SUMMARY: To address the need for improved phage annotation tools that scale, we created an automated throughput annotation pipeline: multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene calling algorithm and assigns putative functions to gene calls using protein-, virus- and phage-centric databases. multiPhATE's modular construction allows the user to implement all or any portion of the analyses by acquiring local instances of the desired databases and specifying the desired analyses in a configuration file. We demonstrate multiPhATE by annotating two newly sequenced Yersinia pestis phage genomes. Within multiPhATE, the PhATE processing pipeline can be readily implemented across multiple processors, making it adaptable for throughput sequencing projects. Software documentation assists the user in configuring the system. AVAILABILITY AND IMPLEMENTATION: multiPhATE was implemented in Python 3.7, and runs as a command-line code under Linux or Unix. multiPhATE is freely available under an open-source BSD3 license from https://github.com/carolzhou/multiPhATE. Instructions for acquiring the databases and third-party codes used by multiPhATE are included in the distribution README file. Users may report bugs by submitting to the github issues page associated with the multiPhATE distribution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Here we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. RESULTS: In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resulting functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. CONCLUSIONS: PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequence-based genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.

BACKGROUND: Most of the currently used methods for protein function prediction rely on sequence-based comparisons between a query protein and those for which a functional annotation is provided. A serious limitation of sequence similarity-based approaches for identifying residue conservation among proteins is the low confidence in assigning residue-residue correspondences among proteins when the level of sequence identity between the compared proteins is poor. Multiple sequence alignment methods are more satisfactory--still, they cannot provide reliable results at low levels of sequence identity. Our goal in the current work was to develop an algorithm that could help overcome these difficulties by facilitating the identification of structurally (and possibly functionally) relevant residue-residue correspondences between compared protein structures. RESULTS: Here we present StralSV (structure-alignment sequence variability), a new algorithm for detecting closely related structure fragments and quantifying residue frequency from tight local structure alignments. We apply StralSV in a study of the RNA-dependent RNA polymerase of poliovirus, and we demonstrate that the algorithm can be used to determine regions of the protein that are relatively unique, or that share structural similarity with proteins that would be considered distantly related. By quantifying residue frequencies among many residue-residue pairs extracted from local structural alignments, one can infer potential structural or functional importance of specific residues that are determined to be highly conserved or that deviate from a consensus. We further demonstrate that considerable detailed structural and phylogenetic information can be derived from StralSV analyses. CONCLUSIONS: StralSV is a new structure-based algorithm for identifying and aligning structure fragments that have similarity to a reference protein. StralSV analysis can be used to quantify residue-residue correspondences and identify residues that may be of particular structural or functional importance, as well as unusual or unexpected residues at a given sequence position. StralSV is provided as a web service at http://proteinmodel.org/AS2TS/STRALSV/.

Here we introduce a quantitative structure-driven computational domain-fusion method, which we used to predict the structures of proteins believed to be involved in regulation of the subtilin pathway in Bacillus subtilis, and used to predict a protein-protein complex formed by interaction between the proteins. Homology modeling of SpaK and SpaR yielded preliminary structural models based on a best template for SpaK comprising a dimer of a histidine kinase, and for SpaR a response regulator protein. Our LGA code was used to identify multi-domain proteins with structure homology to both modeled structures, yielding a set of domain-fusion templates then used to model a hypothetical SpaK/SpaR complex. The models were used to identify putative functional residues and residues at the protein-protein interface, and bioinformatics was used to compare functionally and structurally relevant residues in corresponding positions among proteins with structural homology to the templates. Models of the complex were evaluated in light of known properties of the functional residues within two-component systems involving His-Asp phosphorelays. Based on this analysis, a phosphotransferase complexed with a beryllofluoride was selected as the optimal template for modeling a SpaK/SpaR complex conformation. In vitro phosphorylation studies performed using wild type and site-directed SpaK mutant proteins validated the predictions derived from application of the structure-driven domain-fusion method: SpaK was phosphorylated in the presence of (32)P-ATP and the phosphate moiety was subsequently transferred to SpaR, supporting the hypothesis that SpaK and SpaR function as sensor and response regulator, respectively, in a two-component signal transduction system, and furthermore suggesting that the structure-driven domain-fusion approach correctly predicted a physical interaction between SpaK and SpaR. Our domain-fusion algorithm leverages quantitative structure information and provides a tool for generation of hypotheses regarding protein function, which can then be tested using empirical methods.

