In response to the need for greater cohesion among regional conferences, the Asia Pacific Bioinformatics Network (APBioNET) set out in 2015 to realize a long-held aspiration-a single, unifying bioinformatics "super conference" for the Asia & Pacific community. Nearly a decade of persistence, coordination, and coalition-building led to the inaugural Asia & Pacific Bioinformatics Joint Congress (APBJC2024) in Okinawa, Japan. Now established as a triennial event, APBJC stands as a testament to the power of collective vision and shared purpose, offering a unifying platform for regional collaboration and scientific exchange. Tagline: Bringing a Region Together: The Making of APBJC.

Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification.

Summary: Large-scale peptide mass spectrometry (MS)/MS reference libraries are essential for the comprehensive analysis of data-independent acquisition (DIA) MS datasets, providing a comprehensive set of spectra for identification and quantification of proteins. We have developed a novel web-based R-package (iSwathX) for combining reference libraries that is compatible with different DIA analysis software. This open-source web GUI automates the process of normalization and combination of spectral libraries and provides a user-friendly method for performing library format conversions, analysis and visualizations, with no need for programing familiarity. Availability and implementation: iSwathX is freely accessible at https://biolinfo.shinyapps.io/iSwathX with the R-package and Shiny source code available from GitHub (https://github.com/znoor/iSwathX). Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: (-)-Balanol is an ATP-mimicking inhibitor that non-selectively targets protein kinase C (PKC) isozymes and cAMP-dependent protein kinase (PKA). While PKA constantly shows tumor promoting activities, PKC isozymes can ambiguously be tumor promoters or suppressors. In particular, PKCε is frequently implicated in tumorigenesis and a potential target for anticancer drugs. We recently reported that the C5(S)-fluorinated balanol analogue (balanoid 1c) had improved binding affinity and selectivity for PKCε but not to the other novel PKC isozymes, which share a highly similar ATP site. The underlying basis for this fluorine-based selectivity is not entirely comprehended and needs to be investigated further for the development of ATP mimic inhibitors specific for PKCε. RESULTS: Using molecular dynamics (MD) simulations assisted by homology modelling and sequence analysis, we have studied the fluorine-based selectivity in the highly similar ATP sites of novel PKC (nPKC) isozymes. The study suggests that every nPKC isozyme has different dynamics behaviour in both apo and 1c-bound forms. Interestingly, the apo form of PKCε, where 1c binds strongly, shows the highest degree of flexibility which dramatically decreases after binding 1c. CONCLUSIONS: For the first time to the best of our knowledge, we found that the origin of 1c selectivity for PKCε comes from the unique dynamics feature of each PKC isozyme. Fluorine conformational control in 1c can synergize with and lock down the dynamics of PKCε, which optimize binding interactions with the ATP site residues of the enzyme, particularly the invariant Lys437. This finding has implications for further rational design of balanol-based PKCε inhibitors for cancer drug development.

(-)-Balanol is an ATP mimic that inhibits protein kinase C (PKC) isozymes and cAMP-dependent protein kinase (PKA) with limited selectivity. While PKA is a tumour promoter, PKC isozymes act as tumour promoters or suppressors, depending on the cancer type. In particular, PKCε is frequently implicated in cancer promotion, making it a potential target for anticancer drugs. To improve isozyme selectivity of balanol, exhaustive structural and activity relationship (SAR) studies have been performed in the last two decades, but with limited success. More recently, fluorination on balanol has shown improved selectivity for PKCε, although the fluorine effect is not yet clearly understood. Understanding the origin to this fluorine-based selectivity will be valuable for designing better balanol-based ATP mimicking inhibitors. Computational approaches such as molecular dynamics (MD) simulations can decipher the fluorine effect, provided that correct charges have been assigned to a ligand. Balanol analogues have multiple ionisable functional groups and the effect of fluorine substitutions on the exact charge state of each analogue bound to PKA and to PKCε needs to be thoroughly investigated in order to design highly selective inhibitors for therapeutic applications. We explored the charge states of novel fluorinated balanol analogues using MD simulations. For different potential charge states of these analogues, Molecular Mechanics Generalized Born Surface Area (MMGBSA) binding energy values were computed. This study suggests that balanol and the most potent fluorinated analogue (5 S fluorine substitution on the azepane ring), have charges on the azepane ring (N1), and the phenolic (C6′′OH) and the carboxylate (C15′′O 2 H) groups on the benzophenone moiety, when bound to PKCε as well as PKA. To the best our knowledge, this is the first study showing that the phenolate group is charged in balanol and its analogues binding to the ATP site of PKCε. Correct charge assignments of ligands are important to obtain predicted binding energy values from MD simulations that reflect experimental values. Both fluorination and the local enzymatic environment of the ATP site can influence the exact charge states of balanol analogues. Overall, this study is highly valuable for further rational design of potent balanol analogues selective to PKCε.

The International Conference on Bioinformatics (InCoB) has been publishing peer-reviewed conference papers in BMC Bioinformatics since 2006. Of the 44 articles accepted for publication in supplement issues of BMC Bioinformatics, BMC Genomics, BMC Medical Genomics and BMC Systems Biology, 24 articles with a bioinformatics or systems biology focus are reviewed in this editorial. InCoB2017 is scheduled to be held in Shenzen, China, September 20-22, 2017.

