The SBASE domain sequence library,release 10: domain architecture prediction

SBASE (http://www.icgeb.trieste.it/sbase) is an on-line collection of protein domain sequences and related computational tools designed to facilitate detection of domain homologies based on simple database search. The 10th 'jubilee release' of the SBASE library of protein domain sequences contains 1 052 904 protein sequence segments annotated by structure, function, ligand-binding or cellular topology, clustered into over 6000 domain groups. Domain identification and functional prediction are based on a comparison of BLAST search outputs with a knowledge base of biologically significant similarities extracted from known domain groups. The knowledge base is generated automatically for each domain group from the comparison of within-group ('self') and out-of-group ('non-self') similarities. This is a memory-based approach wherein group-specific similarity functions are automatically learned from the database.     

The SBASE protein domain library, Release 4.0: a collection of annotated protein sequence segments.

SBASE 4.0 is the fourth release of SBASE, a collection of annotated protein domain sequences that represent various structural, functional, ligand binding and topogenic segments of proteins. SBASE was designed to facilitate the detection of functional homologies and can be searched with standard database search tools, such as FASTA and BLAST3. The present release contains 61 137 entries provided with standardized names and cross-referenced to all major protein, nucleic acid and sequence pattern collections. The entries are clustered into 13 155 groups in order to facilitate detection of distant similarities. SBASE 4.0 is freely available by anonymous ftp file transfer from ftp.icgeb.trieste.it. Individual records can be retrieved with the gopher server at icgeb.trieste.it and with a World Wide Web server at http://www.icgeb.trieste.it. Automated searching of SBASE with BLAST can be carried out with the electronic mail server sbase@icgeb.trieste.it, which now also provides a graphic representation of the homologies. A related mail server, domain@hubi.abc.hu, assigns SBASE domain homologies on the basis of SWISS-PROT searches.     

The SBASE protein domain library, release 7.0: a collection of annotated protein sequence segments

SBASE 7.0 is the seventh release of the SBASE protein domain library sequences that contains 237 937 annotated structural, functional, ligand-binding and topogenic segments of proteins, cross-referenced to all major sequence databases and sequence pattern collections. The entries are clustered into over 1811 groups and are provided with two WWW-based search facilities for on-line use. SBASE 7.0 is freely available by anonymous 'ftp' file transfer from ftp.icgeb. trieste.it. Automated searching of SBASE with BLAST can be carried out with the WWW servers http://www.icgeb.trieste.it/sbase/and http://sbase.abc.hu/sbase/     

The SBASE protein domain library, release 8.0: a collection of annotated protein sequence segments

SBASE 8.0 is the eighth release of the SBASE library of protein domain sequences that contains 294 898 annotated structural, functional, ligand-binding and topogenic segments of proteins, cross-referenced to most major sequence databases and sequence pattern collections. The entries are clustered into over 2005 statistically validated domain groups (SBASE-A) and 595 non-validated groups (SBASE-B), provided with several WWW-based search and browsing facilities for online use. A domain-search facility was developed, based on non-parametric pattern recognition methods, including artificial neural networks. SBASE 8.0 is freely available by anonymous 'ftp' file transfer from ftp.icgeb.trieste.it. Automated searching of SBASE can be carried out with the WWW servers http://www.icgeb.trieste.it/sbase/ and http://sbase.abc. hu/sbase/.     

PipeOnline 2.0: automated EST processing and functional data sorting

Expressed sequence tags (ESTs) are generated and deposited in the public domain, as redundant, unannotated, single-pass reactions, with virtually no biological content. PipeOnline automatically analyses and transforms large collections of raw DNA-sequence data from chromatograms or FASTA files by calling the quality of bases, screening and removing vector sequences, assembling and rewriting consensus sequences of redundant input files into a unigene EST data set and finally through translation, amino acid sequence similarity searches, annotation of public databases and functional data. PipeOnline generates an annotated database, retaining the processed unigene sequence, clone/file history, alignments with similar sequences, and proposed functional classification, if available. Functional annotation is automatic and based on a novel method that relies on homology of amino acid sequence multiplicity within GenBank records. Records are examined through a function ordered browser or keyword queries with automated export of results. PipeOnline offers customization for individual projects (MyPipeOnline), automated updating and alert service. PipeOnline is available at http://stress-genomics.org.     

