SELECTpro: effective protein model selection using a structure-based energy function resistant to BLUNDERs

<p>Abstract</p> <p>Background</p> <p>Protein tertiary structure prediction is a fundamental problem in computational biology and identifying the most native-like model from a set of predicted models is a key sub-problem. Consensus methods work well when the redundant models in the set are the most native-like, but fail when the most native-like model is unique. In contrast, structure-based methods score models independently and can be applied to model sets of any size and redundancy level. Additionally, structure-based methods have a variety of important applications including analogous fold recognition, refinement of sequence-structure alignments, and de novo prediction. The purpose of this work was to develop a structure-based model selection method based on predicted structural features that could be applied successfully to any set of models.</p> <p>Results</p> <p>Here we introduce SELECTpro, a novel structure-based model selection method derived from an energy function comprising physical, statistical, and predicted structural terms. Novel and unique energy terms include predicted secondary structure, predicted solvent accessibility, predicted contact map, β-strand pairing, and side-chain hydrogen bonding.</p> <p>SELECTpro participated in the new model quality assessment (QA) category in CASP7, submitting predictions for all 95 targets and achieved top results. The average difference in GDT-TS between models ranked first by SELECTpro and the most native-like model was 5.07. This GDT-TS difference was less than 1% of the GDT-TS of the most native-like model for 18 targets, and less than 10% for 66 targets. SELECTpro also ranked the single most native-like first for 15 targets, in the top five for 39 targets, and in the top ten for 53 targets, more often than any other method. Because the ranking metric is skewed by model redundancy and ignores poor models with a better ranking than the most native-like model, the BLUNDER metric is introduced to overcome these limitations. SELECTpro is also evaluated on a recent benchmark set of 16 small proteins with large decoy sets of 12500 to 20000 models for each protein, where it outperforms the benchmarked method (I-TASSER).</p> <p>Conclusion</p> <p>SELECTpro is an effective model selection method that scores models independently and is appropriate for use on any model set. SELECTpro is available for download as a stand alone application at: <url>http://www.igb.uci.edu/~baldig/selectpro.html</url>. SELECTpro is also available as a public server at the same site.</p

Biosignatures of Exposure/Transmission and Immunity.

A blood test that captures cumulative exposure over time and assesses levels of naturally acquired immunity (NAI) would provide a critical tool to monitor the impact of interventions to reduce malaria transmission and broaden our understanding of how NAI develops around the world as a function of age and exposure. This article describes a collaborative effort in multiple International Centers of Excellence in Malaria Research (ICEMRs) to develop such tests using malaria-specific antibody responses as biosignatures of transmission and immunity. The focus is on the use of Plasmodium falciparum and Plasmodium vivax protein microarrays to identify a panel of the most informative antibody responses in diverse malaria-endemic settings representing an unparalleled spectrum of malaria transmission and malaria species mixes before and after interventions to reduce malaria transmission

Identification of Novel Seroreactive Antigens in Johne’s Disease Cattle by Using the Mycobacterium tuberculosis Protein Array

Johne’s disease, a chronic gastrointestinal inflammatory disease caused by Mycobacterium avium subspecies paratuberculosis, is endemic in dairy cattle and other ruminants worldwide and remains a challenge to diagnose using traditional serological methods. Given the close phylogenetic relationship between M. aviumsubsp. paratuberculosis and the human pathogen Mycobacterium tuberculosis, here, we applied a whole-proteome M. tuberculosis protein array to identify seroreactive and diagnostic M. avium subsp. paratuberculosis antigens. A genome-scale pairwise analysis of amino acid identity levels between orthologous proteins in M. avium subsp. paratuberculosis and M. tuberculosis showed an average of 62% identity, with more than half the orthologous proteins sharing 75% identity. Analysis of the M. tuberculosis protein array probed with sera from M. avium subsp. paratuberculosis- infected cattle showed antibody binding to 729 M. tuberculosis proteins, with 58% of them having 70% identity to M. avium subsp. paratuberculosis orthologs. The results showed that only 4 of the top 40 seroreactive M. tuberculosis antigens were orthologs of previously reported M. avium subsp. paratuberculosis antigens, revealing the existence of a large number of previously unrecognized candidate diagnostic antigens. Enzyme-linked immunosorbent assay (ELISA) testing of 20 M. avium subsp. paratuberculosis recombinant proteins, representing reactive and nonreactive M. tuberculosis orthologs, further confirmed that the M. tuberculosis array has utility as a screening tool for identifying candidate antigens for Johne’s disease diagnostics. Additional ELISA testing of field serum samples collected from dairy herds around the United States revealed that MAP2942c had the strongest seroreactivity with Johne’s disease-positive samples. Collectively, our studies have considerably expanded the number of candidate M. avium subsp. paratuberculosis proteins with potential utility in the next generation of rationally designed Johne’s disease diagnostic assays