GIW/InCoB2015 the joint 26th International Conference on Genome Informatics (GIW) and 14th International Conference on Bioinformatics (InCoB) held in Tokyo, September 9-11, 2015 was attended by over 200 delegates. Fifty-one out of 89 oral presentations were based on research articles accepted for publication in four BMC journal supplements and three other journals. Sixteen articles in this supplement and six articles in the BMC Systems Biology GIW/InCoB2015 Supplement are covered by this introduction. The topics range from genome informatics, protein structure informatics, image analysis to biological networks and biomarker discovery.

BACKGROUND: Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding. RESULTS: Our analysis showed ~60% of protein complexes with surface polarity greater than interface polarity (designated as class A). However, a considerable number of complexes (~40%) have interface polarity greater than surface polarity, (designated as class B), with a significantly different p-value of 1.66E-45 from class A. Comprehensive analyses of protein complexes show that interface features such as interface area, interface polarity abundance, solvation free energy gain upon interface formation, binding energy and the percentage of interface charged residue abundance distinguish among class A and class B complexes, while electrostatic visualization maps also help differentiate interface classes among complexes. CONCLUSIONS: Class A complexes are classical with abundant non-polar interactions at the interface; however class B complexes have abundant polar interactions at the interface, similar to protein surface characteristics. Five physicochemical interface features analyzed from the protein heterodimer dataset are discriminatory among the interface residue-level classes. These novel observations find application in developing residue-level models for protein-protein binding prediction, protein-protein docking studies and interface inhibitor design as drugs.

Since 2006, the International Conference on Bioinformatics (InCoB) has been publishing selected papers in BMC Bioinformatics. Papers within the scope of the journal from the 13th InCoB July 31-2 August, 2014 in Sydney, Australia have been compiled in this supplement. These span protein and proteome informatics, structural bioinformatics, software development and bioimaging to pharmacoinformatics and disease informatics, representing the breadth of bioinformatics research in the Asia-Pacific.

10.1371/journal.pcbi.1003317

BACKGROUND: The transcriptome of an organism can be studied with the analysis of expressed sequence tag (EST) data sets that offers a rapid and cost effective approach with several new and updated bioinformatics approaches and tools for assembly and annotation. The comprehensive analyses comprehend an organism along with the genome and proteome analysis. With the advent of large-scale sequencing projects and generation of sequence data at protein and cDNA levels, automated analysis pipeline is necessary to store, organize and annotate ESTs. RESULTS: TranSeqAnnotator is a workflow for large-scale analysis of transcriptomic data with the most appropriate bioinformatics tools for data management and analysis. The pipeline automatically cleans, clusters, assembles and generates consensus sequences, conceptually translates these into possible protein products and assigns putative function based on various DNA and protein similarity searches. Excretory/secretory (ES) proteins inferred from ESTs/short reads are also identified. The TranSeqAnnotator accepts FASTA format raw and quality ESTs along with protein and short read sequences and are analysed with user selected programs. After pre-processing and assembly, the dataset is annotated at the nucleotide, protein and ES protein levels. CONCLUSION: TranSeqAnnotator has been developed in a Linux cluster, to perform an exhaustive and reliable analysis and provide detailed annotation. TranSeqAnnotator outputs gene ontologies, protein functional identifications in terms of mapping to protein domains and metabolic pathways. The pipeline is applied to annotate large EST datasets to identify several novel and known genes with therapeutic experimental validations and could serve as potential targets for parasite intervention. TransSeqAnnotator is freely available for the scientific community at http://estexplorer.biolinfo.org/TranSeqAnnotator/.

Ten years ago when Asia-Pacific Bioinformatics Network held the first International Conference on Bioinformatics (InCoB) in Bangkok its theme was North-South Networking. At that time InCoB aimed to provide biologists and bioinformatics researchers in the Asia-Pacific region a forum to meet, interact with, and disseminate knowledge about the burgeoning field of bioinformatics. Meanwhile InCoB has evolved into a major regional bioinformatics conference that attracts not only talented and established scientists from the region but increasingly also from East Asia, North America and Europe. Since 2006 InCoB yielded 114 articles in BMC Bioinformatics supplement issues that have been cited nearly 1,000 times to date. In part, these developments reflect the success of bioinformatics education and continuous efforts to integrate and utilize bioinformatics in biotechnology and biosciences in the Asia-Pacific region. A cross-section of research leading from biological data to knowledge and to technological applications, the InCoB2012 theme, is introduced in this editorial. Other highlights included sessions organized by the Pan-Asian Pacific Genome Initiative and a Machine Learning in Immunology competition. InCoB2013 is scheduled for September 18-21, 2013 at Suzhou, China.

UNLABELLED: Sequence-structure-function information is critical in understanding the mechanism of pMHC and TR/pMHC binding and recognition. A database for sequence-structure-function information on pMHC and TR/pMHC interactions, MHC-Peptide Interaction Database-TR version 2 (MPID-T2), is now available augmented with the latest PDB and IMGT/3Dstructure-DB data, advanced features and new parameters for the analysis of pMHC and TR/pMHC structures. AVAILABILITY: http://biolinfo.org/mpid-t2. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Infections due to parasitic nematodes are common causes of morbidity and fatality around the world especially in developing nations. At present however, there are only three major classes of drugs for treating human nematode infections. Additionally the scientific knowledge on the mechanism of action and the reason for the resistance to these drugs is poorly understood. Commercial incentives to design drugs that are endemic to developing countries are limited therefore, virtual screening in academic settings can play a vital role is discovering novel drugs useful against neglected diseases. In this study we propose to build robust machine learning model to classify and screen compounds active against parasitic nematodes. RESULTS: A set of compounds active against parasitic nematodes were collated from various literature sources including PubChem while the inactive set was derived from DrugBank database. The support vector machine (SVM) algorithm was used for model development, and stratified ten-fold cross validation was used to evaluate the performance of each classifier. The best results were obtained using the radial basis function kernel. The SVM method achieved an accuracy of 81.79% on an independent test set. Using the model developed above, we were able to indentify novel compounds with potential anthelmintic activity. CONCLUSION: In this study, we successfully present the SVM approach for predicting compounds active against parasitic nematodes which suggests the effectiveness of computational approaches for antiparasitic drug discovery. Although, the accuracy obtained is lower than the previously reported in a similar study but we believe that our model is more robust because we intentionally employed stringent criteria to select inactive dataset thus making it difficult for the model to classify compounds. The method presents an alternative approach to the existing traditional methods and may be useful for predicting hitherto novel anthelmintic compounds.

