Estimating Biological Age in the Singapore Longitudinal Aging Study.

BACKGROUND: Biological age (BA) is a more accurate measure of the rate of human aging than chronological age (CA). However, there is limited consensus regarding measures of BA in life span and healthspan. METHODS: This study investigated measurement sets of 68 physiological biomarkers using data from 2,844 Chinese Singaporeans in two age subgroups (55-70 and 71-94 years) in the Singapore Longitudinal Aging Study (SLAS-2) with 8-year follow-up frailty and mortality data. We computed BA estimate using three commonly used algorithms: Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Klemera and Doubal (KD) method, and additionally, explored the use of machine learning methods for prediction of mortality and frailty. The most optimal algorithmic estimate of BA compared to CA was evaluated for their associations with risk factors and health outcome. RESULTS: Stepwise selection procedures resulted in the final selection of 8 biomarkers in males and 10 biomarkers in females. The highest-ranking biomarkers were estimated glomerular filtration rate for both genders, and the forced expiratory volume in 1 second in males and females. The BA estimates robustly predicted frailty and mortality and outperformed CA. The best performing KD measure of BA was notably predictive in the younger group (aged 55-70 years). BA estimates obtained using a machine learning train-test method were not more accurate than conventional BA estimates in predicting mortality and frailty in most situations. Biologically older people with the same CA as biologically younger individuals had higher prevalence of frailty and 8-year mortality, and worse health, behavioral, and functional characteristics. CONCLUSIONS: BA is better than CA for measuring life span (mortality) and healthspan (frailty). This measurement set of physiological markers of biological aging among Chinese robustly differentiate biologically old from younger individuals with the same CA.

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest.

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.

Human cytomegalovirus may promote tumour progression by upregulating arginase-2.

BACKGROUND: Both arginase (ARG2) and human cytomegalovirus (HCMV) have been implicated in tumorigenesis. However, the role of ARG2 in the pathogenesis of glioblastoma (GBM) and the HCMV effects on ARG2 are unknown. We hypothesize that HCMV may contribute to tumorigenesis by increasing ARG2 expression. RESULTS: ARG2 promotes tumorigenesis by increasing cellular proliferation, migration, invasion and vasculogenic mimicry in GBM cells, at least in part due to overexpression of MMP2/9. The nor-NOHA significantly reduced migration and tube formation of ARG2-overexpressing cells. HCMV immediate-early proteins (IE1/2) or its downstream pathways upregulated the expression of ARG2 in U-251 MG cells. Immunostaining of GBM tissue sections confirmed the overexpression of ARG2, consistent with data from subsets of Gene Expression Omnibus. Moreover, higher levels of ARG2 expression tended to be associated with poorer survival in GBM patient by analyzing data from TCGA. METHODS: The role of ARG2 in tumorigenesis was examined by proliferation-, migration-, invasion-, wound healing- and tube formation assays using an ARG2-overexpressing cell line and ARG inhibitor, N (omega)-hydroxy-nor-L-arginine (nor-NOHA) and siRNA against ARG2 coupled with functional assays measuring MMP2/9 activity, VEGF levels and nitric oxide synthase activity. Association between HCMV and ARG2 were examined in vitro with 3 different GBM cell lines, and ex vivo with immunostaining on GBM tissue sections. The viral mechanism mediating ARG2 induction was examined by siRNA approach. Correlation between ARG2 expression and patient survival was extrapolated from bioinformatics analysis on data from The Cancer Genome Atlas (TCGA). CONCLUSIONS: ARG2 promotes tumorigenesis, and HCMV may contribute to GBM pathogenesis by upregulating ARG2.

T-Cell Epitope Prediction of Chikungunya Virus.

There has been a growing demand for vaccines against Chikungunya virus (CHIKV), and epitope-based vaccine is a promising solution. Identification of CHIKV T-cell epitopes is critical to ensure successful trigger of immune response for epitope-based vaccine design. Bioinformatics tools are able to significantly reduce time and effort in this process by systematically scanning for immunogenic peptides in CHIKV proteins. This chapter provides the steps in utilizing machine learning algorithms to train on major histocompatibility complex (MHC) class I peptide binding data and build prediction models for the classification of binders and non-binders. The models could then be used in the identification and prediction of CHIKV T-cell epitopes for future vaccine design.

Distinct Host Tropism Protein Signatures to Identify Possible Zoonotic Influenza A Viruses.

