PROSITE Patterns Research Articles

Development of computational tools for the inference of protein interaction specificity rules and functional annotation using structural information

Relatively few protein structures are known, compared to the enormous amount of sequence data produced in the sequencing of different genomes, and relatively few protein complexes are deposited in the PDB with respect to the great amount of interaction data coming from high-throughput experiments (two-hybrid or affinity purification of protein complexes and mass spectrometry). Nevertheless, we can rely on computational techniques for the extraction of high-quality and information-rich data from the known structures and for their spreading in the protein sequence space. We describe here the ongoing research projects in our group: we analyse the protein complexes stored in the PDB and, for each complex involving one domain belonging to a family of interaction domains for which some interaction data are available, we can calculate its probability of interaction with any protein sequence. We analyse the structures of proteins encoding a function specified in a PROSITE pattern, which exhibits relatively low selectivity and specificity, and build extended patterns. To this aim, we consider residues that are well-conserved in the structure, even if their conservation cannot easily be recognized in the sequence alignment of the proteins holding the function. We also analyse protein surface regions and, through the annotation of the solvent-exposed residues, we annotate protein surface patches via a structural comparison performed with stringent parameters and independently of the residue order in the sequence. Local surface comparison may also help in identifying new sequence patterns, which could not be highlighted with other sequence-based methods.

3MATRIX and 3MOTIF: a protein structure visualization system for conserved sequence motifs.

Computational methods such as sequence alignment and motif construction are useful in grouping related proteins into families, as well as helping to annotate new proteins of unknown function. These methods identify conserved amino acids in protein sequences, but cannot determine the specific functional or structural roles of conserved amino acids without additional study. In this work, we present 3MATRIX (http://3matrix.stanford.edu) and 3MOTIF (http://3motif.stanford.edu), a web-based sequence motif visualization system that displays sequence motif information in its appropriate three-dimensional (3D) context. This system is flexible in that users can enter sequences, keywords, structures or sequence motifs to generate visualizations. In 3MOTIF, users can search using discrete sequence motifs such as PROSITE patterns, eMOTIFs, or any other regular expression-like motif. Similarly, 3MATRIX accepts an eMATRIX position-specific scoring matrix, or will convert a multiple sequence alignment block into an eMATRIX for visualization. Each query motif is used to search the protein structure database for matches, in which the motif is then visually highlighted in three dimensions. Important properties of motifs such as sequence conservation and solvent accessible surface area are also displayed in the visualizations, using carefully chosen color shading schemes.

Role of context in the relationship between form and function: structural plasticity of some PROSITE patterns

True positive hits of PROSITE sequence pattern are expected to have a characteristic three-dimensional structure. The combined sequence–structure attributes of PROSITE patterns can be used for function prediction of an uncharacterized protein with known primary and 3D structure, a situation that might arise in structural genomics projects. We have found specific examples of true hits of PROSITE patterns displaying structural plasticity by assuming significantly different local conformation, depending upon the context. Our work highlights the importance of taking into account all the known distinct conformations of PROSITE patterns, while creating a sensitive 3D template for the pattern, for use in functional annotation.

Regular biosequence pattern matching with cellular automata

Could algorithms designed within the computational model of cellular automata fit well into a future biocomputing environment where data and computation are seamlessly and dynamically distributed? We suggest it is possible by presenting a systematic approach for creating 1D linear cellular automata that in parallel can locate all starting positions of complete matches to a given PROSITE pattern in a string. The cellular automaton requires time proportional to the maximal length of a pattern match, and is inherently suited for distribution out on multiple processors.

Pattern searches for the identification of putative lipoprotein genes in Gram-positive bacterial genomes.

N-terminal lipidation is a major mechanism by which bacteria can tether proteins to membranes and one which is of particular importance to Gram-positive bacteria due to the absence of a retentive outer membrane. Lipidation is directed by the presence of a cysteine-containing 'lipobox' within the lipoprotein signal peptide sequence and this feature has greatly facilitated the identification of putative lipoproteins by gene sequence analysis. The properties of lipoprotein signal peptides have been described previously by the Prosite pattern PS00013. Here, a dataset of 33 experimentally verified Gram-positive bacterial lipoproteins (excluding those from Mollicutes) has been identified by an extensive literature review. The signal peptide features of these lipoproteins have been analysed to create a refined pattern, G+LPP, which is more specific for the identification of Gram-positive bacterial lipoproteins. The ability of this pattern to identify probable lipoprotein sequences is demonstrated by a search of the genome of Streptococcus pyogenes, in comparison with sequences identified using PS00013. Greater discrimination against likely false-positives was evident from the use of G+LPP compared with PS00013. These data confirm the likely abundance of lipoproteins in Gram-positive bacterial genomes, with at least 25 probable lipoproteins identified in S. pyogenes

Singular value decomposition analysis of protein sequence alignment score data.

