ProDom and ProDom-CG: tools for protein domain analysis and whole genome comparisons

ProDom contains all protein domain families automatically generated from the SWISS-PROT and TrEMBL sequence databases (http://www. toulouse.inra.fr/prodom.html ). ProDom-CG results from a similar domain analysis as applied to completed genomes (http://www.toulouse. inra.fr/prodomCG.html ). Recent improvements to the ProDom database and its server include: scaling up to include sequences from TrEMBL, addition of Pfam-A entries to the set of expert validated families, assignment of stable accession numbers, consistency indicators for domain families, domain arrangements of sub-families and links to Pfam-A.     

A database of six eukaryotic hypothetical genes and proteins

Assigning functions to proteins of unknown function is of considerable interest to the proteomic researchers as the genes encoding them are conserved over various species. Here, we describe HypoDB, a database of hypothetical genes and proteins in six eukaryotes. The database was collected and organized based on the number of entries in each chromosome with few annotations. Hypothetical protein database contains information related to gene and protein sequences, chromosome number and location, secondary and tertiary structure related data. AVAILABILITY: The database is available for free at http://www.trimslabs.com/database/hypodb/index.html.     

The COG database: a tool for genome-scale analysis of protein functions and evolution

Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The database of Clusters of Orthologous Groups of proteins (COGs) is an attempt on a phylogenetic classification of the proteins encoded in 21 complete genomes of bacteria, archaea and eukaryotes (http://www. ncbi.nlm. nih.gov/COG). The COGs were constructed by applying the criterion of consistency of genome-specific best hits to the results of an exhaustive comparison of all protein sequences from these genomes. The database comprises 2091 COGs that include 56-83% of the gene products from each of the complete bacterial and archaeal genomes and approximately 35% of those from the yeast Saccharomyces cerevisiae genome. The COG database is accompanied by the COGNITOR program that is used to fit new proteins into the COGs and can be applied to functional and phylogenetic annotation of newly sequenced genomes.     

Detection of hydrogen-bond signature patterns in protein families

SUMMARY: We have developed a WWW server, HBPRINT, for the identification of hydrogen-bond signature patterns in protein families from their structures. The server calculates (a) common hydrogen bonds between two structures (b) a hydrogen-bond fingerprint in a set of structural neighbours and (c) details of conserved hydrogen bonds. The server also enables the visualization of the hydrogen bond network comprising the signature pattern. AVAILABILITY: HBPRINT and a tutorial are available from http://144.16.93.115/hb_page/index.html.     

Tool developments for structure-function studies of host defense peptides

Antimicrobial peptides, or host defense peptides, are universal signaling and effector molecules in host defense and innate immunity. This article highlights various tools developed for cathelicidins and defensins, ranging from peptide identification, production, and structural biology, including the eight databases for antimicrobial peptides. Novel peptides can be identified from natural sources at both gene and protein levels. Solid-phase synthesis and bacterial expression are the two important methods for peptide production. Three-dimensional structures of antimicrobial peptides, primarily determined by solution NMR techniques, are essential for an in-depth understanding of the mode of action. The introduction of octanoyl phosphatidylglycerol as a bacterial membrane-mimetic model provides new insights into peptide-lipid interactions. The incorporation of structure and activity data into the antimicrobial peptide database (http://aps.unmc.edu/AP/main.html) will lead to an integrated understanding of these peptides via structural bioinformatics.     

PhoPepMass: A database and search tool assisting human phosphorylation peptide identification from mass spectrometry data

Protein phosphorylation, one of the most important protein post-translational modifications, is involved in various biological processes, and the identification of phosphorylation peptides (phosphopeptides) and their corresponding phosphorylation sites (phosphosites) will facilitate the understanding of the molecular mechanism and function of phosphorylation. Mass spectrometry (MS) provides a high-throughput technology that enables the identification of large numbers of phosphosites. PhoPepMass is designed to assist human phosphopeptide identification from MS data based on a specific database of phophopeptide masses and a multivariate hypergeometric matching algorithm. It contains 244,915 phosphosites from several public sources. Moreover, the accurate masses of peptides and fragments with phosphosites were calculated. It is the first database that provides a systematic resource for the query of phosphosites on peptides and their corresponding masses. This allows researchers to search certain proteins of which phosphosites have been reported, to browse detailed phosphopeptide and fragment information, to match masses from MS analyses with defined threshold to the corresponding phosphopeptide, and to compare proprietary phosphopeptide discovery results with results from previous studies. Additionally, a database search software is created and a “two-stage search strategy” is suggested to identify phosphopeptides from tandem mass spectra of proteomics data. We expect PhoPepMass to be a useful tool and a source of reference for proteomics researchers. PhoPepMass is available at https://www.scbit.org/phopepmass/index.html.     

