TAPPred Prediction of TAP-Binding Peptides in Antigens

Bhasin, Manoj; Lata, Sneh; Raghava, G. P. S.

doi:10.1007/978-1-60327-118-9_28

Cited by 49 publications

(35 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…QMs have been used successfully in the past to predict MHC binders [28] and TAP binders [29]. In this study, we have also generated QMs for both datasets (main and alternate datasets).…”

Section: Methodsmentioning

confidence: 99%

In Silico Approach for Predicting Toxicity of Peptides and Proteins

et al. 2013

View full text Add to dashboard Cite

BackgroundOver the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.DescriptionWe obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.ConclusionToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).

show abstract

“…QMs have been used successfully in the past to predict MHC binders [28] and TAP binders [29]. In this study, we have also generated QMs for both datasets (main and alternate datasets).…”

Section: Methodsmentioning

confidence: 99%

In Silico Approach for Predicting Toxicity of Peptides and Proteins

et al. 2013

View full text Add to dashboard Cite

show abstract

“…At higher pressures, nonspecific aggregates of capsid proteins predominate over seemingly complete reassociated capsids. The formation of imperfect virus particles after a cycle of compression and decompression has also been demonstrated for human norovirus (49) and rotavirus (41,46) by electron microscopy, gel filtration, and spectroscopy (8,50,51,53). In this article, we describe the effects of pressure and chemical treatments on TMV by mapping the linear epitopes through spot synthesis and comparing them to results obtained with epitope prediction software.…”

mentioning

confidence: 83%

“…The prediction of TAP affinity was based on a support vector machine (SVM) (22). The SVMHC (support vector MHC) method was used to predict MHC binding (8). This is an SVM-based method based on verified MHC binding peptides from the SYFPEITHI database (45).…”

Section: An Epitope-based Immunoinformatics Study Of Tmvcpmentioning

confidence: 99%

Influence of High Hydrostatic Pressure on Epitope Mapping of Tobacco Mosaic Virus Coat Protein

Neto

Sampaio²,

Arns³

2014

Viral Immunology

View full text Add to dashboard Cite

In this study, we investigated the effect of high hydrostatic pressure (HHP) on tobacco mosaic virus (TMV), a model virus in immunology and one of the most studied viruses to date. Exposure to HHP significantly altered the recognition epitopes when compared to sera from mice immunized with native virus. These alterations were studied further by combining HHP with urea or low temperature and then inoculating the altered virions into Balb-C mice. The antibody titers and cross-reactivity of the resulting sera were determined by ELISA. The antigenicity of the viral particles was maintained, as assessed by using polyclonal antibodies against native virus. The antigenicity of canonical epitopes was maintained, although binding intensities varied among the treatments. The patterns of recognition determined by epitope mapping were cross checked with the prediction algorithms for the TMVcp amino acid sequence to infer which alterations had occurred. These findings suggest that different cleavage sites were exposed after the treatments and this was confirmed by epitope mapping using sera from mice immunized with virus previously exposed to HHP.

show abstract

“…PAProC then generated peptide sequences and score of estimated strength 29. The peptide binding to the transporter associated with antigen processing (TAP) binding was predicted using TAPPred, http://www.imtech.res.in/raghava/tappred/, which generated protein sequences, their position, and score of predicted binding affinity to each peptide sequence 23. The antigenicity of peptide sequence generated from TAPPred prediction was analyzed using VaxiJen,21 and the antigen-bearing sequences were used to predict epitopes that bind to HLA class I and HLA class II using IEDB analysis resource 25.…”

Section: Methodsmentioning

confidence: 99%

Immunoinformatics Approach in Designing Epitope-based Vaccine against Meningitis-inducing Bacteria (Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae Type b)

Zahroh

Ma’rup

Tambunan

et al. 2016

Drug Target Insights

View full text Add to dashboard Cite

Meningitis infection is one of the major threats during Hajj season in Mecca. Meningitis vaccines are available, but their uses are limited in some countries due to religious reasons. Furthermore, they only give protection to certain serogroups, not to all types of meningitis-inducing bacteria. Recently, research on epitope-based vaccines has been developed intensively. Such vaccines have potential advantages over conventional vaccines in that they are safer to use and well responded to the antibody. In this study, we developed epitope-based vaccine candidates against various meningitis-inducing bacteria, including Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae type b. The epitopes were selected from their protein of polysaccharide capsule. B-cell epitopes were predicted by using BCPred, while T-cell epitope for major histocompatibility complex (MHC) class I was predicted using PAProC, TAPPred, and Immune Epitope Database. Immune Epitope Database was also used to predict T-cell epitope for MHC class II. Population coverage and molecular docking simulation were predicted against previously generated epitope vaccine candidates. The best candidates for MHC class I- and class II-restricted T-cell epitopes were MQYGDKTTF, MKEQNTLEI, ECTEGEPDY, DLSIVVPIY, YPMAMMWRNASNRAI, TLQMTLLGIVPNLNK, ETSLHHIPGISNYFI, and SLLYILEKNAEMEFD, which showed 80% population coverage. The complexes of class I T-cell epitopes–HLA-C*03:03 and class II T-cell epitopes–HLA-DRB1*11:01 showed better affinity than standards as evaluated from their ΔGbinding value and the binding interaction between epitopes and HLA molecules. These peptide constructs may further be undergone in vitro and in vivo testings for the development of targeted vaccine against meningitis infection.

show abstract

TAPPred Prediction of TAP-Binding Peptides in Antigens

Cited by 49 publications

References 11 publications

In Silico Approach for Predicting Toxicity of Peptides and Proteins

In Silico Approach for Predicting Toxicity of Peptides and Proteins

Influence of High Hydrostatic Pressure on Epitope Mapping of Tobacco Mosaic Virus Coat Protein

Immunoinformatics Approach in Designing Epitope-based Vaccine against Meningitis-inducing Bacteria (Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae Type b)

Contact Info

Product

Resources

About