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Heger, Andreas

doi:10.1023/a:1026145703834

Cited by 7 publications

References 18 publications

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Target selection for structural genomics

Frishman

2005

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

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Bioinformatics analyses provide invaluable guidance in finding the shortest path to the ultimate goal of structural genomics – full understanding of structure–function relationships in proteins and exhaustive exploration of the sequence‐structure space. Advanced computational tools allow for predicting currently intractable target proteins and those that already have a known fold, grouping sequences into families, and propagating available structural information to a large number of protein sequences by means of homology modeling. Rational target selection is also a major cost‐saving factor because it prevents the international structural genomics consortia from duplicating effort and helps to identify the most economical set of target proteins. The main strategies in target selection include the systematic determination of protein structures from completely sequenced genomes of selected model organisms, elucidation of all fold types existing in nature, and exhaustive covering of the entire sequence space with structural information.

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Target selection for structural genomics

Frishman

2005

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

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Implications of structural genomics target selection strategies: Pfam5000, whole genome, and random approaches

Chandonia

Brenner

2004

Proteins

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Structural genomics is an international effort to determine the three-dimensional shapes of all important biological macromolecules, with a primary focus on proteins. Target proteins should be selected according to a strategy that is medically and biologically relevant, of good value, and tractable. As an option to consider, we present the "Pfam5000" strategy, which involves selecting the 5000 most important families from the Pfam database as sources for targets. We compare the Pfam5000 strategy to several other proposed strategies that would require similar numbers of targets. These strategies include complete solution of several small to moderately sized bacterial proteomes, partial coverage of the human proteome, and random selection of approximately 5000 targets from sequenced genomes. We measure the impact that successful implementation of these strategies would have upon structural interpretation of the proteins in Swiss-Prot, TrEMBL, and 131 complete proteomes (including 10 of eukaryotes) from the Proteome Analysis database at the European Bioinformatics Institute (EBI). Solving the structures of proteins from the 5000 largest Pfam families would allow accurate fold assignment for approximately 68% of all prokaryotic proteins (covering 59% of residues) and 61% of eukaryotic proteins (40% of residues). More fine-grained coverage that would allow accurate modeling of these proteins would require an order of magnitude more targets. The Pfam5000 strategy may be modified in several ways, for example, to focus on larger families, bacterial sequences, or eukaryotic sequences; as long as secondary consideration is given to large families within Pfam, coverage results vary only slightly. In contrast, focusing structural genomics on a single tractable genome would have only a limited impact in structural knowledge of other proteomes: A significant fraction (about 30 -40% of the proteins and 40 -60% of the residues) of each proteome is classified in small families, which may have little overlap with other species of interest. Random selection of targets from one or more genomes is similar to the Pfam5000 strategy in that proteins from larger families are more likely to be chosen, but substantial effort would be spent on small families. Proteins 2005;58:166 -179.

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Strategies for high‐throughput comparative modeling: Applications to leverage analysis in structural genomics and protein family organization

Mirkovic¹,

Li²,

Parnassa³

et al. 2007

Proteins

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The technological breakthroughs in structural genomics were designed to facilitate the solution of a sufficient number of structures, so that as many protein sequences as possible can be structurally characterized with the aid of comparative modeling. The leverage of a solved structure is the number and quality of the models that can be produced using the structure as a template for modeling and may be viewed as the "currency" with which the success of a structural genomics endeavor can be measured. Moreover, the models obtained in this way should be valuable to all biologists. To this end, at the Northeast Structural Genomics Consortium (NESG), a modular computational pipeline for automated high-throughput leverage analysis was devised and used to assess the leverage of the 186 unique NESG structures solved during the first phase of the Protein Structure Initiative (January 2000 to July 2005). Here, the results of this analysis are presented. The number of sequences in the nonredundant protein sequence database covered by quality models produced by the pipeline is approximately 39,000, so that the average leverage is approximately 210 models per structure. Interestingly, only 7900 of these models fulfill the stringent modeling criterion of being at least 30% sequence-identical to the corresponding NESG structures. This study shows how high-throughput modeling increases the efficiency of structure determination efforts by providing enhanced coverage of protein structure space. In addition, the approach is useful in refining the boundaries of structural domains within larger protein sequences, subclassifying sequence diverse protein families, and defining structure-based strategies specific to a particular family.

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Untitled

Cited by 7 publications

References 18 publications

Target selection for structural genomics

Target selection for structural genomics

Implications of structural genomics target selection strategies: Pfam5000, whole genome, and random approaches

Strategies for high‐throughput comparative modeling: Applications to leverage analysis in structural genomics and protein family organization

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