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PubMed2005

MSDsite: a database search and retrieval system for the analysis and viewing of bound ligands and active sites.

The three-dimensional environments of ligand binding sites have been derived from the parsing and loading of the PDB entries into a relational database. For each bound molecule the biological assembly of the quaternary structure has been used to determine all contact residues and a fast interactive search and retrieval system has been developed. Prosite pattern and short sequence search options are available together with a novel graphical query generator for inter-residue contacts. The database and its query interface are accessible from the Internet through a web server located at: http://www.ebi.ac.uk/msd-srv/msdsite.

Amino Acid Sequence↗
PubMed2005

MOLE: a data management application based on a protein production data model.

MOLE (mining, organizing, and logging experiments) has been developed to meet the growing data management and target tracking needs of molecular biologists and protein crystallographers. The prototype reported here will become a Laboratory Information Management System (LIMS) to help protein scientists manage the large amounts of laboratory data being generated due to the acceleration in proteome research and will furthermore facilitate collaborations between groups based at different sites. To achieve this, MOLE is based on the data model for protein production devised at the European Bioinformatics Institute (Pajon A, et al., Proteins in press).

Algorithms↗
PubMed2005

Assigning new GO annotations to protein data bank sequences by combining structure and sequence homology.

Accompanying the discovery of an increasing number of proteins, there is the need to provide functional annotation that is both highly accurate and consistent. The Gene Ontology (GO) provides consistent annotation in a computer readable and usable form; hence, GO annotation (GOA) has been assigned to a large number of protein sequences based on direct experimental evidence and through inference determined by sequence homology. Here we show that this annotation can be extended and corrected for cases where protein structures are available. Specifically, using the Combinatorial Extension (CE) algorithm for structure comparison, we extend the protein annotation currently provided by GOA at the European Bioinformatics Institute (EBI) to further describe the contents of the Protein Data Bank (PDB). Specific cases of biologically interesting annotations derived by this method are given. Given that the relationship between sequence, structure, and function is complicated, we explore the impact of this relationship on assigning GOA. The effect of superfolds (folds with many functions) is considered and, by comparison to the Structural Classification of Proteins (SCOP), the individual effects of family, superfamily, and fold.

Algorithms↗
PubMed2005

NdPASA: a novel pairwise protein sequence alignment algorithm that incorporates neighbor-dependent amino acid propensities.

Sequence alignment has become one of the essential bioinformatics tools in biomedical research. Existing sequence alignment methods can produce reliable alignments for homologous proteins sharing a high percentage of sequence identity. The performance of these methods deteriorates sharply for the sequence pairs sharing less than 25% sequence identity. We report here a new method, NdPASA, for pairwise sequence alignment. This method employs neighbor-dependent propensities of amino acids as a unique parameter for alignment. The values of neighbor-dependent propensity measure the preference of an amino acid pair adopting a particular secondary structure conformation. NdPASA optimizes alignment by evaluating the likelihood of a residue pair in the query sequence matching against a corresponding residue pair adopting a particular secondary structure in the template sequence. Using superpositions of homologous proteins derived from the PSI-BLAST analysis and the Structural Classification of Proteins (SCOP) classification of a nonredundant Protein Data Bank (PDB) database as a gold standard, we show that NdPASA has improved pairwise alignment. Statistical analyses of the performance of NdPASA indicate that the introduction of sequence patterns of secondary structure derived from neighbor-dependent sequence analysis clearly improves alignment performance for sequence pairs sharing less than 20% sequence identity. For sequence pairs sharing 13-21% sequence identity, NdPASA improves the accuracy of alignment over the conventional global alignment (GA) algorithm using the BLOSUM62 by an average of 8.6%. NdPASA is most effective for aligning query sequences with template sequences whose structure is known. NdPASA can be accessed online at http://astro.temple.edu/feng/Servers/BioinformaticServers.htm.

Algorithms↗
PubMed2005

A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators.

