A comprehensive analysis of 40 blind protein structure predictions

BACKGROUND: We thoroughly analyse the results of 40 blind predictions for which an experimental answer was made available at the fourth meeting on the critical assessment of protein structure methods (CASP4). Using our comparative modelling and fold recognition methodologies, we made 29 predictions for targets that had sequence identities ranging from 50% to 10% to the nearest related protein with known structure. Using our ab initio methodologies, we made eleven predictions for targets that had no detectable sequence relationships. RESULTS: For 23 of these proteins, we produced models ranging from 1.0 to 6.0 Å root mean square deviation (RMSD) for the C(α) atoms between the model and the corresponding experimental structure for all or large parts of the protein, with model accuracies scaling fairly linearly with respect to sequence identity (i.e., the higher the sequence identity, the better the prediction). We produced nine models with accuracies ranging from 4.0 to 6.0 Å C(α) RMSD for 60–100 residue proteins (or large fragments of a protein), with a prediction accuracy of 4.0 Å C(α) RMSD for residues 1–80 for T110/rbfa. CONCLUSIONS: The areas of protein structure prediction that work well, and areas that need improvement, are discernable by examining how our methods have performed over the past four CASP experiments. These results have implications for modelling the structure of all tractable proteins encoded by the genome of an organism

Improving the accuracy of template-based predictions by mixing and matching between initial models

BACKGROUND: Comparative modeling is a technique to predict the three dimensional structure of a given protein sequence based primarily on its alignment to one or more proteins with experimentally determined structures. A major bottleneck of current comparative modeling methods is the lack of methods to accurately refine a starting initial model so that it approaches the resolution of the corresponding experimental structure. We investigate the effectiveness of a graph-theoretic clique finding approach to solve this problem. RESULTS: Our method takes into account the information presented in multiple templates/alignments at the three-dimensional level by mixing and matching regions between different initial comparative models. This method enables us to obtain an optimized conformation ensemble representing the best combination of secondary structures, resulting in the refined models of higher quality. In addition, the process of mixing and matching accumulates near-native conformations, resulting in discriminating the native-like conformation in a more effective manner. In the seventh Critical Assessment of Structure Prediction (CASP7) experiment, the refined models produced are more accurate than the starting initial models. CONCLUSION: This novel approach can be applied without any manual intervention to improve the quality of comparative predictions where multiple template/alignment combinations are available for modeling, producing conformational models of higher quality than the starting initial predictions

Homo-dimerization and ligand binding by the leucine-rich repeat domain at RHG1/RFS2 underlying resistance to two soybean pathogens

BACKGROUND: The protein encoded by GmRLK18-1 (Glyma_18_02680 on chromosome 18) was a receptor like kinase (RLK) encoded within the soybean (Glycine max L. Merr.) Rhg1/Rfs2 locus. The locus underlies resistance to the soybean cyst nematode (SCN) Heterodera glycines (I.) and causal agent of sudden death syndrome (SDS) Fusarium virguliforme (Aoki). Previously the leucine rich repeat (LRR) domain was expressed in Escherichia coli. RESULTS: The aims here were to evaluate the LRRs ability to; homo-dimerize; bind larger proteins; and bind to small peptides. Western analysis suggested homo-dimers could form after protein extraction from roots. The purified LRR domain, from residue 131–485, was seen to form a mixture of monomers and homo-dimers in vitro. Cross-linking experiments in vitro showed the H274N region was close (<11.1 A) to the highly conserved cysteine residue C196 on the second homo-dimer subunit. Binding constants of 20–142 nM for peptides found in plant and nematode secretions were found. Effects on plant phenotypes including wilting, stem bending and resistance to infection by SCN were observed when roots were treated with 50 pM of the peptides. Far-Western analyses followed by MS showed methionine synthase and cyclophilin bound strongly to the LRR domain. A second LRR from GmRLK08-1 (Glyma_08_g11350) did not show these strong interactions. CONCLUSIONS: The LRR domain of the GmRLK18-1 protein formed both a monomer and a homo-dimer. The LRR domain bound avidly to 4 different CLE peptides, a cyclophilin and a methionine synthase. The CLE peptides GmTGIF, GmCLE34, GmCLE3 and HgCLE were previously reported to be involved in root growth inhibition but here GmTGIF and HgCLE were shown to alter stem morphology and resistance to SCN. One of several models from homology and ab-initio modeling was partially validated by cross-linking. The effect of the 3 amino acid replacements present among RLK allotypes, A87V, Q115K and H274N were predicted to alter domain stability and function. Therefore, the LRR domain of GmRLK18-1 might underlie both root development and disease resistance in soybean and provide an avenue to develop new variants and ligands that might promote reduced losses to SCN

