PROSITE: a documented database using patterns and profiles as motif descriptors

Among the various databases dedicated to the identification of protein families and domains, PROSITE is the first one created and has continuously evolved since. PROSITE currently consists of a large collection of biologically meaningful motifs that are described as patterns or profiles, and linked to documentation briefly describing the protein family or domain they are designed to detect. The close relationship of PROSITE with the SWISS-PROT protein database allows the evaluation of the sensitivity and specificity of the PROSITE motifs and their periodic reviewing. In return, PROSITE is used to help annotate SWISS-PROT entries. The main characteristics and the techniques of family and domain identification used by PROSITE are reviewed in this paper.     

Scan2S: increasing the precision of PROSITE pattern motifs using secondary structure constraints

Sequence signature databases such as PROSITE, which include protein pattern motifs indicative of a protein's function, are widely used for function prediction studies, cellular localization annotation, and sequence classification. Correct annotation relies on high precision of the motifs. We present a new and general approach for increasing the precision of established protein pattern motifs by including secondary structure constraints (SSCs). We use Scan2S, the first sequence motif-scanning program to optionally include SSCs, to augment PROSITE pattern motifs. The constraints were derived from either the DSSP secondary structure assignment or the PSIPRED predictions for PROSITE-documented true positive hits. The secondary structure-augmented motifs were scanned against all SwissProt sequences, for which secondary structure predictions were precalculated. Against this dataset, motifs with PSIPRED-derived SSCs exhibited improved performance over motifs with DSSP-derived constraints. The precision of 763 of the 782 PSIPRED-augmented motifs remained unchanged or increased compared to the original motifs; 26 motifs showed an absolute precision increase of 10-30%. We provide the complete set of augmented motifs and the Scan2S program at http://physiology.med.cornell.edu/go/scan2s. Our results suggest a general protocol for increasing the precision of protein pattern detection via the inclusion of SSCs.     

PROMOT: a FORTRAN program to scan protein sequences against a library of known motifs

Information about the three-dimensional structure or functionof a newly determined protein sequence can be obtained if theprotein is found to contain a characterized motif or patternof residues. Recently a database (PROSITE) has been establishedthat contains 337 known motifs encoded as a list of allowedresidue types at specific positions along the sequence. PROMOTis a FORTRAN computer program that takes a protein sequenceand examines if it contains any of the motifs in PROSITE. Theprogram also extends the definitions of patterns beyond thoseused in PROSITE to provide a simple, yet flexible, method toscan either a PROSITE or a user-defined pattern against a proteinsequence database. Received on October 17, 1990; accepted on November 15, 1990     

Identification of function-associated loop motifs and application to protein function prediction

MOTIVATION: The detection of function-related local 3D-motifs in protein structures can provide insights towards protein function in absence of sequence or fold similarity. Protein loops are known to play important roles in protein function and several loop classifications have been described, but the automated identification of putative functional 3D-motifs in such classifications has not yet been addressed. This identification can be used on sequence annotations. RESULTS: We evaluated three different scoring methods for their ability to identify known motifs from the PROSITE database in ArchDB. More than 500 new putative function-related motifs not reported in PROSITE were identified. Sequence patterns derived from these motifs were especially useful at predicting precise annotations. The number of reliable sequence annotations could be increased up to 100% with respect to standard BLAST. CONTACT: boliva@imim.es SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.     

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/.     

XFINGER: A tool for searching and visualising protein fingerprints and patterns

A tool for searching pattern and fingerprint databases is described.Fingerprints are groups of motifs excised from conserved regionsof sequence alignments and used for iterative database scanning.The constituent motifs are thus encoded as small alignmentsin which sequence information is maximised with each databasepass; they therefore differ from regular-expression patterns,in which alignments are reduced to single consensus sequences.Different database formats have evolved to store these disparatetypes of information, namely the PROSITE dictionary of patternsand the PRINTS fingerprint database, but programs have not beenavailable with the flexibility to search them both. We havedeveloped a facility to do this: the system allows query sequencesto be scanned against either PROSITE, the full PRINTS database,or against individual fingerprints. The results of fingerprintsearches are displayed simultaneously in both text and graphicalwindows to render them more tangible to the user. Where structuralcoordinates are available, identified motifs may be visualisedin a 3D context. The program runs on Silicon Graphics machinesusing GL graphics libraries and on machines with X servers supportingthe PEX extension: its use is illustrated here by depictingthe location of low-density lipoprotein-binding (LDL) motifsand leucine-rich repeats in a mosaic G-protein-coupled receptor(GPCR).     

Construction and analysis of a profile library characterizing groups of structurally known proteins.