Zoonotic influenza A viruses constantly pose a health threat to humans as novel strains occasionally emerge from the avian population to cause human infections. Many past epidemic as well as pandemic strains have originated from avian species. While most viruses are restricted to their primary hosts, zoonotic strains can sometimes arise from mutations or reassortment, leading them to acquire the capability to escape host species barrier and successfully infect a new host. Phylogenetic analyses and genetic markers are useful in tracing the origins of zoonotic infections, but there are still no effective means to identify high risk strains prior to an outbreak. Here we show that distinct host tropism protein signatures can be used to identify possible zoonotic strains in avian species which have the potential to cause human infections. We have discovered that influenza A viruses can now be classified into avian, human, or zoonotic strains based on their host tropism protein signatures. Analysis of all influenza A viruses with complete proteome using the host tropism prediction system, based on machine learning classifications of avian and human viral proteins has uncovered distinct signatures of zoonotic strains as mosaics of avian and human viral proteins. This is in contrast with typical avian or human strains where they show mostly avian or human viral proteins in their signatures respectively. Moreover, we have found that zoonotic strains from the same influenza outbreaks carry similar host tropism protein signatures characteristic of a common ancestry. Our results demonstrate that the distinct host tropism protein signature in zoonotic strains may prove useful in influenza surveillance to rapidly identify potential high risk strains circulating in avian species, which may grant us the foresight in anticipating an impending influenza outbreak.

Building MHC class II epitope predictor using machine learning approaches.

Identification of T-cell epitopes binding to MHC class II molecules is an important step in epitope-based vaccine development. This process has since been accelerated with the use of bioinformatics tools to aid in the prediction of peptide binding to MHC class II molecules and also to systematically scan for candidate peptides in antigenic proteins. There have been many prediction software developed over the years using various methods and algorithms and they are becoming increasingly sophisticated. Here, we illustrate the use of machine learning algorithms to train on MHC class II peptide data represented by feature vectors describing their amino acid physicochemical properties. The developed prediction model can then be used to predict new peptide data.

Novel SNP improves differential survivability and mortality in non-small cell lung cancer patients.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a major cause of cancer-related death worldwide due to poor patient prognosis and clinical outcome. Here, we studied the genetic variations underlying NSCLC pathogenesis based on their association to patient outcome after gemcitabine therapy. RESULTS: Bioinformatics analysis was used to investigate possible effects of POLA2 G583R (POLA2+1747 GG/GA, dbSNP ID: rs487989) in terms of protein function. Using biostatistics, POLA2+1747 GG/GA (rs487989, POLA2 G583R) was identified as strongly associated with mortality rate and survival time among NSCLC patients. It was also shown that POLA2+1747 GG/GA is functionally significant for protein localization via green fluorescent protein (GFP)-tagging and confocal laser scanning microscopy analysis. The single nucleotide polymorphism (SNP) causes DNA polymerase alpha subunit B to localize in the cytoplasm instead of the nucleus. This inhibits DNA replication in cancer cells and confers a protective effect in individuals with this SNP. CONCLUSIONS: The results suggest that POLA2+1747 GG/GA may be used as a prognostic biomarker of patient outcome in NSCLC pathogenesis.

Predicting host tropism of influenza A virus proteins using random forest.

BACKGROUND: Majority of influenza A viruses reside and circulate among animal populations, seldom infecting humans due to host range restriction. Yet when some avian strains do acquire the ability to overcome species barrier, they might become adapted to humans, replicating efficiently and causing diseases, leading to potential pandemic. With the huge influenza A virus reservoir in wild birds, it is a cause for concern when a new influenza strain emerges with the ability to cross host species barrier, as shown in light of the recent H7N9 outbreak in China. Several influenza proteins have been shown to be major determinants in host tropism. Further understanding and determining host tropism would be important in identifying zoonotic influenza virus strains capable of crossing species barrier and infecting humans. RESULTS: In this study, computational models for 11 influenza proteins have been constructed using the machine learning algorithm random forest for prediction of host tropism. The prediction models were trained on influenza protein sequences isolated from both avian and human samples, which were transformed into amino acid physicochemical properties feature vectors. The results were highly accurate prediction models (ACC>96.57; AUC>0.980; MCC>0.916) capable of determining host tropism of individual influenza proteins. In addition, features from all 11 proteins were used to construct a combined model to predict host tropism of influenza virus strains. This would help assess a novel influenza strain's host range capability. CONCLUSIONS: From the prediction models constructed, all achieved high prediction performance, indicating clear distinctions in both avian and human proteins. When used together as a host tropism prediction system, zoonotic strains could potentially be identified based on different protein prediction results. Understanding and predicting host tropism of influenza proteins lay an important foundation for future work in constructing computation models capable of directly predicting interspecies transmission of influenza viruses. The models are available for prediction at http://fluleap.bic.nus.edu.sg.