Mass spectrometry-based proteomics combined with bioinformatic tools for bacterial classification

Timely classification and identification of bacteria is of vital importance in many areas of public health. We present a mass spectrometry (MS)-based proteomics approach for bacterial classification. In this method, a bacterial proteome database is derived from all potential protein coding open reading frames (ORFs) found in 170 fully sequenced bacterial genomes. Amino acid sequences of tryptic peptides obtained by LC-ESI MS/MS analysis of the digest of bacterial cell extracts are assigned to individual bacterial proteomes in the database. Phylogenetic profiles of these peptides are used to create a matrix of sequence-to-bacterium assignments. These matrixes, viewed as specific assignment bitmaps, are analyzed using statistical tools to reveal the relatedness between a test bacterial sample and the microorganism database. It is shown that, if a sufficient amount of sequence information is obtained from the MS/MS experiments, a bacterial sample can be classified to a strain level by using this proteomics method, leading to its positive identification.     

Recent improvements of the ProDom database of protein domain families.

The ProDom database contains protein domain families generated from the SWISS-PROT database by automated sequence comparisons. The current version was built with a new improved procedure based on recursive PSI-BLAST homology searches. ProDom can be searched on the World Wide Web to study domain arrangements within either known families or new proteins, with the help of a user-friendly graphical interface (http://www.toulouse.inra.fr/prodom.html). Recent improvements to the ProDom server include: ProDom queries under the SRS Sequence Retrieval System; links to the PredictProtein server; phylogenetic trees and condensed multiple alignments for a better representation of large domain families, with zooming in and out capabilities. In addition, a similar server was set up to display the outcome of whole genome domain analysis as applied to 17 completed microbial genomes (http://www.toulouse.inra.fr/prodomCG.html ).     

Selecton: a server for detecting evolutionary forces at a single amino-acid site

SUMMARY: We present an algorithmic tool for the identification of biologically significant amino acids in proteins of known three dimensional structure. We estimate the degree of purifying selection and positive Darwinian selection at each site and project these estimates onto the molecular surface of the protein. Thus, patches of functional residues (undergoing either positive or purifying selection), which may be discontinuous in the linear sequence, are revealed. We test for the statistical significance of the site-specific scores in order to obtain reliable and valid estimates. AVAILABILITY: The Selecton web server is available at: http://selecton.bioinfo.tau.ac.il SUPPLEMENTARY INFORMATION: More information is available at http://selecton.bioinfo.tau.ac.il/overview.html. A set of examples is available at http://selecton.bioinfo.tau.ac.il/gallery.html.     

Disordered patterns in clustered Protein Data Bank and in eukaryotic and bacterial proteomes

We have constructed the clustered Protein Data Bank and obtained clusters of chains of different identity inside each cluster, http://bioinfo.protres.ru/st_pdb/. We have compiled the largest database of disordered patterns (141) from the clustered PDB where identity between chains inside of a cluster is larger or equal to 75% (version of 28 June 2010) by using simple rules of selection. The results of these analyses would help to further our understanding of the physicochemical and structural determinants of intrinsically disordered regions that serve as molecular recognition elements. We have analyzed the occurrence of the selected patterns in 97 eukaryotic and in 26 bacterial proteomes. The disordered patterns appear more often in eukaryotic than in bacterial proteomes. The matrix of correlation coefficients between numbers of proteins where a disordered pattern from the library of 141 disordered patterns appears at least once in 9 kingdoms of eukaryota and 5 phyla of bacteria have been calculated. As a rule, the correlation coefficients are higher inside of the considered kingdom than between them. The patterns with the frequent occurrence in proteomes have low complexity (PPPPP, GGGGG, EEEED, HHHH, KKKKK, SSTSS, QQQQQP), and the type of patterns vary across different proteomes, http://bioinfo.protres.ru/fp/search_new_pattern.html.     

An hierarchical artificial neural network system for the classification of transmembrane proteins.

