Biomedical subjects

Richard A George

Publications and source records attributed to Richard A George.

9 recordsLinked to original sources

PubMed2006

Analysis of protein sequence and interaction data for candidate disease gene prediction.

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein-protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

Algorithms↗

PubMed2005

Effective function annotation through catalytic residue conservation.

Because of the extreme impact of genome sequencing projects, protein sequences without accompanying experimental data now dominate public databases. Homology searches, by providing an opportunity to transfer functional information between related proteins, have become the de facto way to address this. Although a single, well annotated, close relationship will often facilitate sufficient annotation, this situation is not always the case, particularly if mutations are present in important functional residues. When only distant relationships are available, the transfer of function information is more tenuous, and the likelihood of encountering several well annotated proteins with different functions is increased. The consequence for a researcher is a range of candidate functions with little way of knowing which, if any, are correct. Here, we address the problem directly by introducing a computational approach to accurately identify and segregate related proteins into those with a functional similarity and those where function differs. This approach should find a wide range of applications, including the interpretation of genomics/proteomics data and the prioritization of targets for high-throughput structure determination. The method is generic, but here we concentrate on enzymes and apply high-quality catalytic site data. In addition to providing a series of comprehensive benchmarks to show the overall performance of our approach, we illustrate its utility with specific examples that include the correct identification of haptoglobin as a nonenzymatic relative of trypsin, discrimination of acid-d-amino acid ligases from a much larger ligase pool, and the successful annotation of BioH, a structural genomics target.

Amino Acid Sequence↗

PubMed2005

Scooby-domain: prediction of globular domains in protein sequence.

Scooby-domain (sequence hydrophobicity predicts domains) is a fast and simple method to identify globular domains in protein sequence, based on the observed lengths and hydrophobicities of domains from proteins with known tertiary structure. The prediction method successfully identifies sequence regions that will form a globular structure and those that are likely to be unstructured. The method does not rely on homology searches and, therefore, can identify previously unknown domains for structural elucidation. Scooby-domain is available as a Java applet at http://ibivu.cs.vu.nl/programs/scoobywww. It may be used to visualize local properties within a protein sequence, such as average hydrophobicity, secondary structure propensity and domain boundaries, as well as being a method for fast domain assignment of large sequence sets.

Hydrophobic and Hydrophilic Interactions↗

PubMed2005

The complement of enzymatic sets in different species.

We present here a comprehensive analysis of the complement of enzymes in a large variety of species. As enzymes are a relatively conserved group there are several classification systems available that are common to all species and link a protein sequence to an enzymatic function. Enzymes are therefore an ideal functional group to study the relationship between sequence expansion, functional divergence and phenotypic changes. By using information retrieved from the well annotated SWISS-PROT database together with sequence information from a variety of fully sequenced genomes and information from the EC functional scheme we have aimed here to estimate the fraction of enzymes in genomes, to determine the extent of their functional redundancy in different domains of life and to identify functional innovations and lineage specific expansions in the metazoa lineage. We found that prokaryote and eukaryote species differ both in the fraction of enzymes in their genomes and in the pattern of expansion of their enzymatic sets. We observe an increase in functional redundancy accompanying an increase in species complexity. A quantitative assessment was performed in order to determine the degree of functional redundancy in different species. Finally, we report a massive expansion in the number of mammalian enzymes involved in signalling and degradation.

Animals↗

PubMed2005

A ligand-centric analysis of the diversity and evolution of protein-ligand relationships in E.coli.

As enzymes evolve and diverge from common ancestor sequences, they often keep their overall reaction chemistry but specialize in the binding of different cognate ligands. This study borrows methods for the computational assessment of 2D similarity of small molecules from the field of chemoinformatics, to examine the extent of structure conservation of cognate ligands binding to similar proteins. Proteins from 87 structural superfamilies from Escherichia coli form the core dataset, which is extended using homologues with functional assignments from any organism. We find that correlation of the substrate similarity with protein similarity (measured by either sequence-based or structure-based scores) can only be clearly established for very similar proteins. At low sequence identities, the superfamily to which a protein belongs can give helpful clues to its function, and more importantly, the confidence attached to such clues is superfamily-dependent. Our data indicate that only a few superfamilies show great substrate diversity, and that most exhibit conservation of at least part of the structural scaffold of the substrate.