One of the standard tools for the analysis of data arranged in matrix form is singular value decomposition (SVD). Few applications to genomic data have been reported to date mainly for the analysis of gene expression microarray data. We review SVD properties, examine mathematical terms and assumptions implicit in the SVD formalism, and show that SVD can be applied to the analysis of matrices representing pairwise alignment scores between large sets of protein sequences. In particular, we illustrate SVD capabilities for data dimension reduction and for clustering protein sequences. A comparison is performed between SVD-generated clusters of proteins and annotation reported in the SWISS-PROT Database for a set of protein sequences forming the calycin superfamily, entailing all entries corresponding to the lipocalin, cytosolic fatty acid-binding protein, and avidin-streptavidin Prosite patterns.

A Web-based classification system of DNA-binding protein families.

Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The family of DNA-binding proteins is one of the most populated and studied amongst the various genomes of bacteria, archaea and eukaryotes and the Web-based system presented here is an approach to their classification. The DnaProt resource is an annotated and searchable collection of protein sequences for the families of DNA-binding proteins. The database contains 3238 full-length sequences (retrieved from the SWISS-PROT database, release 38) that include, at least, a DNA-binding domain. Sequence entries are organized into families defined by PROSITE patterns, PRINTS motifs and de novo excised signatures. Combining global similarities and functional motifs into a single classification scheme, DNA-binding proteins are classified into 33 unique classes, which helps to reveal comprehensive family relationships. To maximize family information retrieval, DnaProt contains a collection of multiple alignments for each DNA-binding family while the recognized motifs can be used as diagnostically functional fingerprints. All available structural class representatives have been referenced. The resource was developed as a Web-based management system for online free access of customized data sets. Entries are fully hyperlinked to facilitate easy retrieval of the original records from the source databases while functional and phylogenetic annotation will be applied to newly sequenced genomes. The database is freely available for online search of a library containing specific patterns of the identified DNA-binding protein classes and retrieval of individual entries from our WWW server (http://kronos.biol.uoa.gr/~mariak/dbDNA.html).

1P030PROSITEパターン近傍の局所構造の解析

1P030PROSITEパターン近傍の局所構造の解析

Analysis of protein structural motifs in terms of sets of codes representing local structures

An amino acid sequence pattern conserved among a family of proteins is called motif. It is usually related to the specific function of the family. On the other hand, functions of proteins are realized through their 3D structures. Specific local structures, called structural motifs, are considered as related to their functions. However, searching for common structural motifs in different proteins is much more difficult than for common sequence motifs. We are attempting in this study to convert the information about the structural motifs into a set of one-dimensional digital strings, i.e., a set of codes, to compare them more easily by computer and to investigate their relationship to functions more quantitatively. By applying the Delaunay tessellation to a 3D structure of a protein, we can assign each local structure to a unique code that is defined so as to reflect its structural feature. Since a structural motif is defined as a set of the local structures in this paper, the structural motif is represented by a set of the codes. In order to examine the ability of the set of the codes to distinguish differences among the sets of local structures with a given PROSITE pattern that contain both true and false positives, we clustered them by introducing a similarity measure among the set of the codes. The obtained clustering shows a good agreement with other results by direct structural comparison methods such as a superposition method. The structural motifs in homologous proteins are also properly clustered according to their sources. These results suggest that the structural motifs can be well characterized by these sets of the codes, and that the method can be utilized in comparing structural motifs and relating them with function.

PDBsum: summaries and analyses of PDB structures

PDBsum is a web-based database providing a largely pictorial summary of the key information on each macromolecular structure deposited at the Protein Data Bank (PDB). It includes images of the structure, annotated plots of each protein chain's secondary structure, detailed structural analyses generated by the PROMOTIF program, summary PROCHECK results and schematic diagrams of protein-ligand and protein-DNA interactions. RasMol scripts highlight key aspects of the structure, such as the protein's domains, PROSITE patterns and protein-ligand interactions, for interactive viewing in 3D. Numerous links take the user to related sites. PDBsum is updated whenever any new structures are released by the PDB and is freely accessible via http://www.biochem.ucl.ac.uk/bsm/pdbsum.

HUNT: launch of a full-length cDNA database from the Helix Research Institute.