This work presents a simple artificial neural network which classifies proteins into two classes from their sequences alone: the membrane protein class and the non-membrane protein class. This may be important in the functional assignment and analysis of open reading frames (ORF's) identified in complete genomes and, especially, those ORF's that correspond to proteins with unknown function. The network described here has a simple hierarchical feed-forward topology and a limited number of neurons which makes it very fast. By using only information contained in 11 protein sequences, the method was able to identify, with 100% accuracy, all membrane proteins with reliable topologies collected from several papers in the literature. Applied to a test set of 995 globular, water-soluble proteins, the neural network classified falsely 23 of them in the membrane protein class (97.7% of correct assignment). The method was also applied to the complete SWISS-PROT database with considerable success and on ORF's of several complete genomes. The neural network developed was associated with the PRED-TMR algorithm (Pasquier,C., Promponas,V.J., Palaios,G.A., Hamodrakas,J.S. and Hamodrakas,S.J., 1999) in a new application package called PRED-TMR2. A WWW server running the PRED-TMR2 software is available at http://o2.db.uoa.gr/PRED-TMR2     

Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method

A substantial percentage of the putative protein-encoding open reading frames (ORFs) in bacterial genomes have no homolog of known function, and their function cannot be confidently assigned on the basis of sequence similarity. Methods not based on sequence similarity are needed and being developed. One method, SVMProt (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi), predicts protein functional family irrespective of sequence similarity (Nucleic Acids Res. 2003;31:3692-3697). While it has been tested on a large number of proteins, its capability for non-homologous proteins has so far been evaluated for a relatively small number of proteins, and additional tests are needed to more fully assess SVMProt. In this work, 90 novel bacterial proteins (non-homologous to known proteins) are used to evaluate the capability of SVMProt. These proteins are such that none of their homologs are in the Swiss-Prot database, their functions not clearly described in the literature, and they themselves and their homologs are not included in the training sets of SVMProt. They represent proteins whose function cannot be confidently predicted by sequence similarity methods at present. The predicted functional class of 76.7% of each of these proteins shows various levels of consistency with the literature-described function, compared to the overall accuracy of 87% for the SVMProt functional class assignment of 34,582 proteins that have at least one homolog of known function. Our study suggests that SVMProt is capable of assigning functional class for novel bacterial proteins at a level not too much lower than that of sequence alignment methods for homologous proteins.     

DDBASE2.0: updated domain database with improved identification of structural domains

MOTIVATION: Although many methods are available for the identification of structural domains from protein three-dimensional structures, accurate definition of protein domains and the curation of such data for a large number of proteins are often possible only after manual intervention. The availability of domain definitions for protein structural entries is useful for the sequence analysis of aligned domains, structure comparison, fold recognition procedures and understanding protein folding, domain stability and flexibility. RESULTS: We have improved our method of domain identification starting from the concept of clustering secondary structural elements, but with an intention of reducing the number of discontinuous segments in identified domains. The results of our modified and automatic approach have been compared with the domain definitions from other databases. On a test data set of 55 proteins, this method acquires high agreement (88%) in the number of domains with the crystallographers' definition and resources such as SCOP, CATH, DALI, 3Dee and PDP databases. This method also obtains 98% overlap score with the other resources in the definition of domain boundaries of the 55 proteins. We have examined the domain arrangements of 4592 non-redundant protein chains using the improved method to include 5409 domains leading to an update of the structural domain database. AVAILABILITY: The latest version of the domain database and online domain identification methods are available from http://www.ncbs.res.in/~faculty/mini/ddbase/ddbase.html Supplementary information: http://www.ncbs.res.in/~faculty/mini/ddbase/supplementary/supplementary.html     

Fast assignment of protein structures to sequences using the intermediate sequence library PDB-ISL

MOTIVATION: For large-scale structural assignment to sequences, as in computational structural genomics, a fast yet sensitive sequence search procedure is essential. A new approach using intermediate sequences was tested as a shortcut to iterative multiple sequence search methods such as PSI-BLAST. RESULTS: A library containing potential intermediate sequences for proteins of known structure (PDB-ISL) was constructed. The sequences in the library were collected from a large sequence database using the sequences of the domains of proteins of known structure as the query sequences and the program PSI-BLAST. Sequences of proteins of unknown structure can be matched to distantly related proteins of known structure by using pairwise sequence comparison methods to find homologues in PDB-ISL. Searches of PDB-ISL were calibrated, and the number of correct matches found at a given error rate was the same as that found by PSI-BLAST. The advantage of this library is that it uses pairwise sequence comparison methods, such as FASTA or BLAST2, and can, therefore, be searched easily and, in many cases, much more quickly than an iterative multiple sequence comparison method. The procedure is roughly 20 times faster than PSI-BLAST for small genomes and several hundred times for large genomes. AVAILABILITY: Sequences can be submitted to the PDB-ISL servers at http://stash.mrc-lmb.cam.ac.uk/PDB_ISL/ or http://cyrah.ebi.ac.uk:1111/Serv/PDB_ISL/ and can be downloaded from ftp://ftp.ebi.ac.uk/pub/contrib/jong/PDB_+ ++ISL/ CONTACT: sat@mrc-lmb.cam.ac.uk and jong@ebi.ac.uk     

