My research interests
This page provides a list of all peer-reviewed journal and conference articles that I have authored or co-authored. A summary view of this page is available here.
26) Highly conserved influenza A sequences as T cell epitopes-based vaccine targets to address the viral variability
Tan PT, Khan AM, August JT.
Hum Vaccin. 2011 Apr 1;7(4)
PUBMED PMID: 21471731
Out link: Full-text
Impact Factor Year 2009: 1.94
No. of Citations: ?
ABSTRACT
Vaccines are the only proven effective method for prevention of human infectious diseases. Almost all traditional vaccines require activating immunological memory B cells to secrete neutralizing antibodies against invading pathogens. The complication with influenza viruses is the high viral mutation rate that results in immune escape through modification of the B cell epitopes. Studies of T cell immunity to influenza infection provide an alternative vaccine strategy based on highly conserved T cell epitopes. In this review, we discuss the importance of T cell-mediated immunity in influenza infection and the need for a targeted vaccine approach focused on highly conserved T cell epitopes to mitigate immune escape. We propose 15 highly conserved pan-influenza sequences as possible T cell epitopes-based vaccine targets for broad protection and lasting immunity against variant influenza strains.
This article has been cited by other articles/sites:
25) T3SEdb: Data warehousing of virulence effectors secreted by the bacterial Type III Secretion System
Tay DMM*, Govindarajan KR*, Khan AM, Ong TYR, Samad HM, Soh WW, Tong M, Zhang F, Tan TW
BMC Bioinformatics. 2010; 11 Suppl 7:S4.
PUBMED PMID: 21106126
Out link: Full-text
Impact Factor Year 2009: 3.428
No. of Citations: ?
* Contributed equally
ABSTRACT
Background
Effectors of Type III Secretion System (T3SS) play a pivotal role in establishing and maintaining pathogenicity in the host and therefore the identification of these effectors is important in understanding virulence. However, the effectors display high level of sequence diversity, therefore making the identification a difficult process. There is a need to collate and annotate existing effector sequences in public databases to enable systematic analyses of these sequences for development of models for screening and selection of putative novel effectors from bacterial genomes that can be validated by a smaller number of key experiments.
Results
Herein, we present T3SEdb http://effectors.bic.nus.edu.sg/T3SEdb, a specialized database of annotated T3SS effector (T3SE) sequences containing 1089 records from 46 bacterial species compiled from the literature and public protein databases. Procedures have been defined for i) comprehensive annotation of experimental status of effectors, ii) submission and curation review of records by users of the database, and iii) the regular update of T3SEdb existing and new records. Keyword fielded and sequence searches (BLAST, regular expression) are supported for both experimentally verified and hypothetical T3SEs. More than 171 clusters of T3SEs were detected based on sequence identity comparisons (intra-cluster difference up to ~60%). Owing to this high level of sequence diversity of T3SEs, the T3SEdb provides a large number of experimentally known effector sequences with wide species representation for creation of effector predictors. We created a reliable effector prediction tool, integrated into the database, to demonstrate the application of the database for such endeavours.
Conclusions
T3SEdb is the first specialised database reported for T3SS effectors, enriched with manual annotations that facilitated systematic construction of a reliable prediction model for identification of novel effectors. The T3SEdb represents a platform for inclusion of additional annotations of metadata for future developments of sophisticated effector prediction models for screening and selection of putative novel effectors from bacterial genomes/proteomes that can be validated by a small number of key experiments.
This article has been cited by other articles/sites:
24) Cheminformatics-based drug design approach for identification of inhibitors targeting the characteristic residues of MMP-13 hemopexin domain
Kothapalli R, Khan AM, Basappa, Gopalsamy A, Chong YS and Annamalai L
PLoS One. 2010 (In Press).
PUBMED PMID: 20824169
Out link: Full-text
Impact Factor Year 2009: 4.351
No. of Citations: 0
ABSTRACT
Background
MMP-13, a zinc dependent protease which catalyses the cleavage of type II collagen, is expressed in osteoarthritis (OA) and rheumatoid arthritis (RA) patients, but not in normal adult tissues. Therefore, the protease has been intensively studied as a target for the inhibition of progression of OA and RA. Recent reports suggest that selective inhibition of MMP-13 may be achieved by targeting the hemopexin (Hpx) domain of the protease, which is critical for substrate specificity. In this study, we applied a cheminformatics-based drug design approach for the identification and characterization of inhibitors targeting the amino acid residues characteristic to Hpx domain of MMP-13; these inhibitors may potentially be employed in the treatment of OA and RA.
Methods/Principal Findings
Sequence-based mutual information analysis revealed five characteristic (completely conserved and unique), putative functional residues of the Hpx domain of MMP-13 (these residues hereafter are referred to as HCR-13(pf)). Binding of a ligand to as many of the HCR-13(pf) is postulated to result in an increased selective inhibition of the Hpx domain of MMP-13. Through the in silico structure-based high-throughput virtual screening (HTVS) method of Glide, against a large public library of 16908 molecules from Maybridge, PubChem and Binding, we identified 25 ligands that interact with at least one of the HCR-13(pf). Assessment of cross-reactivity of the 25 ligands with MMP-1 and MMP-8, members of the collagenase family as MMP-13, returned seven lead molecules that did not bind to any one of the putative functional residues of Hpx domain of MMP-1 and any of the catalytic active site residues of MMP-1 and -8, suggesting that the ligands are not likely to interact with the functional or catalytic residues of other MMPs. Further, in silico analysis of physicochemical and pharmacokinetic parameters based on Lipinski's rule of five and ADMET (absorption, distribution, metabolism, excretion and toxicity) respectively, suggested potential utility of the compounds as drug leads.
Conclusions
We have identified seven distinct drug-like molecules binding to the HCR-13(pf) of MMP-13 with no observable cross-reactivity to MMP-1 and MMP-8. These molecules are potential selective inhibitors of MMP-13 that can be experimentally validated and their backbone structural scaffold could serve as building blocks in designing drug-like molecules for OA, RA and other inflammatory disorders. The systematic cheminformatics-based drug design approach applied herein can be used for rational search of other public/commercial combinatorial libraries for more potent molecules, capable of selectively inhibiting the collagenolytic activity of MMP-13.
This article has been cited by other articles/sites:
23) Classification of dengue fever patients based on gene expression data using support vector machines.
Gomes AL*, Wee LJ*, Khan AM, Gil LH, Marques ET Jr, Calzavara-Silva CE, Tan TW.
PLoS One. 2010 Jun 23;5(6):e11267.
PUBMED PMID: 20585645
Out link: Full-text
Impact Factor Year 2009: 4.351
No. of Citations: 0
*Contributed equally
ABSTRACT
Background
Symptomatic infection by dengue virus (DENV) can range from dengue fever (DF) to dengue haemorrhagic fever (DHF), however, the determinants of DF or DHF progression are not completely understood. It is hypothesised that host innate immune response factors are involved in modulating the disease outcome and the expression levels of genes involved in this response could be used as early prognostic markers for disease severity.
Results
mRNA expression levels of genes involved in DENV innate immune responses were measured using quantitative real time PCR (qPCR). Here, we present a novel application of the support vector machines (SVM) algorithm to analyze the expression pattern of 12 genes in peripheral blood mononuclear cells (PBMCs) of 28 dengue patients (13 DHF and 15 DF) during acute viral infection. The SVM model was trained using gene expression data of these genes and achieved the highest accuracy of ~85% with leave-one-out cross-validation. Through selective removal of gene expression data from the SVM model, we have identified seven genes (MYD88, TLR7, TLR3, MDA5, IRF3, IFN-α and CLEC5A) that may be central in differentiating DF patients from DHF, with MYD88 and TLR7 observed to be the most important. Though the individual removal of expression data of five other genes had no impact on the overall accuracy, a significant combined role was observed when the SVM model of the two main genes (MYD88 and TLR7) was re-trained to include the five genes, increasing the overall accuracy to ~96%.
Conclusion
Here, we present a novel use of the SVM algorithm to classify DF and DHF patients, as well as to elucidate the significance of the various genes involved. It was observed that seven genes are critical in classifying DF and DHF patients: TLR3, MDA5, IRF3, IFN-α, CLEC5A, and the two most important MYD88 and TLR7. While these preliminary results are promising, further experimental investigation is necessary to validate their specific roles in dengue disease.
This article has been cited by other articles/sites:
22) Dendritic cell mediated delivery of plasmid DNA encoding LAMP/HIV-1 Gag fusion immunogen enhances T cell epitope responses in HLA DR4 Tg mice
Simon GG, Hu Y, Khan AM, Zhou J, Salmon J, Chikhlikar PR, Jung KO, Marques ET, August JT.
PLoS One. 2010 Jan 5;5(1):e8574.
PUBMED PMID: 20052293
Out link: Full-text
Impact Factor Year 2009: 4.351
No. of Citations: 0
Background
This report describes the identification and bioinformatics analysis of HLA-DR4-restricted HIV-1 Gag epitope peptides, and the application of dendritic cell mediated immunization of DNA plasmid constructs. BALB/c (H-2d) and HLA-DR4 (DRA1*0101, DRB1*0401) transgenic mice were immunized with immature dendritic cells transfected by a recombinant DNA plasmid encoding the lysosome-associated membrane protein-1/HIV-1 Gag (pLAMP/gag) chimera antigen. Three immunization protocols were compared: 1) primary subcutaneous immunization with 1×105 immature dendritic cells transfected by electroporation with the pLAMP/gag DNA plasmid, and a second subcutaneous immunization with the naked pLAMP/gag DNA plasmid; 2) primary immunization as above, and a second subcutaneous immunization with a pool of overlapping peptides spanning the HIV-1 Gag sequence; and 3) immunization twice by subcutaneous injection of the pLAMP/gag DNA plasmid.
Results
Primary immunization with pLAMP/gag-transfected dendritic cells elicited the greatest number of peptide specific T-cell responses, as measured by ex vivo IFN-γ ELISpot assay, both in BALB/c and HLA-DR4 transgenic mice. The pLAMP/gag-transfected dendritic cells prime and naked DNA boost immunization protocol also resulted in an increased apparent avidity of peptide in the ELISpot assay. Strikingly, 20 of 25 peptide-specific T-cell responses in the HLA-DR4 transgenic mice contained sequences that corresponded, entirely or partially to 18 of the 19 human HLA-DR4 epitopes listed in the HIV molecular immunology database. Selection of the most conserved epitope peptides as vaccine targets was facilitated by analysis of their representation and variability in all reported sequences.
