A semi‐empirical method for prediction of antigenic determinants on protein antigens

Kolaskar, A. S.; Tongaonkar, Prasad

doi:10.1016/0014-5793(90)80535-q

Cited by 1,277 publications

(1,031 citation statements)

References 9 publications

Supporting

Mentioning

1,020

Contrasting

Unclassified

Order By: Relevance

“…The antigenic domains of goat and all 12 animal species were predicted by (Kolaskar & Tongaonkar, 1990) method using online software tool (http://imed.med.ucm.es/tool/antigenic.pl). The results revealed that 11 antigenic domains of IL‐33 from goat lie from 4th to 225th amino acids (Figure 4) and 143 antigenic domains for 12 animal species including goat lie from 4th to 3114th amino acids (Figure 5).…”

Section: Resultsmentioning

confidence: 99%

Positive selection ofIL‐33 in adaptive immunity of domestic Chinese goats

Asif

Awais

Qadri

et al. 2017

Ecology and Evolution

View full text Add to dashboard Cite

The identification of the candidate genes that play key role in phenotypic variation in livestock populations can provide new information about evolution and positive selection. IL‐33 (71954) (Interleukin) gene is associated with the increased nematode resistance in small ruminants; however, the role of IL‐33 for the genetic control of different diseases in Chinese goat breeds is poorly described in scientific literature. Therefore, the current investigation was performed for the better understanding of the molecular evolution and the positive selection of single‐nucleotide polymorphism in IL‐33 gene. Fixation Index (F ST)‐based method was used for the outlier loci determination and found that IL‐33 was present in outlier area with the provisional combined allocation of mean heterozygosity and F ST. Positively selected IL‐33 gene was significantly, that is, p(Simul F ST < sample F ST = 0.98*) present in corresponding positive selection area. Hence, our study provided novel information about the nucleotide variations in IL‐33 gene and found to be nonsynonymous which may helpful for the genetic control of diseases by enhancing the immune system in local Chinese goat breeds as well as in other analyzed vertebrate species.

show abstract

Section: Resultsmentioning

confidence: 99%

Positive selection ofIL‐33 in adaptive immunity of domestic Chinese goats

Asif

Awais

Qadri

et al. 2017

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…Linear B cell epitopes: Linear epitope constitutes a single continuous stretch of amino acids within a protein sequence antigen recognized by soluble or membrane bound antibodies (Vivona et al 2008). Among the algorithms used for linear B cell epitope prediction, ABCPred is based on artificial neural networks and BcePred uses physico-chemical properties for epitope prediction (Kolaskar and Tongaonkar 1990;Raghava 2006a, b, 2007 (Zhang et al 2008;Bui et al 2005;Wang et al 2010;Moutaftsi et al 2006;Parker et al 1994;Lundegaard et al 2008). 4.…”

Section: Immunoinformaticsmentioning

confidence: 99%

Integrative immunoinformatics for Mycobacterial diseases in R platform

et al. 2014

View full text Add to dashboard Cite

The sequencing of genomes of the pathogenic Mycobacterial species causing pulmonary and extrapulmonary tuberculosis, leprosy and other atypical mycobacterial infections, offer immense opportunities for discovering new therapeutics and identifying new vaccine candidates. Enhanced RV, which uses additional algorithms to Reverse Vaccinology (RV), has increased potential to reduce likelihood of undesirable features including allergenicity and immune cross reactivity to host. The starting point for MycobacRV database construction includes collection of known vaccine candidates and a set of predicted vaccine candidates identified from the whole genome sequences of 22 mycobacterium species and strains pathogenic to human and one non-pathogenic Mycobacterium tuberculosis H37Ra strain. These predicted vaccine candidates are the adhesins and adhesin-like proteins obtained using SPAAN at P ad [ 0.6 and screening for putative extracellular or surface localization characteristics using PSORTb v.3.0 at very stringent cutoff. Subsequently, these protein sequences were analyzed through 21 publicly available algorithms to obtain Orthologs, Paralogs, BetaWrap Motifs, Transmembrane Domains, Signal Peptides, Conserved Domains, and similarity to human proteins, T cell epitopes, B cell epitopes, Discotopes and potential Allergens predictions. The Enhanced RV information was analysed in R platform through scripts following well structured decision trees to derive a set of nonredundant 233 most probable vaccine candidates. Additionally, the degree of conservation of potential epitopes across all orthologs has been obtained with reference to the M. tuberculosis H37Rv strain, the most commonly used strain in M. tuberculosis studies. Utilities for the vaccine candidate search and analysis of epitope conservation across the orthologs with reference to M. tuberculosis H37Rv strain are available in the mycobacrvR package in R platform accessible from the ''Download'' tab of MycobacRV webserver. MycobacRV an immunoinformatics database of known and predicted mycobacterial vaccine candidates has been developed and is freely available at http://mycobacteriarv.igib.res.in.

show abstract

“…The amino acid sequence heterogeneity at each residue position is indicated in the red regions in Figure 1 (higher peaks indicate greater sequence diversity). The antigenicity of the sequences, which was calculated using the algorithm of Kolaskar and Tongaonkar, 52 is indicated by the black lines (higher peaks, either above or below the midline indicate greater antigenicity). Many of the regions of high antigenicity (black lines) also show highly variable amino acid sequences (red regions), suggesting the possibility of strain-to-strain variation in antigenicity.…”

Section: Why Are There Disparities In the Published Reports Of Test Pmentioning

confidence: 99%

Laboratory Diagnosis of Clostridium difficile Infection

Tenover¹,

Baron²,

Peterson

et al. 2011

The Journal of Molecular Diagnostics

View full text Add to dashboard Cite

The laboratory diagnosis of Clostridium difficile infection (CDI) continues to be challenging. Recent guidelines from professional societies in the United States note that enzyme immunoassays for toxins A and B do not have adequate sensitivity to be used alone for detecting CDI, yet the optimal method for diagnosing this infection remains unclear. Nucleic acid amplification tests (NAATs) that target chromosomal toxin genes (usually the toxin B gene, tcdB) show high sensitivity and specificity, provide rapid results, and are amenable to both batch and on-demand testing, but these tests were not universally recommended for routine use in the recent guidelines. Rather, two-step algorithms that use glutamate dehydrogenase (GDH) assays to screen for C. difficile in stool specimens, followed by either direct cytotoxin testing or culture to identify toxin-producing C. difficile isolates, were recommended in one guideline and either GDH algorithms or NAATs were recommended in another guideline. Unfortunately, neither culture nor direct cytotoxin testing is widely available. In addition, this two-step approach requires 48 to 92 hours to complete, which may delay the initiation of therapy and critical infection control measures. Recent studies also show the sensitivity of several GDH assays to be <90%. This review considers the role of NAATs for diagnosing CDI and explores their potential advantages over two-step algorithms, including shorter time to results, while providing comparable, if not superior, accuracy.

show abstract

A semi‐empirical method for prediction of antigenic determinants on protein antigens

Cited by 1,277 publications

References 9 publications

Positive selection ofIL‐33 in adaptive immunity of domestic Chinese goats

Positive selection ofIL‐33 in adaptive immunity of domestic Chinese goats

Integrative immunoinformatics for Mycobacterial diseases in R platform

Laboratory Diagnosis of Clostridium difficile Infection

Contact Info

Product

Resources

About