The SBASE protein domain library, release 5.0: a collection of annotated protein sequence segments.

SBASE 5.0 is the fifth release of SBASE, a collection of annotated protein domain sequences that represent various structural, functional, ligand-binding and topogenic segments of proteins. SBASE was designed to facilitate the detection of functional homologies and can be searched with standard database-search programs. The present release contains over 79863 entries provided with standardized names and is cross-referenced to all major sequence databases and sequence pattern collections. The information is assigned to individual domains rather than to entire protein sequences, thus SBASE contains substantially more cross-references and links than do the protein sequence databases. The entries are clustered into >16 000 groups in order to facilitate the detection of distant similarities. SBASE 5.0 is freely available by anonymous 'ftp' file transfer from <ftp.icgeb.trieste.it >. Automated searching of SBASE with BLAST can be carried out with the WWW-server <http://www.icgeb.trieste.it/sbase/ >. and with the electronic mail server <sbase@icgeb.trieste.it >which now also provides a graphic representation of the homologies. A related WWW-server <http://www.abc.hu/blast.html > and e-mail server <domain@hubi.abc.hu > predicts SBASE domain homologies on the basis of SWISS-PROT searches.     

The SBASE protein domain library, release 2.0: a collection of annotated protein sequence segments.

SBASE 2.0 is the second release of SBASE, a collection of annotated protein domain sequences. SBASE entries represent various structural, functional, ligand-binding and topogenic segments of proteins [Pongor, S. et al. (1993) Prot. Eng., in press]. This release contains 34,518 entries provided with standardized names and it is cross-referenced to the major protein and nucleic acid databanks as well as to the PROSITE catalog of protein sequence patterns [Bairoch, A. (1992) Nucl. Acids Res., 20 suppl, 2013-2018]. SBASE can be used for establishing domain homologies using different database-search tools such as FASTA [Lipman and Pearson (1985) Science, 227, 1436-1441], FASTDB [Brutlag et al. (1990) Comp. Appl. Biosci., 6, 237-245] or BLAST3 [Altschul and Lipman (1990) Proc. Natl. Acad. Sci. USA, 87, 5509-5513] which is especially useful in the case of loosely defined domain types for which efficient consensus patterns can not be established. SBASE 2.0 and a set of search and retrieval tools are freely available on request to the authors or by anonymous 'ftp' file transfer from mean value of ftp.icgeb.trieste.it.     

The SBASE protein domain library, release 3.0: a collection of annotated protein sequence segments.

SBASE 3.0 is the third release of SBASE, a collection of annotated protein domain sequences. SBASE entries represent various structural, functional, ligand-binding and topogenic segments of proteins as defined by their publishing authors. SBASE can be used for establishing domain homologies using different database-search tools such as FASTA [Lipman and Pearson (1985) Science, 227, 1436-1441], and BLAST3 [Altschul and Lipman (1990) Proc. Natl. Acad. Sci. USA, 87, 5509-5513] which is especially useful in the case of loosely defined domain types for which efficient consensus patterns can not be established. The present release contains 41,749 entries provided with standardized names and cross-referenced to the major protein and nucleic acid databanks as well as to the PROSITE catalogue of protein sequence patterns. The entries are clustered into 2285 groups using the BLAST algorithm for computing similarity measures. SBASE 3.0 is freely available on request to the authors or by anonymous 'ftp' file transfer from < ftp.icgeb.trieste.it >. Individual records can be retrieved with the gopher server at < icgeb.trieste.it > and with a www-server at < http:@www.icgeb.trieste.it >. Automated searching of SBASE by BLAST can be carried out with the electronic mail server < sbase@icgeb.trieste.it >. Another mail server < domain@hubi.abc.hu > assigns SBASE domain homologies on the basis of SWISS-PROT searches. A comparison of pertinent search strategies is presented.     