The 2011 International Conference on Bioinformatics (InCoB) conference, which is the annual scientific conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted by Kuala Lumpur, Malaysia, is co-organized with the first ISCB-Asia conference of the International Society for Computational Biology (ISCB). InCoB and the sequencing of the human genome are both celebrating their tenth anniversaries and InCoB's goalposts for the next decade, implementing standards in bioinformatics and globally distributed computational networks, will be discussed and adopted at this conference. Of the 49 manuscripts (selected from 104 submissions) accepted to BMC Genomics and BMC Bioinformatics conference supplements, 24 are featured in this issue, covering software tools, genome/proteome analysis, systems biology (networks, pathways, bioimaging) and drug discovery and design.

BACKGROUND: Understanding cellular systems requires the knowledge of a protein's subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary for developing a robust SCL prediction methodology. RESULTS: Based on protein-protein interaction (PPI) and metabolite-linked protein interaction (MLPI) networks of proteins, we have compared, contrasted and analysed the statistical properties across different subcellular compartments. We integrated PPI and metabolic datasets with SCL information of human proteins from LOCATE and GOA (Gene Ontology Annotation) and estimated three statistical properties: Chi-square (χ2) test, Paired Localisation Correlation Profile (PLCP) and network topological measures. For the PPI network, Pearson's chi-square test shows that for the same SCL category, twice as many interacting protein pairs are observed than estimated when compared to non-interacting protein pairs (χ2 = 1270.19, P-value < 2.2 × 10(-16)), whereas for MLPI, metabolite-linked protein pairs having the same SCL are observed 20% more than expected, compared to non-metabolite linked proteins (χ2 = 110.02, P-value < 2.2 x10(-16)). To address the issue of proteins with multiple SCLs, we have specifically used the PLCP (Pair Localization Correlation Profile) measure. PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins. Metabolite-linked protein pairs are restricted to specific compartments such as the mitochondrion (P-value < 6.0e-07), the lysosome (P-value < 4.7e-05) and the Golgi apparatus (P-value < 1.0e-15). These findings indicate that the metabolic network adds value to the information in the PPI network for the localisation process of proteins in human subcellular compartments. CONCLUSIONS: The MLPI network differs significantly from the PPI network in its SCL distribution. The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.

The International Conference on Bioinformatics (InCoB), the annual conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted in one of countries of the Asia-Pacific region. The 2010 conference was awarded to Japan and has attracted more than one hundred high-quality research paper submissions. Thorough peer reviewing resulted in 47 (43.5%) accepted papers out of 108 submissions. Submissions from Japan, R.O. Korea, P.R. China, Australia, Singapore and U.S.A totaled 43.8% and contributed to 57.4% of accepted papers. Manuscripts originating from Taiwan and India added up to 42.8% of submissions and 28.3% of acceptances. The fifteen articles published in this BMC Bioinformatics supplement cover disease informatics, structural bioinformatics and drug design, biological databases and software tools, signaling pathways, gene regulatory and biochemical networks, evolution and sequence analysis.

BACKGROUND: Amino-terminal signal peptides (SPs) are short regions that guide the targeting of secretory proteins to the correct subcellular compartments in the cell. They are cleaved off upon the passenger protein reaching its destination. The explosive growth in sequencing technologies has led to the deposition of vast numbers of protein sequences necessitating rapid functional annotation techniques, with subcellular localization being a key feature. Of the myriad software prediction tools developed to automate the task of assigning the SP cleavage site of these new sequences, we review here, the performance and reliability of commonly used SP prediction tools. RESULTS: The available signal peptide data has been manually curated and organized into three datasets representing eukaryotes, Gram-positive and Gram-negative bacteria. These datasets are used to evaluate thirteen prediction tools that are publicly available. SignalP (both the HMM and ANN versions) maintains consistency and achieves the best overall accuracy in all three benchmarking experiments, ranging from 0.872 to 0.914 although other prediction tools are narrowing the performance gap. CONCLUSION: The majority of the tools evaluated in this study encounter no difficulty in discriminating between secretory and non-secretory proteins. The challenge clearly remains with pinpointing the correct SP cleavage site. The composite scoring schemes employed by SignalP may help to explain its accuracy. Prediction task is divided into a number of separate steps, thus allowing each score to tackle a particular aspect of the prediction.

BACKGROUND: The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets. RESULTS: We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets. CONCLUSION: The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.

