Abstract not found

A principle aim of structural and functional genomics is to elucidate the structures and functions of all the gene products in the genome. However, to adequately comprehend and analyze such a large amount of information we need new descriptions of proteins that scale to the genomic level. In short, we need a unified ontology for proteomics. Here we review progress towards this end, surveying the diverse approaches to systematic structural and functional classification and their progress towards developing standardized, unified descriptions for proteins. We focus particularly on systems to organize protein properties (both biophysical and biochemical) - as opposed to the classification of 3D protein folds, a subject has been reviewed extensively elsewhere.

This paper describes the development of a new method for multiple sequence alignment based on fold-level protein structure alignments, which provides an improvement in accuracy compared to the most commonly used sequence only based techniques. This method integrates the widely used progressive multiple sequence alignment approach ClustalW with the TOPS topology based alignment algorithm. The TOPS approach produces a structure alignment for the input protein set by using a topology-based pattern discovery program, providing a set of matched sequence regions that can be used to guide a sequence alignment using ClustalW. The resulting alignments are more reliable than a sequence-only alignment, as determined by 20-fold cross validation with a set of 106 protein examples from the CATH database, distributed in 7 superfold families. The method is particularly effective for sets of proteins which have similar structures at the fold level, but low sequence identity. The aim of this research is to contribute towards bridging the gap between protein sequence and structure analysis, in the hope that this can be used to assist the understanding of the relationship between sequence, structure and function. The tool is available at http://balabio.dcs.gla.ac.uk/msat/

Abstract: We describe how knowledge of three dimensional protein structure can add to the understanding of as yet functionally unannotated protein sequences. Structure determination may reveal that the new protein shares structural similarity with a previously observed structure or that it is a novel fold. The manner in which structure can be used to suggest the function of a protein will depend on the number and diversity of homologous sequences and the extent to which these sequences are functionally characterized. These factors are discussed with reference to a number of illustrative examples of structures solved by structural genomics initiatives. 1.

many areas of bioinformatics, but mainly in the area of protein structure prediction and analysis. He is also one of the Founders of Inpharmatica, a bioinformatics driven drug discovery company located in Central London.

The Conserved Key Amino Acid Positions DataBase (CKAAPs DB) provides access to an analysis of structurally similar proteins with dissimilar sequences where key residues within a common fold are identified. CKAAPs may be important in protein folding and structural stability and function, and hence useful for protein engineering studies. This paper provides an update to the initial report of CKAAPs DB [Li et al. (2001) Nucleic Acids Res., 29, 329--331]. CKAAPs DB contains CKAAPs for the representative set of polypeptide chains derived from the CE and FSSP databases, as well as subdomains (conserved regions of the order of 100 residues within a domain) identified by CE. The new version now offers different perspectives on the CKAAPs. First, CKAAPs are mapped onto their respective Protein Data Bank (PDB) structures rendered by Molscript, providing a spatial context for the CKAAPs. Secondly, CKAAPs may be highlighted within a structure-based sequence alignment, as well as secondary structure alignment. Thirdly, the resulting sequence homologs from the structure alignment may be viewed in alignments colorized based on identities and property groups using Mview. New search capabilities have also been provided for searching by keyword combinations, PDB IDs, EC numbers, GI numbers, LocusLink ID, taxonomy, gene ontology and pathways. A new custom CKAAPs analysis interface has been implemented where a user may change the criteria for inclusion of chains, initiate CKAAPs analysis and retrieve results. CKAAPs DB is accessible through the web at http://ckaaps.sdsc.edu/. Plain text analysis results are available by FTP at ftp://ftp.sdsc.edu/pub/ sdsc/biology/ckaap.

Background: We thoroughly analyse the results of 40 blind predictions for which an experimental answer was made available at the fourth meeting on the critical assessment of protein structure methods (CASP4). Using our comparative modelling and fold recognition methodologies, we made 29 predictions for targets that had sequence identities ranging from 50% to 10% to the nearest related protein with known structure. Using our ab initio methodologies, we made eleven predictions for targets that had no detectable sequence relationships.

Background: It is a major challenge of computational biology to provide a comprehensive functional classification of all known proteins. Most existing methods seek recurrent patterns in known proteins based on manually-validated alignments of known protein families. Such methods can achieve high sensitivity, but are limited by the necessary manual labor. This makes our current view of the protein world incomplete and biased. This paper concerns ProtoNet, a automatic unsupervised global clustering system that generates a hierarchical tree of over 1,000,000 proteins, based solely on sequence similarity.

De novo protein structure prediction methods attempt to predict tertiary structures from sequences based on general principles that govern protein folding energetics and/or statistical tendencies of conformational features that native structures acquire, without the use of explicit templates. A general paradigm for de novo prediction involves sampling the conformational space, guided by scoring functions and other sequence-dependent biases, such that a large set of candidate (“decoy”) structures are generated, and then selecting native-like conformations from those decoys using scoring functions as well as conformer clustering. High-resolution refinement is sometimes used as a final step to fine-tune native-like structures. There are two major classes of scoring functions. Physics-based functions are based on mathematical models describing aspects of the known physics of molecular interaction. Knowledge-based functions are formed with statistical models capturing aspects of the properties of native protein conformations. We discuss the implementation and use of some of the scoring functions from these two classes for de novo structure prediction in this chapter. Key Words: De novo; physics-based; knowledge-based; potential; protein folding. 1.

Motivation: One of the primary aims of the structural genomics initiative is the determination of representative structures from each protein fold family. Given this objective, it is important to rapidly identify proteins that belong to a family that is already well populated (so they can be eliminated from further studies), or more importantly identify proteins that represent new families of fold. Results: Amethod for rapid classification to a fold family by the statistical analyses of unassigned the 15 N – 1 H residual dipolar couplings is presented. The required NMR data can be quickly acquired and analyzed. Using this method, structure determination efforts can be focused on more unique and interesting structures, and the overall efficiency in the construction of an information-rich library can be increased. Contact: