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Intensive growth in 3D structure data on DNA-protein complexes as reflected in the Protein Data Bank (PDB) demands new approaches to the annotation and characterization of these data and will lead to a new understanding of critical biological processes involving these data. These data and those from other protein structure classifications will become increasingly important for the modeling of complete proteomes. We propose a fully automated classification of DNA-binding protein domains based on existing 3D-structures from the PDB. The classification, by domain, relies on the Protein Domain Parser (PDP) and the Combinatorial Extension (CE) algorithm for structural alignment. The approach involves the analysis of 3D-interaction patterns in DNA-protein interfaces, assignment of structural domains interacting with DNA, clustering of domains based on structural similarity and DNA-interacting patterns. Comparison with existing resources on describing structural and functional classifications of DNA-binding proteins was used to validate and improve the approach proposed here. In the course of our study we defined a set of criteria and heuristics allowing us to automatically build a biologically meaningful classification and define classes of functionally related protein domains. It was shown that taking into consideration interactions between protein domains and DNA considerably improves the classification accuracy. Our approach provides a high-throughput and up-to-date annotation of DNA-binding protein families which can be found at http://spdc.sdsc.edu.Contact: shindyal@sdsc.edu

Crystallographers were motivated 10 years ago to develop a simple and consistent data representation for the exchange and archiving of data associated with the crystallographic experiment and the final structure. As this process evolved (and the data grew at near exponential rates) came the recognition that this representation should also facilitate the automated management of the data and, with the aid of additional software for verification and validation, provide improved consistency and accuracy and hence improved scientific inquiry. This realization led to a new Dictionary Definition Language (DDL) and an extensive dictionary based on this DDL for describing macromolecular structure. In broad terms this could be considered an ontology. An important feature in the development of the ontology was the endorsement and ongoing maintenance and support of the International Union of Crystallography (IUCr). While the description of macromolecular structure and the x-ray crystallographic experiment used to derive it represent explicit data, the ontology is extensible and applicable to other less well-characterized data domains.

Biological databases, with their rapidly expanding contents, are indispensable tools in the quest to understand more about biological function. However, a serious user of a database that comprises a large collection of data, collected over a long period, will likely be struck by the inconsistency in reporting individual items of data. This paper takes a critical look at the Protein Data Bank (PDB) to explore the seriousness of the problem in one particular data set and to explore the implications to those actively engaged in comparative analysis of these data.

An object metamodel based on a standard scientific ontology has been developed and used to generate a CORBA interface, an SQL schema and an XML representation for macromolecular structure (MMS) data. In addition to the interface and schema definitions, the metamodel was also used to generate the core elements of a CORBA reference server and a JDBC database loader. The Java source code which implements this metamodel, the CORBA server, database loader and XML converter along with detailed documentation and code examples are available as part of the OpenMMS toolkit.

Motivation: Analysis of large biological data sets using a variety of parallel processor computer architectures is a common task in bioinformatics. The efficiency of the analysis can be significantly improved by properly handling redundancy present in these data combined with taking advantage of the unique features of these compute architectures. Results: We describe a generalized approach to this analysis, but present specific results using the program CEPAR, an efficient implementation of the Combinatorial Extension algorithm in a massively parallel (PAR) mode for finding pairwise protein structure similarities and aligning protein structures from the Protein Data Bank. CEPAR design and implementation are described and results provided for the efficiency of the algorithm when run on a large number of processors. Availability: Source code is available by contacting one of the authors.

Motivation: Assignment of putative protein functional annotation by comparative analysis using pre-defined experimental annotations is performed routinely by molecular biologists. The number and statistical significance of these assignments remains a challenge in this era of high-throughput proteomics. A combined statistical method that enables robust, automated protein annotation by reliably expanding existing annotation sets is described. An existing clustering scheme, based on relevant experimental information (e.g. sequence identity, keywords or gene expression data) is required. The method assigns new proteins to these clusters with a measure of reliability. It can also provide human reviewers with a reliability score for both new and previously classified proteins. Results: A dataset of 27 000 annotated Protein Data Bank (PDB) polypeptide chains (of 36 000 chains currently in the PDB) was generated from 23 000 chains classified a priori. Availability: PDB annotations and sample software implementation are freely accessible on the Web at http://pmr.sdsc.edu/go