Identification of Sero-Diagnostic Antigens for the Early Diagnosis of Johne’s Disease using MAP Protein Microarrays

Considerable effort has been directed toward controlling Johne’s disease (JD), a chronic granulomatous intestinal inflammatory disease caused by Mycobacterium avium subsp. paratuberculosis (MAP) in cattle and other ruminants. However, progress in controlling the spread of MAP infection has been impeded by the lack of reliable diagnostic tests that can identify animals early in the infection process and help break the transmission chain. To identify reliable antigens for early diagnosis of MAP infection, we constructed a MAP protein array with 868 purified recombinant MAP proteins, and screened a total of 180 well-characterized serum samples from cows assigned to 4 groups based on previous serological and fecal test results: negative low exposure (NL, n = 30); negative high exposure (NH, n = 30); fecal- positive, ELISA-negative (F + E−, n = 60); and both fecal- and ELISA-positive (F + E+, n = 60). The analyses identified a total of 49 candidate antigens in the NH, F + E−, and F + E+ with reactivity compared with the NL group (p \u3c 0.01), a majority of which have not been previously identified. While some of the antigens were identified as reactive in only one of the groups, others showed reactivity in multiple groups, including NH (n = 28), F + E− (n = 26), and F + E+ (n = 17) groups. Using combinations of top reactive antigens in each group, the results reveal sensitivities of 60.0%, 73.3%, and 81.7% in the NH, F + E−, and F + E+, respectively at 90% specificity, suggesting that early detection of infection in animals may be possible and enable better opportunities to reduce within herd transmission that may be otherwise missed by traditional serological assays that are biased towards more heavily infected animals. Together, the results suggest that several of the novel candidate antigens identified in this study, particularly those that were reactive in the NH and F + E− groups, have potential utility for the early sero-diagnosis of MAP infection

Identification of Novel Serodiagnostic Signatures of Typhoid Fever Using a Salmonella Proteome Array.

Current diagnostic tests for typhoid fever, the disease caused by Salmonella Typhi, are poor. We aimed to identify serodiagnostic signatures of typhoid fever by assessing microarray signals to 4,445 S. Typhi antigens in sera from 41 participants challenged with oral S. Typhi. We found broad, heterogeneous antibody responses with increasing IgM/IgA signals at diagnosis. In down-selected 250-antigen arrays we validated responses in a second challenge cohort (n = 30), and selected diagnostic signatures using machine learning and multivariable modeling. In four models containing responses to antigens including flagellin, OmpA, HlyE, sipC, and LPS, multi-antigen signatures discriminated typhoid (n = 100) from other febrile bacteremia (n = 52) in Nepal. These models contained combinatorial IgM, IgA, and IgG responses to 5 antigens (ROC AUC, 0.67 and 0.71) or 3 antigens (0.87), although IgA responses to LPS also performed well (0.88). Using a novel systematic approach we have identified and validated optimal serological diagnostic signatures of typhoid fever

SELECTpro: effective protein model selection using a structure-based energy function resistant to BLUNDERs

Background: Protein tertiary structure prediction is a fundamental problem in computational biology and identifying the most native-like model from a set of predicted models is a key sub-problem. Consensus methods work well when the redundant models in the set are the most native-like, but fail when the most native-like model is unique. In contrast, structure-based methods score models independently and can be applied to model sets of any size and redundancy level. Additionally, structure-based methods have a variety of important applications including analogous fold recognition, refinement of sequence-structure alignments, and de novo prediction. The purpose of this work was to develop a structure-based model selection method based on predicted structural features that could be applied successfully to any set of models. Results: Here we introduce SELECTpro, a novel structure-based model selection method derived from an energy function comprising physical, statistical, and predicted structural terms. Novel and unique energy terms include predicted secondary structure, predicted solvent accessibility, predicted contact map, beta-strand pairing, and side-chain hydrogen bonding. SELECTpro participated in the new model quality assessment (QA) category in CASP7, submitting predictions for all 95 targets and achieved top results. The average difference in GDT-TS between models ranked first by SELECTpro and the most native-like model was 5.07. This GDT-TS difference was less than 1% of the GDT-TS of the most native-like model for 18 targets, and less than 10% for 66 targets. SELECTpro also ranked the single most native-like first for 15 targets, in the top five for 39 targets, and in the top ten for 53 targets, more often than any other method. Because the ranking metric is skewed by model redundancy and ignores poor models with a better ranking than the most native-like model, the BLUNDER metric is introduced to overcome these limitations. SELECTpro is also evaluated on a recent benchmark set of 16 small proteins with large decoy sets of 12500 to 20000 models for each protein, where it outperforms the benchmarked method (I-TASSER). Conclusion: SELECTpro is an effective model selection method that scores models independently and is appropriate for use on any model set. SELECTpro is available for download as a stand alone application at: http://www.igb.uci.edu/similar to baldig/selectpro.html. SELECTpro is also available as a public server at the same site