The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation from 1998, was organized as the 8th International Conference on Bioinformatics (InCoB), Sept. 9-11, 2009 at Biopolis, Singapore. InCoB has actively engaged researchers from the area of life sciences, systems biology and clinicians, to facilitate greater synergy between these groups. To encourage bioinformatics students and new researchers, tutorials and student symposium, the Singapore Symposium on Computational Biology (SYMBIO) were organized, along with the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and the Clinical Bioinformatics (CBAS) Symposium. However, to many students and young researchers, pursuing a career in a multi-disciplinary area such as bioinformatics poses a Himalayan challenge. A collection to tips is presented here to provide signposts on the road to a career in bioinformatics. An overview of the application of bioinformatics to traditional and emerging areas, published in this supplement, is also presented to provide possible future avenues of bioinformatics investigation. A case study on the application of e-learning tools in undergraduate bioinformatics curriculum provides information on how to go impart targeted education, to sustain bioinformatics in the Asia-Pacific region. The next InCoB is scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010.

Scientific advances over the last 50 years have provided a basis for parallel revolutions in engineering and in biomedicine. Advances in automation and miniaturization have enabled the development of modern instrumentation that supports large-scale measurement of biological entities. The burgeoning fields of genomics and proteomics, as well as other ‘omics’, keep generating large amounts of molecular expression and interaction data. Advances in cytometry enable quantification of cellular states and related functional properties. The latest scanning and imaging technologies have made it possible to scan and make detailed measurements of whole organisms. Clinical data complement biological data, enabling detailed descriptions of various healthy and diseased states, progression and responses to therapies. The availability of data representing various biological states, processes, and their time dependencies enable the study of biological systems at various levels of organization, from molecule to organism, and even population levels. Multiple sources of data support a rapidly growing body of biomedical knowledge; however our ability to analyze and interpret these data lags far behind data generation and storage capacity. The field of bioinformatics is experiencing rapid growth, principally in three directions: management, analysis and modeling of biological data. Data management refers to acquisition, management, dissemination and basic interpretation of biological data. Analysis refers to integrative analysis, as well as complex analysis of biological data. Modeling of biological data refers to in silico approaches such as mathematical and computational modeling, simulation and prediction of biological systems and processes. Bioinformatics uses information technologies to gather data from biomedicine and translate it into information and various forms of knowledge. Information technologies support this quest: hardware, networking, databases, algorithms and computational models are used for tasks ranging from simple retrieval of publication abstracts or data from molecular databases, to complex simulations that complement multi-step experimentation. Early bioinformatics infrastructure of the 1980s comprised simple molecular databases containing several thousand molecular entries, basic sequence comparison algorithms and simple mathematical models. Current infrastructure comprises a network of databases, web-accessible analytical tools, computer networks and supercomputers that connect users to bioinformatics resources across the world. The revolutions in information technology and biotechnology and related advances in instrumentation produce huge amounts of data. The interpretation of these data and extraction of new knowledge requires bioinformatics approaches of ever increasing sophistication. This special issue of Briefings in Bioinformatics covers eight bioinformatics technologies which have made a significant impact on both the theory and practice of biomedical research. These are representative of important bioinformatics topics that can be described as enabling technologies for generating and sharing of biological knowledge. Lefranc et al. describe IMGT, a system that integrates complex immunogenetic data, ontologies, analysis tools and web resources. This system offers a formal description of objects, processes and relations necessary for conceptualization of knowledge in molecular immunogenetics and provides tools for multi-scale approaches at various levels of hierarchy from molecule to organism and, possibly, population level. In particular, this review offers an insight on merging the conceptual models and ontologies with sequence databases and their combination with related analytical tools. The IMGT technology demonstrates the conversion of precisely defined biological concepts into an integrated bioinformatics environment. The article by Standley et al. describes the latest developments in molecular structure databases, in particular the enhancement of structural information with biochemical, functional and experimental information. A new generation of tools enable querying by keywords/text, sequence similarity and structure similarity. Advanced visualization tools have been developed to assist the analysis and interpretation of molecular structures. The new generation of tools improves identification of distant functional relationship relative to traditional sequence comparison-based methods. Multiple sequence alignment (MSA) methods are among the most important bioinformatics tools whereby biological sequences are compared to identify the regions of similarity that confer evolutionary, structural or functional relatedness. Because the MSA problem is NP-hard, the optimal solution is not possible unless MSA involves a small number of sequences. The rapid growth of biological sequence data, both in length and number of sequences, the need for identification of distant homologs and for web accessibility require increasingly sophisticated algorithms. Katoh and Toh have described the latest version of the MAFFT program for MSA. MAFFT is an example of tools where high-quality MSAs can be performed rapidly over the Internet. Kumar et al. have described MEGA, a tool for evolutionary analysis of biological sequences. MEGA is designed for comparative analysis of genes and proteins and investigation of their evolutionary relationships. It offers a convenient means for managing data from local files and web repositories, for statistical analysis of data, and visualization of results. In their article, the authors describe the design features that make MEGA a biologist-centric tool, that enable complex analyses where the results are clearly, unambiguously presented to the user through advanced visualization tools. The analysis of complex biological data brings formidable challenges to the developers of advanced bioinformatics tools. Formal mathematical and statistical approaches are complemented with tools of computational intelligence that employ heuristic algorithms. Computational intelligence methods employ fuzzy logic, search and classification to study systems than learn, evolve and adapt. Fogel provides a primer of computational intelligence methods and their application to selected biological problems. Systems biology is concerned with the study of complex interactions in biological systems. It uses integrative rather than reductionist approach to discover properties arising from combination of multiple biological entities and their effects on multiple levels of biological organization. Hu et al. have described a visual data mining system VisANT that integrates multiple data types, performs the analysis of complex biological networks, and displays the results through visualization tools. Transcriptional regulation is a key area of systems biology. Wingender's article describes the TRANSFAC database and analysis of system whose original aim was to provide a genome-wide map of interaction sites for transcription factors. Over the years, TRANSFAC became the industry standard. However, with the growth of genomic information and genome mapping of model organisms, the original development evolved into a system that provides classifications and ontologies for the domain, and also integrates a catalog of transcription networks and their parts with a set of prediction tools. Hunter et al. have described the Physiome project that focuses on multi-scale modeling necessary for relating molecular and cellular processes to the events observed at the organ or whole organism. Such multi-scale modeling requires advanced techniques of mechanistic modeling, visualization and numerical techniques. Advances in bioinformatics are reshaping biomedical research and applications in biotechnology. Integration of large quantities of data, instant access to information, and the ability to perform complex analyses and simulations provide the capacity to amplify the results of biomedical research many-fold. Biomedicine is increasingly becoming an information-rich field. Deciphering qualitative relationships between components of living organisms, and quantification of interactions will depend on further development of bioinformatics technologies. The combination of large-scale screening experiments with large-scale simulations in silico is already used for selection of a limited number of key experiments, resulting in decreased cost and time required for biomedical research and discovery.