Conclusion
These data provide a model system that demonstrates a) the superiority of immunization with dendritic cells transfected with LAMP/gag plasmid DNA, as compared to naked DNA, b) the value of HLA transgenic mice as a model system for the identification and evaluation of epitope-based vaccine strategies, and c) the application of variability analysis across reported sequences in public databases for selection of historically conserved HIV epitopes as vaccine targets.
This article has been cited by other articles/sites:
21) The implementation of e-learning tools to enhance undergraduate bioinformatics teaching and learning: a case study in the Nat. Uni. of Singapore
Lim SJ, Khan AM, De Silva M, Lim KS, Hu Y, Tan CH, Tan TW.
BMC Bioinformatics. 2009 Dec 3;10 Suppl 15:S12.
PUBMED PMID: 19958511
Out link: Full-text
Impact Factor Year 2009: 3.428
No. of Citations: 1
ABSTRACT
Background
The rapid advancement of computer and information technology in recent years has resulted in the rise of e-learning technologies to enhance and complement traditional classroom teaching in many fields, including bioinformatics. This paper records the experience of implementing e-learning technology to support problem-based learning (PBL) in the teaching of two undergraduate bioinformatics classes in the National University of Singapore.
Results
Survey results further established the efficiency and suitability of e-learning tools to supplement PBL in bioinformatics education. 63.16% of year three bioinformatics students showed a positive response regarding the usefulness of the Learning Activity Management System (LAMS) e-learning tool in guiding the learning and discussion process involved in PBL and in enhancing the learning experience by breaking down PBL activities into a sequential workflow. On the other hand, 89.81% of year two bioinformatics students indicated that their revision process was positively impacted with the use of LAMS for guiding the learning process, while 60.19% agreed that the breakdown of activities into a sequential step-by-step workflow by LAMS enhances the learning experience
Conclusion
We show that e-learning tools are useful for supplementing PBL in bioinformatics education. The results suggest that it is feasible to develop and adopt e-learning tools to supplement a variety of instructional strategies in the future.
This article has been cited by other articles/sites:
1) Towards a career in bioinformatics.
Ranganathan S.
BMC Bioinformatics. 2009 Dec 3;10 Suppl 15:S1.
PMID: 19958508
20) A proposed minimum skill set for university graduates to meet the informatics needs and challenges of the "-omics" era.
Tan TW, Lim SJ, Khan AM, Ranganathan S.
BMC Genomics. 2009 Dec 3;10 Suppl 3:S36.
PUBMED PMID: 19958501
Out link: Full-text
Impact Factor Year 2009: 3.759
No. of Citations: 1
ABSTRACT
Background
The development of high throughput experimental technologies have given rise to the "-omics" era where terabyte-scale datasets for systems-level measurements of various cellular and molecular phenomena pose considerable challenges in data processing and extraction of biological meaning. Moreover, it has created an unmet need for the effective integration of these datasets to achieve insights into biological systems. While it has increased the demand for bioinformatics experts who can interface with biologists, it has also raised the requirement for biologists to possess a basic capability in bioinformatics and to communicate seamlessly with these experts. This may be achieved by embedding in their undergraduate and graduate life science education, basic training in bioinformatics geared towards acquiring a minimum skill set in computation and informatics.
Results
Based on previous attempts to define curricula suitable for addressing the bioinformatics capability gap, an initiative was taken during the Workshops on Education in Bioinformatics and Computational Biology (WEBCB) in 2008 and 2009 to identify a minimum skill set for the training of future bioinformaticians and molecular biologists with informatics capabilities. The minimum skill set proposed is cross-disciplinary in nature, involving a combination of knowledge and proficiency from the fields of biology, computer science, mathematics and statistics, and can be tailored to the needs of the "-omics".
Conclusion
The proposed bioinformatics minimum skill set serves as a guideline for biology curriculum design and development in universities at both the undergraduate and graduate levels.
This article has been cited by other articles/sites:
1) Extending Asia Pacific bioinformatics into new realms in the "-omics" era.
Ranganathan S, Eisenhaber F, Tong JC, Tan TW.
BMC Genomics. 2009 Dec 3;10 Suppl 3:S1.
PMID: 19958472
19) Conservation and variability of West Nile virus proteins
Koo QY, Khan AM, Jung KO, Ramdas S, Miotto O, Tan TW, Brusic V, Salmon J, August JT.
PLoS One. 2009;4(4):e5352. Epub 2009 Apr 29.
PUBMED PMID: 19401763
Out link: Full-text
Impact Factor Year 2009: 4.351
No. of Citations: 0
ABSTRACT
Background
West Nile virus (WNV) has emerged globally as an increasingly important pathogen for humans and domestic animals. Studies of the evolutionary diversity of the virus over its known history will help to elucidate conserved sites, and characterize their correspondence to other pathogens and their relevance to the immune system. We describe a large-scale analysis of the entire WNV proteome, aimed at identifying and characterizing evolutionarily conserved amino acid sequences.
Methodology/Principal Findings
This study, which used 2,746 WNV protein sequences collected from the NCBI GenPept database, focused on analysis of peptides of length 9 amino acids or more, which are immunologically relevant as potential T-cell epitopes. Entropy-based analysis of the diversity of WNV sequences, revealed the presence of numerous evolutionarily stable nonamer positions across the proteome (entropy value of ≤1). The representation (frequency) of nonamers variant to the predominant peptide at these stable positions was, generally, low (≤10% of the WNV sequences analyzed). Eighty-eight fragments of length 9–29 amino acids, representing ~34% of the WNV polyprotein length, were identified to be identical and evolutionarily stable in all analyzed WNV sequences. Of the 88 completely conserved sequences, 67 are also present in other flaviviruses, and several have been associated with the functional and structural properties of viral proteins. Immunoinformatic analysis revealed that the majority (78/88) of conserved sequences are potentially immunogenic, while 44 contained experimentally confirmed human T-cell epitopes.
Conclusions
This study identified a comprehensive catalogue of completely conserved WNV sequences, many of which are shared by other flaviviruses, and majority are potential epitopes. The complete conservation of these immunologically relevant sequences through the entire recorded WNV history suggests they will be valuable as components of peptide-specific vaccines or other therapeutic applications, for sequence-specific diagnosis of a wide-range of Flavivivirus infections, and for studies of homologous sequences among other flaviviruses.
This article has been cited by other articles/sites:
18) Conservation and variability of dengue virus proteins: implications for vaccine design
Khan AM, Miotto O, Nascimento EJ, Srinivasan KN, Heiny AT, Zhang GL, Marques ET, Tan TW, Brusic V, Salmon J, August JT.
Virus Reviews and Research, 2008, 13 Supp 1, 74-75 & Proceedings of First Pan American Dengue Research Network Meeting, Recife, Brazil, July 2008
PUBMED PMID: N.A
Out link: N.A
Impact Factor Year 2009: N.A
No. of Citations: 0
ABSTRACT
Background
Genetic variation and rapid evolution are hallmarks of RNA viruses, the result of high mutation rates in RNA replication and selection of mutants that enhance viral adaptation, including the escape from host immune responses. Variability is uneven across the genome because mutations resulting in a deleterious effect on viral fitness are restricted. RNA viruses are thus marked by protein sites permissive to multiple mutations and sites critical to viral structure-function that are evolutionarily robust and highly conserved. Identification and characterization of the historical dynamics of the conserved sites have relevance to multiple applications, including potential targets for diagnosis, and prophylactic and therapeutic purposes.
Methodology/Principal Findings
We describe a large-scale identification and analysis of evolutionarily highly conserved amino acid sequences of the entire dengue virus (DENV) proteome, with a focus on sequences of 9 amino acids or more, and thus immune-relevant as potential T-cell determinants. DENV protein sequence data were collected from the NCBI Entrez protein database in 2005 (9,512 sequences) and again in 2007 (12,404 sequences). Forty-four (44) sequences (pan-DENV sequences), mainly those of nonstructural proteins and representing ~15% of the DENV polyprotein length, were identical in 80% or more of all recorded DENV sequences. Of these 44 sequences, 34 (~77%) were present in ≥95% of sequences of each DENV type, and 27 (~61%) were conserved in other Flaviviruses. The frequencies of variants of the pan-DENV sequences were low (0 to ~5%), as compared to variant frequencies of ~60 to ~85% in the non pan-DENV sequence regions. We further showed that the majority of the conserved sequences were immunologically relevant: 34 contained numerous predicted human leukocyte antigen (HLA) supertype-restricted peptide sequences, and 26 contained T-cell determinants identified by studies with HLA-transgenic mice and/or reported to be immunogenic in humans.
Conclusions
Forty-four (44) pan-DENV sequences of at least 9 amino acids were highly conserved and identical in 80% or more of all recorded DENV sequences, and the majority were found to be immune-relevant by their correspondence to known or putative HLA-restricted T-cell determinants. The conservation of these sequences through the entire recorded DENV genetic history supports their possible value for diagnosis, prophylactic and/or therapeutic applications. The combination of bioinformatics and experimental approaches applied herein provides a framework for large-scale and systematic analysis of conserved and variable sequences of other pathogens, in particular, for rapidly mutating viruses, such as influenza A virus and HIV.
This article has been cited by other articles/sites:
17) Conservation and variability of dengue virus proteins: implications for vaccine design
Khan AM, Miotto O, Nascimento EJ, Srinivasan KN, Heiny AT, Zhang GL, Marques ET, Tan TW, Brusic V, Salmon J, August JT.
PLoS Negl Trop Dis. 2008 Aug 13;2(8):e272.
PUBMED PMID: 18698358
Out link: Full-text
Impact Factor Year 2009: 4.693
No. of Citations: 7 (total): 5 (non-self) & 2 (self)
ABSTRACT
Background
Genetic variation and rapid evolution are hallmarks of RNA viruses, the result of high mutation rates in RNA replication and selection of mutants that enhance viral adaptation, including the escape from host immune responses. Variability is uneven across the genome because mutations resulting in a deleterious effect on viral fitness are restricted. RNA viruses are thus marked by protein sites permissive to multiple mutations and sites critical to viral structure-function that are evolutionarily robust and highly conserved. Identification and characterization of the historical dynamics of the conserved sites have relevance to multiple applications, including potential targets for diagnosis, and prophylactic and therapeutic purposes.
Methodology/Principal Findings
We describe a large-scale identification and analysis of evolutionarily highly conserved amino acid sequences of the entire dengue virus (DENV) proteome, with a focus on sequences of 9 amino acids or more, and thus immune-relevant as potential T-cell determinants. DENV protein sequence data were collected from the NCBI Entrez protein database in 2005 (9,512 sequences) and again in 2007 (12,404 sequences). Forty-four (44) sequences (pan-DENV sequences), mainly those of nonstructural proteins and representing ~15% of the DENV polyprotein length, were identical in 80% or more of all recorded DENV sequences. Of these 44 sequences, 34 (~77%) were present in ≥95% of sequences of each DENV type, and 27 (~61%) were conserved in other Flaviviruses. The frequencies of variants of the pan-DENV sequences were low (0 to ~5%), as compared to variant frequencies of ~60 to ~85% in the non pan-DENV sequence regions. We further showed that the majority of the conserved sequences were immunologically relevant: 34 contained numerous predicted human leukocyte antigen (HLA) supertype-restricted peptide sequences, and 26 contained T-cell determinants identified by studies with HLA-transgenic mice and/or reported to be immunogenic in humans.
