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Comparing and subsequently classifying protein structures information has received significant attention concurrent with the increase in the number of experimentally derived 3-dimensional structures. Classification schemes have focused on biological function found within protein domains and on structure classification based on topology. Here an alternative view is presented that groups substructures. Substructures are long (50-150 residue) highly repetitive near-contiguous pieces of polypeptide chain that occur frequently in a set of proteins from the PDB defined as structurally non-redundant over the complete polypeptide chain. The substructure classification is based on a previously reported Combinatorial Extension (CE) algorithm that provides a significantly different set of structure alignments than those previously described, having, for example, only a 40% overlap with FSSP. Qualitatively the algorithm provides longer contiguous aligned segments at the price of a slightly higher root-mean-square deviation (rmsd). Clustering these alignments gives a discreet and highly repetitive set of substructures not detectable by sequence similarity alone. In some cases different substructures represent all or different parts of well known folds indicative of the Russian doll effect--the continuity of protein fold space. In other cases they fall into different structure and functional classifications. It is too early to determine whether these newly classified substructures represent new insights into the evolution of a structural framework important to many proteins. What is apparent from on-going work is that these substructures have the potential to be useful probes in finding remote sequence homology and in structure prediction studies. The characteristics of the complete all-by-all comparison of the polypeptide chains present in the PDB and details of the filtering procedure by pair-wise structure alignment that led to the emergent substructure gallery are discussed. Substructure classification, alignments, and tools to analyze them are available at http://cl.sdsc.edu/ce.html.

An all-against-all protein structure comparison using the Combinatorial Extension (CE) algorithm applied to a representative set of PDB structures revealed a gallery of common substructures in proteins (http://cl.sdsc.edu/ce.html). These substructures represent commonly identified folds, domains, or components thereof. Most of the subsequences forming these similar substructures have no significant sequence similarity. We present a method to identify conserved amino acid positions and residue-dependent property clusters within these subsequences starting with structure alignments. Each of the subsequences is aligned to its homologues in SWALL, a nonredundant protein sequence database. The most similar sequences are purged into a common frequency matrix, and weighted homologues of each one of the subsequences are used in scoring for conserved key amino acid positions (CKAAPs). We have set the top 20% of the high-scoring positions in each substructure to be CKAAPs. It is hypothesized that CKAAPs may be responsible for the common folding patterns in either a local or global view of the protein-folding pathway. Where a significant number of structures exist, CKAAPs have also been identified in structure alignments of complete polypeptide chains from the same protein family or superfamily. Evidence to support the presence of CKAAPs comes from other computational approaches and experimental studies of mutation and protein-folding experiments, notably the Paracelsus challenge. Finally, the structural environment of CKAAPs versus non-CKAAPs is examined for solvent accessibility, hydrogen bonding, and secondary structure. The identification of CKAAPs has important implications for protein engineering, fold recognition, modeling, and structure prediction studies and is dependent on the availability of structures and an accurate structure alignment methodology. Proteins 2001;42:148–163. © 2000 Wiley-Liss, Inc.

Databases containing macromolecular structure data provide a crystallographer with important tools for use in solving, refining and understanding the functional significance of their protein structures. Given this importance, this paper briefly summarizes past progress by outlining the features of the significant number of relevant databases developed to date. One recent database, PDB+, containing all current and obsolete structures deposited with the Protein Data Bank (PDB) is discussed in more detail. PDB+ has been used to analyze the self-consistency of the current (1 January 1998) corpus of over 7000 structures. A summary of those findings is presented (a full discussion will appear elsewhere) in the form of global and temporal trends within the data. These trends indicate that challenges exist if crystallographers are to provide the community with complete and consistent structural results in the future. It is argued that better information management practices are required to meet these challenges.

WPDB version 2.0 is a Microsoft-Windows-based program for browsing and interrogating native and derived structural features of biological macromolecules using data obtained from the Protein Data Bank (PDB). Major features of WPDB are a 20-fold compression of PDB files and query and analysis tools. The latter permit the geometric and sequence properties of structures to be analyzed individually or through comparative analysis. The object-oriented software design provides a high level of interaction between display windows, which facilitates information discovery. Three examples are given to illustrate the capabilities of the software, namely : location of the distribution of the most hydrophobic residues in the acid proteases ; exploration of the geometric features of a 4-helix bundle motif ; and examination of the effect of antibody binding by comparison of a neuraminidase with a neuraminidase-antibody complex. Additional details are available to World Wide Web (WWW) users at the URL http ://www.sdsc.edu/CCMS/Packages/wpdb.html.