Sequence patterns derived from the automated prediction of functional residues in structurally-aligned homologous protein families

MOTIVATION: Most proteins have evolved to perform specific functions that are dependent on the adoption of well-defined three-dimensional (3D) structures. Specific patterns of conserved residues in amino acid sequences of divergently evolved proteins are frequently observed; these may reflect evolutionary restraints arising both from the need to maintain tertiary structure and the requirement to conserve residues more directly involved in function. Databases of such sequence patterns are valuable in identifying distant homologues, in predicting function and in the study of evolution. RESULTS: A fully automated database of protein sequence patterns, Functional Protein Sequence Pattern Database (FPSPD), has been derived from the analysis of the conserved residues that are predicted to be functional in structurally aligned homologous families in the HOMSTRAD database. Environment-dependent substitution tables, evolutionary trace analysis, solvent accessibility calculations and 3D-structures were used to obtain the FPSPD. The method yielded 3584 patterns that are considered functional and 3049 patterns that are probably functional. FPSPD could be useful for assigning a protein to a homologous superfamily and thereby providing clues about function. AVAILABILITY: FPSPD is available at http://www-cryst.bioc.cam.ac.uk/~fpspd/     

Support-vector-machine classification of linear functional motifs in proteins

Our algorithm predicts short linear functional motifs in proteins using only sequence information. Statistical models for short linear functional motifs in proteins are built using the database of short sequence fragments taken from proteins in the current release of the Swiss-Prot database. Those segments are confirmed by experiments to have single-residue post-translational modification. The sensitivities of the classification for various types of short linear motifs are in the range of 70%. The query protein sequence is dissected into short overlapping fragments. All segments are represented as vectors. Each vector is then classified by a machine learning algorithm (Support Vector Machine) as potentially modifiable or not. The resulting list of plausible post-translational sites in the query protein is returned to the user. We also present a study of the human protein kinase C family as a biological application of our method.     

Prediction of subcellular protein localization based on functional domain composition

Assigning subcellular localization (SL) to proteins is one of the major tasks of functional proteomics. Despite the impressive technical advances of the past decades, it is still time-consuming and laborious to experimentally determine SL on a high throughput scale. Thus, computational predictions are the preferred method for large-scale assignment of protein SL, and if appropriate, followed up by experimental studies. In this report, using a machine learning approach, the Nearest Neighbor Algorithm (NNA), we developed a prediction system for protein SL in which we incorporated a protein functional domain profile. The overall accuracy achieved by this system is 93.96%. Furthermore, comparisons with other methods have been conducted to demonstrate the validity and efficiency of our prediction system. We also provide an implementation of our Subcellular Location Prediction System (SLPS), which is available at http://pcal.biosino.org.     

The CATH database: an extended protein family resource for structural and functional genomics

The CATH database of protein domain structures (http://www.biochem.ucl.ac.uk/bsm/cath_new) currently contains 34 287 domain structures classified into 1383 superfamilies and 3285 sequence families. Each structural family is expanded with domain sequence relatives recruited from GenBank using a variety of efficient sequence search protocols and reliable thresholds. This extended resource, known as the CATH-protein family database (CATH-PFDB) contains a total of 310 000 domain sequences classified into 26 812 sequence families. New sequence search protocols have been designed, based on these intermediate sequence libraries, to allow more regular updating of the classification. Further developments include the adaptation of a recently developed method for rapid structure comparison, based on secondary structure matching, for domain boundary assignment. The philosophy behind CATHEDRAL is the recognition of recurrent folds already classified in CATH. Benchmarking of CATHEDRAL, using manually validated domain assignments, demonstrated that 43% of domains boundaries could be completely automatically assigned. This is an improvement on a previous consensus approach for which only 10-20% of domains could be reliably processed in a completely automated fashion. Since domain boundary assignment is a significant bottleneck in the classification of new structures, CATHEDRAL will also help to increase the frequency of CATH updates.     

Fold independent structural comparisons of protein-ligand binding sites for exploring functional relationships

The rapid growth in protein structural data and the emergence of structural genomics projects have increased the need for automatic structure analysis and tools for function prediction. Small molecule recognition is critical to the function of many proteins; therefore, determination of ligand binding site similarity is important for understanding ligand interactions and may allow their functional classification. Here, we present a binding sites database (SitesBase) that given a known protein-ligand binding site allows rapid retrieval of other binding sites with similar structure independent of overall sequence or fold similarity. However, each match is also annotated with sequence similarity and fold information to aid interpretation of structure and functional similarity. Similarity in ligand binding sites can indicate common binding modes and recognition of similar molecules, allowing potential inference of function for an uncharacterised protein or providing additional evidence of common function where sequence or fold similarity is already known. Alternatively, the resource can provide valuable information for detailed studies of molecular recognition including structure-based ligand design and in understanding ligand cross-reactivity. Here, we show examples of atomic similarity between superfamily or more distant fold relatives as well as between seemingly unrelated proteins. Assignment of unclassified proteins to structural superfamiles is also undertaken and in most cases substantiates assignments made using sequence similarity. Correct assignment is also possible where sequence similarity fails to find significant matches, illustrating the potential use of binding site comparisons for newly determined proteins.     