Conclusions
Forty-four (44) pan-DENV sequences of at least 9 amino acids were highly conserved and identical in 80% or more of all recorded DENV sequences, and the majority were found to be immune-relevant by their correspondence to known or putative HLA-restricted T-cell determinants. The conservation of these sequences through the entire recorded DENV genetic history supports their possible value for diagnosis, prophylactic and/or therapeutic applications. The combination of bioinformatics and experimental approaches applied herein provides a framework for large-scale and systematic analysis of conserved and variable sequences of other pathogens, in particular, for rapidly mutating viruses, such as influenza A virus and HIV.
This article has been cited by other articles/sites:
1. Wired Science @ Wired Blog Network
2. Koo QY, Khan AM, Jung KO, Ramdas S, Miotto O, Tan TW, Brusic V, Salmon J, August JT. Conservation and variability of West Nile virus proteins. PLoS One. 2009;4(4):e5352. Epub 2009 Apr 29. PMID: 19401763
3. Lin HH, Zhang GL, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S22. PMID: 19091022
4. Nevis Amin, Alicia Aguilar, Frank Chamacho, Yaime Vázquez, Maritza Pupo, Juan Carlos Ramirez, Luis Izquierdo, Felix Dafhnis, David Ian Stott, Ela Maria Perez, Armando Acosta. Identification of Dengue-specific B-cell Epitopes by Phage-display Random Peptide Library. Malaysian Journal of Medical Sciences , Vol. 16, No. 4, Oct-Dec, 2009 pp. 4-14
5. HLA and other gene associations with dengue disease severity. Stephens HA. Curr Top Microbiol Immunol. 2010;338:99-114. Review. PMID: 19802581
6. Darwinian interventions: taming pathogens through evolutionary ecology. Williams PD. Trends Parasitol. 2010 Feb;26(2):83-92. Epub 2009 Dec 25. Review. PMID: 20036799
7. Tropenkrankheiten und Molekularbiologie- neue Horizonte, Mechthild Regenass- Klotz, Urs Regenass, Springer, 2009 - Immunology - 148 pages
16) Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes
Zhang GL*, Khan AM*, Srinivasan KN, Heiny AT, Lee KX, Kwoh CK, August JT, Brusic V.
BMC Bioinformatics. 2008, 9(Suppl 1):S19
* Contributed equally
PUBMED PMID: In Process
Out link: Full-text
Impact Factor Year 2009: 3.428
No. of Citations: 6 (total): 4 (non-self) & 2 (self)
ABSTRACT :
BACKGROUND : T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots.
RESULTS : Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and Support Vector Machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the formulation of an epitope-based vaccine that could be effective against multiple variants of the pathogen and applicable to a large proportion of the human population.
CONCLUSION : Hotspot Hunter is publicly accessible at http://antigen.i2r.a-star.edu.sg/hh/. It is a new generation computational tool aiding in epitope-based vaccine design.
This article has been cited by other articles/sites:
1. Ranganathan S, Gribskov M, Tan TW (2008) Bioinformatics research in the Asia Pacific: a 2007 update. BMC Bioinformatics. 9 Suppl 1, S1. PMID: 18315840
2. Toussaint NC, Kohlbacher O.
OptiTope--a web server for the selection of an optimal set of peptides for epitope-based vaccines. Nucleic Acids Res. 2009 May 11. [Epub ahead of print]
PMID: 19420066
3. De Groot AS, McMurry J, Moise L.
Prediction of immunogenicity: in silico paradigms, ex vivo and in vivo correlates.
Curr Opin Pharmacol. 2008 Oct;8(5):620-6. Epub 2008 Sep 19. Review.
PMID: 18775515
4. Koo QY, Khan AM, Jung KO, Ramdas S, Miotto O, Tan TW, Brusic V, Salmon J, August JT. Conservation and variability of West Nile virus proteins. PLoS One. 2009;4(4):e5352. Epub 2009 Apr 29. PMID: 19401763
5. Vaccine development against Trypanosoma cruzi and Leishmania species in the post-genomic era
Dumonteil E
Infection Genetics and Evolution, 2009. 9(6); SI: 1075-1082
6. OptiTope-a web server for the selection of an optimal set of peptides for epitope-based vaccines
Toussaint NC, Kohlbacher O
Nucleic Acids Research, 2009. 37(S): W617-W622
15) Automatic Synchronization and Distribution of Biological Databases and Software over Low-Bandwidth Networks among Developing Countries.
Sangket U, Phongdara A, Chotigeat W, Nathan D, Kim WY, Bhak J, Ngamphiw C, Tongsima S, Khan AM, Lin H, Tan TW.
Bioinformatics. 2008 Jan 15;24(2):299-301. Epub 2007 Nov 23.
PUBMED PMID: 18037613
Out link: Full-text
Impact Factor Year 2009: 4.926
No. of Citations: 2 (total): 0 (non-self) & 2 (self)
ABSTRACT :
Bioinformatics involves the collection, organization, and analysis of large amounts of biological data, using networks of computers and databases. Developing countries in the Asia-Pacific region are just moving into this new field of information-based biotechnology. However, the computational infrastructure and network bandwidths available in these countries are still at a basic level compared to that in developed countries. In this study, we assessed the utility of a BitTorrent-based Peer-to-Peer (btP2P) file distribution model for automatic synchronization and distribution of large amounts of biological data among developing countries. The initial country-level nodes in the Asia-Pacific region comprised Thailand, Korea, and Singapore. The results showed a significant improvement in download performance using btP2P - three times faster overall download performance than conventional File Transfer Protocol (FTP). This study demonstrated the reliability of btP2P in the dissemination of continuously growing multi-gigabyte biological databases across the three Asia-Pacific countries. The download performance for btP2P can be further improved by including more nodes from other countries into the network. This suggests that the btP2P technology is appropriate for automatic synchronization and distribution of biological databases and software over low-bandwidth networks among developing countries in the Asia-Pacific region. AVAILABILITY: http://everest.bic.nus.edu.sg/p2p/
This article has been cited by other articles/sites:
1. Ranganathan S, Gribskov M, Tan TW. Bioinformatics research in the Asia Pacific: a 2007 update. BMC Bioinformatics. 2008;9 Suppl 1:S1.
2. Ranganathan S, Hsu WL, Yang UC, Tan TW. Emerging strengths in Asia Pacific bioinformatics.
BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S1. PMID: 19091008
14) Evolutionarily conserved protein sequences of influenza a viruses, avian and human, as vaccine targets.
Heiny AT, Miotto O, Srinivasan KN, Khan AM, Zhang GL, Brusic V, Tan TW, August JT.
PLoS ONE. 2007 Nov 21;2(11):e1190.
PUBMED PMID: 18030326
Out link: Full-text
Impact Factor Year 2009: 4.351
No. of Citations: 33 (total): 28 (non-self) & 5 (self)
ABSTRACT :
BACKGROUND: Influenza A viruses generate an extreme genetic diversity through point mutation and gene segment exchange, resulting in many new strains that emerge from the animal reservoirs, among which was the recent highly pathogenic H5N1 virus. This genetic diversity also endows these viruses with a dynamic adaptability to their habitats, one result being the rapid selection of genomic variants that resist the immune responses of infected hosts. With the possibility of an influenza A pandemic, a critical need is a vaccine that will recognize and protect against any influenza A pathogen. One feasible approach is a vaccine containing conserved immunogenic protein sequences that represent the genotypic diversity of all current and future avian and human influenza viruses as an alternative to current vaccines that address only the known circulating virus strains.
METHODOLOGY/PRINCIPAL FINDINGS: Methodologies for large-scale analysis of the evolutionary variability of the influenza A virus proteins recorded in public databases were developed and used to elucidate the amino acid sequence diversity and conservation of 36,343 sequences of the 11 viral proteins of the recorded virus isolates of the past 30 years. Technologies were also applied to identify the conserved amino acid sequences from isolates of the past decade, and to evaluate the predicted human lymphocyte antigen (HLA) supertype-restricted class I and II T-cell epitopes of the conserved sequences. Fifty-five (55) sequences of 9 or more amino acids of the polymerases (PB2, PB1, and PA), nucleoprotein (NP), and matrix 1 (M1) proteins were completely conserved in at least 80%, many in 95 to 100%, of the avian and human influenza A virus isolates despite the marked evolutionary variability of the viruses. Almost all (50) of these conserved sequences contained putative supertype HLA class I or class II epitopes as predicted by 4 peptide-HLA binding algorithms. Additionally, data of the Immune Epitope Database (IEDB) include 29 experimentally identified HLA class I and II T-cell epitopes present in 14 of the conserved sequences.
CONCLUSIONS/SIGNIFICANCE: This study of all reported influenza A virus protein sequences, avian and human, has identified 55 highly conserved sequences, most of which are predicted to have immune relevance as T-cell epitopes. This is a necessary first step in the design and analysis of a polyepitope, pan-influenza A vaccine. In addition to the application described herein, these technologies can be applied to other pathogens and to other therapeutic modalities designed to attack DNA, RNA, or protein sequences critical to pathogen function.
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1. Tripp RA, Tompkins SM Recombinant vaccines for influenza virus. Curr Opin Investig Drugs. (2008). 9 (8), 836-45. PMID: 18666031
2. Lin HH, Zhang GL, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S22. PMID: 19091022
3. Khan AM, Miotto O, Nascimento EJ, Srinivasan KN, Heiny AT, Zhang GL, Marques ET, Tan TW, Brusic V, Salmon J, August JT. Conservation and variability of dengue virus proteins: implications for vaccine design. PLoS Negl Trop Dis. 2008 Aug 13;2(8):e272. PMID: 18698358
4. H Rashid, E Haworth, J Ellis, R Booy, S Shafi. Reverse Transcriptase-Polymerase Chain Reaction on QuickVue Influenza Test Strips: A Pilot Study. The Journal of Near-Patient Testing & Technology. 8(1):1-3, March 2009
5. Pappalardo F, Halling-Brown MD, Rapin N, Zhang P, Alemani D, Emerson A, Paci P, Duroux P, Pennisi M, Palladini A, Miotto O, Churchill D, Rossi E, Shepherd AJ, Moss DS, Castiglione F, Bernaschi M, Lefranc MP, Brunak S, Motta S, Lollini PL, Basford KE, Brusic V. ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization. Brief Bioinform. 2009 May;10(3):330-40. PMID: 19383844
6. Chon H, Choi B, Jeong G, Mo I. Evaluation system for an experimental study of low-pathogenic avian influenza virus (H9N2) infection in specific pathogen free chickens using lactic acid bacteria, Lactobacillus plantarum KFCC11389P. Avian Pathol. 2008 Dec;37(6):593-7.PMID: 18923971
7. Tong JC, Ren EC. Immunoinformatics: Current trends and future directions. Drug Discov Today. 2009 Apr 18. [Epub ahead of print]. PMID: 19379830
8. Hemerka JN, Wang D, Weng Y, Lu W, Kaushik RS, Jin J, Harmon AF, Li F.
Detection and characterization of influenza A virus PA-PB2 interaction through a bimolecular fluorescence complementation assay. J Virol. 2009 Apr;83(8):3944-55. Epub 2009 Feb 4.