GPU-Q-J, a fast method for calculating root mean square deviation (RMSD) after optimal superposition

<p>Abstract</p> <p>Background</p> <p>Calculation of the root mean square deviation (RMSD) between the atomic coordinates of two optimally superposed structures is a basic component of structural comparison techniques. We describe a quaternion based method, GPU-Q-J, that is stable with single precision calculations and suitable for graphics processor units (GPUs). The application was implemented on an ATI 4770 graphics card in C/C++ and Brook+ in Linux where it was 260 to 760 times faster than existing unoptimized CPU methods. Source code is available from the Compbio website <url>http://software.compbio.washington.edu/misc/downloads/st_gpu_fit/</url> or from the author LHH.</p> <p>Findings</p> <p>The Nutritious Rice for the World Project (NRW) on World Community Grid predicted <it>de novo</it>, the structures of over 62,000 small proteins and protein domains returning a total of 10 billion candidate structures. Clustering ensembles of structures on this scale requires calculation of large similarity matrices consisting of RMSDs between each pair of structures in the set. As a real-world test, we calculated the matrices for 6 different ensembles from NRW. The GPU method was 260 times faster that the fastest existing CPU based method and over 500 times faster than the method that had been previously used.</p> <p>Conclusions</p> <p>GPU-Q-J is a significant advance over previous CPU methods. It relieves a major bottleneck in the clustering of large numbers of structures for NRW. It also has applications in structure comparison methods that involve multiple superposition and RMSD determination steps, particularly when such methods are applied on a proteome and genome wide scale.</p

Comprehensive computational analysis of Hmd enzymes and paralogs in methanogenic Archaea

<p>Abstract</p> <p>Background</p> <p>Methanogenesis is the sole means of energy production in methanogenic Archaea. H₂-forming methylenetetrahydromethanopterin dehydrogenase (Hmd) catalyzes a step in the hydrogenotrophic methanogenesis pathway in class I methanogens. At least one <it>hmd </it>paralog has been identified in nine of the eleven complete genome sequences of class I hydrogenotrophic methanogens. The products of these paralog genes have thus far eluded any detailed functional characterization.</p> <p>Results</p> <p>Here we present a thorough computational analysis of Hmd enzymes and paralogs that includes state of the art phylogenetic inference, structure prediction, and functional site prediction techniques. We determine that the Hmd enzymes are phylogenetically distinct from Hmd paralogs but share a common overall structure. We predict that the active site of the Hmd enzyme is conserved as a functional site in Hmd paralogs and use this observation to propose possible molecular functions of the paralog that are consistent with previous experimental evidence. We also identify an uncharacterized site in the N-terminal domains of both proteins that is predicted by our methods to directly impart function.</p> <p>Conclusion</p> <p>This study contributes to our understanding of the evolutionary history, structural conservation, and functional roles, of the Hmd enzymes and paralogs. The results of our phylogenetic and structural analysis constitute datasets that will aid in the future study of the Hmd protein family. Our functional site predictions generate several testable hypotheses that will guide further experimental characterization of the Hmd paralog. This work also represents a novel approach to protein function prediction in which multiple computational methods are integrated to achieve a detailed characterization of proteins that are not well understood.</p

The evolution and functional repertoire of translation proteins following the origin of life

<p>Abstract</p> <p>Background</p> <p>The RNA world hypothesis posits that the earliest genetic system consisted of informational RNA molecules that directed the synthesis of modestly functional RNA molecules. Further evidence suggests that it was within this RNA-based genetic system that life developed the ability to synthesize proteins by translating genetic code. Here we investigate the early development of the translation system through an evolutionary survey of protein architectures associated with modern translation.</p> <p>Results</p> <p>Our analysis reveals a structural expansion of translation proteins immediately following the RNA world and well before the establishment of the DNA genome. Subsequent functional annotation shows that representatives of the ten most ancestral protein architectures are responsible for all of the core protein functions found in modern translation.</p> <p>Conclusions</p> <p>We propose that this early robust translation system evolved by virtue of a positive feedback cycle in which the system was able to create increasingly complex proteins to further enhance its own function.</p> <p>Reviewers</p> <p>This article was reviewed by Janet Siefert, George Fox, and Antonio Lazcano (nominated by Laura Landweber)</p

Improved protein structure selection using decoy-dependent discriminatory functions

BACKGROUND: A key component in protein structure prediction is a scoring or discriminatory function that can distinguish near-native conformations from misfolded ones. Various types of scoring functions have been developed to accomplish this goal, but their performance is not adequate to solve the structure selection problem. In addition, there is poor correlation between the scores and the accuracy of the generated conformations. RESULTS: We present a simple and nonparametric formula to estimate the accuracy of predicted conformations (or decoys). This scoring function, called the density score function, evaluates decoy conformations by performing an all-against-all C(α )RMSD (Root Mean Square Deviation) calculation in a given decoy set. We tested the density score function on 83 decoy sets grouped by their generation methods (4state_reduced, fisa, fisa_casp3, lmds, lattice_ssfit, semfold and Rosetta). The density scores have correlations as high as 0.9 with the C(α )RMSDs of the decoy conformations, measured relative to the experimental conformation for each decoy. We previously developed a residue-specific all-atom probability discriminatory function (RAPDF), which compiles statistics from a database of experimentally determined conformations, to aid in structure selection. Here, we present a decoy-dependent discriminatory function called self-RAPDF, where we compiled the atom-atom contact probabilities from all the conformations in a decoy set instead of using an ensemble of native conformations, with a weighting scheme based on the density scores. The self-RAPDF has a higher correlation with C(α )RMSD than RAPDF for 76/83 decoy sets, and selects better near-native conformations for 62/83 decoy sets. Self-RAPDF may be useful not only for selecting near-native conformations from decoy sets, but also for fold simulations and protein structure refinement. CONCLUSIONS: Both the density score and the self-RAPDF functions are decoy-dependent scoring functions for improved protein structure selection. Their success indicates that information from the ensemble of decoy conformations can be used to derive statistical probabilities and facilitate the identification of near-native structures

Combating Ebola with Repurposed Therapeutics Using the CANDO Platform

Ebola virus disease (EVD) is extremely virulent with an estimated mortality rate of up to 90%. However, the state-of-the-art treatment for EVD is limited to quarantine and supportive care. The 2014 Ebola epidemic in West Africa, the largest in history, is believed to have caused more than 11,000 fatalities. The countries worst affected are also among the poorest in the world. Given the complexities, time, and resources required for a novel drug development, finding efficient drug discovery pathways is going to be crucial in the fight against future outbreaks. We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for rapid therapeutic repurposing and discovery. We used the CANDO platform to identify and rank FDA-approved drug candidates that bind and inhibit all proteins encoded by the genomes of five different Ebola virus strains. Top ranking drug candidates for EVD treatment generated by CANDO were compared to in vitro screening studies against Ebola virus-like particles (VLPs) by Kouznetsova et al. and genetically engineered Ebola virus and cell viability studies by Johansen et al. to identify drug overlaps between the in virtuale and in vitro studies as putative treatments for future EVD outbreaks. Our results indicate that integrating computational docking predictions on a proteomic scale with results from in vitro screening studies may be used to select and prioritize compounds for further in vivo and clinical testing. This approach will significantly reduce the lead time, risk, cost, and resources required to determine efficacious therapies against future EVD outbreaks

PROTINFO: new algorithms for enhanced protein structure predictions

We describe new algorithms and modules for protein structure prediction available as part of the PROTINFO web server. The modules, comparative and de novo modelling, have significantly improved back-end algorithms that were rigorously evaluated at the sixth meeting on the Critical Assessment of Protein Structure Prediction methods. We were one of four server groups invited to make an oral presentation (only the best performing groups are asked to do so). These two modules allow a user to submit a protein sequence and return atomic coordinates representing the tertiary structure of that protein. The PROTINFO server is available at

INTEGRATOR: interactive graphical search of large protein interactomes over the Web

BACKGROUND: The rapid growth of protein interactome data has elevated the necessity and importance of network analysis tools. However, unlike pure text data, network search spaces are of exponential complexity. This poses special challenges for storing, searching, and navigating this data efficiently. Moreover, development of effective web interfaces has been difficult. RESULTS: We present Integrator, a web-integrated graphical search tool for protein-protein interaction networks across 50+ genomes. CONCLUSION: Integrator provides single and multiple protein searches of the Bioverse database containing experimentally-derived and predicted protein-protein interactions. The interface provides animated local network views, rapid subgraph manipulation, and cross-referencing of functional annotations. Integrator is available at