Structure-based clustering of major histocompatibility complex (MHC) proteins for broad-based T-cell vaccine design.

Structure-based clustering technique is useful for identifying superfamilies of major histocompatibility complex (MHC) proteins with similar binding specificities. The resolved MHC superfamilies play an important role in vaccine development, from discovering new targets for broad-based vaccines and therapeutics to optimizing the affinity and selectivity of hits. Here, we describe a protocol and provide a summary for grouping MHC proteins according to their structural interaction characteristics.

Clustering HLA class I superfamilies using structural interaction patterns.

Human leukocyte antigen (HLA) class I molecules are critical components of the cell-mediated immune system that bind and present intracellular antigenic peptides to CD8(+) T cell receptors. To understand the interaction mechanism underlying human leukocyte antigen (HLA) class I specificity in detail, we studied the structural interaction characteristics of 16,393 nonameric peptides binding to 58 HLA-A and -B molecules. Our analysis showed for the first time that HLA-peptide intermolecular bonding patterns vary among different alleles and may be grouped in a superfamily dependent manner. Through the use of these HLA class I 'fingerprints', a high resolution HLA class I superfamily classification schema was developed. This classification is capable of separating HLA alleles into well resolved, non-overlapping clusters, which is consistent with known HLA superfamily definitions. Such structural interaction approach serves as an excellent alternative to the traditional methods of HLA superfamily definitions that use peptide binding motifs or receptor information, and will help identify appropriate antigens suitable for broad-based subunit vaccine design.

Simple re-instantiation of small databases using cloud computing.

BACKGROUND: Small bioinformatics databases, unlike institutionally funded large databases, are vulnerable to discontinuation and many reported in publications are no longer accessible. This leads to irreproducible scientific work and redundant effort, impeding the pace of scientific progress. RESULTS: We describe a Web-accessible system, available online at http://biodb100.apbionet.org, for archival and future on demand re-instantiation of small databases within minutes. Depositors can rebuild their databases by downloading a Linux live operating system (http://www.bioslax.com), preinstalled with bioinformatics and UNIX tools. The database and its dependencies can be compressed into an ".lzm" file for deposition. End-users can search for archived databases and activate them on dynamically re-instantiated BioSlax instances, run as virtual machines over the two popular full virtualization standard cloud-computing platforms, Xen Hypervisor or vSphere. The system is adaptable to increasing demand for disk storage or computational load and allows database developers to use the re-instantiated databases for integration and development of new databases. CONCLUSIONS: Herein, we demonstrate that a relatively inexpensive solution can be implemented for archival of bioinformatics databases and their rapid re-instantiation should the live databases disappear.

Early neutralizing IgG response to Chikungunya virus in infected patients targets a dominant linear epitope on the E2 glycoprotein.

Chikungunya virus (CHIKV) and related arboviruses have been responsible for large epidemic outbreaks with serious economic and social impact. The immune mechanisms, which control viral multiplication and dissemination, are not yet known. Here, we studied the antibody response against the CHIKV surface antigens in infected patients. With plasma samples obtained during the early convalescent phase, we showed that the naturally-acquired IgG response is dominated by IgG3 antibodies specific mostly for a single linear epitope 'E2EP3'. E2EP3 is located at the N-terminus of the E2 glycoprotein and prominently exposed on the viral envelope. E2EP3-specific antibodies are neutralizing and their removal from the plasma reduced the CHIKV-specific antibody titer by up to 80%. Screening of E2EP3 across different patient cohorts and in non-human primates demonstrated the value of this epitope as a good serology detection marker for CHIKV infection already at an early stage. Mice vaccinated by E2EP3 peptides were protected against CHIKV with reduced viremia and joint inflammation, providing a pre-clinical basis for the design of effective vaccine against arthralgia-inducing CHIKV and other alphaviruses.

In silico design of small molecules.

Computational methods now play an integral role in modern drug discovery, and include the design and management of small molecule libraries, initial hit identification through virtual screening, optimization of the affinity and selectivity of hits, and improving the physicochemical properties of the lead compounds. In this chapter, we survey the most important data sources for the discovery of new molecular entities, and discuss the key considerations and guidelines for virtual chemical library design.

Immunoinformatics: how in silico methods are re-shaping the investigation of peptide immune specificity.