PMID: 19193801
9. Koo QY, Khan AM, Jung KO, Ramdas S, Miotto O, Tan TW, Brusic V, Salmon J, August JT. Conservation and variability of West Nile virus proteins. PLoS One. 2009;4(4):e5352. Epub 2009 Apr 29. PMID: 19401763
10. http://www.cqvip.com/qk/94860x/2008010/28567073.html
11. Wahl A, Schafer F, Bardet W, Buchli R, Air GM, Hildebrand WH.
HLA class I molecules consistently present internal influenza epitopes.
Proc Natl Acad Sci U S A. 2009 Jan 13;106(2):540-5. Epub 2009 Jan 2.
PMID: 19122146
12. Thompson, W.A., Fan, S., Weltman, J.K. Information entropy of influenza a segment 7 . Entropy. 2008, 10 (4), pp. 736-744
13. Pu, Z., Yu-Shu, Z., Chuan-Ling, Q., Bei-Bei, J.I.A., Xing-You, L.I.U. Strategies exploited by influenza a virus for evading immune responses. Progress in Biochemistry and Biophysics. 2008. 35 (10), pp. 1137-1141
14) A live attenuated H1N1 M1 mutant provides broad cross-protection against influenza A viruses, including highly pathogenic A/Vietnam/1203/2004, in mice.
Xie H, Liu TM, Lu X, Wu Z, Belser JA, Katz JM, Tumpey TM, Ye Z.
J Infect Dis. 2009 Dec 15;200(12):1874-83.
PMID: 19909080
15) A 型流感病毒逃避免疫应答的策略
赵朴， 郑玉姝， 乔传玲， 贾贝贝， 刘兴友 - 生物化学与生物物理进展, 2008 - cqvip.com
http://www.cqvip.com/qk/94860x/2008010/28567073.html
16) Complete-proteome mapping of human influenza A adaptive mutations: implications for human transmissibility of zoonotic strains.
Miotto O, Heiny AT, Albrecht R, García-Sastre A, Tan TW, August JT, Brusic V.
PLoS One. 2010 Feb 3;5(2):e9025.
PMID: 20140252
17) Vaccines: the fourth century.
Plotkin SA.
Clin Vaccine Immunol. 2009 Dec;16(12):1709-19. Epub 2009 Sep 30. Review.
PMID: 19793898
18) 统计学筛选电脑预测的人 H5N1 毒株核蛋白 B 细胞表位
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http://www.cqvip.com/qk/95714x/2008004/27156030.html
19) Heterosubtypic anti-avian H5N1 influenza antibodies in intravenous immunoglobulins from globally separate populations protect against H5N1 infection in cell culture.
Sullivan JS, Selleck PW, Downton T, Boehm I, Axell AM, Ayob Y, Kapitza NM, Dyer W, Fitzgerald A, Walsh B, Lynch GW.
J Mol Genet Med. 2009 Dec 23;3(2):217-24.
PMID: 20076794
20) Controlling influenza by cytotoxic T-cells: calling for help from destroyers.
Schotsaert M, Ibañez LI, Fiers W, Saelens X.
J Biomed Biotechnol. 2010;2010:863985. Epub 2010 May 24.
PMID: 20508820
21) Conservation and diversity of influenza A H1N1 HLA-restricted T cell epitope candidates for epitope-based vaccines.
Tan PT, Heiny AT, Miotto O, Salmon J, Marques ET, Lemonnier F, August JT.
PLoS One. 2010 Jan 18;5(1):e8754.
PMID: 20090904
22) Two novel HLA-A*0201 T-cell epitopes in avian H5N1 viral nucleoprotein induced specific immune responses in HHD mice.
Cheung YK, Cheng SC, Ke Y, Xie Y.
Vet Res. 2010 Mar-Apr;41(2):24. Epub 2009 Nov 27.
PMID: 19941812
23) Approach to the profiling and characterization of influenza vaccine constituents by the combined use of size-exclusion chromatography, gel electrophoresis and mass spectrometry.
Garcia-Cañas V, Lorbetskie B, Cyr TD, Hefford MA, Smith S, Girard M.
Biologicals. 2010 Mar;38(2):294-302. Epub 2010 Jan 13.
PMID: 20074977
24) Surveillance of influenza virus B circulation in Northern Italy: summer-fall 2008 isolation of three strains and phylogenetic analysis.
Gerna G, Percivalle E, Piralla A, Rognoni V, Marchi A, Baldanti F.
New Microbiol. 2009 Oct;32(4):405-10.
PMID: 20128448
25) Standardization and validation of assays determining cellular immune responses against influenza.
Gijzen K, Liu WM, Visontai I, Oftung F, van der Werf S, Korsvold GE, Pronk I, Aaberge IS, Tütto A, Jankovics I, Jankovics M, Gentleman B, McElhaney JE, Soethout EC.
Vaccine. 2010 Apr 26;28(19):3416-22. Epub 2010 Mar 4.
PMID: 20206285
26) Discovery of novel antiviral agents directed against the influenza A virus nucleoprotein using photo-cross-linked chemical arrays.
Hagiwara K, Kondoh Y, Ueda A, Yamada K, Goto H, Watanabe T, Nakata T, Osada H, Aida Y.
Biochem Biophys Res Commun. 2010 Apr 9;394(3):721-7. Epub 2010 Mar 16.
PMID: 20230782
27) Identification and structural definition of H5-specific CTL epitopes restricted by HLA-A*0201 derived from the H5N1 subtype of influenza A viruses.
Sun Y, Liu J, Yang M, Gao F, Zhou J, Kitamura Y, Gao B, Tien P, Shu Y, Iwamoto A, Chen Z, Gao GF.
J Gen Virol. 2010 Apr;91(Pt 4):919-30. Epub 2009 Dec 2.
PMID: 19955560
28) Acquired heterosubtypic antibodies in human immunity for avian H5N1 influenza.
Lynch GW, Selleck P, Sullivan JS.
J Mol Genet Med. 2009 Dec 15;3(2):205-9.
PMID: 20076792
29) Heterosubtypic immunity to influenza mediated by liposome adjuvanted H5N1 recombinant protein vaccines.
Thueng-In K, Maneewatch S, Srimanote P, Songserm T, Tapchaisri P, Sookrung N, Tongtawe P, Channarong S, Chaicumpa W.
Vaccine. 2010 Aug 2. [Epub ahead of print]
PMID: 20688037
30) H5N1 型流感病毒 M1 蛋白的基因克隆与表达
李雪辉， 陈杭薇， 李明刚， 张秋雨， 张励力 - 西北国防医学杂志, 2009 - cqvip.com
http://www.cqvip.com/qk/91335x/2009006/32576099.html
31) 抗甲型流感病毒短发夹状 RNA 分子的体外生物合成及功能鉴定
郭述良， 罗永艾 - 第三军医大学学报, 2009 - cqvip.com
http://www.cqvip.com/qk/92156x/2009010/30342873.html
32) Human H1 promoter expressed short hairpin RNAs (shRNAs) suppress avian influenza virus replication in chicken CH-SAH and canine MDCK cells.
Abrahamyan A, Nagy E, Golovan SP.
Antiviral Res. 2009 Nov;84(2):159-67. Epub 2009 Sep 6.
PMID: 19737578
33) Strategies Exploited by Influenza A Virus for Evading Immune Responses
Zhao P, Zheng YS, Qiao CL, et al.
Progress in Biochemistry and Biophysics. 2008; 35(10):1137-1141
13) Temporal and antigenic analysis of dengue virus serotype 1 genome polyprotein sequences
Rajapakse M, Veeramani A, Gopalakrishnan K, Ananthanarayan S, Srinivasan KN, August JT, Khan AM, Brusic V.
International Conference on Biomedical and Pharmaceutical Engineering, 2006. Singapore, December 11, 2006.
Out link: Full-text
Impact Factor Year 2009: NA
No. of Citations: 0
ABSTRACT :
Dengue virus poses a significant health threat. The new emerging strains continue to cause annual epidemics. In this paper, we report results of an analysis of dengue virus serotype 1 (DV1) genome polyprotein sequences in the context of time and geographical distribution. We studied the clustering of 60 DV1 genome polyprotein sequences of different geographical distributions, reported in the past 30 years, to identify geographic and temporal patterns of genetic and antigenic variation. Our analysis showed distinct clustering of sequences into two main geographical regions: i) Western and South Pacific and South-East Asian region (with three distinct sub-clusters) and ii) South American region. The results provide evidence suggesting temporal changes of the isolated strains. In addition, our analysis indicated that potential T-cell epitope hotspots within DV1 are shifting with time and recurrent epidemics, resulting in disappearing of some hotspots and emerging of new hotspots.
12) A systematic bioinformatics approach for selection of epitope-based vaccine targets
Khan AM, Miotto O, Heiny AT, Salmon J, Srinivasan KN, Nascimento EJ, Marques ET, Brusic V, Tan TW, August JT.
Cell Immunol. 2006 Dec;244(2):141-147. Epub 2007 Apr 16.
PUBMED PMID: 17434154
Out link: Full-text
Impact Factor Year 2009: 2.698
No. of Citations: 15 (total): 9 (non-self) & 6 (self)
ABSTRACT :
Epitope-based vaccines provide a new strategy for prophylactic and therapeutic application of pathogen-specific immunity. A critical requirement of this strategy is the identification and selection of T-cell epitopes that act as vaccine targets. This study describes current methodologies for the selection process, with dengue virus as a model system. A combination of publicly available bioinformatics algorithms and computational tools are used to screen and select antigen sequences as potential T-cell epitopes of supertype human leukocyte antigen (HLA) alleles. The selected sequences are tested for biological function by their activation of T-cells of HLA transgenic mice and of pathogen infected subjects. This approach provides an experimental basis for the design of pathogen specific, T-cell epitope-based vaccines that are targeted to majority of the genetic variants of the pathogen, and are effective for a broad range of differences in human leukocyte antigens among the global human population.
