CEP: a conformational epitope prediction server

Kulkarni‐Kale, Urmila; Bhosle, Shriram G.; Kolaskar, A. S.

doi:10.1093/nar/gki460

Cited by 242 publications

(198 citation statements)

References 14 publications

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“…Number of methods that utilize 3D structure of antigens for discontinuous epitope prediction have also been developed. These methods use different approaches for prediction such as solvent accessibility of surface residues [123,124], solvent accessibility with propensity scores [125], and propensity scores with packing density of amino acids [126]. An account of major tools/servers that are involved in conformational epitope prediction is provided in Studies have shown that the analysis of antigen-antibody complex structures is very useful for the characterization of conformational epitopes [134].…”

Section: Computational Prediction Of Allergen Epitopesmentioning

confidence: 99%

Databases and Algorithms in Allergen Informatics

Kadam¹,

Sawant²,

Jayaraman³

et al. 2016

Bioinformatics - Updated Features and Applications

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Allergic diseases are considered as one of the major health problems worldwide due to their increasing prevalence. Advancements in genomic, proteomic, and analytical techniques have resulted in considerable progress in the field of allergology, which has led to accumulation of huge amount of data. Allergen bioinformatics comprises allergenrelated data resources and computational methods/tools, which deal with an efficient archival, management, and analysis of allergological data. Significant work has been done in the area of allergen bioinformatics that has proven pivotal for the development and progress of this field. In this chapter, we describe the current status of databases and algorithms, encompassing the field of allergen bioinformatics by examining work carried out thus far with respect to features such as allergens and allergenicity, allergen databases, algorithms/tools for allergen/allergenicity prediction, allergen epitope prediction, and allergenic cross-reactivity assessment. This chapter illustrates concepts and algorithms in allergen bioinformatics, as well as it outlines the key areas for potential development in allergology field.

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Section: Computational Prediction Of Allergen Epitopesmentioning

confidence: 99%

Databases and Algorithms in Allergen Informatics

Kadam¹,

Sawant²,

Jayaraman³

et al. 2016

Bioinformatics - Updated Features and Applications

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“…However, a few recent studies propose bioinformatics tools based on 3D structure to predict epitopes such as SUPERFICIAL (Goede et al, 2010), Disco tope (Haste Andersen et al, 2006), Conformational Epitope Predictor (CEP) (Kulkarni-Kale et al, 2005), and PEPOP (Moreau et al, 2008). Although, computational methods have different performance; identification and prediction of suitable epitope peptides for vaccine design using these methods are cheaper and quicker than that of experimental screening.…”

Section: Figure 2 Predicted Structures Of B-cell Epitope Peptides Anmentioning

confidence: 99%

“…Although, computational methods have different performance; identification and prediction of suitable epitope peptides for vaccine design using these methods are cheaper and quicker than that of experimental screening. For example, CEP correctly identifies 76% of the amino acid residues known to describe the selected epitopes in data sets consisting of 63 antigen-antibody complexes (Kulkarni-Kale et al, 2005) but Disco Tope detects 15.5% (sensitivity) of residues located in discontinuous epitopes with a specificity of 95%. So, CEP can predict more accurately than Disco tope (Roggen, 2008).…”

Section: Figure 2 Predicted Structures Of B-cell Epitope Peptides Anmentioning

confidence: 99%

In silico Design of Discontinuous Peptides Representative of B and T-cell Epitopes from HER2-ECD as Potential Novel Cancer Peptide Vaccines