We developed a pharmacophore-based evolutionary approach for virtual screening. This tool, termed the Generic Evolutionary Method for molecular DOCKing (GEMDOCK), combines an evolutionary approach with a new pharmacophore-based scoring function. The former integrates discrete and continuous global search strategies with local search strategies to expedite convergence. The latter, integrating an empirical-based energy function and pharmacological preferences (binding-site pharmacological interactions and ligand preferences), simultaneously serves as the scoring function for both molecular docking and postdocking analyses to improve screening accuracy. We apply pharmacological interaction preferences to select the ligands that form pharmacological interactions with target proteins, and use the ligand preferences to eliminate the ligands that violate the electrostatic or hydrophilic constraints. We assessed the accuracy of our approach using human estrogen receptor (ER) and a ligand database from the comparative studies of Bissantz et al. (J Med Chem 2000;43:4759-4767). Using GEMDOCK, the average goodness-of-hit (GH) score was 0.83 and the average false-positive rate was 0.13% for ER antagonists, and the average GH score was 0.48 and the average false-positive rate was 0.75% for ER agonists. The performance of GEMDOCK was superior to competing methods such as GOLD and DOCK. We found that our pharmacophore-based scoring function indeed was able to reduce the number of false positives; moreover, the resulting pharmacological interactions at the binding site, as well as ligand preferences, were important to the screening accuracy of our experiments. These results suggest that GEMDOCK constitutes a robust tool for virtual database screening.

Binding Sites↗
PubMed2005

Main-chain conformational tendencies of amino acids.

A Ramachandran plot is a visual representation of the main-chain conformational tendencies of an amino acid. Despite forty years of research, the shape of Ramachandran plots is still a matter of debate. The issue in making a Ramachandran plot based on experimental data is deciding whether sparse data represent genuine conformations. We present here a simple solution to settle the ambiguities of the sparse data, and explain how we verified the accuracies of our plots using an independent dataset. To obtain our results, we then measured the pair-wise distances of main-chain conformational tendencies among amino acids, and showed that the conformational relationships of amino acids are well preserved in a two-dimensional map, leading to the conclusion that the conformational diversity space of amino acids is largely two dimensional. We further noticed that amino acids in early and late evolutionary stages are located in different zones in the two-dimensional map. In addition to these conclusions, we here present an amino acid substitution table derived from experimental data.

Amino Acids↗
PubMed2006

Protein kinase resource: an integrated environment for phosphorylation research.

The protein kinase superfamily is an important group of enzymes controlling cellular signaling cascades. The increasing amount of available experimental data provides a foundation for deeper understanding of details of signaling systems and the underlying cellular processes. Here, we describe the Protein Kinase Resource, an integrated online service that provides access to information relevant to cell signaling and enables kinase researchers to visualize and analyze the data directly in an online environment. The data set is synchronized with Uniprot and Protein Data Bank (PDB) databases and is regularly updated and verified. Additional annotation includes interactive display of domain composition, cross-references between orthologs and functional mapping to OMIM records. The Protein Kinase Resource provides an integrated view of the protein kinase superfamily by linking data with their visual representation. Thus, human kinases can be mapped onto the human kinome tree via an interactive display. Sequence and structure data can be easily displayed using applications developed for the PKR and integrated with the website and the underlying database. Advanced search mechanisms, such as multiparameter lookup, sequence pattern, and blast search, enable fast access to the desired information, while statistics tools provide the ability to analyze the relationships among the kinases under study. The integration of data presentation and visualization implemented in the Protein Kinase Resource can be adapted by other online providers of scientific data and should become an effective way to access available experimental information.

Amino Acid Sequence↗
PubMed1995

Huntington disease: a case study describing the complexities and nuances of predictive testing of monozygotic twins.

When a candidate for predictive testing for the Huntington disease gene is a monozygotic twin, confidentiality of the co-twin's diagnosis and autonomy of participation are among the critical genetic counseling issues. Predictive testing can proceed when twins voluntarily and simultaneously request counseling and evaluation in an HD testing program. This case describes a young man referred for predictive testing to an HD testing site on the East Coast of the United States. Family history revealed a twin brother of unknown zygosity who resided on the West Coast of the United States. The genetic counselors on opposite coasts collaborated to provide genetic counseling and evaluation for voluntary, informed predictive testing of the twins, protecting their rights while observing national protocol guidelines

Adult↗
PubMedDate not supplied

TransMiner: mining transitive associations among biological objects from text.

