Current Bioinformatics - Volume 2, Issue 1, 2007

DBMODELING is a relational database of annotated comparative protein structure models and their metabolic pathway characterization. It is focused on enzymes identified in the genomes of Mycobacterium tuberculosis and Xylella fastidiosa. The main goal of the present database is to provide structural models to be used in docking simulations and drug design. However, since the accuracy of structural models is highly dependent on sequence identity between template and target, it is necessary to make clear to the user that only models which show high structural quality should be used in such efforts. Molecular modeling of these genomes generated a database, in which all structural models were built using alignments presenting more than 30% of sequence identity, generating models with medium and high accuracy. All models in the database are publicly accessible at http://www.biocristalografia.df.ibilce.unesp.br/tools. DBMODELING user interface provides users friendly menus, so that all information can be printed in one step from any web browser. Furthermore, DBMODELING also provides a docking interface, which allows the user to carry out geometric docking simulation, against the molecular models available in the database. There are three other important homology model databases: MODBASE, SWISSMODEL, and GTOP. The main applications of these databases are described in the present article.

Novel biomarker identification and drug target validation are highly complex and resource-intensive processes, requiring an integral use of various tools, approaches and information. The recently developed proteomic technology features high-throughput parallel analysis of thousands of proteins in individual patients and amount populations and thus opens up the possibility of providing more details at a global level on the molecular mechanisms. With regularly updated public databases, bioinformatics can contribute to these processes by providing functional information of target candidates and correlating this information to the biological pathways. In this review, we outline recent advances of bioinformatic application in proteomic research on biomarker discovery and drug target validation. Specifically, we highlight how bioinformatics can facilitate the proteomic studies of biomarker identification and drug target validation.

IMGT®, the international ImMunoGeneTics information system® (http://imgt.cines.fr) provides a common access to expertly annotated data on the genome, proteome, genetics and structure of immunoglobulins (IG), T cell receptors (TR), major histocompatibility complex (MHC) of human and other vertebrates, and related proteins of the immune system (RPI) of any species. RPI include proteins that belong to the immunoglobulin superfamily (IgSF) and MHC superfamily (MhcSF). IMGT has set up a unique numbering system, which takes into account the structural features of the Ig-like and Mhc-like domains. In this paper, we describe the IMGT Scientific chart rules for the description of the IgSF V type and C type and of the MhcSF G type domains. These rules are based on the IMGT-ONTOLOGY concepts and are applicable for the sequence and structure analysis, whatever the species, the IgSF or MhcSF protein, or the chain type. We present examples of IMGT Colliers de Perles of IgSF V type (V-DOMAIN and V-LIKE-DOMAIN), C type (C-DOMAIN and C-LIKE-DOMAIN) and MhcSF G type (G-DOMAIN and G-LIKE-DOMAIN) based on the IMGT unique numbering. These standardized two-dimensional graphical representations are particularly useful for antibody engineering, sequence- structure analysis, visualization and comparison of positions for mutations, polymorphisms and contact analysis.

Computational approaches to predict protein structure have gained much attention in the fields of protein engineering and protein folding studies. Due to the vastness of conformational space, one of the major tasks is to restrain the flexibility of protein structure and reduce the search space. Many studies have revealed that, with the information of disulfide connectivity available, the search in conformational space can be dramatically reduced and lead to significant improvements in the prediction accuracy. As a result, predicting disulfide connectivity using bioinformatics approaches is of great interest nowadays. In this review, recent advances in disulfide connectivity predictions will be presented in detail. The predictions of disulfide bonding state and disulfide connectivity patterns will be covered. The effects of the features on the prediction accuracy will be compared and discussed. Finally, the practical uses and applications of the predicted disulfide bonding patterns will be illustrated. This review should serve as a reference for issues related to protein structure predictions.

Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks. We then consider the major bioinformatics applications, such as alignment, labeling, and profiling of sequences, protein structure prediction, and pattern recognition. We finally provide a critical appraisal of the use and perspectives of HMMs in bioinformatics.

With the advent of whole genome sequencing, large-scale proteomics has rapidly come to dominate the postgenomic age. As such, tandem mass spectrometry has emerged as the most promising and powerful technique in this area but analysis of raw spectra remains one of the principle bottlenecks to making effective use of the technology. Analytical approaches for identifying proteins from MS/MS data fall into two categories: comparing measured fragment spectra to theoretical spectra from sequence databases and de novo peptide sequencing. Available methods still have weaknesses, highlighting the need for new powerful algorithms that are able to exploit the enormous volume of data generated by proteomic experiments. Recent efforts have also been directed towards the identification of post-translational modifications, biomarker discovery and quantitative proteomics. Overall, the intended goal of this review is to give as thorough as possible an overview of state-of-the-art approaches and tools developed to analyze tandem mass spectra in different fields and discuss future directions aimed at overcoming the limits of present methods.