Mahdavi

Keyhanfar²,

Moreau³

et al. 2013

Asian Pacific Journal of Cancer Prevention

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At present, the most common cause of cancer-related death in women is breast cancer. In a large proportion of breast cancers, there is the overexpression of human epidermal growth factor receptor 2 (HER2). This receptor is a 185 KDa growth factor glycoprotein, also known as the first tumor-associated antigen for different types of breast cancers. Moreover, HER2 is an appropriate cell-surface specific antigen for passive immunotherapy, which relies on the repeated application of monoclonal antibodies that are transferred to the patient. However, vaccination is preferable because it would stimulate a patient's own immune system to actively respond to a disease. In the current study, several bioinformatics tools were used for designing synthetic peptide vaccines. PEPOP was used to predict peptides from HER2 ECD subdomain III in the form of discontinuous-continuous B-cell epitopes. Then, T-cell epitope prediction web servers MHCPred, SYFPEITHI, HLA peptide motif search, Propred, and SVMHC were used to identify class-I and II MHC peptides. In this way, PEPOP selected 12 discontinuous peptides from the 3D structure of the HER2 ECD subdomain III. Furthermore, T-cell epitope prediction analyses identified four peptides containing the segments 77 (384-391) and 99 (495-503) for both B and T-cell epitopes. This work is the only study to our knowledge focusing on design of in silico potential novel cancer peptide vaccines of the HER2 ECD subdomain III that contain epitopes for both B and T-cells. These findings based on bioinformatics analyses may be used in vaccine design and cancer therapy; saving time and minimizing the number of tests needed to select the best possible epitopes.

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“…Some structural prediction programs evaluate similarities in the overall sequences of proteins for specific secondary structures (e.g., likely disulfide bonds, alpha helices, turns) that are likely to dominate secondary shape and may represent an antibody epitope. Others focus on segments of the full-sequence, predicting local structures independent of the overall structure or they predict the overall structure and plot surface exposure [22]. For selected cross-reactive pairs of allergenic proteins, one or more of these structural prediction algorithms is likely to be highly predictive for estimating potential cross-reactivity.…”

Section: Structural Comparisonsmentioning

confidence: 99%

Practical and predictive bioinformatics methods for the identification of potentially cross‐reactive protein matches

Goodman

2006

Molecular Nutrition Food Res

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A bioinformatics comparison of proteins introduced into food crops through genetic engineering provides a mechanism to identify those proteins that may present an increased risk of allergic reactions for individuals with existing allergies. The goal is to identify proteins that are known to be allergens or are so similar to an allergen that they may induce allergic cross‐reactions. Three comparative approaches have traditionally been used, or considered for safety evaluations. One identifies any short (6–8) amino acid segment of the protein that exactly matches a known allergen sequence. The second is an overall primary sequence comparison using Basic Local Alignment Search Tool (BLAST) or FASTA to find matches of greater than 35% identity over 80 amino acids. The third is based on 3‐D prediction programs to identify 3‐D similarities that might predict potential cross‐reactivity. The utility of each of these approaches was debated in the bioinformatics workshop. The consensus agreement from the expert workshop participants was that the short‐segment match (e. g., 6–8 amino acids) provides an unacceptably high rate of false positive matches and an uncertain rate of true positive matches, and was not particularly useful for an allergenicity evaluation performed in the context of comprehensive safety evaluation. There was no consensus regarding the most appropriate bioinformatics method, an acceptable scoring criteria for triggering closer examination subsequent to a positive match, or an acceptable scoring mechanism for ranking the utility of the various 3‐D approaches that were discussed during the workshop. However, the general consensus was that the most practical approach at this time is to evaluate primary sequence identities to known allergens using either FASTA or BLAST. While there was good agreement that identities of greater than 35% over 80 or more amino acids (recommended by Codex in 2003) is quite conservative, the conclusion was that additional data or studies would be needed to justify changing this criterion as there is some evidence that some individuals sensitized to proteins in evolutionarily conserved protein families may experience cross‐reactions to proteins sharing approximately 40% identity.

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CEP: a conformational epitope prediction server

Cited by 242 publications

References 14 publications

Databases and Algorithms in Allergen Informatics

Databases and Algorithms in Allergen Informatics

In silico Design of Discontinuous Peptides Representative of B and T-cell Epitopes from HER2-ECD as Potential Novel Cancer Peptide Vaccines

Practical and predictive bioinformatics methods for the identification of potentially cross‐reactive protein matches

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