A new sequence motif library StrProf was constructed characterizing the groups of related proteins in the PDB three-dimensional structure database. For a representative member of each protein family, which was identified by cross-referencing the PDB with the PIR superfamily classification, a group of related sequences was collected by the BLAST search against the nonredundant protein sequence database. For every group, the motifs were identified automatically according to the criteria of conservation and uniqueness of pentapeptide patterns and with a dual dynamic programming algorithm. In the StrProf library, motifs are represented by profile matrices rather than consensus patterns to allow more flexible search capabilities. Another dynamic programming algorithm was then developed to search this motif library. When the computationally derived StrProf was compared with PROSITE, which is a manually derived motif library in the best consensus pattern representation, the numbers of identified patterns were comparable. StrProf missed about one third of the PROSITE motifs, but there were also new motifs lacking in PROSITE. The new library was incorporated in SMART (Sequence Motif Analysis and Retrieval Tool), a computer tool designed to help search and annotate biologically important sites in an unknown protein sequence. The client program is available free of charge through the Internet.     

Finding flexible patterns in unaligned protein sequences.

We present a new method for the identification of conserved patterns in a set of unaligned related protein sequences. It is able to discover patterns of a quite general form, allowing for both ambiguous positions and for variable length wildcard regions. It allows the user to define a class of patterns (e.g., the degree of ambiguity allowed and the length and number of gaps), and the method is then guaranteed to find the conserved patterns in this class scoring highest according to a significance measure defined. Identified patterns may be refined using one of two new algorithms. We present a new (nonstatistical) significance measure for flexible patterns. The method is shown to recover known motifs for PROSITE families and is also applied to some recently described families from the literature.     

iGibbs: improving Gibbs motif sampler for proteins by sequence clustering and iterative pattern sampling

The motif prediction problem is to predict short, conserved subsequences that are part of a family of sequences, and it is a very important biological problem. Gibbs is one of the first successful motif algorithms and it runs very fast compared with other algorithms, and its search behavior is based on the well-studied Gibbs random sampling. However, motif prediction is a very difficult problem and Gibbs may not predict true motifs in some cases. Thus, the authors explored a possibility of improving the prediction accuracy of Gibbs while retaining its fast runtime performance. In this paper, the authors considered Gibbs only for proteins, not for DNA binding sites. The authors have developed iGibbs, an integrated motif search framework for proteins that employs two previous techniques of their own: one for guiding motif search by clustering sequences and another by pattern refinement. These two techniques are combined to a new double clustering approach to guiding motif search. The unique feature of their framework is that users do not have to specify the number of motifs to be predicted when motifs occur in different subsets of the input sequences since it automatically clusters input sequences into clusters and predict motifs from the clusters. Tests on the PROSITE database show that their framework improved the prediction accuracy of Gibbs significantly. Compared with more exhaustive search methods like MEME, iGibbs predicted motifs more accurately and runs one order of magnitude faster.     

DOMPLOT: a program to generate schematic diagrams of the structural domain organization within proteins, annotated by ligand contacts.

A program is described for automatically generating schematic linear representations of protein chains in terms of their structural domains. The program requires the co-ordinates of the chain, the domain assignment, PROSITE information and a file listing all intermolecular interactions in the protein structure. The output is a PostScript file in which each protein is represented by a set of linked boxes, each box corresponding to all or part of a structural domain. PROSITE motifs and residues involved in ligand interactions are highlighted. The diagrams allow immediate visualization of the domain arrangement within a protein chain, and by providing information on sequence motifs, and metal ion, ligand and DNA binding at the domain level, the program facilitates detection of remote evolutionary relationships between proteins.     

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.     

Analysis of structural motifs of proteins using sets of codes, describing 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 achieved by their 3D structures. Specific local structures, called structural motifs, are considered 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.     

Discovery of local packing motifs in protein structures

We present a language for describing structural patterns of residues in protein structures and a method for the discovery of such patterns that recur in a set of protein structures. The patterns impose restrictions on the spatial position of each residue, their order along the amino acid chain, and which amino acids are allowed in each position. Unlike other methods for comparing sets of protein structures, our method is not based on the use of pairwise structure comparisons which is often time consuming and can produce inconsistent results. Instead, the method simultaneously takes into account information from all structures in the search for conserved structure patterns which are potential structure motifs. The method is based on describing the spatial neighborhoods of each residue in each structure as a string and applying a sequence pattern discovery method to find patterns common to subsets of these strings. Finally it is checked whether the similarities between the neighborhood strings correspond to spatially similar substructures. We apply the method to analyze sets of very disparate proteins from the four different protein families: serine proteases, cuprodoxins, cysteine proteinases, and ferredoxins. The motifs found by the method correspond well to the site and motif information given in the annotation of these proteins in PDB, Swiss-Prot, and PROSITE. Furthermore, the motifs are confirmed by using the motif data to constrain the structural alignment of the proteins obtained with the program SAP. This gave the best superposition/alignment of the proteins given the motif assignment.     