Incorporating post-translational modifications and unnatural amino acids into high-throughput modeling of protein structures

Motivation: Accurately predicting protein side-chain conformations is an important subproblem of the broader protein structure prediction problem. Several methods exist for generating fairly accurate models for moderate-size proteins in seconds or less. However, a major limitation of these methods is their inability to model post-translational modifications (PTMs) and unnatural amino acids. In natural living systems, the chemical groups added following translation are often critical for the function of the protein. In engineered systems, unnatural amino acids are incorporated into proteins to explore structure–function relationships and create novel proteins. Results: We present a new version of SIDEpro to predict the side chains of proteins containing non-standard amino acids, including 15 of the most frequently observed PTMs in the Protein Data Bank and all types of phosphorylation. SIDEpro uses energy functions that are parameterized by neural networks trained from available data. For PTMs, the [Image: see text] and [Image: see text] accuracies are comparable with those obtained for the precursor amino acid, and so are the RMSD values for the atoms shared with the precursor amino acid. In addition, SIDEpro can accommodate any PTM or unnatural amino acid, thus providing a flexible prediction system for high-throughput modeling of proteins beyond the standard amino acids. Availability and implementation: SIDEpro programs and Web server, rotamer libraries and data are available through the SCRATCH suite of protein structure predictors at http://scratch.proteomics.ics.uci.edu/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Incorporating post-translational modifications and unnatural amino acids into high-throughput modeling of protein structures

MotivationAccurately predicting protein side-chain conformations is an important subproblem of the broader protein structure prediction problem. Several methods exist for generating fairly accurate models for moderate-size proteins in seconds or less. However, a major limitation of these methods is their inability to model post-translational modifications (PTMs) and unnatural amino acids. In natural living systems, the chemical groups added following translation are often critical for the function of the protein. In engineered systems, unnatural amino acids are incorporated into proteins to explore structure-function relationships and create novel proteins.ResultsWe present a new version of SIDEpro to predict the side chains of proteins containing non-standard amino acids, including 15 of the most frequently observed PTMs in the Protein Data Bank and all types of phosphorylation. SIDEpro uses energy functions that are parameterized by neural networks trained from available data. For PTMs, the [Formula: see text] and [Formula: see text] accuracies are comparable with those obtained for the precursor amino acid, and so are the RMSD values for the atoms shared with the precursor amino acid. In addition, SIDEpro can accommodate any PTM or unnatural amino acid, thus providing a flexible prediction system for high-throughput modeling of proteins beyond the standard amino acids.Availability and implementationSIDEpro programs and Web server, rotamer libraries and data are available through the SCRATCH suite of protein structure predictors at http://scratch.proteomics.ics.uci.edu

Inferring Epitopes of a Polymorphic Antigen Amidst Broadly Cross-Reactive Antibodies Using Protein Microarrays: A Study of OspC Proteins of <i>Borrelia burgdorferi</i>

<div><p>Epitope mapping studies aim to identify the binding sites of antibody-antigen interactions to enhance the development of vaccines, diagnostics and immunotherapeutic compounds. However, mapping is a laborious process employing time- and resource-consuming ‘wet bench’ techniques or epitope prediction software that are still in their infancy. For polymorphic antigens, another challenge is characterizing cross-reactivity between epitopes, teasing out distinctions between broadly cross-reactive responses, limited cross-reactions among variants and the truly type-specific responses. A refined understanding of cross-reactive antibody binding could guide the selection of the most informative subsets of variants for diagnostics and multivalent subunit vaccines. We explored the antibody binding reactivity of sera from human patients and <i>Peromyscus leucopus</i> rodents infected with <i>Borrelia burgdorferi</i> to the polymorphic outer surface protein C (OspC), an attractive candidate antigen for vaccine and improved diagnostics for Lyme disease. We constructed a protein microarray displaying 23 natural variants of OspC and quantified the degree of cross-reactive antibody binding between all pairs of variants, using Pearson correlation calculated on the reactivity values using three independent transforms of the raw data: (1) logarithmic, (2) rank, and (3) binary indicators. We observed that the global amino acid sequence identity between OspC pairs was a poor predictor of cross-reactive antibody binding. Then we asked if specific regions of the protein would better explain the observed cross-reactive binding and performed <i>in silico</i> screening of the linear sequence and 3-dimensional structure of OspC. This analysis pointed to residues 179 through 188 the fifth C-terminal helix of the structure as a major determinant of type-specific cross-reactive antibody binding. We developed bioinformatics methods to systematically analyze the relationship between local sequence/structure variation and cross-reactive antibody binding patterns among variants of a polymorphic antigen, and this method can be applied to other polymorphic antigens for which immune response data is available for multiple variants.</p></div