Databases, Protein↗

PubMed2004

SCOPEC: a database of protein catalytic domains.

MOTIVATION: Domains are the units of protein structure, function and evolution. It is therefore essential to utilize knowledge of domains when studying the evolution of function, or when assigning function to genome sequence data. For this purpose, we have developed a database of catalytic domains, SCOPEC, by combining structural domain information from SCOP, full-length sequence information from Swiss-Prot, and verified functional information from the Enzyme Classification (EC) database. Two major problems need to be overcome to create a database of domain-function relationships; (1) for sequences, EC numbers are typically assigned to whole sequences rather than the functional unit, and (2) The Protein Data Bank (PDB) structures elucidated from a larger multi-domain protein will often have EC annotation although the relevant catalytic domain may lie elsewhere. RESULTS: SCOPEC entries have high quality enzyme assignments; having passed both computational and manual checks. SCOPEC currently contains entries for 75% of all EC annotations in the PDB. Overall, EC number is fairly well conserved within a superfamily, even when the proteins are distantly related. Initial analysis is encouraging; suggesting that there is a 50:50 chance of conserved function in distant homologues first detected by a third iteration PSI-BLAST search. Therefore, we envisage that a knowledge-based approach to function assignment using the domain-EC relationships in SCOPEC will gain a marked improvement over this base line. AVAILABILITY: The SCOPEC database is a valuable resource in the analysis and prediction of protein structure and function. It can be obtained or queried at our website http://www.enzome.com

Catalysis↗

PubMed2002

Protein domain identification and improved sequence similarity searching using PSI-BLAST.

Protein sequences containing more than one structural domain are problematic when used in homology searches where they can either stop an iterative database search prematurely or cause an explosion of a search to common domains. We describe a method, DOMAINATION, that infers domains and their boundaries in a query sequence from local gapped alignments generated using PSI-BLAST. Through a new technique to recognize domain insertions and permutations, DOMAINATION submits delineated domains as successive database queries in further iterative steps. Assessed over a set of 452 multidomain proteins, the method predicts structural domain boundaries with an overall accuracy of 50% and improves finding distant homologies by 14% compared with PSI-BLAST. DOMAINATION is available as a web based tool at http://mathbio.nimr.mrc.ac.uk, and the source code is available from the authors upon request.

Animals↗

PubMed2002

SnapDRAGON: a method to delineate protein structural domains from sequence data.

We describe a method to identify protein domain boundaries from sequence information alone based on the assumption that hydrophobic residues cluster together in space. SnapDRAGON is a suite of programs developed to predict domain boundaries based on the consistency observed in a set of alternative ab initio three-dimensional (3D) models generated for a given protein multiple sequence alignment. This is achieved by running a distance geometry-based folding technique in conjunction with a 3D-domain assignment algorithm. The overall accuracy of our method in predicting the number of domains for a non-redundant data set of 414 multiple alignments, representing 185 single and 231 multiple-domain proteins, is 72.4 %. Using domain linker regions observed in the tertiary structures associated with each query alignment as the standard of truth, inter-domain boundary positions are delineated with an accuracy of 63.9 % for proteins comprising continuous domains only, and 35.4 % for proteins with discontinuous domains. Overall, domain boundaries are delineated with an accuracy of 51.8 %. The prediction accuracy values are independent of the pair-wise sequence similarities within each of the alignments. These results demonstrate the capability of our method to delineate domains in protein sequences associated with a wide variety of structural domain organisation.

Algorithms↗

PubMed2002

An analysis of protein domain linkers: their classification and role in protein folding.

Recent advances in protein engineering have come from creating multi-functional chimeric proteins containing modules from various proteins. These modules are typically joined via an oligopeptide linker, the correct design of which is crucial for the desired function of the chimeric protein. Here we analyse the properties of naturally occurring inter-domain linkers with the aim to design linkers for domain fusion. Two main types of linker were identified; helical and non-helical. Helical linkers are thought to act as rigid spacers separating two domains. Non-helical linkers are rich in prolines, which also leads to structural rigidity and isolation of the linker from the attached domains. This means that both linker types are likely to act as a scaffold to prevent unfavourable interactions between folding domains. Based on these results we have constructed a linker database intended for the rational design of linkers for domain fusion, which can be accessed via the Internet at http://mathbio.nimr.mrc.ac.uk.

Chi-Square Distribution↗