsc.es/pid/protein-rna-benchmark.     

Structure determination of a new protein from backbone-centered NMR data and NMR-assisted structure prediction

Targeting of proteins for structure determination in structural genomic programs often includes the use of threading and fold recognition methods to exclude proteins belonging to well-populated fold families, but such methods can still fail to recognize preexisting folds. The authors illustrate here a method in which limited amounts of structural data are used to improve an initial homology search and the data are subsequently used to produce a structure by data-constrained refinement of an identified structural template. The data used are primarily NMR-based residual dipolar couplings, but they also include additional chemical shift and backbone-nuclear Overhauser effect data. Using this methodology, a backbone structure was efficiently produced for a 10 kDa protein (PF1455) from Pyrococcus furiosus. Its relationship to existing structures and its probable function are discussed.     

Successful protein fold recognition by optimal sequence threading validated by rigorous blind testing

Analysis of the results of the recent protein structure prediction experiment for our method shows that we achieved a high level of success, Of the 18 available prediction targets of known structure, the assessors have identified 11 chains which either entirely match a previously known fold, or which partially match a substantial region of a known fold. Of these 11 chains, we made predictions for 9, and correctly assigned the folds in 5 cases. We have also identified a further 2 chains which also partially match known folds, and both of these were correctly predicted. The success rate for our method under blind testing is therefore 7 out of 11 chains. A further 2 folds could have easily been recognized but failed due to either overzealous filtering of potential matches, or to simple human error on our part. One of the two targets for which we did not submit a prediction, prosubtilisin, would not have been recognized by our usual criteria, but even in this case, it is possible that a correct prediction could have been made by considerin a combination of pairwise energy and solvation energy Z-scores. Inspection of the threading alignments for the (αβ)₈ barrels provides clues as to how fold recognition by threading works, in that these folds are recognized by parts rather than as a whole. The prospects for developing sequence threading technology further is discussed. © 1995 Wiley-Liss, Inc.     

NMR for structural proteomics of Thermotoga maritima: Screening and structure determination

This paper describes the NMR screening of 141 small (<15 kDa) recombinant Thermotoga maritima proteins for globular folding. The experimental data shows that approximately 25% of the screened proteins are folded under our screening conditions, which makes this procedure an important step for selecting those proteins that are suitable for structure determination. A comparison of screening based either on 1D 1H NMR with unlabeled proteins or on 2D [1H,15N]-COSY with uniformly 15N-labeled proteins is presented, and a comprehensive analysis of the 1D 1H NMR screening data is described. As an illustration of the utility of these methods to structural proteomics, the NMR structure determination of TM1492 (ribosomal protein L29) is presented. This 66-residue protein consists of a N-terminal 3(10)-helix and two long alpha-helices connected by a tight turn centered about glycine 35, where conserved leucine and isoleucine residues in the two alpha-helices form a small hydrophobic core.     

Analysis of a data set of paired uncomplexed protein structures: new metrics for side-chain flexibility and model evaluation

We compiled and analyzed a data set of paired protein structures containing proteins for which multiple high-quality uncomplexed atomic structures were available in the Protein Data Bank. Side-chain flexibility was quantified, yielding a set of residue- and environment-specific confidence levels describing the range of motion around chi1 and chi2 angles. As expected, buried residues were inflexible, adopting similar conformations in different crystal structure analyses. Ile, Thr, Asn, Asp, and the large aromatics also showed limited flexibility when exposed on the protein surface, whereas exposed Ser, Lys, Arg, Met, Gln, and Glu residues were very flexible. This information is different from and complementary to the information available from rotamer surveys. The confidence levels are useful for assessing the significance of observed side-chain motion and estimating the extent of side-chain motion in protein structure prediction. We compare the performance of a simple 40 degrees threshold with these quantitative confidence levels in a critical evaluation of side-chain prediction with the program SCWRL.