Algorithm for predicting functionally equivalent proteins from BLAST and HMMER searches.

In order to predict biologically significant attributes such as function from protein sequences, searching against large databases for homologous proteins is a common practice. In particular, BLAST and HMMER are widely used in a variety of biological fields. However, sequencehomologous proteins determined by BLAST and proteins having the same domains predicted by HMMER are not always functionally equivalent, even though their sequences are aligning with high similarity. Thus, accurate assignment of functionally equivalent proteins from aligned sequences remains a challenge in bioinformatics. We have developed the FEP-BH algorithm to predict functionally equivalent proteins from protein-protein pairs identified by BLAST and from protein-domain pairs predicted by HMMER. When examined against domain classes of the Pfam-A seed database, FEP-BH showed 71.53% accuracy, whereas BLAST and HMMER were 57.72% and 36.62%, respectively. We expect that the FEP-BH algorithm will be effective in predicting functionally equivalent proteins from BLAST and HMMER outputs and will also suit biologists who want to search out functionally equivalent proteins from among sequence-homologous proteins.

ASMPKS: an analysis system for modular polyketide synthases.

BACKGROUND: Polyketides are secondary metabolites of microorganisms with diverse biological activities, including pharmacological functions such as antibiotic, antitumor and agrochemical properties. Polyketides are synthesized by serialized reactions of a set of enzymes called polyketide synthase(PKS)s, which coordinate the elongation of carbon skeletons by the stepwise condensation of short carbon precursors. Due to their importance as drugs, the volume of data on polyketides is rapidly increasing and creating a need for computational analysis methods for efficient polyketide research. Moreover, the increasing use of genetic engineering to research new kinds of polyketides requires genome wide analysis. RESULTS: We describe a system named ASMPKS (Analysis System for Modular Polyketide Synthesis) for computational analysis of PKSs against genome sequences. It also provides overall management of information on modular PKS, including polyketide database construction, new PKS assembly, and chain visualization. ASMPKS operates on a web interface to construct the database and to analyze PKSs, allowing polyketide researchers to add their data to this database and to use it easily. In addition, the ASMPKS can predict functional modules for a protein sequence submitted by users, estimate the chemical composition of a polyketide synthesized from the modules, and display the carbon chain structure on the web interface. CONCLUSION: ASMPKS has powerful computation features to aid modular PKS research. As various factors, such as starter units and post-processing, are related to polyketide biosynthesis, ASMPKS will be improved through further development for study of the factors.

Identification of novel anti-angiogenic factors by in silico functional gene screening method.

Angiogenesis, the formation of new blood vessels out of pre-existing capillaries, occurs in a variety of pathophysiological conditions, and is regulated by a balance of angiogenic activators and inhibitors. To identify novel angiogenic factors, we developed a gene screening method by combining the prediction analysis of transcription factor (TF) binding site and the chromosomal localization analysis. First, we analyzed the promoter sequences from known angiogenesis-related factors using the MATINSPECTOR program in TRANSFAC database. Interestingly, we found that the binding site of LMO2 complex is highly conserved in the promoter regions of these factors. Second, we analyzed chromosome loci based on the hypothesis that angiogenesis-related factors might be co-localized in a specific chromosomal band. We found that angiogenesis-related factors are localized in specific 14 chromosomal bands including 5q31 and 19q13 using AngioDB and LocusLink database mining. From these two approaches, we identified 32 novel candidates that have the LMO2 complex binding site in their promoter and are located on one of 14 chromosomal bands. Among them, human recombinant troponin T and spectrin markedly inhibited the neovascularization in vivo and in vitro. Collectively, we suggest that the combination of the prediction analysis of TF binding site and the chromosomal localization analysis might be a useful strategy for gene screening of angiogenesis.

A method for identifying splice sites and translation start sites in human genomic sequences.

We describe a new method for identifying the sequences that signal the start of translation, and the boundaries between exons and introns (donor and acceptor sites) in human mRNA. According to the mandatory keyword, ORGANISM, and feature key, CDS, a large set of standard data for each signal site was extracted from the ASCII flat file, gbpri.seq, in the GenBank release 108.0. This was used to generate the scoring matrices, which summarize the sequence information for each signal site. The scoring matrices take into account the independent nucleotide frequencies between adjacent bases in each position within the signal site regions, and the relative weight on each nucleotide in proportion to their probabilities in the known signal sites. Using a scoring scheme that is based on the nucleotide scoring matrices, the method has great sensitivity and specificity when used to locate signals in uncharacterized human genomic DNA. These matrices are especially effective at distinguishing true and false sites.