BACKGROUND: Signal peptides (SPs) mediate the targeting of secretory precursor proteins to the correct subcellular compartments in prokaryotes and eukaryotes. Identifying these transient peptides is crucial to the medical, food and beverage and biotechnology industries yet our understanding of these peptides remains limited. This paper examines the most common type of signal peptides cleavable by the endoprotease signal peptidase I (SPase I), and the residues flanking the cleavage sites of three groups of signal peptide sequences, namely (i) eukaryotes (Euk) (ii) Gram-positive (Gram+) bacteria, and (iii) Gram-negative (Gram-) bacteria. RESULTS: In this study, 2352 secretory peptide sequences from a variety of organisms with amino-terminal SPs are extracted from the manually curated SPdb database for analysis based on physicochemical properties such as pI, aliphatic index, GRAVY score, hydrophobicity, net charge and position-specific residue preferences. Our findings show that the three groups share several similarities in general, but they display distinctive features upon examination in terms of their amino acid compositions and frequencies, and various physico-chemical properties. Thus, analysis or prediction of their sequences should be separated and treated as distinct groups. CONCLUSION: We conclude that the peptide segment recognized by SPase I extends to the start of the mature protein to a limited extent, upon our survey of the amino acid residues surrounding the cleavage processing site. These flanking residues possibly influence the cleavage processing and contribute to non-canonical cleavage sites. Our findings are applicable in defining more accurate prediction tools for recognition and identification of cleavage site of SPs.

BACKGROUND: Type I signal peptidases (SPases) are essential membrane-bound serine proteases responsible for the cleavage of signal peptides from proteins that are translocated across biological membranes. The crystal structure of SPase in complex with signal peptide has not been solved and their substrate-binding site and binding specificities remain poorly understood. We report here a structure-based model for Escherichia coli DsbA 13-25 in complex with its endogenous type I SPase. RESULTS: The bound structure of DsbA 13-25 in complex with its endogenous type I SPase reported here reveals the existence of an extended conformation of the precursor protein with a pronounced backbone twist between positions P3 and P1'. Residues 13-25 of DsbA occupy, and thereby define 13 subsites, S7 to S6', within the SPase substrate-binding site. The newly defined subsites, S1' to S6' play critical roles in the substrate specificities of E. coli SPase. Our results are in accord with available experimental data. CONCLUSION: Collectively, the results of this study provide interesting new insights into the binding conformation of signal peptides and the substrate-binding site of E. coli SPase. This is the first report on the modeling of a precursor protein into the entire SPase binding site. Together with the conserved precursor protein binding conformation, the existing and newly identified substrate binding sites readily explain SPase cleavage fidelity, consistent with existing biochemical results and solution structures of inhibitors in complex with E. coli SPase. Our data suggests that both signal and mature moiety sequences play important roles and should be considered in the development of predictive tools.

BACKGROUND: The customary medicinal plant knowledge possessed by the Australian Aboriginal people is a significant resource. Published information on it is scattered throughout the literature, in heterogeneous data formats, and is scattered among various Aboriginal communities across Australia, due to a multiplicity of languages. This ancient knowledge is at risk due to loss of biodiversity, cultural impact and the demise of many of its custodians. We have developed the Customary Medicinal Knowledgebase (CMKb), an integrated multidisciplinary resource, to document, conserve and disseminate this knowledge. DESCRIPTION: CMKb is an online relational database for collating, disseminating, visualising and analysing initially public domain data on customary medicinal plants. The database stores information related to taxonomy, phytochemistry, biogeography, biological activities of customary medicinal plant species as well as images of individual species. The database can be accessed at http://biolinfo.org/cmkb. Known bioactive molecules are characterized within the chemoinformatics module of CMKb, with functions available for molecular editing and visualization. CONCLUSION: CMKb has been developed as a prototype data resource for documenting, integrating, disseminating, analysing multidisciplinary customary medicinal plant data from Australia and to facilitate user-defined complex querying. Each species in CMKb is linked to online resources such as the Integrated Taxonomic Information System (ITIS), NCBI Taxonomy, Australia's SpeciesLinks-Integrated Botanical Information System (IBIS) and Google images. The bioactive compounds are linked to the PubChem database. Overall, CMKb serves as a single knowledgebase for holistic plant-derived therapeutics and can be used as an information resource for biodiversity conservation, to lead discovery and conservation of customary medicinal knowledge.