In the past decade, information technology has enabled synergistic advances in key domains of immunological research including the development of diagnostics and vaccines. Computational methods of epitope mapping now play instrumental roles in bench experiments, by facilitating the selection of immunogenic targets and the modeling of downstream cellular responses. In this article, we summarize the latest development and application of immune epitope prediction methods and discuss future directions in this field which could enhance our understanding of immune specificity.

MPID-T2: a database for sequence-structure-function analyses of pMHC and TR/pMHC structures.

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: shoba.ranganathan@mq.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Understanding infectious agents from an in silico perspective.

Knowledge of infectious diseases now emerging from genomic, proteomic, epidemiological and clinical data can provide insights into the mechanisms of immune function, disease pathogenesis and epidemiology. Here, we describe how considerable advances in computational methods of data mining, mathematical modeling in epidemiology and simulation have been used to enhance our understanding of infectious agents and discuss their impact on the discovery of new therapeutics and controlling their spread.

CLEVER: A general design tool for combinatorial libraries.

CLEVER is a computational tool designed to support the creation, manipulation, enumeration, and visualization of combinatorial libraries. The system also provides a summary of the diversity, coverage, and distribution of selected compound collections. When deployed in conjunction with large-scale virtual screening campaigns, CLEVER can offer insights into what chemical compounds to synthesize, and, more importantly, what not to synthesize. In this chapter, we describe how CLEVER is used and offer advice in interpreting the results.

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction.

BACKGROUND: The identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naïve modeling methods difficult to apply. RESULTS: We analyzed two benchmark datasets and found that linear B-cell epitopes possess distinctive residue conservation and position-specific residue propensities which could be exploited for epitope discrimination in silico. We developed a support vector machines (SVM) prediction model employing Bayes Feature Extraction to predict linear B-cell epitopes of diverse lengths (12- to 20-mers). The best SVM classifier achieved an accuracy of 74.50% and AROC of 0.84 on an independent test set and was shown to outperform existing linear B-cell epitope prediction algorithms. In addition, we applied our model to a dataset of antigenic proteins with experimentally-verified epitopes and found it to be generally effective for discriminating the epitopes from non-epitopes. CONCLUSION: We developed a SVM prediction model utilizing Bayes Feature Extraction and showed that it was effective in discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins. A web server for predicting linear B-cell epitopes was developed and is available, together with supplementary materials, at http://www.immunopred.org/bayesb/index.html.

Advancing standards for bioinformatics activities: persistence, reproducibility, disambiguation and Minimum Information About a Bioinformatics investigation (MIABi).

The 2010 International Conference on Bioinformatics, InCoB2010, which is the annual conference of the Asia-Pacific Bioinformatics Network (APBioNet) has agreed to publish conference papers in compliance with the proposed Minimum Information about a Bioinformatics investigation (MIABi), proposed in June 2009. Authors of the conference supplements in BMC Bioinformatics, BMC Genomics and Immunome Research have consented to cooperate in this process, which will include the procedures described herein, where appropriate, to ensure data and software persistence and perpetuity, database and resource re-instantiability and reproducibility of results, author and contributor identity disambiguation and MIABi-compliance. Wherever possible, datasets and databases will be submitted to depositories with standardized terminologies. As standards are evolving, this process is intended as a prelude to the 100 BioDatabases (BioDB100) initiative whereby APBioNet collaborators will contribute exemplar databases to demonstrate the feasibility of standards-compliance and participate in refining the process for peer-review of such publications and validation of scientific claims and standards compliance. This testbed represents another step in advancing standards-based processes in the bioinformatics community which is essential to the growing interoperability of biological data, information, knowledge and computational resources.

Computational Epigenetics: the new scientific paradigm.

Epigenetics has recently emerged as a critical field for studying how non-gene factors can influence the traits and functions of an organism. At the core of this new wave of research is the use of computational tools that play critical roles not only in directing the selection of key experiments, but also in formulating new testable hypotheses through detailed analysis of complex genomic information that is not achievable using traditional approaches alone. Epigenomics, which combines traditional genomics with computer science, mathematics, chemistry, biochemistry and proteomics for the large-scale analysis of heritable changes in phenotype, gene function or gene expression that are not dependent on gene sequence, offers new opportunities to further our understanding of transcriptional regulation, nuclear organization, development and disease. This article examines existing computational strategies for the study of epigenetic factors. The most important databases and bioinformatic tools in this rapidly growing field have been reviewed.