Summary: BioEditor is an application to enable scientists and educators to prepare and present structure annotations containing formatted text, graphics, sequence data, and interactive molecular views. It is intended to bridge the gap between printed journal articles and Internet presentation formats. BioEditor is relevant in the era of structural genomics, where annotation and publication could become the rate determining step in structure determination. Availability: BioEditor is available at http://bioeditor.sdsc.edu. The Web site includes the latest version of the software for Microsoft Windows, including documentation, the opportunity to submit bug reports and suggestions, example documentaries prepared with BioEditor and a repository where users can submit documentaries for posting to the site. Contact: bourne@sdsc.edu * To whom correspondence should be addressed.

Motivation: To provide data management tools to maintain and query efficiently experimental and derived protein data with the goal of providing new insights into structure-function relationships. The tools should be portable, extensible, and accessible locally, or via the World Wide Web, providing data that would not otherwise be available.Results: The initial phase of the work, the data representation and query of all available macromolecular structure data, including real-time access to complex property patterns based on the amino acid sequence, is reported. Protein structure data taken from the Protein Data Bank (PDB) are decomposed into native and derived elementary properties, and represented as compact indexed objects minimizing storage requirements and query time for select types of query. In addition, collections of indices representing a particular property are maintained and can be queried for specific property patterns found across the whole database. The approach is proving applicable to a wide variety of data available on specific protein families.Availability: Three resources an available using this approach, (i) The query of basic structural components and property patterns of the complete PDB is available via the World Wide Web at the URL http://www.sdsc.edu/moose. (ii) WPDB, a PC-based compressed macromolecular structure database and loader with a Microsoft Windows inteiface, is available fivm ftp:llftpsdsc.edu/publsdsdbiologyl WPDB/. (iii) A data base supporting real-time three-dimensional substructure searching will be reported elsewhere. Source code is available by contacting the authors.Contact: E-mail:{shindyal.bourne}@sdsc.edu

PDBlib is an extensible object-oriented class library written in C++ for representing the three-dimensional structure of biological macromolecules. The software design strategy, features of many of the 129 classes currently distributed with the library, and two sample applications which use the library are described. Version 1.0 of the library represents the structural features of proteins, DNA, RNA and complexes thereof at a level of detail on a par with that which can be parsed from a Protein Data Bank (PDB) entry. However, the memor, v-resident representation of the macromolecule is independent of the PDB entr and can be obtained from other sources, e.g. relational and object orien ted databases. PDBlib classes are organized into four categories. (i) classes that model the macromolecule; (ii) classes that enhance the extensibility of the library; (iii) classes that provide navigation facilities of the object orien ted macromolecular structure representation; and (iv) a class that loads a PDB file into the memory-resident object-oriented representation. A number of general- purpose procedures that return features of this representa tion and that are relevant to all biological disciplines are included in (i). The library has been used to develop PDBtool, a prototype structure ver tool, and PDBview, a structure rendering tool that requires no specialized graphics hardware and software. Current work centers on making the macromolecular structures repre sented by PDBlib persistent using a commercial object-oriented database and providing an additional class library, MMQLlib, to query those structures.

An algorithm is described for mapping DNA contigs based on an interval graph (IG) representation. In general terms, the input to the algorithm is a set of binary overlapping relations among finite intervals spread along a real line, from which the algorithm generates sets of ordered overlapping fragments spanning that line. The implications of a more general case of the IG, called a probe interval graph (PIG), in which only a subset of cosmids are used as probes, are also discussed. In the specific case of cosmids hybridizing to regions of a YAC, the algorithm takes cross-hybridization information using the cosmids as probes, and orders them along the YAC; if gaps exist due to insufficient coverage of cosmid contigs along the length of the YAC, repetitive use of the algorithm generates sets of ordered overlapping fragments. Both the IG and the PIG can expose problems caused by false overlaps, such as hybridizations due to repetitive elements. The algorithm, has been coded in C; CPU time is essentially linear with respect to the number of cosmids analyzed. Results are presented for the application of a PIG to cosmid contig assembly along a human chromosome 13-specific YAC. An alignment of 67 cosmids spanning a YAC took 0.28 seconds of CPU time on a Convex 220 computer.