BACKGROUND: The Potato type II (Pot II) family of proteinase inhibitors plays critical roles in the defense system of plants from Solanaceae family against pests. To better understand the evolution of this family, we investigated the correlation between sequence and structural repeats within this family and the evolution and molecular adaptation of Pot II genes through computational analysis, using the putative ancestral domain sequence as the basic repeat unit. RESULTS: Our analysis discovered the following interesting findings in Pot II family. (1) We classified the structural domains in Pot II family into three types (original repeat domain, circularly permuted domain, the two-chain domain) according to the existence of two linkers between the two domain components, which clearly show the circular permutation relationship between the original repeat domain and circularly permuted domain. (2) The permuted domains appear more stable than original repeat domain, from available structural information. Therefore, we proposed a multiple-repeat sequence is likely to adopt the permuted domain from contiguous sequence segments, with the N- and C-termini forming a single non-contiguous structural domain, linking the bracelet of tandem repeats. (3) The analysis of nonsynonymous/synonymous substitution rates ratio in Pot II domain revealed heterogeneous selective pressures among amino acid sites: the reactive site is under positive Darwinian selection (providing different specificity to target varieties of proteinases) while the cysteine scaffold is under purifying selection (essential for maintaining the fold). (4) For multi-repeat Pot II genes from Nicotiana genus, the proteolytic processing site is under positive Darwinian selection (which may improve the cleavage efficiency). CONCLUSION: This paper provides comprehensive analysis and characterization of Pot II family, and enlightens our understanding on the strategies (Gene and domain duplication, structural circular permutation and molecular adaptation) of Solanaceae plants for defending pathogenic attacks through the evolution of Pot II genes.

BACKGROUND: The analysis of expressed sequence tags (EST) offers a rapid and cost effective approach to elucidate the transcriptome of an organism, but requires several computational methods for assembly and annotation. Researchers frequently analyse each step manually, which is laborious and time consuming. We have recently developed ESTExplorer, a semi-automated computational workflow system, in order to achieve the rapid analysis of EST datasets. In this study, we evaluated EST data analysis for the parasitic nematode Trichostrongylus vitrinus (order Strongylida) using ESTExplorer, compared with database matching alone. RESULTS: We functionally annotated 1776 ESTs obtained via suppressive-subtractive hybridisation from T. vitrinus, an important parasitic trichostrongylid of small ruminants. Cluster and comparative genomic analyses of the transcripts using ESTExplorer indicated that 290 (41%) sequences had homologues in Caenorhabditis elegans, 329 (42%) in parasitic nematodes, 202 (28%) in organisms other than nematodes, and 218 (31%) had no significant match to any sequence in the current databases. Of the C. elegans homologues, 90 were associated with 'non-wildtype' double-stranded RNA interference (RNAi) phenotypes, including embryonic lethality, maternal sterility, sterile progeny, larval arrest and slow growth. We could functionally classify 267 (38%) sequences using the Gene Ontologies (GO) and establish pathway associations for 230 (33%) sequences using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Further examination of this EST dataset revealed a number of signalling molecules, proteases, protease inhibitors, enzymes, ion channels and immune-related genes. In addition, we identified 40 putative secreted proteins that could represent potential candidates for developing novel anthelmintics or vaccines. We further compared the automated EST sequence annotations, using ESTExplorer, with database search results for individual T. vitrinus ESTs. ESTExplorer reliably and rapidly annotated 301 ESTs, with pathway and GO information, eliminating 60 low quality hits from database searches. CONCLUSION: We evaluated the efficacy of ESTExplorer in analysing EST data, and demonstrate that computational tools can be used to accelerate the process of gene discovery in EST sequencing projects. The present study has elucidated sets of relatively conserved and potentially novel genes for biological investigation, and the annotated EST set provides further insight into the molecular biology of T. vitrinus, towards the identification of novel drug targets.

We provide a 2007 update on the bioinformatics research in the Asia-Pacific from the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation set up in 1998. From 2002, APBioNet has organized the first International Conference on Bioinformatics (InCoB) bringing together scientists working in the field of bioinformatics in the region. This year, the InCoB2007 Conference was organized as the 6th annual conference of the Asia-Pacific Bioinformatics Network, on Aug. 27-30, 2007 at Hong Kong, following a series of successful events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea) and New Delhi (India). Besides a scientific meeting at Hong Kong, satellite events organized are a pre-conference training workshop at Hanoi, Vietnam and a post-conference workshop at Nansha, China. This Introduction provides a brief overview of the peer-reviewed manuscripts accepted for publication in this Supplement. We have organized the papers into thematic areas, highlighting the growing contribution of research excellence from this region, to global bioinformatics endeavours.

The 2008 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation set up in 1998, was organized as the 7th International Conference on Bioinformatics (InCoB), jointly with the Bioinformatics and Systems Biology in Taiwan (BIT 2008) Conference, Oct. 20-23, 2008 at Taipei, Taiwan. Besides bringing together scientists from the field of bioinformatics in this region, InCoB is actively involving researchers from the area of systems biology, to facilitate greater synergy between these two groups. Marking the 10th Anniversary of APBioNet, this InCoB 2008 meeting followed on from a series of successful annual events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India) and Hong Kong. Additionally, tutorials and the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) immediately prior to the 20th Federation of Asian and Oceanian Biochemists and Molecular Biologists (FAOBMB) Taipei Conference provided ample opportunity for inducting mainstream biochemists and molecular biologists from the region into a greater level of awareness of the importance of bioinformatics in their craft. In this editorial, we provide a brief overview of the peer-reviewed manuscripts accepted for publication herein, grouped into thematic areas. As the regional research expertise in bioinformatics matures, the papers fall into thematic areas, illustrating the specific contributions made by APBioNet to global bioinformatics efforts.