Associations among biological objects such as genes, proteins, and drugs can be discovered automatically from the scientific literature. TransMiner is a system for finding associations among objects by mining the Medline database of the scientific literature. The direct associations among the objects are discovered based on the principle of co-occurrence in the form of an association graph. The principle of transitive closure is applied to the association graph to find potential transitive associations. The potential transitive associations that are indeed direct are discovered by iterative retrieval and mining of the Medline documents. Those associations that are not found explicitly in the entire Medline database are transitive associations and are the candidates for hypothesis generation. The transitive associations were ranked based on the sum of weight of terms that co-occur with both the objects. The direct and transitive associations are visualized using a graph visualization applet. TransMiner was tested by finding associations among 56 breast cancer genes and among 24 objects in the calpain signal transduction pathway. TransMiner was also used to rediscover associations between magnesium and migraine.

Abstracting and Indexing↗
PubMed2003

Panel of human cancer cell lines provides valuable database for drug discovery and bioinformatics.

Studies conducted at the US National Cancer Institute (NCI) and in our laboratory show that databases including the drug sensitivities of panels of many human cancer cell lines provide valuable information on the molecular pharmacology of anticancer drugs. We established a panel of 39 cell lines of various human cancers and developed a database of their chemosensitivities. Drugs were profiled in terms of their "fingerprints", patterns of differential activity against the cell lines. There was a significant correlation between a drug's fingerprint and its mode of action, as observed in the NCI panel of 60 cell lines. Therefore our cell-line panel is a powerful tool to predict the modes of action of new compounds. We have been using this system for drug discovery, coupled with various target-based drug screenings. We used the system to identify a novel DNA minor-groove binder, MS-247, which has inhibitory activity against topoisomerases I and II, and potent in vivo antitumor activity against various human cancer xenografts. We also discovered a potent novel telomerase inhibitor, FJ5002, by mining our database with the COMPARE algorithm, followed by experimental validation. We investigated the gene expression profiles of the cell lines by using DNA microarrays to find profiles determining cellular chemosensitivity and new targets for anticancer drugs. Our integrated database, including the chemosensitivities and gene expression profiles of the cell-line panel, could provide a basis for drug discovery and personalized therapy.

Databases, Factual↗
PubMed2006

Bioinformatics and cancer: an essential alliance.

Modern research in cancer has been revolutionized by the introduction of new high-throughput methodologies such as DNA microarrays. Keeping the pace with these technologies, the bioinformatics offer new solutions for data analysis and, what is more important, it permits to formulate a new class of hypothesis inspired in systems biology, more oriented to blocks of functionally-related genes. Although software implementations for this new methodologies is new there are some options already available. Bioinformatic solutions for other high-throughput techniques such as array-CGH of large-scale genotyping is also revised.

Chromosome Aberrations↗
PubMed2004

Scale-free networks versus evolutionary drift.

Recent studies of properties of various biological networks revealed that many of them display scale-free characteristics. Since the theory of scale-free networks is applicable to evolving networks, one can hope that it provides not only a model of a biological network in its current state but also sheds some insight into the evolution of the network. In this work, we investigate the probability distributions and scaling properties underlying some models for biological networks and protein domain evolution. The analysis of evolutionary models for domain similarity networks indicates that models which include evolutionary drift are typically not scale free. Instead they adhere quite closely to the Yule distribution. This finding indicates that the direct applicability of scale-free models in understanding the evolution of biological network may not be as wide as it has been hoped for.

Computational Biology↗
PubMed2005

ONTOFUSION: ontology-based integration of genomic and clinical databases.

ONTOFUSION is an ontology-based system designed for biomedical database integration. It is based on two processes: mapping and unification. Mapping is a semi-automated process that uses ontologies to link a database schema with a conceptual framework-named virtual schema. There are three methodologies for creating virtual schemas, according to the origin of the domain ontology used: (1) top-down--e.g. using an existing ontology, such as the UMLS or Gene Ontology--, (2) bottom-up--building a new domain ontology-- and (3) a hybrid combination. Unification is an automated process for integrating ontologies and hence the database to which they are linked. Using these methods, we employed ONTOFUSION to integrate a large number of public genomic and clinical databases, as well as biomedical ontologies.

