Biomedical subjects

Hedi Hegyi

Publications and source records attributed to Hedi Hegyi.

4 recordsLinked to original sources

PubMed2004

Automatic target selection for structural genomics on eukaryotes.

A central goal of structural genomics is to experimentally determine representative structures for all protein families. At least 14 structural genomics pilot projects are currently investigating the feasibility of high-throughput structure determination; the National Institutes of Health funded nine of these in the United States. Initiatives differ in the particular subset of "all families" on which they focus. At the NorthEast Structural Genomics consortium (NESG), we target eukaryotic protein domain families. The automatic target selection procedure has three aims: 1) identify all protein domain families from currently five entirely sequenced eukaryotic target organisms based on their sequence homology, 2) discard those families that can be modeled on the basis of structural information already present in the PDB, and 3) target representatives of the remaining families for structure determination. To guarantee that all members of one family share a common foldlike region, we had to begin by dissecting proteins into structural domain-like regions before clustering. Our hierarchical approach, CHOP, utilizing homology to PrISM, Pfam-A, and SWISS-PROT chopped the 103,796 eukaryotic proteins/ORFs into 247,222 fragments. Of these fragments, 122,999 appeared suitable targets that were grouped into >27,000 singletons and >18,000 multifragment clusters. Thus, our results suggested that it might be necessary to determine >40,000 structures to minimally cover the subset of five eukaryotic proteomes.

Algorithms↗

PubMed2002

Structural genomics analysis: characteristics of atypical, common, and horizontally transferred folds.

We conducted a structural genomics analysis of the folds and structural superfamilies in the first 20 completely sequenced genomes by focusing on the patterns of fold usage and trying to identify structural characteristics of typical and atypical folds. We assigned folds to sequences using PSI-blast, run with a systematic protocol to reduce the amount of computational overhead. On average, folds could be assigned to about a fourth of the ORFs in the genomes and about a fifth of the amino acids in the proteomes. More than 80% of all the folds in the SCOP structural classification were identified in one of the 20 organisms, with worm and E. coli having the largest number of distinct folds. Folds are particularly effective at comprehensively measuring levels of gene duplication, because they group together even very remote homologues. Using folds, we find the average level of duplication varies depending on the complexity of the organism, ranging from 2.4 in M. genitalium to 32 for the worm, values significantly higher than those observed based purely on sequence similarity. We rank the common folds in the 20 organisms, finding that the top three are the P-loop NTP hydrolase, the ferrodoxin fold, and the TIM-barrel, and discuss in detail the many factors that affect and bias these rankings. We also identify atypical folds that are "unique" to one of the organisms in our study and compare the characteristics of these folds with the most common ones. We find that common folds tend be more multifunctional and associated with more regular, "symmetrical" structures than the unique ones. In addition, many of the unique folds are associated with proteins involved in cell defense (e.g., toxins). We analyze specific patterns of fold occurrence in the genomes by associating some of them with instances of horizontal transfer and others with gene loss. In particular, we find three possible examples of transfer between archaea and bacteria and six between eukarya and bacteria. We make available our detailed results at http://genecensus.org/20.

Animals↗

PubMed2002

Molecular fossils in the human genome: identification and analysis of the pseudogenes in chromosomes 21 and 22.

We have developed an initial approach for annotating and surveying pseudogenes in the human genome. We search human genomic DNA for regions that are similar to known protein sequences and contain obvious disablements (i.e., mid-sequence stop codons or frameshifts), while ensuring minimal overlap with annotations of known genes. Pseudogenes can be divided into "processed" and "nonprocessed"; the former are reverse transcribed from mRNA (and therefore have no intron structure), whereas the latter presumably arise from genomic duplications. We annotate putative processed pseudogenes based on whether there is a continuous span of homology that is >70% of the length of the closest matching human protein (i.e., with introns removed), or whether there is evidence of polyadenylation. We have applied our approach to chromosomes 21 and 22, the first parts of the human genome completely sequenced, finding 190 new pseudogene annotations beyond the 264 reported by the sequencing centers. In total, on chromosomes 21 and 22, there are 189 processed pseudogenes, 195 nonprocessed pseudogenes, and, additionally, 70 pseudogenic immunoglobulin gene segments. (Detailed assignments are available at http://bioinfo.mbb.yale.edu/genome/pseudogene or http://genecensus.org/pseudogene.) By extrapolation, we predict that there could be up to approximately 20,000 pseudogenes in the whole human genome, with a little more than half of them processed. We have determined the main populations and clusters of pseudogenes on chromosomes 21 and 22. There are notable excesses of pseudogenes relative to genes near the centromeres of both chromosomes, indicating the existence of pseudogenic "hot-spots" in the genome. We have looked at the distribution of InterPro families and Gene Ontology (GO) functional categories in our pseudogenes. Overall, the families in both processed and nonprocessed pseudogene populations occur according to a similar power-law distribution as that found for the occurrence of gene families, with a few big families and many small ones. The processed population is, in particular, enriched in highly expressed ribosomal-protein sequences (approximately 20%), which appear fairly evenly distributed across the chromosomes. We compared processed pseudogenes of different evolutionary ages, observing a high degree of similarity between "ancient" and "modern" subpopulations. This may be attributable to the consistently high expression of ribosomal proteins over evolutionary time. Finally, we find that chromosome 22 pseudogene population is dominated by immunoglobulin segments, which have a greater rate of disablement per amino acid than the other pseudogene populations and are also substantially more diverged.

Chromosome Mapping↗

PubMed2002

SNPs on human chromosomes 21 and 22 -- analysis in terms of protein features and pseudogenes.

SNPs are useful for genome-wide mapping and the study of disease genes. Previous studies have focused on SNPs in specific genes or SNPs pooled from a variety of different sources. Here, a systematic approach to the analysis of SNPs in relation to various features on a genome-wide scale, with emphasis on protein features and pseudogenes, is presented. We have performed a comprehensive analysis of 39,408 SNPs on human chromosomes 21 and 22 from the SNP consortium (TSC) database, where SNPs are obtained by random sequencing using consistent and uniform methods. Our study indicates that the occurrence of SNPs is lowest in exons and higher in repeats, introns and pseudogenes. Moreover, in comparing genes and pseudogenes, we find that the SNP density is higher in pseudogenes and the ratio of nonsynonymous to synonymous changes is also much higher. These observations may be explained by the increased rate of SNP accumulation in pseudogenes, which presumably are not under selective pressure. We have also performed secondary structure prediction on all coding regions and found that there is no preferential distribution of SNPs in a -helices, b -sheets or coils. This could imply that protein structures, in general, can tolerate a wide degree of substitutions. Tables relating to our results are available from http://genecensus.org/pseudogene.

Algorithms↗