Expressed sequence tag (EST) sequencing projects are underway for numerous organisms, generating millions of short, single-pass nucleotide sequence reads, accumulating in EST databases. Extensive computational strategies have been developed to organize and analyse both small- and large-scale EST data for gene discovery, transcript and single nucleotide polymorphism analysis as well as functional annotation of putative gene products. We provide an overview of the significance of ESTs in the genomic era, their properties and the applications of ESTs. Methods adopted for each step of EST analysis by various research groups have been compared. Challenges that lie ahead in organizing and analysing the ever increasing EST data have also been identified. The most appropriate software tools for EST pre-processing, clustering and assembly, database matching and functional annotation have been compiled (available online from http://biolinfo.org/EST). We propose a road map for EST analysis to accelerate the effective analyses of EST data sets. An investigation of EST analysis platforms reveals that they all terminate prior to downstream functional annotation including gene ontologies, motif/pattern analysis and pathway mapping.

T-cell recognition of peptide/major histocompatibility complex (MHC) is a prerequisite for cellular immunity. Recently, there has been an influx of bioinformatics tools to facilitate the identification of T-cell epitopes to specific MHC alleles. This article examines existing computational strategies for the study of peptide/MHC interactions. The most important bioinformatics tools and methods with relevance to the study of peptide/MHC interactions have been reviewed. We have also provided guidelines for predicting antigenic peptides based on the availability of existing experimental data.

MOTIVATION: Classification of human leukocyte antigen (HLA) proteins into supertypes underpins the development of epitope-based vaccines with wide population coverage. Current methods for HLA supertype definition, based on common structural features of HLA proteins and/or their functional binding specificities, leave structural interaction characteristics among different HLA supertypes with antigenic peptides unexplored. METHODS: We describe the use of structural interaction descriptors for the analysis of 68 peptide/HLA class I crystallographic structures. Interaction parameters computed include the number of intermolecular hydrogen bonds between each HLA protein and its corresponding bound peptide, solvent accessibility, gap volume and gap index. RESULTS: The structural interactions patterns of peptide/HLA class I complexes investigated herein vary among individual alleles and may be grouped in a supertype dependent manner. Using the proposed methodology, eight HLA class I supertypes were defined based on existing experimental crystallographic structures which largely overlaps (77% consensus) with the definitions by binding motifs. This mode of classification, which considers conformational information of both peptide and HLA proteins, provides an alternative to the characterization of supertypes using either peptide or HLA protein information alone.

UNLABELLED: Caspases belong to a unique class of cysteine proteases which function as critical effectors of apoptosis, inflammation and other important cellular processes. Caspases cleave substrates at specific tetrapeptide sites after a highly conserved aspartic acid residue. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. We have recently developed a support vector machines (SVM) method to address this issue. Our algorithm achieved an accuracy ranging from 81.25 to 97.92%, making it one of the best methods currently available. CASVM is the web server implementation of our SVM algorithms, written in Perl and hosted on a Linux platform. The server can be used for predicting non-canonical caspase substrate cleavage sites. We have also included a relational database containing experimentally verified caspase substrates retrievable using accession IDs, keywords or sequence similarity. AVAILABILITY: http://www.casbase.org/casvm/index.html

MOTIVATION: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. METHODS: We describe a structure-based prediction model for modeling peptide-DQ3.2beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2beta binding and non-binding peptides obtained from biochemical and functional studies. RESULTS: Our model predicts DQ3.2beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.

In 1998, the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation was set up to champion the advancement of bioinformatics in the Asia Pacific. By 2002, APBioNet was able to gain sufficient critical mass to initiate the first International Conference on Bioinformatics (InCoB) bringing together scientists working in the field of bioinformatics in the region. This year, the InCoB2006 Conference was organized as the 5 th annual conference of the Asia-Pacific Bioinformatics Network, on Dec. 18–20, 2006 in New Delhi, India, following a series of successful events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand) and Busan (South Korea). This Introduction provides a brief overview of the peer-reviewed manuscripts accepted for publication in this Supplement. It exemplifies a typical snapshot of the growing research excellence in bioinformatics of the region as we embark on a trajectory of establishing a solid bioinformatics research culture in the Asia Pacific that is able to contribute fully to the global bioinformatics community.