Data Collection↗
PubMed2004

Comparison of character-level and part of speech features for name recognition in biomedical texts.

The immense volume of data which is now available from experiments in molecular biology has led to an explosion in reported results most of which are available only in unstructured text format. For this reason there has been great interest in the task of text mining to aid in fact extraction, document screening, citation analysis, and linkage with large gene and gene-product databases. In particular there has been an intensive investigation into the named entity (NE) task as a core technology in all of these tasks which has been driven by the availability of high volume training sets such as the GENIA v3.02 corpus. Despite such large training sets accuracy for biology NE has proven to be consistently far below the high levels of performance in the news domain where F scores above 90 are commonly reported which can be considered near to human performance. We argue that it is crucial that more rigorous analysis of the factors that contribute to the model's performance be applied to discover where the underlying limitations are and what our future research direction should be. Our investigation in this paper reports on variations of two widely used feature types, part of speech (POS) tags and character-level orthographic features, and makes a comparison of how these variations influence performance. We base our experiments on a proven state-of-the-art model, support vector machines using a high quality subset of 100 annotated MEDLINE abstracts. Experiments reveal that the best performing features are orthographic features with F score of 72.6. Although the Brill tagger trained in-domain on the GENIA v3.02p POS corpus gives the best overall performance of any POS tagger, at an F score of 68.6, this is still significantly below the orthographic features. In combination these two features types appear to interfere with each other and degrade performance slightly to an F score of 72.3.

Abbreviations as Topic↗
PubMed2004

A novel sequence similarity searching and visualization method based on overlappingly translated nucleic acids: the blastNP.

Sequence data are stored in nucleic acid and protein databases. Searching the nucleic acid databases is very specific but rather insensitive method. Searching protein databases is sensitive but not very specific procedure. It was expected that the combination of these methods might provide an optimal approach. Therefore an alternative method to TblastX has been developed, known as blastNP. Nucleic acids in database and query sequences were translated into overlapping protein-like sequences (overlappingly translated sequences or OTSs) before searching with blastP. Thus, each nucleic acid sequence is represented by a single "protein like" sequence (instead of three hypothetical proteins in different reading frames). The blastNP method is defined as a blastP that is performed on an overlappingly translated nucleic acid database using a similarly converted nucleic acid query. The specificity and sensitivity of blastNP and TblastX is very similar, however blastNP is more sensitive to detect short sequence similarities (less than 50 residues). BlastNP combines the advantages of nucleotide and protein blasts and bypasses many difficulties: (1). it is more sensitive to weak sequence similarities than blastN, (2). codon redundancy is eliminated, (3). the sensitivity to single nucleotide polymorphism, mutation and sequencing errors are reduced, (4). it is insensitive to frame shifts. This novel method was proved to find significant sequence similarities which remained hidden for other methods and is a promising tool for further understanding (and annotating) the function of many old and new sequences.

Amino Acid Sequence↗
PubMed2005

Inference of protein function from protein structure.

Structural genomics has brought us three-dimensional structures of proteins with unknown functions. To shed light on such structures, we have developed ProKnow (http://www.doe-mbi.ucla.edu/Services/ProKnow/), which annotates proteins with Gene Ontology functional terms. The method extracts features from the protein such as 3D fold, sequence, motif, and functional linkages and relates them to function via the ProKnow knowledgebase of features, which links features to annotated functions via annotation profiles. Bayes' theorem is used to compute weights of the functions assigned, using likelihoods based on the extracted features. The description level of the assigned function is quantified by the ontology depth (from 1 = general to 9 = specific). Jackknife tests show approximately 89% correct assignments at ontology depth 1 and 40% at depth 9, with 93% coverage of 1507 distinct folded proteins. Overall, about 70% of the assignments were inferred correctly. This level of performance suggests that ProKnow is a useful resource in functional assessments of novel proteins.

Amino Acid Motifs↗