This article has been cited by other articles:
1) Heiny AT, Miotto O, Srinivasan KN, Khan AM, Zhang GL, Brusic V, Tan TW, August JT. Evolutionarily conserved protein sequences of influenza a viruses, avian and human, as vaccine targets. PLoS ONE. 2007 Nov 21;2(11):e1190. PMID: 18030326
2) Miotto O, Heiny A, Tan TW, August JT, Brusic V. Identification of human-to-human transmissibility factors in PB2 proteins of influenza A by large-scale mutual information analysis. BMC Bioinformatics. 2008;9 Suppl 1:S18. PMID: 18315849
3) Miotto O, Tan TW, Brusic V. Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses. BMC Bioinformatics. 2008;9 Suppl 1:S7. PMID: 18315860
4) Sidney J, Peters B, Frahm N, Brander C, Sette A. HLA class I supertypes: a revised and updated classification. BMC Immunol. 2008 Jan 22;9:1. PMID: 18211710
5) Koo QY, Khan AM, Jung KO, Ramdas S, Miotto O, Tan TW, Brusic V, Salmon J, August JT. Conservation and variability of West Nile virus proteins. PLoS One. 2009;4(4):e5352. Epub 2009 Apr 29. PMID: 19401763
6) De Groot AS, McMurry J, Moise L. Prediction of immunogenicity: in silico paradigms, ex vivo and in vivo correlates. Curr Opin Pharmacol. 2008 Oct;8(5):620-6. Epub 2008 Sep 19. Review.
PMID: 18775515
7) Khan AM, Miotto O, Nascimento EJ, Srinivasan KN, Heiny AT, Zhang GL, Marques ET, Tan TW, Brusic V, Salmon J, August JT. Conservation and variability of dengue virus proteins: implications for vaccine design. PLoS Negl Trop Dis. 2008 Aug 13;2(8):e272. PMID: 18698358
8) Yauch LE, Zellweger RM, Kotturi MF, Qutubuddin A, Sidney J, Peters B, Prestwood TR, Sette A, Shresta S. A protective role for dengue virus-specific CD8+ T cells. J Immunol. 2009 Apr 15;182(8):4865-73. PMID: 19342665
9) Complete-proteome mapping of human influenza A adaptive mutations: implications for human transmissibility of zoonotic strains.
Miotto O, Heiny AT, Albrecht R, García-Sastre A, Tan TW, August JT, Brusic V.
PLoS One. 2010 Feb 3;5(2):e9025.
PMID: 20140252
10) CD4+ T cell epitope discovery and rational vaccine design.
Rosa DS, Ribeiro SP, Cunha-Neto E.
Arch Immunol Ther Exp (Warsz). 2010 Apr;58(2):121-30. Epub 2010 Feb 14. Review.
PMID: 20155490
11) Computational analysis of cysteine proteases (Clan CA, Family Cl) of Leishmania major to find potential epitopic regions.
Saffari B, Mohabatkar H.
Genomics Proteomics Bioinformatics. 2009 Sep;7(3):87-95.
PMID: 19944381
12) Computational characterization of Plasmodium falciparum proteomic data for screening of potential vaccine candidates.
Singh SP, Khan F, Mishra BN.
Hum Immunol. 2010 Feb;71(2):136-43. Epub 2009 Dec 31.
PMID: 19945493
13) Immunoinformatics and its role in microbes and vaccines
Chate P.B., Kayarkar N.A., Durgude S.G., Maurya B.D., Pawar S.V. and Gomase V.S
International Journal of Parasitology Research, ISSN: 0975-3702, Volume 1, Issue 2, 2009, pp-01-07
14) Applied informatics manipulation for fight against dengue
V Wiwanitkit
138 Dengue Bulletin–Volume 32, 2008
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15) Inflammatory and autoimmune reactions in atherosclerosis and vaccine design informatics.
Jan M, Meng S, Chen NC, Mai J, Wang H, Yang XF.
J Biomed Biotechnol. 2010;2010:459798. Epub 2010 Apr 15. Review.
PMID: 20414374
11) Large-scale analysis of antigenic diversity of T-cell epitopes in dengue virus.
Khan AM, Heiny A, Lee KX, Srinivasan K, Tan TW, August JT, Brusic V.
BMC Bioinformatics. 2006 Dec 18;7 Suppl 5:S4.
PUBMED PMID: 17254309
Out link: Full-text
Impact Factor Year 2009: 3.428
No. of Citations: 16 (total): 8 (non-self) & 8 (self)
ABSTRACT :
BACKGROUND : Antigenic diversity in dengue virus strains has been studied, but large-scale and detailed systematic analyses have not been reported. In this study, we report a bioinformatics method for analyzing viral antigenic diversity in the context of T-cell mediated immune responses. We applied this method to study the relationship between short-peptide antigenic diversity and protein sequence diversity of dengue virus. We also studied the effects of sequence determinants on viral antigenic diversity. Short peptides, principally 9-mers were studied because they represent the predominant length of binding cores of T-cell epitopes, which are important for formulation of vaccines.
RESULTS : Our analysis showed that the number of unique protein sequences required to represent complete antigenic diversity of short peptides in dengue virus is significantly smaller than that required to represent complete protein sequence diversity. Short-peptide antigenic diversity shows an asymptotic relationship to the number of unique protein sequences, indicating that for large sequence sets (~200) the addition of new protein sequences has marginal effect to increasing antigenic diversity. A near-linear relationship was observed between the extent of antigenic diversity and the length of protein sequences, suggesting that, for the practical purpose of vaccine development, antigenic diversity of short peptides from dengue virus can be represented by short regions of sequences (~<100>
CONCLUSION : This study provides evidence that there are limited numbers of antigenic combinations in protein sequence variants of a viral species and that short regions of the viral protein are sufficient to capture antigenic diversity of T-cell epitopes. The approach described herein has direct application to the analysis of other viruses, in particular those that show high diversity and/or rapid evolution, such as influenza A virus and human immunodeficiency virus (HIV).
This article has been cited by other articles/sites:
1) Ranganathan S, Tammi M, Gribskov M, Tan TW. Establishing bioinformatics research in the asia pacific - introduction. Dec 2006; BMC Bioinformatics 7: S1 suppl. 5
2) Khan AM, Miotto O, Heiny AT, Salmon J, Srinivasan KN, Nascimento EJ, Marques ET Jr, Brusic V, Tan TW, August JT. A systematic bioinformatics approach for selection of epitope-based vaccine targets. Cell Immunol. 2006 Dec;244(2):141-7. Epub 2007 Apr 16. PMID: 17434154
3) Muzzi A, Masignani V, Rappuoli R. The pan-genome: towards a knowledge-based discovery of novel targets for vaccines and antibacterials. Drug Discov Today. 2007 Jun;12(11-12):429-39. Epub 2007 May 7. Review. PMID: 17532526
4) Mazumder R, Hu ZZ, Vinayaka CR, Sagripanti JL, Frost SD, Kosakovsky Pond SL, Wu CH. Computational analysis and identification of amino acid sites in dengue E proteins relevant to development of diagnostics and vaccines. Virus Genes. 2007 Oct;35(2):175-86. Epub 2007 May 17. PMID: 17508277
5) Scharnagl NC, Klade CS. Experimental discovery of T-cell epitopes: combining the best of classical and contemporary approaches. Expert Rev Vaccines. 2007 Aug;6(4):605-15. Review. PMID: 17669013
6) Dimitrios Vlachakis. An Introduction to Molecular Modelling, from Theory to Application. (United States), Paperback, 2007.
7) Judice, Lie Yong Koh. Correlation-Based Methods for Biological Data Cleaning. PhD thesis. 2007, National University of Singapore
8) Zhang, GL, Khan, AM, Srinivasan, KN, Heiny, AT, Lee, KX, Kwoh, CK, August JT, Brusic, V. Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes. BMC Bioinformatics 2008, 9(Suppl 1):S19
9) Khan AM, Miotto O, Nascimento EJ, Srinivasan KN, Heiny AT, Zhang GL, Marques ET, Tan TW, Brusic V, Salmon J, August JT. Conservation and variability of dengue virus proteins: implications for vaccine design. PLoS Negl Trop Dis. 2008 Aug 13;2(8):e272. PMID: 18698358
10) Tongchusak S, Leelayuwat C, Brusic V, Chaiyaroj SC. In silico prediction and immunological validation of common HLA-DRB1-restricted T cell epitopes of Candida albicans secretory aspartyl proteinase 2. Microbiol Immunol. 2008 Apr;52(4):231-42. PMID: 18426398
11) Tong JC, Ren EC. Immunoinformatics: Current trends and future directions. Drug Discov Today. 2009 Apr 18. [Epub ahead of print]. PMID: 19379830
12) Somvanshi P and Seth PK. Prediction of T cell epitopes for the utility of vaccine development from structural proteins of dengue virus variants using in silico methods. Indian Journal of Biotechnology, 2009 Apr, 8, 193-198.
13) Applied informatics manipulation for fight against dengue
V Wiwanitkit
138 Dengue Bulletin–Volume 32, 2008
searo.who.int
14) Heiny Tan.
Characterizing evolutionarily conserved influenza A virus sequences as vaccine targets
MSc Thesis 2009, National University of Singapore
https://scholarbank.nus.edu/handle/10635/16641
15) HLA class I restriction as a possible driving force for Chikungunya evolution.
Tong JC, Simarmata D, Lin RT, Rénia L, Ng LF.
PLoS One. 2010 Feb 26;5(2):e9291.
PMID: 20195467
16) Judice, Lie Yong Koh. Correlation-based methods for data cleaning, with application to biological databases.
PhD thesis. 2007, National University of Singapore
10) Neural models for predicting viral vaccine targets.
Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V.
J Bioinform Comput Biol. 2005 Oct;3(5):1207-25.
PUBMED PMID: 16278955.
Out link: Full-text
Impact Factor Year 2009: NA
No. of Citations: 16 (total): 5 (non-self) & 11 (self)
ABSTRACT :
We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve--AROC>0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC>0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) membrane protein were predicted by using MULTIPRED.
This article has been cited by other articles:
1) Srinivasan KN, Zhang GL, Khan AM, August JT, Brusic V. Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens. Bioinformatics. 2004 Aug 4;20 Suppl 1:i297-302. PMID: 15262812
2) Zhang GL, Srinivasan KN, Veeramani A, August JT, Brusic V. PREDBALB/c: a system for the prediction of peptide binding to H2d molecules, a haplotype of the BALB/c mouse. Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W180-3. PMID: 15980450
3) Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V. MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W172-9. PMID: 15980449
4) Zhang, GL, August, JT, Kwo, CK, Brusic, V. Performance Evaluation of MULTIPRED1 on Prediction of MHC Class I Binders. Proceedings of the International Conference on Biomedical and Pharmaceutical Engineering, 2006. 2006 Page(s):307-313.
