Position-specific scoring matrix and hidden Markov model complement each other for the prediction of conopeptide superfamilies.

Classified into 16 superfamilies, conopeptides are the main component of cone snail venoms that attract growing interest in pharmacology and drug discovery. The conventional approach to assigning a conopeptide to a superfamily is based on a consensus signal peptide of the precursor sequence. While this information is available at the genomic or transcriptomic levels, it is not present in amino acid sequences of mature bioactives generated by proteomic studies. As the number of conopeptide sequences is increasing exponentially with the improvement in sequencing techniques, there is a growing need for automating superfamily elucidation. To face this challenge we have defined distinct models of the signal sequence, propeptide region and mature peptides for each of the superfamilies containing more than 5 members (14 out of 16). These models rely on two robust techniques namely, Position-Specific Scoring Matrices (PSSM, also named generalized profiles) and hidden Markov models (HMM). A total of 50 PSSMs and 47 HMM profiles were generated. We confirm that propeptide and mature regions can be used to efficiently classify conopeptides lacking a signal sequence. Furthermore, the combination of all three-region models demonstrated improvement in the classification rates and results emphasise how PSSM and HMM approaches complement each other for superfamily determination. The 97 models were validated and offer a straightforward method applicable to large sequence datasets.

ConoDictor: a tool for prediction of conopeptide superfamilies.

ConoDictor is a tool that enables fast and accurate classification of conopeptides into superfamilies based on their amino acid sequence. ConoDictor combines predictions from two complementary approaches-profile hidden Markov models and generalized profiles. Results appear in a browser as tables that can be downloaded in various formats. This application is particularly valuable in view of the exponentially increasing number of conopeptides that are being identified. ConoDictor was written in Perl using the common gateway interface module with a php submission page. Sequence matching is performed with hmmsearch from HMMER 3 and ps_scan.pl from the pftools 2.3 package. ConoDictor is freely accessible at http://conco.ebc.ee.

Identification and classification of conopeptides using profile Hidden Markov Models.

Conopeptides are small toxins produced by predatory marine snails of the genus Conus. They are studied with increasing intensity due to their potential in neurosciences and pharmacology. The number of existing conopeptides is estimated to be 1 million, but only about 1000 have been described to date. Thanks to new high-throughput sequencing technologies the number of known conopeptides is likely to increase exponentially in the near future. There is therefore a need for a fast and accurate computational method for identification and classification of the novel conopeptides in large data sets. 62 profile Hidden Markov Models (pHMMs) were built for prediction and classification of all described conopeptide superfamilies and families, based on the different parts of the corresponding protein sequences. These models showed very high specificity in detection of new peptides. 56 out of 62 models do not give a single false positive in a test with the entire UniProtKB/Swiss-Prot protein sequence database. Our study demonstrates the usefulness of mature peptide models for automatic classification with accuracy of 96% for the mature peptide models and 100% for the pro- and signal peptide models. Our conopeptide profile HMMs can be used for finding and annotation of new conopeptides from large datasets generated by transcriptome or genome sequencing. To our knowledge this is the first time this kind of computational method has been applied to predict all known conopeptide superfamilies and some conopeptide families.

Generation and characterization of a single-chain Fv antibody against G, a hedgehog signaling pathway transcription factor.

Gli3 is a key regulator of development, controlling multiple patterning steps. Here we report the generation of a scFv antibody specific to the repressor domain of human Gli3. We show that this scFv retains the binding capacity of its parent anti-Gli3 monoclonal antibody derived from hybridoma clone 5E1. When expressed in mammalian cells, the anti-Gli3 scFv co-localizes with intracellular Gli3. Immunocytochemical staining of the intrabody in Gli3-positive TM4 cells shows a distinct perinuclear cytoplasmic localization. Such a scFv constitutes a useful tool for studying transcriptional regulation of the hedgehog pathway in mammals and offers a starting point for developing novel Gli-related therapeutic intrabodies.

Cloning and characterization of glucokinase from a methylotrophic yeast Hansenula polymorpha: different effects on glucose repression in H. polymorpha and Saccharomyces cerevisiae.

Glucokinase gene (HPGLK1) was cloned from a methylotrophic yeast Hansenula polymorpha by complementation of glucose-phosphorylation deficiency in a H. polymorpha double kinase-negative mutant A31-10 by a genomic library. An open reading frame of 1416 nt encoding a 471-amino-acid protein with calculated molecular weight 51.6 kDa was characterized in the genomic insert of the plasmid pH3. The protein sequence deduced from HPGLK1 exhibited 55 and 46% identity with glucokinases from Saccharomyces cerevisiae and Aspergillus niger, respectively. The enzyme phosphorylated glucose, mannose and 2-deoxyglucose, but not fructose. Transformation of HPGLK1 into A31-10 restored glucose repression of alcohol oxidase and catalase in the mutant. Transformation of HPGLK1 into S. cerevisiae triple kinase-negative mutant DFY632 showed that H. polymorpha glucokinase cannot transmit the glucose repression signal in S. CEREVSIAE: synthesis of invertase and maltase in respective transformants was insensitive to glucose repression similarly to S. cerevisiae DFY568 possessing only glucokinase.