The Helix Research Institute (HRI) in Japan is releasing 4356 HUman Novel Transcripts and related information in the newly established HUNT database. The institute is a joint research project principally funded by the Japanese Ministry of International Trade and Industry, and the clones were sequenced in the governmental New Energy and Industrial Technology Development Organization (NEDO) Human cDNA Sequencing Project. The HUNT database contains an extensive amount of annotation from advanced analysis and represents an essential bioinformatics contribution towards understanding of the gene function. The HRI human cDNA clones were obtained from full-length enriched cDNA libraries constructed with the oligo-capping method and have resulted in novel full-length cDNA sequences. A large fraction has little similarity to any proteins of known function and to obtain clues about possible function we have developed original analysis procedures. Any putative function deduced here can be validated or refuted by complementary analysis results. The user can also extract information from specific categories like PROSITE patterns, PFAM domains, PSORT localization, transmembrane helices and clones with GENIUS structure assignments. The HUNT database can be accessed at http://www.hri.co.jp/HUNT.

Searching the Protein Structure Databank with Weak Sequence Patterns and Structural Constraints

A method is described in which proteins that match PROSITE patterns are filtered by the root-mean-square deviation of the local 3D structures of the probe and target over the pattern components. This was found to increase the discrimination between true and false members of the protein family but was dependent on how unique the structural features in the pattern were compared to equivalent fragments extracted from the structure databank (for example; if the pattern fell in an α-helix, then discrimination was poor.) We then generalised the sequence patterns (by widening the range of amino acid residues allowed at each position) and monitored how well the structural information helped retain specificity. While the discrimination of the pure sequence pattern had generally disappeared at information content values less than ten bits, the discrimination of the combined sequence structure probe remained high at this point before following a similar decay. The displacement between these curves indicates that the structural component is, on average, equivalent to about ten bits. The sequence patterns were also filtered using the structure comparison program SAP, giving a global, rather than local “view” of the proteins. This allowed the information content of the sequence patterns to become even less specific but raised problems of whether some proteins encountered with the same fold but no PROSITE pattern should constitute family members.

Systematic and fully automated identification of protein sequence patterns.

We present an efficient algorithm to systematically and automatically identify patterns in protein sequence families. The procedure is based on the Splash deterministic pattern discovery algorithm and on a framework to assess the statistical significance of patterns. We demonstrate its application to the fully automated discovery of patterns in 974 PROSITE families (the complete subset of PROSITE families which are defined by patterns and contain DR records). Splash generates patterns with better specificity and undiminished sensitivity, or vice versa, in 28% of the families; identical statistics were obtained in 48% of the families, worse statistics in 15%, and mixed behavior in the remaining 9%. In about 75% of the cases, Splash patterns identify sequence sites that overlap more than 50% with the corresponding PROSITE pattern. The procedure is sufficiently rapid to enable its use for daily curation of existing motif and profile databases. Third, our results show that the statistical significance of discovered patterns correlates well with their biological significance. The trypsin subfamily of serine proteases is used to illustrate this method's ability to exhaustively discover all motifs in a family that are statistically and biologically significant. Finally, we discuss applications of sequence patterns to multiple sequence alignment and the training of more sensitive score-based motif models, akin to the procedure used by PSI-BLAST. All results are available at httpl//www.research.ibm.com/spat/.

Conformational analysis of long spacers in PROSITE patterns

To determine if variable sequences (spacers) between conserved positions in a sequence motif or pattern share a consensus structure, three-dimensional structures containing PROSITE patterns with spacers of fixed length greater than three residues were analyzed. Structural similarities of a given pattern were evaluated by computing the backbone φ, ψ and side-chain χ1 dihedral order parameters. The exact bias information in analyzing the conformational variability of the patterns was taken into account by introducing a new parameter, the bias coefficient, which describes the number and distribution of residue types found at each position of a pattern in the structures. The results of the analyses show that backbone conformational heterogeneity at a given position in a sequence motif does not necessarily correlate with the residue-type variability at that position, and the long spacer region can adopt a well-defined backbone conformation, in addition to the conserved residues. Furthermore, a PROSITE pattern may be redefined to yield two or more “refined” regular expressions, each corresponding to a distinct backbone conformation. A way in which the observed structural consensus in a pattern may be employed to improve the accuracy of function prediction from sequence is suggested.

ProClass protein family database.

ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PIR superfamilies and PROSITE patterns. By combining global similarities and functional motifs into a single classification scheme, ProClass helps to reveal domain and family relationships and classify multi-domain proteins. The database currently consists of >155 000 sequence entries retrieved from both PIR-International and SWISS-PROT databases. Approximately 92 000 or 60% of the ProClass entries are classified into approximately 6000 families, including a large number of new members detected by our GeneFIND family identification system. The ProClass motif collection contains approximately 72 000 motif sequences and >1300 multiple alignments for all PROSITE patterns, including >21 000 matches not listed in PROSITE and mostly detected from unique PIR sequences. To maximize family information retrieval, the database provides links to various protein family, domain, alignment and structural class databases. With its high classification rate and comprehensive family relationships, ProClass can be used to support full-scale genomic annotation. The database, now being implemented in an object-relational database management system, is available for online sequence search and record retrieval from our WWW server at http://pir.georgetown.edu/gfserver/proclass.html

Three-dimensional structure analysis of PROSITE patterns

Pattern matches for each of the sequence patterns in PROSITE, a database of sequence patterns, were searched in all protein sequences in the Brookhaven Protein Data Bank (PDB). The three-dimensional structures of the pattern matches for the 20 patterns with the largest numbers of hits were analysed. We found that the true positives have a common three-dimensional structure for each pattern; the structures of false positives, found for six of the 20 patterns, were clearly different from those of the true positives. The results suggest that the true pattern matches each have a characteristic common three-dimensional structure, which could be used to create a template to define a three-dimensional functional pattern.

ProClass Protein Family Database.

ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PROSITE patterns and PIR superfamilies. By combining global similarities and functional motifs into a single classification scheme, ProClass helps to reveal domain and family relationships and classify multi-domain proteins. The database currently consists of more than 120 000 sequence entries, approximately 60% of which is classified into about 3500 families. To maximize family information retrieval, the database provides links to various protein family/domain and structural class databases and contains multiple motif alignments of all PROSITE patterns as well as global alignments of PIR superfamilies. The motif sequences are retrieved from both PIR-International and SWISS-PROT databases, including a large number of new members detected by our GeneFIND family identification system. ProClass can be used to support full-scale genomic annotation, because of its high classification rate. The ProClass database is available for on-line search and record retrieval from our WWW server at http://diana.uthct.edu/proclass.html

Variations of the C2H2 zinc finger motif in the yeast genome and classification of yeast zinc finger proteins.

The PROSITE pattern Zinc_Finger_C2H2 was extended to permit the detection of all C2H2 zinc fingers and their parent proteins in the recently completed sequence of the yeast genome. Additionally, a new computer program was written that extracts other zinc binding motifs (non C2H2 'fingers'), overlapping with the classical zinc finger pattern, from the found set of yeast C2H2 fingers. The complete and correct detection of all fingers is a prerequisite for the classification of the yeast zinc finger proteins in functional terms. The detected 53 yeast C2H2 zinc finger proteins do not contain finger clusters with 10 or more repeats, as is frequently found in higher eukaryotes. Only three proteins contain four or more fingers in a cluster. Moreover, nearly all 27 yeast proteins with tandem arrays of two or three finger domains can be classified into nine subgroups with high sequence conservation in their finger clusters, in particular of their DNA recognition helices. These results and application of the recently elaborated finger/DNA recognition rules suggest that the yeast proteins belonging to the same subgroup may recognize identical or very similar DNA sites.

A protein class database organized with ProSite protein groups and PIR superfamilies.

A protein class (ProClass) database is developed as a "value-added" "second-generation" database organized according to family relationships. The database collects non-redundant protein sequence entries from SwissProt and PIR databases, and classifies them in families defined collectively by the ProSite protein groups and PIR superfamilies. The major objectives of the database are to maximize family information retrieval, to provide speedy family identification, and to help organizing existing protein sequence databases. The database has two sub-databases: PCFam (ProClass Family) to define protein families and provide links to ProSite patterns and PIR superfamilies, and PCSeq (ProClass Sequence) to describe sequence entries and provide links to PCFam, SwissProt, PIR, and ProSite databases. The current ProClass release has a total of 85,165 sequence entries, about half of which are classified in 3072 ProClass families; it also contains 10,431 newly established SwissProt-PIR links. The database can help reveal domain structures of related families, define new ProSite and PIR families, and provide family assignments for unclassified sequence entries. New ProSite and PIR family members are readily identified via database cross-reference, including 9437 SwissProt entries and 8522 PIR entries. False negative family members missed by both ProSite and PIR are detected using a neural network family identification system. The newly identified superfamily memberships are being incorporated into the current PIR database releases in a collaborative effort with the PIR. The ProClass database is accessible through anonymous FTP and on-line search on the World Wide Web.

Method for calculation of probability of matching a bounded regular expression in a random data string.

A method is presented for determining within strict bounds the probability of matching a regular expression with a match start point in a given section of a random data string. The method in general requires time and space exponential in the number of optional characters in the regular expression, but in practice was used to determine bounds for probabilities of matching all the ProSite patterns without difficulty.