HyPaLib: a database of RNAs and RNA structural elements defined by hybrid patterns

The database, called HyPaLib (for Hybrid Pattern Library), contains annotated structural elements characteristic for certain classes of structural and/or functional RNAs. These elements are described in a language specifically designed for this purpose. The language allows convenient specification of hybrid patterns, i.e. motifs consisting of sequence features and structural elements together with sequence similarity and thermodynamic constraints. We are currently developing software tools that allow a user to search sequence databases for any pattern in HyPaLib, thus providing functionality which is similar to PROSITE, but dedicated to the more complex patterns in RNA sequences. HyPaLib is available at http://bibiserv. techfak.uni-bielefeld.de/HyPa/.     

Assessing the exceptionality of network motifs.

Getting and analyzing biological interaction networks is at the core of systems biology. To help understanding these complex networks, many recent works have suggested to focus on motifs which occur more frequently than expected in random. To identify such exceptional motifs in a given network, we propose a statistical and analytical method which does not require any simulation. For this, we first provide an analytical expression of the mean and variance of the count under any exchangeable random graph model. Then we approximate the motif count distribution by a compound Poisson distribution whose parameters are derived from the mean and variance of the count. Thanks to simulations, we show that the compound Poisson approximation outperforms the Gaussian approximation. The compound Poisson distribution can then be used to get an approximate p-value and to decide if an observed count is significantly high or not. Our methodology is applied on protein-protein interaction (PPI) networks, and statistical issues related to exceptional motif detection are discussed.     

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     

Comparative Analysis of Evolutionarily Conserved Motifs of Epidermal Growth Factor Receptor 2 (HER2) Predicts Novel Potential Therapeutic Epitopes

Overexpression of human epidermal growth factor receptor 2 (HER2) is associated with tumor aggressiveness and poor prognosis in breast cancer. With the availability of therapeutic antibodies against HER2, great strides have been made in the clinical management of HER2 overexpressing breast cancer. However, de novo and acquired resistance to these antibodies presents a serious limitation to successful HER2 targeting treatment. The identification of novel epitopes of HER2 that can be used for functional/region-specific blockade could represent a central step in the development of new clinically relevant anti-HER2 antibodies. In the present study, we present a novel computational approach as an auxiliary tool for identification of novel HER2 epitopes. We hypothesized that the structurally and linearly evolutionarily conserved motifs of the extracellular domain of HER2 (ECD HER2) contain potential druggable epitopes/targets. We employed the PROSITE Scan to detect structurally conserved motifs and PRINTS to search for linearly conserved motifs of ECD HER2. We found that the epitopes recognized by trastuzumab and pertuzumab are located in the predicted conserved motifs of ECD HER2, supporting our initial hypothesis. Considering that structurally and linearly conserved motifs can provide functional specific configurations, we propose that by comparing the two types of conserved motifs, additional druggable epitopes/targets in the ECD HER2 protein can be identified, which can be further modified for potential therapeutic application. Thus, this novel computational process for predicting or searching for potential epitopes or key target sites may contribute to epitope-based vaccine and function-selected drug design, especially when x-ray crystal structure protein data is not available.     

Metabolic pathway reconstruction of eugenol to vanillin bioconversion in Aspergillus niger

Identification of missing genes or proteins participating in the metabolic pathways as enzymes are of great interest. One such class of pathway is involved in the eugenol to vanillin bioconversion. Our goal is to develop an integral approach for identifying the topology of a reference or known pathway in other organism. We successfully identify the missing enzymes and then reconstruct the vanillin biosynthetic pathway in Aspergillus niger. The procedure combines enzyme sequence similarity searched through BLAST homology search and orthologs detection through COG & KEGG databases. Conservation of protein domains and motifs was searched through CDD, PFAM & PROSITE databases. Predictions regarding how proteins act in pathway were validated experimentally and also compared with reported data. The bioconversion of vanillin was screened on UV-TLC plates and later confirmed through GC and GC-MS techniques. We applied a procedure for identifying missing enzymes on the basis of conserved functional motifs and later reconstruct the metabolic pathway in target organism. Using the vanillin biosynthetic pathway of Pseudomonas fluorescens as a case study, we indicate how this approach can be used to reconstruct the reference pathway in A. niger and later results were experimentally validated through chromatography and spectroscopy techniques.     

ScanProsite: a reference implementation of a PROSITE scanning tool

Many different software tools are available publicly to scan the PROSITE database of protein families. However, none of them, to our knowledge, wholly implements the PROSITE syntax, or satisfies all the rules for scanning a pattern against a sequence. We hereby propose a strict definition of how a PROSITE pattern is to be scanned against a sequence, and provide a reference implementation of a tool to scan PROSITE patterns, rules and profiles against protein sequences.