5) Handoko SD, Keong KC, Soon OY, Zhang GL, Brusic V. Extreme learning machine for predicting HLA-peptide binding. Proceedings of Advances in neural networks, pt 3. 2006; 3973:716-721.
6) Zhang, GL, Tong, JC, Zhang, ZH, August, JT, Zheng, Y, Kwoh, CK, Brusic, V. Computational Models for Identifying Promiscuous HLA-B7 Binders based on Information Theory and Support Vector Machine. International Conference on Biomedical and Pharmaceutical Engineering, 2006. Pages:319-323.
7) Zhang GL, Bozic I, Kwoh CK, August JT, Brusic V. Prediction of supertype-specific HLA class I binding peptides using support vector machines. J Immunol Methods. 2007 Mar 30;320(1-2):143-54. Epub 2007 Jan 25. PMID: 17303158
8) Zhang, GL, Khan, AM, Srinivasan, KN, Heiny, AT, Lee, KX, Kwoh, CK, August JT, Brusic, V. Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes. BMC Bioinformatics 2008, 9(Suppl 1):S19
9) Nanni L, Lumini A. A genetic approach for building different alphabets for peptide and protein classification. BMC Bioinformatics. 2008 Jan 24;9:45. PMID: 18218100
10) Nanni, L., Lumini, A. Machine learning multi-classifiers for peptide classification. Neural Computing and Applications. 2009, 18 (2), pp. 185-192
11) Predictive Vaccinology: Optimisation of Predictions Using Support Vector Machine Classifiers
Ivana Bozic, Guang Lan Zhang and Vladimir Brusic
Intelligent Data Engineering and Automated Learning - IDEAL 2005
Lecture Notes in Computer Science, 2005, Volume 3578/2005, 375-381, DOI: 10.1007/11508069_49
12) CD4+ T cell epitope discovery and rational vaccine design.
Rosa DS, Ribeiro SP, Cunha-Neto E.
Arch Immunol Ther Exp (Warsz). 2010 Apr;58(2):121-30. Epub 2010 Feb 14. Review.
PMID: 20155490
13) Khan AM
Mapping targets of immune responses in complete dengue viral genomes
MSc Thesis 2006, National University of Singapore
https://scholarbank.nus.edu.sg/handle/10635/15081
14) Vaccines: Data Driven Prediction of Binders, Epitopes and Immunogenicity
G Santayana - Bioinformatics for Vaccinology - ebooks.lib.unair.ac.id
15) Characterizing evolutionarily conserved influenza A virus sequences as vaccine targets
Heiny Tan.
MSc Thesis 2009, National University of Singapore
https://scholarbank.nus.edu/handle/10635/16641
16) 刺苋花粉泛过敏原 Profilin 的抗原性评估与三级结构分析
陶爱林， 刘林川， 王永飞， 邹泽红， 马三梅， 赖 … - 中华微生物学和免疫学 …, 2008 - cqvip.com
http://www.cqvip.com/qk/95714x/2008007/27915213.html
9) MULTIPRED: a computational system for prediction of promiscuous HLA binding.
Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V.
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W172-9.
PUBMED PMID: 15980449
Out link: Full-text
Impact Factor Year 2009: 7.479
No. of Citations: 70 (total): 51 (non-self) & 19 (self)
ABSTRACT :
MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability (area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets--termed T-cell epitope hotspots. MULTIPRED is available at http://antigen.i2r.a-star.edu.sg/multipred/.
This article has been cited by other articles:
1) Zhang GL, Srinivasan KN, Veeramani A, August JT, Brusic V. PREDBALB/c: a system for the prediction of peptide binding to H2d molecules, a haplotype of the BALB/c mouse. Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W180-3. PMID: 15980450
2) Tongchusak, S, Chaiyaroj, SC, Veeramani, A, JLY Koh, Brusic, V. CandiVF – Candida albicans Virulence Factor Database. International Journal of Peptide Research and Therapeutics, Vol. 11, No. 4, December 2005, pp. 271–277.
3) Pietersz GA, Pouniotis DS, Apostolopoulos V. Design of peptide-based vaccines for cancer. Curr Med Chem. 2006;13(14):1591-607. Review. PMID: 16787206
4) Cui J, Han LY, Lin HH, Tang ZQ, Jiang L, Cao ZW, Chen YZ. MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties. Immunogenetics. 2006 Aug;58(8):607-13. Epub 2006 Jul 11.
PMID: 16832638
5) Braga-Neto UM, Marques ET Jr. From functional genomics to functional immunomics: new challenges, old problems, big rewards. PLoS Comput Biol. 2006 Jul 28;2(7):e81. Review. PMID: 16863395
6) Peters B, Bui HH, Frankild S, Nielson M, Lundegaard C, Kostem E, Basch D, Lamberth K, Harndahl M, Fleri W, Wilson SS, Sidney J, Lund O, Buus S, Sette A. A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput Biol. 2006 Jun 9;2(6):e65. Epub 2006 Jun 9. PMID: 16789818
7) Mishra S, Sinha S. Prediction and molecular modeling of T-cell epitopes derived from placental alkaline phosphatase for use in cancer immunotherapy. J Biomol Struct Dyn. 2006 Oct;24(2):109-21. PMID: 16928134
8) Wan J, Liu W, Xu Q, Ren Y, Flower DR, Li T. SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics. 2006 Oct 23;7:463. PMID: 17059589
9) Petrovsky N, Brusic V. Bioinformatics for study of autoimmunity. Autoimmunity. 2006 Dec;39(8):635-43. Review. PMID: 17178560
10) Zhang, GL, August, JT, Kwo, CK, Brusic, V. Performance Evaluation of MULTIPRED1 on Prediction of MHC Class I Binders. Proceedings of the International Conference on Biomedical and Pharmaceutical Engineering, 2006. 2006 Page(s):307-313.
11) Zhang C, Bickis MG, Wu FX, Kusalik AJ. Optimally-connected hidden markov models for predicting MHC-binding peptides. J Bioinform Comput Biol. 2006 Oct;4(5):959-80.
PMID: 17099936.
12) Kosmopoulou, A., Vlassi, M., Stavrakoudis, A., Sakarellos, C. and Sakarellos-Daitsiotis, M. (2006) T-cell epitopes of the La/SSB autoantigen: prediction based on the homology modeling of HLA-DQ2/DQ7 with the insulin-B peptide/HLA-DQ8 complex, J Comput Chem, 27, 1033-1044.
13) Zhang, GL, Tong, JC, Zhang, ZH, August, JT, Zheng, Y, Kwoh, CK, Brusic, V. Computational Models for Identifying Promiscuous HLA-B7 Binders based on Information Theory and Support Vector Machine. International Conference on Biomedical and Pharmaceutical Engineering, 2006. Pages:319-323.
14) Rajapakse, M, Veeramani, A, Gopalakrishnan, K, Ananthanarayan, S, Srinivasan KN, August, JT, Khan, AM, Brusic, V. Temporal and antigenic analysis of dengue virus serotype 1 genome polyprotein sequences. Proceedings of the International Conference on Biomedical and Pharmaceutical Engineering, 2006. 2006 Page(s):301-306.
15) Khan AM, Miotto O, Heiny AT, Salmon J, Srinivasan KN, Nascimento EJ, Marques ET Jr, Brusic V, Tan TW, August JT. A systematic bioinformatics approach for selection of epitope-based vaccine targets. Cell Immunol. 2006 Dec;244(2):141-7. Epub 2007 Apr 16. PMID: 17434154
Development of an OspC-based tetravalent, recombinant, chimeric vaccinogen that elicits bactericidal antibody against diverse Lyme disease spirochete strains. Vaccine. 2007 Jan 5;25(3):466-80. Epub 2006 Aug 8.PMID: 16996663
17) Cui J, Han LY, Lin HH, Zhang HL, Tang ZQ, Zheng CJ, Cao ZW, Chen YZ. Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties. Mol Immunol. 2007 Feb;44(5):866-77. Epub 2006 Jun 27. PMID: 16806474
18) Baker MP, Jones TD. Identification and removal of immunogenicity in therapeutic proteins. Curr Opin Drug Discov Devel. 2007 Mar;10(2):219-27. Review. PMID: 17436557
19) Brusic V, Marina O, Wu CJ, Reinherz EL. Proteome informatics for cancer research: from molecules to clinic. Proteomics. 2007 Mar;7(6):976-91. Review. PMID: 17370257
20) Zhang GL, Bozic I, Kwoh CK, August JT, Brusic V. Prediction of supertype-specific HLA class I binding peptides using support vector machines. J Immunol Methods. 2007 Mar 30;320(1-2):143-54. Epub 2007 Jan 25. PMID: 17303158
21) Tong JC, Tan TW, Ranganathan S. Methods and protocols for prediction of immunogenic epitopes. Brief Bioinform. 2007 Mar;8(2):96-108. Epub 2006 Oct 31. Review. PMID: 17077136
22) Kessler JH, Melief CJ. Identification of T-cell epitopes for cancer immunotherapy. Leukemia. 2007 Sep;21(9):1859-74. Epub 2007 Jul 5. Review. PMID: 17611570
23) Ostrout ND, McHugh MM, Tisch DJ, Moormann AM, Brusic V, Kazura JW. Long-term T cell memory to human leucocyte antigen-A2 supertype epitopes in humans vaccinated against smallpox. Clin Exp Immunol. 2007 Aug;149(2):265-73. Epub 2007 May 4. PMID: 17488297
24) Trost B, Bickis M, Kusalik A. Strength in numbers: achieving greater accuracy in MHC-I binding prediction by combining the results from multiple prediction tools.
Immunome Res. 2007 Mar 24;3:5. PMID: 17381846
25) Calvo-Calle JM, Strug I, Nastke MD, Baker SP, Stern LJ. Human CD4+ T cell epitopes from vaccinia virus induced by vaccination or infection. PLoS Pathog. 2007 Oct 12;3(10):1511-29. PMID: 17937498
Specificity in molecular design: a physical framework for probing the determinants of binding specificity and promiscuity in a biological environment.