BACKGROUND: Pemphigus vulgaris (PV) is a severe autoimmune blistering skin disorder that is strongly associated with major histocompatibility complex class II alleles DRB1*0402 and DQB1*0503. The target antigen of PV, desmoglein 3 (Dsg3), is crucial for initiating T-cell response in early disease. Although a number of T-cell specificities within Dsg3 have been reported, the number is limited and the role of T-cells in the pathogenesis of PV remains poorly understood. We report here a structure-based model for the prediction of peptide binding to DRB1*0402 and DQB1*0503. The scoring functions were rigorously trained, tested and validated using experimentally verified peptide sequences. RESULTS: High predictivity is obtained for both DRB1*0402 (r2 = 0.90, s = 1.20 kJ/mol, q2 = 0.82, s(press) = 1.61 kJ/mol) and DQB1*0503 (r2 = 0.95, s = 1.20 kJ/mol, q2 = 0.75, s(press) = 2.15 kJ/mol) models, compared to experimental data. We investigated the binding patterns of Dsg3 peptides and illustrate the existence of multiple immunodominant epitopes that may be responsible for both disease initiation and propagation in PV. Further analysis reveals that DRB1*0402 and DQB1*0503 may share similar specificities by binding peptides at different binding registers, thus providing a molecular mechanism for the dual HLA association observed in PV. CONCLUSION: Collectively, the results of this study provide interesting new insights into the pathology of PV. This is the first report illustrating high-level of cross-reactivity between both PV-implicated alleles, DRB1*0402 and DQB1*0503, as well as the existence of a potentially large number of T-cell epitopes throughout the entire Dsg3 extracellular domain (ECD) and transmembrane region. Our results reveal that DR4 and DR6 PV may initiate in the ECD and transmembrane region respectively, with implications for immunotherapeutic strategies for the treatment of this autoimmune disease.

BACKGROUND: Caspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM) algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain. RESULTS: A set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i) the tetrapeptide cleavage sites, (ii) the tetrapeptide cleavage sites, augmented by two adjacent residues, P1' and P2' amino acids and (iii) the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available) were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants. CONCLUSION: This study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.

BACKGROUND: The signal peptide plays an important role in protein targeting and protein translocation in both prokaryotic and eukaryotic cells. This transient, short peptide sequence functions like a postal address on an envelope by targeting proteins for secretion or for transfer to specific organelles for further processing. Understanding how signal peptides function is crucial in predicting where proteins are translocated. To support this understanding, we present SPdb signal peptide database http://proline.bic.nus.edu.sg/spdb, a repository of experimentally determined and computationally predicted signal peptides. RESULTS: SPdb integrates information from two sources (a) Swiss-Prot protein sequence database which is now part of UniProt and (b) EMBL nucleotide sequence database. The database update is semi-automated with human checking and verification of the data to ensure the correctness of the data stored. The latest release SPdb release 3.2 contains 18,146 entries of which 2,584 entries are experimentally verified signal sequences; the remaining 15,562 entries are either signal sequences that fail to meet our filtering criteria or entries that contain unverified signal sequences. CONCLUSION: SPdb is a manually curated database constructed to support the understanding and analysis of signal peptides. SPdb tracks the major updates of the two underlying primary databases thereby ensuring that its information remains up-to-date.

10.1371/journal.pcbi.0010052

The delineation of domain boundaries of a given sequence in the absence of known 3D structures or detectable sequence homology to known domains benefits many areas in protein science, such as protein engineering, protein 3D structure determination and protein structure prediction. With the exponential growth of newly determined sequences, our ability to predict domain boundaries rapidly and accurately from sequence information alone is both essential and critical from the viewpoint of gene function annotation. Anyone attempting to predict domain boundaries for a single protein sequence is invariably confronted with a plethora of databases that contain boundary information available from the internet and a variety of methods for domain boundary prediction. How are these derived and how well do they work? What definition of 'domain' do they use? We will first clarify the different definitions of protein domains, and then describe the available public databases with domain boundary information. Finally, we will review existing domain boundary prediction methods and discuss their strengths and weaknesses.

BACKGROUND: A wealth of quality genomic and mRNA/EST sequences in recent years has provided the data required for large-scale genome-wide analysis of alternative splicing. We have capitalized on this by constructing a database that contains alternative splicing information organized as splicing graphs, where all transcripts arising from a single gene are collected, organized and classified. The splicing graph then serves as the basis for the classification of the various types of alternative splicing events. DESCRIPTION: DEDB http://proline.bic.nus.edu.sg/dedb/index.html is a database of Drosophila melanogaster exons obtained from FlyBase arranged in a splicing graph form that permits the creation of simple rules allowing for the classification of alternative splicing events. Pfam domains were also mapped onto the protein sequences allowing users to access the impact of alternative splicing events on domain organization. CONCLUSIONS: DEDB's catalogue of splicing graphs facilitates genome-wide classification of alternative splicing events for genome analysis. The splicing graph viewer brings together genome, transcript, protein and domain information to facilitate biologists in understanding the implications of alternative splicing.

SUMMARY: Binding of short antigenic peptides to Major histocompatibility complex (MHC) proteins is the first step in T-cell mediated immune response. To understand the structural principles governing MHC-specific peptide recognition and binding, we have developed the MHC-Peptide Interaction Database (MPID), containing sequence-structure-function information. MPID (version 1.2) contains curated x-ray crystallographic data on 86 MHC peptide complexes, with precomputed interaction parameters (solvent accessibility, hydrogen bonds, gap volume and gap index). A user-friendly web interface and query tools will facilitate the development of predictive algorithms for MHC-peptide binding from a structural viewpoint. AVAILABILITY: Freely accessible from http://surya.bic.nus.edu.sg/mpid.

SUMMARY: The relationship between intron distribution in the eukaryotic gene and protein structural elements is essential for understanding the origin and evolution of genes. XdomView is a web-based viewer mapping protein structural domains and intron positions in eukaryotic homologues to its tertiary structure. The association of sequence signals to 3D structure in XdomView provides a valuable visualization environment for eukaryotic gene organization, gene evolution, protein folding and protein structure classification. AVAILABILITY: Freely available from http://surya.bic.nus.edu.sg/xdom.