Macromolecular query language (MMQL) is an extensible interpretive language in which to pose questions concerning the experimental or derived features of the 3-D structure of biological macromolecules. MMQL portends to be intuitive with a simple syntax, so that from a user's perspective complex queries are easily written. A number of basic queries and a more complex query--determination of structures containing a five-strand Greek key motif--are presented to illustrate the strengths and weaknesses of the language. The predominant features of MMQL are a filter and pattern grammar which are combined to express a wide range of interesting biological queries. Filters permit the selection of object attributes, for example, compound name and resolution, whereas the patterns currently implemented query primary sequence, close contacts, hydrogen bonding, secondary structure, conformation and amino acid properties (volume, polarity, isoelectric point, hydrophobicity and different forms of exposure). MMQL queries are processed by MMQLlib; a C++ class library, to which new query methods and pattern types are easily added. The prototype implementation described uses PDBlib, another C(++)-based class library from representing the features of biological macromolecules at the level of detail parsable from a PDB file. Since PDBlib can represent data stored in relational and object-oriented databases, as well as PDB files, once these data are loaded they too can be queried by MMQL. Performance metrics are given for queries of PDB files for which all derived data are calculated at run time and compared to a preliminary version of OOPDB, a prototype object-oriented database with a schema based on a persistent version of PDBlib which offers more efficient data access and the potential to maintain derived information. MMQLlib, PDBlib and associated software are available via anonymous ftp from cuhhca.hhmi.columbia.edu.

A method has been developed to detect pairs of positions with correlated mutations in protein multiple sequence alignments. The method is based on reconstruction of the phylogenetic tree for a set of sequences and statistical analysis of the distribution of mutations in the branches of the tree. The database of homology-derived protein structures (HSSP) is used as the source of multiple sequence alignments for proteins of known three-dimensional structure. We analyse pairs of positions with correlated mutations in 67 protein families and show quantitatively that the presence of such positions is a typical feature of protein families. A significant but weak tendency is observed for correlated residue pairs to be close in the three-dimensional structure. With further improvements, methods of this type may be useful for the prediction of residue--residue contacts and subsequent prediction of protein structure using distance geometry algorithms. In conclusion, we suggest a new experimental approach to protein structure determination in which selection of functional mutants after random mutagenesis and analysis of correlated mutations provide sufficient proximity constraints for calculation of the protein fold.

In 1998, members of the Research Collaboratory for Structural Bioinformatics became the managers of the Protein Data Bank archive. This chapter details the systems used for the deposition, annotation and distribution of the data in the archive. Keywords: databases; nuclear magnetic resonance; NMR; Protein Data Bank; structure validation

CE-MC server (http://cemc.sdsc.edu) provides a web-based facility for the alignment of multiple protein structures based on C-alpha coordinate distances, using combinatorial extension (CE) and Monte Carlo (MC) optimization methods. Alignments are possible for user-selected PDB (Protein Data Bank) chains as well as for user-uploaded structures or the combination of the two. The whole process of generating multiple structure alignments involves three distinct steps, i.e. all-to-all pairwise alignment using the CE algorithm, iterative global optimization of a multiple alignment using the MC algorithm and formatting MC results using the JOY program. The server can be used to get multiple alignments for up to 25 protein structural chains with the flexibility of uploading multiple coordinate files and performing multiple structure alignment for user-selected PDB chains. For large-scale jobs and local installation of the CE-MC program, users can download the source code and precompiled binaries from the web server.

Publisher Summary CEPAR is a parallel, efficient software tool for family classification and all-to-all comparison of proteins in a given database, such as the protein data bank (PDB) based on comparing their 3D structure, using the CE (combinatorial extension) algorithm. In designing the CEPAR algorithm, the main themes were efficiently using the redundancy of data in the input database to reduce the amount of work and eliminating load imbalance to achieve high scalability. These ideas can be equally applicable to other biological applications dealing with redundant data sets. This chapter describes these themes that influenced the algorithm design choices. Resulting parallel performance is also presented. The limitations of the algorithm should be recognized, in particular, the fact the software should be run on a machine with the number of processors much less than the number of entities in the database. Alternatively, the software can be run in two steps. Post-processing step is necessary to convert the family classification to the all-against-all comparison information. The post-processing software can be run either in serial or in parallel, on a shared memory computer using OpenMP. Production run involve the entire PDB was performed on 64 processors of a LINUX cluster at SDSC. This run took 6 weeks, and achieved processor utilization close to 100%. This demonstrates that CEPAR is capable of utilizing loosely coupled clusters. It took another day to post-process the results on 24 processors of IBM Power4+ Regatta shared memory node. CEPAR software can also be used for incremental updates of the results to take into account the growth of the PDB itself.