27) Heiny AT, Miotto O, Srinivasan KN, Khan AM, Zhang GL, Brusic V, Tan TW, August JT. Evolutionarily conserved protein sequences of influenza a viruses, avian and human, as vaccine targets. PLoS ONE. 2007 Nov 21;2(11):e1190. PMID: 18030326J Phys Chem B. 2007 Nov 29;111(47):13419-35. Epub 2007 Nov 3. PMID: 17979267
28) Jacob, L, Vert, JP. Epitope prediction improved by multitask support vector machines. http://arxiv.org/abs/q-bio/0702008
29) Zhang, GL, Khan, AM, Srinivasan, KN, Heiny, AT, Lee, KX, Kwoh, CK, August JT, Brusic, V. Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes. BMC Bioinformatics 2008, 9(Suppl 1):S19
30) Promiscuous T cell epitope prediction of Candida albicans secretory aspartyl proteinase family of proteins. Infect Genet Evol. 2008 Jul;8(4):467-73. Epub 2007 Sep 25. PMID: 17974505
31) A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol. 2008 Apr 4;4(4):e1000048. PMID: 18389056
32) Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol. 2008 Mar 16;9:8. PMID: 18366636
33) Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res. 2008 Jan 25;4:2. PMID: 18221540
34) A comprehensive analysis of the thermodynamic events involved in ligand-receptor binding using CoRIA and its variants. J Comput Aided Mol Des. 2008 Feb;22(2):91-104. Epub 2008 Jan 25. PMID: 18219446
35) Efficient peptide-MHC-I binding prediction for alleles with few known binders. Bioinformatics. 2008 Feb 1;24(3):358-66. Epub 2007 Dec 14.PMID: 18083718
36) HLA class I supertypes: a revised and updated classification. BMC Immunol. 2008 Jan 22;9:1.PMID: 18211710
37) A probabilistic meta-predictor for the MHC class II binding peptides. Immunogenetics. 2008 Jan;60(1):25-36. Epub 2007 Dec 19. PMID: 18092156
38) Mallik, B., Morikis, D. Applications of molecular dynamics simulations in immunology: A useful computational method in aiding vaccine design. Current Proteomics, 2006 3 (4), pp. 259-270
39) Tong JC, Sinha AA. Immunological hotspots analyzed by docking simulations: evidence for a general mechanism in pemphigus vulgaris pathology and transformation. BMC Immunol. 2008 Jun 19;9:30. PMID: 18564435
40) Khan AM, Miotto O, Nascimento EJ, Srinivasan KN, Heiny AT, Zhang GL, Marques ET, Tan TW, Brusic V, Salmon J, August JT. Conservation and variability of dengue virus proteins: implications for vaccine design. PLoS Negl Trop Dis. 2008 Aug 13;2(8):e272. PMID: 18698358
41) Lara J, Wohlhueter RM, Dimitrova Z, Khudyakov YE.
Artificial neural network for prediction of antigenic activity for a major conformational epitope in the hepatitis C virus NS3 protein. Bioinformatics. 2008 Sep 1;24(17):1858-64. Epub 2008 Jul 15. PMID: 18628290
42) Lin HH, Zhang GL, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S22. PMID: 19091022
43) Zhang H, Lundegaard C, Nielsen M. Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods. Bioinformatics. 2009 Jan 1;25(1):83-9. Epub 2008 Nov 7. PMID: 18996943
44) Hoof I, Peters B, Sidney J, Pedersen LE, Sette A, Lund O, Buus S, Nielsen M. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics. 2009 Jan;61(1):1-13. Epub 2008 Nov 12. PMID: 19002680
45) Tambunan, U.S.F., Parikesit, A.A., Hendra, Taufik, R.I., Amelia, F., Syamsudin. In silico analysis of envelope dengue virus-2 and envelope dengue virus-3 protein as the backbone of dengue virus tetravalent vaccine by using homology modeling method. OnLine Journal of Biological Sciences. 2009, 9 (1), pp. 6-16
46) Koo QY, Khan AM, Jung KO, Ramdas S, Miotto O, Tan TW, Brusic V, Salmon J, August JT. Conservation and variability of West Nile virus proteins. PLoS One. 2009;4(4):e5352. Epub 2009 Apr 29. PMID: 19401763
47) Zhang H, Lund O, Nielsen M. The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding.
Bioinformatics. 2009 May 15;25(10):1293-9. Epub 2009 Mar 17. PMID: 19297351
48) Pappalardo F, Halling-Brown MD, Rapin N, Zhang P, Alemani D, Emerson A, Paci P, Duroux P, Pennisi M, Palladini A, Miotto O, Churchill D, Rossi E, Shepherd AJ, Moss DS, Castiglione F, Bernaschi M, Lefranc MP, Brunak S, Motta S, Lollini PL, Basford KE, Brusic V. ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization. Brief Bioinform. 2009 May;10(3):330-40. PMID: 19383844
49) Escobar H, Crockett DK, Reyes-Vargas E, Baena A, Rockwood AL, Jensen PE, Delgado JC.
Large scale mass spectrometric profiling of peptides eluted from HLA molecules reveals N-terminal-extended peptide motifs. J Immunol. 2008 Oct 1;181(7):4874-82.
PMID: 18802091
50) Delayed-Type hypersensitivity to latex: Computational prediction of MHC class II epitopes on latex allergens
Bell R. Eapen
Nature Precedings : hdl:10101/npre.2009.2931.1 : Posted 6 Mar 2009
http://precedings.nature.com/documents/2931/version/1/files/npre20092931-1.pdf
51) R Holland Cheng, Tatsuo Miyamura. Structure-based study of viral replication: with CD-ROM. World Scientific, 2008
52) http://www.cqvip.com/qk/90053x/2008009/28245363.html
53) http://www.cqvip.com/qk/83872x/2008006/27369733.html
52) http://www.cqvip.com/qk/90053x/2008009/28245363.html
53) http://www.cqvip.com/qk/83872x/2008006/27369733.html
54) HLA-DR: molecular insights and vaccine design.
Stern LJ, Calvo-Calle JM.
Curr Pharm Des. 2009;15(28):3249-61. Review.
PMID: 19860674
55) Prediction of MHC-peptide binding: a systematic and comprehensive overview.
Lafuente EM, Reche PA.
Curr Pharm Des. 2009;15(28):3209-20. Review.
PMID: 19860671
56) Identification of continuous human B-cell epitopes in the envelope glycoprotein of dengue virus type 3 (DENV-3).
da Silva AN, Nascimento EJ, Cordeiro MT, Gil LH, Abath FG, Montenegro SM, Marques ET.
PLoS One. 2009 Oct 13;4(10):e7425. Erratum in: PLoS One. 2009;4(10). doi: 10.1371/annotation/238cbff8-6794-4796-8a8a-a80d3b246757.
PMID: 1982663
57) Predicting MHC class I epitopes in large datasets.
Roomp K, Antes I, Lengauer T.
BMC Bioinformatics. 2010 Feb 17;11:90.
PMID: 20163709
58) Optimization algorithms for functional deimmunization of therapeutic proteins.
Parker AS, Zheng W, Griswold KE, Bailey-Kellogg C.
BMC Bioinformatics. 2010 Apr 9;11:180.
PMID: 20380721
59) Computational analysis of cysteine proteases (Clan CA, Family Cl) of Leishmania major to find potential epitopic regions.
Saffari B, Mohabatkar H.
Genomics Proteomics Bioinformatics. 2009 Sep;7(3):87-95.
PMID: 19944381
60) MHC Class II epitope predictive algorithms.
Nielsen M, Lund O, Buus S, Lundegaard C.
Immunology. 2010 Jul;130(3):319-28. Epub 2010 Apr 12.
PMID: 20408898
61) Computational characterization of Plasmodium falciparum proteomic data for screening of potential vaccine candidates.
Singh SP, Khan F, Mishra BN.
Hum Immunol. 2010 Feb;71(2):136-43. Epub 2009 Dec 31.
PMID: 19945493
62) Prediction of immunogenicity of therapeutic proteins: validity of computational tools.
Bryson CJ, Jones TD, Baker MP.
BioDrugs. 2010 Feb 1;24(1):1-8. doi: 10.2165/11318560-000000000-00000. Review.
PMID: 20055528
63) Nicholas Daniel Ostrout
Vaccinia and dengue viruses: exploring current fundamental issues of memory T cells and utilizing quantitative immunology to compare correlates of protection following smallpox immunization
PhD Thesis, 2008. Case Western Reserve University
64) The ImmunoGrid Simulator: How to Use It
Francesco Pappalardo, Mark Halling-Brown, Marzio Pennisi, Ferdinando Chiacchio, Clare E. Sansom, Adrian J. Shepherd, David S. Moss, Santo Motta and Vladimir Brusic
Computational Intelligence Methods FOR Bioinformatics and Biostatistics
Lecture Notes in Computer Science, 2010, Volume 6160/2010, 1-19, DOI:
10.1007/978-3-642-14571-1_1
65) MetaMHC: a meta approach to predict peptides binding to MHC molecules.
Hu X, Zhou W, Udaka K, Mamitsuka H, Zhu S.
Nucleic Acids Res. 2010 Jul 1;38 Suppl:W474-9. Epub 2010 May 18.
PMID: 20483919
66) Identification of conserved epitopes in Japanese Encephalitis Virus for rational design of vaccines
AY KC, AM Khan, TW Tan, A JT - nus.edu.sg
http://www.nus.edu.sg/nurop/2009/FoS/14th%20NUROP%20Congress_FoS/Life%20Sciences/Biochemistry/Au%20Yeung%20Kiu%20Chi_U052614J.pdf
67) Prediction of MHC class I binding peptides using probability distribution functions.
Soam SS, Khan F, Bhasker B, Mishra BN.
Bioinformation. 2009 Jun 28;3(9):403-8.
PMID: 19759816
68) 刺苋花粉泛过敏原 Profilin 的抗原性评估与三级结构分析
陶爱林， 刘林川， 王永飞， 邹泽红， 马三梅， 赖 … - 中华微生物学和免疫学 …, 2008 - cqvip.com
http://www.cqvip.com/qk/95714x/2008007/27915213.html
69) Immunoinformatics and modeling perspective of T cell epitope-based cancer immunotherapy: a holistic picture.
Mishra S, Sinha S.
J Biomol Struct Dyn. 2009 Dec;27(3):293-306. Review.
PMID: 19795913
70) Homology-free prediction of functional class of proteins and peptides by support vector machines.
Zhu F, Han LY, Chen X, Lin HH, Ong S, Xie B, Zhang HL, Chen YZ.
Curr Protein Pept Sci. 2008 Feb;9(1):70-95. Review.
PMID: 18336324
8) BioWare: A framework for bioinformatics data retrieval, annotation and publishing.
Koh JLY, Krishnan SPT, Seah SH, Tan PT, Khan AM, Lee ML, Brusic V.
27th Annual International ACM SIGIR Conference on Research and Development in IR. Sheffield, UK, July 29, 2004.