BACKGROUND: MannDB was created to meet a need for rapid, comprehensive automated protein sequence analyses to support selection of proteins suitable as targets for driving the development of reagents for pathogen or protein toxin detection. Because a large number of open-source tools were needed, it was necessary to produce a software system to scale the computations for whole-proteome analysis. Thus, we built a fully automated system for executing software tools and for storage, integration, and display of automated protein sequence analysis and annotation data. DESCRIPTION: MannDB is a relational database that organizes data resulting from fully automated, high-throughput protein-sequence analyses using open-source tools. Types of analyses provided include predictions of cleavage, chemical properties, classification, features, functional assignment, post-translational modifications, motifs, antigenicity, and secondary structure. Proteomes (lists of hypothetical and known proteins) are downloaded and parsed from Genbank and then inserted into MannDB, and annotations from SwissProt are downloaded when identifiers are found in the Genbank entry or when identical sequences are identified. Currently 36 open-source tools are run against MannDB protein sequences either on local systems or by means of batch submission to external servers. In addition, BLAST against protein entries in MvirDB, our database of microbial virulence factors, is performed. A web client browser enables viewing of computational results and downloaded annotations, and a query tool enables structured and free-text search capabilities. When available, links to external databases, including MvirDB, are provided. MannDB contains whole-proteome analyses for at least one representative organism from each category of biological threat organism listed by APHIS, CDC, HHS, NIAID, USDA, USFDA, and WHO. CONCLUSION: MannDB comprises a large number of genomes and comprehensive protein sequence analyses representing organisms listed as high-priority agents on the websites of several governmental organizations concerned with bio-terrorism. MannDB provides the user with a BLAST interface for comparison of native and non-native sequences and a query tool for conveniently selecting proteins of interest. In addition, the user has access to a web-based browser that compiles comprehensive and extensive reports. Access to MannDB is freely available at http://manndb.llnl.gov/.

MOTIVATION: Specific and sensitive ligand-based protein detection assays that employ antibodies or small molecules such as peptides, aptamers or other small molecules require that the corresponding surface region of the protein be accessible and that there be minimal cross-reactivity with non-target proteins. To reduce the time and cost of laboratory screening efforts for diagnostic reagents, we developed new methods for evaluating and selecting protein surface regions for ligand targeting. RESULTS: We devised combined structure- and sequence-based methods for identifying 3D epitopes and binding pockets on the surface of the A chain of ricin that are conserved with respect to a set of ricin A chains and unique with respect to other proteins. We (1) used structure alignment software to detect structural deviations and extracted from this analysis the residue-residue correspondence, (2) devised a method to compare corresponding residues across sets of ricin structures and structures of closely related proteins, (3) devised a sequence-based approach to determine residue infrequency in local sequence context and (4) modified a pocket-finding algorithm to identify surface crevices in close proximity to residues determined to be conserved/unique based on our structure- and sequence-based methods. In applying this combined informatics approach to ricin A, we identified a conserved/unique pocket in close proximity (but not overlapping) the active site that is suitable for bi-dentate ligand development. These methods are generally applicable to identification of surface epitopes and binding pockets for development of diagnostic reagents, therapeutics and vaccines.

Rapid advances in the genomic sequencing of bacteria and viruses over the past few years have made it possible to consider sequencing the genomes of all pathogens that affect humans and the crops and livestock upon which our lives depend. Recent events make it imperative that full genome sequencing be accomplished as soon as possible for pathogens that could be used as weapons of mass destruction or disruption. This sequence information must be exploited to provide rapid and accurate diagnostics to identify pathogens and distinguish them from harmless near-neighbours and hoaxes. The Chem-Bio Non-Proliferation (CBNP) programme of the US Department of Energy (DOE) began a large-scale effort of pathogen detection in early 2000 when it was announced that the DOE would be providing bio-security at the 2002 Winter Olympic Games in Salt Lake City, Utah. Our team at the Lawrence Livermore National Lab (LLNL) was given the task of developing reliable and validated assays for a number of the most likely bioterrorist agents. The short timeline led us to devise a novel system that utilised whole-genome comparison methods to rapidly focus on parts of the pathogen genomes that had a high probability of being unique. Assays developed with this approach have been validated by the Centers for Disease Control (CDC). They were used at the 2002 Winter Olympics, have entered the public health system, and have been in continual use for non-publicised aspects of homeland defence since autumn 2001. Assays have been developed for all major threat list agents for which adequate genomic sequence is available, as well as for other pathogens requested by various government agencies. Collaborations with comparative genomics algorithm developers have enabled our LLNL team to make major advances in pathogen detection, since many of the existing tools simply did not scale well enough to be of practical use for this application. It is hoped that a discussion of a real-life practical application of comparative genomics algorithms may help spur algorithm developers to tackle some of the many remaining problems that need to be addressed. Solutions to these problems will advance a wide range of biological disciplines, only one of which is pathogen detection. For example, exploration in evolution and phylogenetics, annotating gene coding regions, predicting and understanding gene function and regulation, and untangling gene networks all rely on tools for aligning multiple sequences, detecting gene rearrangements and duplications, and visualising genomic data. Two key problems currently needing improved solutions are: (1) aligning incomplete, fragmentary sequence (eg draft genome contigs or arbitrary genome regions) with both complete genomes and other fragmentary sequences; and (2) ordering, aligning and visualising non-colinear gene rearrangements and inversions in addition to the colinear alignments handled by current tools.