Predicting protein N-glycosylation by combining functional domain and secretion information

Protein N-glycosylation plays an important role in protein function. Yet, at present, few computational methods are available for the prediction of this protein modification. This prompted our development of a support vector machine (SVM)-based method for this task, as well as a partial least squares (PLS) regression based prediction method for comparison. A functional domain feature space was used to create SVM and PLS models, which achieved accuracies of 83.91% and 79.89%, respectively, as evaluated by a leave-one-out cross-validation. Subsequently, SVM and PLS models were developed based on functional domain and protein secretion information, which yielded accuracies of 89.13% and 86%, respectively. This analysis demonstrates that the protein functional domain and secretion information are both efficient predictors of N-glycosylation.     

Artificial protein sequences enable recognition of vicinal and distant protein functional relationships

High divergence in protein sequences makes the detection of distant protein relationships through homology-based approaches challenging. Grouping protein sequences into families, through similarities in either sequence or 3-D structure, facilitates in the improved recognition of protein relationships. In addition, strategically designed protein-like sequences have been shown to bridge distant structural domain families by serving as artificial linkers. In this study, we have augmented a search database of known protein domain families with such designed sequences, with the intention of providing functional clues to domain families of unknown structure. When assessed using representative query sequences from each family, we obtain a success rate of 94% in protein domain families of known structure. Further, we demonstrate that the augmented search space enabled fold recognition for 582 families with no structural information available a priori. Additionally, we were able to provide reliable functional relationships for 610 orphan families. We discuss the application of our method in predicting functional roles through select examples for DUF4922, DUF5131, and DUF5085. Our approach also detects new associations between families that were previously not known to be related, as demonstrated through new sub-groups of the RNA polymerase domain among three distinct RNA viruses. Taken together, designed sequences-augmented search databases direct the detection of meaningful relationships between distant protein families. In turn, they enable fold recognition and offer reliable pointers to potential functional sites that may be probed further through direct mutagenesis studies.     

Using multiple interdependency to separate functional from phylogenetic correlations in protein alignments

MOTIVATION: Multiple sequence alignments of homologous proteins are useful for inferring their phylogenetic history and to reveal functionally important regions in the proteins. Functional constraints may lead to co-variation of two or more amino acids in the sequence, such that a substitution at one site is accompanied by compensatory substitutions at another site. It is not sufficient to find the statistical correlations between sites in the alignment because these may be the result of several undetermined causes. In particular, phylogenetic clustering will lead to many strong correlations. RESULTS: A procedure is developed to detect statistical correlations stemming from functional interaction by removing the strong phylogenetic signal that leads to the correlations of each site with many others in the sequence. Our method relies upon the accuracy of the alignment but it does not require any assumptions about the phylogeny or the substitution process. The effectiveness of the method was verified using computer simulations and then applied to predict functional interactions between amino acids in the Pfam database of alignments.     

Inferring functional information from domain co-evolution

MOTIVATION: Co-evolution is a powerful mechanism for understanding protein function. Prior work in this area has shown that co-evolving proteins are more likely to share the same function than those that do not because of functional constraints. Many of the efforts founded on this observation, however, are at the level of entire sequences, implicitly assuming that the complete protein sequence follows a single evolutionary trajectory. Since it is well known that a domain can exist in various contexts, this assumption is not valid for numerous multi-domain proteins. Motivated by these observations, we introduce a novel technique called Coevolutionary-Matrix that captures co-evolution between regions of two proteins. Instead of using existing domain information, the method exploits residue-level conservation to identify co-evolving regions that might correspond to domains. RESULTS: We show that the Coevolutionary-Matrix method can detect greater number of known functional associations for the Escherichia coli proteins when compared with earlier implementations of phylogenetic profiles. Furthermore, co-evolving regions of proteins detected by our method enable us to make hypotheses about their specific functions, many of which are supported by existing biochemical studies.