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Hugo O Villar

Publications and source records attributed to Hugo O Villar.

7 recordsLinked to original sources
PubMed2005

Ring systems in mutagenicity databases.

The distribution of ring systems in public mutagenicity databases is analyzed. An automated enumeration of substructures permits determination of the occurrence of different scaffolds in data sets. The counts are used to perform population analysis via proportions and odds ratios of mutagenic compounds. Pairwise calculations of odds ratios between scaffolds allow comparison of ring systems for isostere replacement studies. These findings are presented in tables that readily show which scaffold is likely to occur in mutagenic compounds. Also, rings identified in public domain mutagenicity data sets are compared to rings in drugs data sets; unfortunately, public mutagenicity data sets do not reflect the types of scaffolds in drugs and those typically used in medicinal chemistry. The findings bring into question the utility of predictive models that were derived from public domain data sets. The automated ring identification and statistical approaches used here can be applied to other pharmacological properties to yield information about chemical scaffolds.

Confidence Intervals↗
PubMed2004

Novel cyclooxygenase-1 inhibitors discovered using affinity fingerprints.

We used protein affinity fingerprints to discover structurally novel inhibitors of cyclooxygenase-1 (COX-1) by screening a selected number of compounds, thus providing an alternative to extensive screening. From the affinity fingerprints of 19 known COX-1 inhibitors, a computational model for COX-1 inhibition was constructed and used to select candidate inhibitors from our compound library to be tested in the COX-1 assay. Subsequent refinement of the model by including affinity fingerprints of inactive compounds identified three molecules that were more potent than ibuprofen, a commonly used COX-1 inhibitor. These compounds are structurally distinct from those used to build the model and were discovered by testing only 62 library compounds. The discovery of these leads demonstrates the efficiency with which affinity fingerprints can identify novel bioactive chemotypes from known drugs.

Anti-Inflammatory Agents, Non-Steroidal↗
PubMed2004

Using NMR for ligand discovery and optimization.

Several recent technology-driven advances in the area of NMR have rekindled an interest in the application of the technology to problems in drug discovery and development. A unique aspect of NMR is that it has applicability in broadly different areas of the drug discovery and optimization processes. NMR techniques for screening aimed at the discovery of novel ligands or low molecular weight structures for fragment-based build up procedures are being applied commonly in the industry. Application of NMR in structure-guided drug design and metabonomics are also becoming routine. We present an overview of some of the most recent NMR developments in these areas.

Drug Design↗
PubMed2003

A path from primary protein sequence to ligand recognition.

A novel method to organize protein structural information based solely on sequence is presented. The method clusters proteins into families that correlate with the three-dimensional protein structure and the conformation of the bound ligands. This procedure was applied to nicotinamide adenine dinucleotide [NAD(P)]-utilizing enzymes to identify a total of 94 sequence families, 53 of which are structurally characterized. Each of the structurally characterized proteins within a sequence family correlates to a single protein fold and to a common bound conformation of NAD(P). A wide range of structural folds is identified that recognize NAD(P), including Rossmann folds and beta/alpha barrels. The defined sequence families can be used to identify the type and prevalence of NAD(P)-utilizing enzymes in the proteomes of sequenced organisms. The proteome of Mycobacterium tuberculosis was mined to generate a proteome-wide profile of NAD(P)-utilizing enzymes coded by this organism. This enzyme family comprises approximately 6% of the open reading frames, with the largest subgroup being the Rossmann fold, short-chain dehydrogenases. The preponderance of short-chain dehydrogenases correlates strongly with the phenotype of M. tuberculosis, which is characterized as having one of the most complex prokaryotic cell walls.

Amino Acid Sequence↗
PubMed2002

Chemoproteomics as a basis for post-genomic drug discovery.

The large number of small organic compounds now available for drug-lead screening has led to numerous methods for classifying molecular similarity and diversity, the aim being to restore a balance between the quantity and drug-like quality of compounds in small-molecule libraries. Whereas structural and physicochemical attributes continue to be emphasized in compound selection for drug-lead screening, chemoproteomics--the use of biological information to guide chemistry--offers a highly efficient alternative to small-molecule characterization that can accelerate drug discovery in the post-genomic era.

Databases, Factual↗
PubMedDate not supplied

Genome-wide profile of oxidoreductases in viruses, prokaryotes, and eukaryotes.

Enzymes that utilize nicotinamide adenine dinucleotide (NAD) or its 2'-phosphate derivative (NADP) are found throughout the kingdoms of life. These enzymes are fundamental to many biochemical pathways, including central intermediary metabolism and mechanisms for cell survival and defense. The complete genomes of 25 organisms representing bacteria, protists, fungi, plants, and animals, and 811 viruses, were mined to identify and classify NAD(P)-dependent enzymes. An average of 3.4% of the proteins in these genomes was categorized as NAD(P)-utilizing proteins, with highest prevalence in the medium-chain oxidoreductase and short-chain oxidoreductase families. In general, the distribution of these enzymes by oxidoreductase family was correlated to the number of different catalytic mechanisms in each family. Organisms with smaller genomes encoded a larger proportion of NAD(P)-dependent enzymes in their proteome (approximately 6%) as compared to the larger genomes of eukaryotes (approximately 3%). Among viruses, those with large, double-strand DNA genomes were shown to encode oxidoreductases. Gram-positive and gram-negative bacteria showed some differences in the distribution of NAD(P)-dependent proteins. Several organisms such as M. tuberculosis, P. falciparum, and A. thaliana showed unique distributions of oxidoreductases corresponding to some phenotypic features.

Animals↗