| Current
Bioinformatics
ISSN: 1574-8936
Current Bioinformatics
Volume 2, Number 1, January 2007
Contents
Molecular Modeling Databases: A New Way in the Search
of Protein Targets for Drug Development Pp. 1-10
Nelson José Freitas da Silveira, Carlos Eduardo
Bonalumi, Helen Andrade Arcuri and Walter Filgueira de Azevedo
Junior
[Abstract] [Full
text article]
Bioinformatic Application in Proteomic Research on
Biomarker Discovery and Drug Target Validation Pp.
11-20
Ying Wang, Jen-Fu Chiu and Qing-Yu He
[Abstract] [Full
text article]
IMGT Colliers de Perles: Standardized Sequence-Structure
Representations of the IgSF and MhcSF Superfamily Domains
Pp. 21-30
Quentin Kaas and Marie-Paule Lefranc
[Abstract] [Full
text article]
Recent Advances in Disulfide Connectivity Predictions
Pp. 31-47
Hsuan-Liang Liu
[Abstract] [Full
text article]
Hidden Markov Models in Bioinformatics Pp.
49-61
Valeria De Fonzo, Filippo Aluffi-Pentini and Valerio Parisi
[Abstract] [Full
text article]
Mass Spectrometry Data Analysis in the Proteomics
Era Pp. 63-93
Francesca Forner, Leonard J. Foster and Stefano Toppo
[Abstract] [Full
text article]
Abstracts
[Back to top]
Molecular Modeling Databases: A New Way in the Search of Protein
Targets for Drug Development
Nelson José Freitas da Silveira, Carlos Eduardo
Bonalumi, Helen Andrade Arcuri and Walter Filgueira de Azevedo
Junior
[Full
text article]
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.
[Back to top]
Bioinformatic Application in Proteomic Research on
Biomarker Discovery and Drug Target Validation
Ying Wang, Jen-Fu Chiu and Qing-Yu He
[Full
text 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.
[Back to top]
IMGT Colliers de Perles: Standardized Sequence-Structure
Representations of the IgSF and MhcSF Superfamily Domains
Quentin Kaas and Marie-Paule Lefranc
[Full
text article]
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.
[Back to top]
Recent Advances in Disulfide Connectivity Predictions
Hsuan-Liang Liu
[Full
text article]
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.
[Back to top]
Hidden Markov Models in Bioinformatics
Valeria De Fonzo, Filippo Aluffi-Pentini and Valerio Parisi
[Full
text article]
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.
[Back to top]
Mass Spectrometry Data Analysis in the Proteomics
Era
Francesca Forner, Leonard J. Foster and Stefano Toppo
[Full
text article]
With the advent of whole genome sequencing, large-scale proteomics
has rapidly come to dominate the post-genomic 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.
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