Rapid phylogenetic and functional classification of short genomic fragments with signature peptides

ABSTRACT: BACKGROUND: Classification is difficult for shotgun metagenomics data from environments such as soils, where the diversity of sequences is high and where reference sequences from close relatives may not exist. Approaches based on sequence-similarity scores must deal with the confounding effects that inheritance and functional pressures exert on the relation between scores and phylogenetic distance, while approaches based on sequence alignment and tree-building are typically limited to a small fraction of gene families. We describe an approach based on finding one or more exact matches between a read and a precomputed set of peptide 10-mers. RESULTS: At even the largest phylogenetic distances, thousands of 10-mer peptide exact matches can be found between pairs of bacterial genomes. Genes that share one or more peptide 10-mers typically have high reciprocal BLAST scores. Among a set of 403 representative bacterial genomes, some 20 million 10-mer peptides were found to be shared. We assign each of these peptides as a signature of a particular node in a phylogenetic reference tree based on the RNA polymerase genes. We classify the phylogeny of a genomic fragment (e.g., read) at the most specific node on the reference tree that is consistent with the phylogeny of observed signature peptides it contains. Using both synthetic data from four newly-sequenced soil-bacterium genomes and ten real soil metagenomics data sets, we demonstrate a sensitivity and specificity comparable to that of the MEGAN metagenomics analysis package using BLASTX against the NR database. Phylogenetic and functional similarity metrics applied to real metagenomics data indicates a signal-to-noise ratio of approximately 400 for distinguishing among environments. Our method assigns ~6.6 Gbp/hr on a single CPU, compared with 25 kbp/hr for methods based on BLASTX against the NR database. CONCLUSIONS: Classification by exact matching against a precomputed list of signature peptides provides comparable results to existing techniques for reads longer than about 300 bp and does not degrade severely with shorter reads. Orders of magnitude faster than existing methods, the approach is suitable now for inclusion in analysis pipelines and appears to be extensible in several different directions.     

Classification and identification of bacteria using mass spectrometry-based proteomics

Timely classification and identification of bacteria is of vital importance in many areas of public health. Mass spectrometry-based methods provide an attractive alternative to well-established microbiologic procedures. Mass spectrometry methods can be characterized by the relatively high speed of acquiring taxonomically relevant information. Gel-free mass spectrometry proteomics techniques allow for rapid fingerprinting of bacterial proteins using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or, for high-throughput sequencing of peptides from protease-digested cellular proteins, using mass analysis of fragments from collision-induced dissociation of peptide ions. The latter technique uses database searching of product ion mass spectra. A database contains a comprehensive list of protein sequences translated from protein-encoding open reading frames found in bacterial genomes. The results of such searches allow the assignment of experimental peptide sequences to matching theoretical bacterial proteomes. Phylogenetic profiles of sequenced peptides are then used to create a matrix of sequence-to-bacterium assignments, which are analyzed using numerical taxonomy tools. The results thereof reveal the relatedness between bacteria, and allow the taxonomic position of an investigated strain to be inferred.     

An automated comparative analysis of 17 complete microbial genomes

MOTIVATION: As sequenced genomes become larger and sequencing becomes faster, there is a need to develop accurate automated genome comparison techniques and databases to facilitate derivation of genome functionality; identification of enzymes, putative operons and metabolic pathways; and to derive phylogenetic classification of microbes. RESULTS: This paper extends an automated pair-wise genome comparison technique (Bansal et al., Math. Model. Sci. Comput., 9, 1-23, 1998, Bansal and Bork, in First International Workshop of Declarative Languages, Springer, pp. 275-289, 1999) used to identify orthologs and gene groups to derive orthologous genes in a group of genomes and to identify genes with conserved functionality. Seventeen microbial genomes archived at ftp://ncbi.nlm.nih.gov/genbank/genomes have been compared using the automated technique. Data related to orthologs, gene groups, gene duplication, gene fusion, orthologs with conserved functionality, and genes specifically orthologous to Escherichia coli and pathogens has been presented and analyzed. AVAILABILITY: A prototype database is available at ftp://www.mcs.kent.edu/arvind/intellibio / orthos.html. The software is free for academic research under an academic license. The detailed database for every microbial genome in NCBI is commercially available through intellibio software and consultancy corporation (Web site: http://www.mcs.kent.edu/?rvind/intellibio . html). CONTACT: arvind@mcs.kent.edu.