Out link: Full-text
Impact Factor Year 2009: NA
No. of Citations: 10 (total): 3 (non-self) & 7 (self)
ABSTRACT :
In-depth analysis about a specific subject in molecular biology, specifically those associated with the structural and functional properties of a particular group of sequences typically requires access to an extensive knowledge base. The knowledge base may take the form of a specialist database or subject-specific data warehouse (SSDW) to facilitate the organisation of specialized data and the extraction of new knowledge. These SSDWs are particularly useful for data mining or knowledge discovery processes which require the relevant information from multiple data sources. The construction of a specialist database is a multistep process which typically involves enrichment of annotations (by domain experts), development and integration of analytical tools (by computer programmers), and construction of the system (by database experts). The SSDWs contain focused subsets of data compiled from multiple data sources and enriched with user annotations. In this article we present and describe the BioWare system which enables its users to collect, annotate, publish, and update specialized molecular data in personalized WWW accessible databases. BioWare contains four data warehouse enabling components: (i) BioWare-Retrieve searches and extracts data from selected sources and integrates them into a standardized format, (ii) BioWare-Prep provides a semi-automated mechanism for user-driven cleaning, preliminary analysis and annotation of the data, (iii) TEMPLAR enables users to rapidly create searchable WWW-accessible SSDWs, and (iv) BioWare-Update enables incremental updating of the SSDWs with new data from the sources. We have used BioWare system for the creation and maintenance of several bioinformatic databases.
This article has been cited by other articles:
1) Zhang ZH, Tan SC, Koh JL, Falus A, Brusic V. ALLERDB database and integrated bioinformatic tools for assessment of allergenicity and allergic cross-reactivity.
Cell Immunol. 2006 Dec;244(2):90-6. Epub 2007 Apr 30. PMID: 17467675
2) Zhang ZH, Koh JL, Zhang GL, Choo KH, Tammi MT, Tong JC. AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins. Bioinformatics. 2007 Feb 15;23(4):504-6. Epub 2006 Dec 6. PMID: 17150996
3) Tagger, B. A Framework forManaging Changes in Biological Data. The EngD First Year Report. November 16, 2005. http://www.cs.ucl.ac.uk/staff/btagger/FirstYearRep.pdf
4) Tagger, B. A Framework forManaging Changes in Biological Data. The EngD Second Year Report. October 18, 2006. http://www.cs.ucl.ac.uk/staff/btagger/Second.pdf
5) Tagger, B. A Literature Review for the Problem of Biological Data Versioning. 28th July 2005. http://www.cs.ucl.ac.uk/staff/btagger/LitReview.pdf
6) Tongchusak, S, Chaiyaroj, SC, Veeramani, A, JLY Koh, Brusic, V. CandiVF – Candida albicans Virulence Factor Database. International Journal of Peptide Research and Therapeutics, Vol. 11, No. 4, December 2005, pp. 271–277.
7) Judice, Lie Yong Koh. Correlation-Based Methods for Biological Data Cleaning, with application to biological databases. PhD thesis. 2007, National University of Singapore
8) Lam, K.-T., Koh, J.L.Y., Veeravalli, B., Brusic, V. Incremental maintenance of biological databases using association rule mining. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4146 LNBI, pp. 140-150
9) Miotto, O., Tan, T.W., Brusic, V. Extraction by example: Induction of structural rules for the analysis of molecular sequence data from heterogeneous sources. Lecture Notes in Computer Science. 2005, 3578, pp. 398-405
10) Tan Thiam Joo, Paul
Functional prediction of bioactive toxins in scorpion venom through bioinformatics
PhD Thesis, 2006
https://scholarbank.nus.edu.sg/handle/10635/15536
7) Duplicate Detection in Biological Data using Association.
Koh JLY, Lee ML, Khan AM, Tan PTJ, Brusic V
2nd European Workshop on Data Mining and Text Mining for Bioinformatics.
Pisa, Italy, September 24, 2004.
Out link: Full-text
Impact Factor Year 2009: NA
No. of Citations: 19 (total): 16 (non-self) & 3 (self)
ABSTRACT :
Recent advancement in biotechnology has produced a massive amount of raw biological data which are accumulating at an exponential rate. Errors, redundancy and discrepancies are prevalent in the raw data, and there is a serious need for systematic approaches towards biological data cleaning. This work examines the extent of redundancy in biological data and proposes a method for detecting duplicates in biological data. Duplicate relations in a real-world biological dataset are modeled
into forms of association rules so that these duplicate relations or rules can be induced from data with known duplicates using association rule mining. Our approach of using association rule induction to find duplicate relations is new. Evaluation of our method on a real-world dataset shows that our duplicate association rules can accurately identify up to 96.8% of the duplicates in the dataset at the accuracy of 0.3% false positives and 0.0038% false negatives.
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1) McCallum, A, Bellare, K, Pereira, F. A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance. Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence (UAI 2005). http://www.seas.upenn.edu/~strctlrn/bib/PDF/crfstredit.pdf
2) Jakoniene, V, Rundqvist, D, Lambrix, P. A method for similarity-based grouping of biological data. Lecture Notes in Computer Science Series. In the book Data Integration in the Life Sciences. Page: 136-151.
3) David Rundqvist. Grouping Biological Data. PhD Thesis. Dept. of Computer and Information Science at Link¨oping University. http://www.diva-portal.org/diva/getDocument?urn_nbn_se_liu_diva-6327-1__fulltext.pdf
4) Veeramani, A, Gopalakrishnan, K, Brusic, V, Koh, JLY. Biodart - Catalogue of Biological Data Artifact Examples. International Conference on Biomedical and Pharmaceutical Engineering, 2006. 2006 Page(s):c1 - xiii. http://antigen.i2r.a-star.edu.sg/ani/textmin/kddinfo/pdf/261_1.pdf
5) Zhu YY, Xiong Y. DNA sequence data mining technique. Journal of Software, 2007,18(11):2766-2781. http://www.jos.org.cn/1000-9825/18/2766.htm
6) Judice, Lie Yong Koh. Correlation-Based Methods for Biological Data Cleaning. PhD thesis. 2007, National University of Singapore
7) van Grootel RJ, van der Bilt A, van der Glas HW. Long-term reliable change of pain scores in individual myogenous TMD patients. Eur J Pain. 2007 Aug;11(6):635-43. Epub 2006 Nov 22. PMID: 17118682
8) Arraes LC, de Souza PR, Bruneska D, Castelo Filho A, Cavada Bde S, de Lima Filho JL, Crovella S. A cost-effective melting temperature assay for the detection of single-nucleotide polymorphism in the MBL2 gene of HIV-1-infected children. Braz J Med Biol Res. 2006 Jun;39(6):719-23. Epub 2006 Jun 2. PMID: 16751976
9) Szalma SJ, Buckler ES 4th, Snook ME, McMullen MD.
Association analysis of candidate genes for maysin and chlorogenic acid accumulation in maize silks. Theor Appl Genet. 2005 May;110(7):1324-33. Epub 2005 Apr 2. PMID: 15806344
10) Ferguson M, Heath A. Report of a collaborative study to calibrate the Second International Standard for parvovirus B19 antibody. Biologicals. 2004 Dec;32(4):207-12. PMID: 15572102
11) D Apiletti, G Bruno, E Ficarra, E Baralis. Data Cleaning and Semantic Improvement in Biological Databases. Journal of Integrative Bioinformatics, 2006 - imbio.de
12) http://www.cqvip.com/qk/96857x/2007011/25789686.html
13) A classification of biological data artifacts
JLY Koh, ML Lee, V Brusic
Workshop on Database Issues in Biological Databases
January 8-9, 2005
National e-Science Centre, Edinburgh, UK
Organized by the European Bioinformatics Institute & Edinburgh Database Group
homepages.inf.ed.ac.uk
14) A tool for evaluating strategies for grouping of biological data
V Jakoniene, P Lambrix - Journal of Integrative Bioinformatics, 2007
15) Quantitative association rules mining
F Karel - Proc. 10th Int. Conf. Knowl.-Based Intell. Inf. Eng. Syst, 2006
16) Detecting duplicate biological entities using Markov random field-based edit distance
M Song, A Rudniy
Bioinformatics and Biomedicine, 2008 (BIBM '08)
ieeexplore.ieee.org
17) A Comprehensive Review of Significant Researches on Duplicate Record Detection in Databases
Deepa K & Rangarajan R
Advances in Computational Sciences and Technology. 2009; 2(2)
18) Extraction of Constraints from Biological Data
Daniele Apiletti, Giulia Bruno, Elisa Ficarra and Elena Baralis
BOMEDICAL DATA AND APPLICATIONS
Studies in Computational Intelligence, 2009, Volume 224/2009, 169-186, DOI: 10.1007/978-3-642-02193-0_7
19) C Markschies
Describing Differences between Overlapping Databases
PhD Thesis, 2008
edoc.hu-berlin.de
Humboldt-Universität zu Berlin:
6) Systematic analysis of snake neurotoxins' functional classification using a data warehousing approach.
Siew JP, Khan AM, Tan PT, Koh JL, Seah SH, Koo CY, Chai SC, Armugam A, Brusic V, Jeyaseelan K.
Bioinformatics. 2004 Dec 12;20(18):3466-80. Epub 2004 Jul 22.
PUBMED PMID: 15271784
Out link: Full-text
Impact Factor Year 2009: 4.926
No. of Citations: 9 (total): 5 (non-self) & 4 (self)
ABSTRACT:
MOTIVATION: Sequence annotations, functional and structural data on snake venom neurotoxins (svNTXs) are scattered across multiple databases and literature sources. Sequence annotations and structural data are available in the public molecular databases, while functional data are almost exclusively available in the published articles. There is a need for a specialized svNTXs database that contains NTX entries, which are organized, well annotated and classified in a systematic manner.
RESULTS: We have systematically analyzed svNTXs and classified them using structure-function groups based on their structural, functional and phylogenetic properties. Using conserved motifs in each phylogenetic group, we built an intelligent module for the prediction of structural and functional properties of unknown NTXs. We also developed an annotation tool to aid the functional prediction of newly identified NTXs as an additional resource for the venom research community.
AVAILABILITY: We created a searchable online database of NTX proteins sequences (http://research.i2r.a-star.edu.sg/Templar/DB/snake_neurotoxin). This database can also be found under Swiss-Prot Toxin Annotation Project website (http://www.expasy.org/sprot/).
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Conference Information: 9th International Conference on Control, Automation, Robotics and Vision, DEC 05-08, 2006 Singapore, SINGAPORE
2006 9th International Conference on Control, Automation, Robotics and Vision, Vols 1- 5 Pages: 2295-2298 Published: 2006
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