Computer prediction of potential immunogenic determinants from protein amino acid sequence

Krchňák, Viktor; Mach, O; Malý, A

doi:10.1016/0003-2697(87)90220-x

Cited by 44 publications

(12 citation statements)

References 38 publications

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“…Region 285-300 hFSHR has no homology with its corresponding regions from hLHR and hTSHR, indicating a unique sequence and a probable determinant of hormone specificity. This stretch also has a potential -turn, and is the only region that is positive for nine of the 10 parameters in the PEPTIDE program, indicative of probable surface orientation (Krchnak et al 1987) and further strengthening a possible role in protein-protein interaction.…”

Section: Discussionmentioning

confidence: 85%

“…Potential surface-oriented regions of hFSHR-ECD were identified by the computer program Peptide (Krchnak et al 1987). This program analyzes protein sequences by parameters such as hydrophilicity, accessibility, hydrophobicity, antigenicity, acrophilicity, hydropathy, large-sphere accessibility and presence of -turns.…”

Section: Identification Of Potential Surface-oriented Regions Of Hfshrmentioning

confidence: 99%

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Identification of the structural and functional determinants of the extracellular domain of the human follicle stimulating hormone receptor

Kene

Nalavadi²,

Dighe³

et al. 2004

Journal of Endocrinology

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The extracellular domain (ECD) of the human folliclestimulating hormone receptor (hFSHR) is believed to be the major determinant of hormone selectivity. Different discrete, discontinuous regions on the ECD of the hFSHR have been suggested to be crucial for hormone binding. However, the role of the ECD in signal transduction is not well understood. This study provides some insight into these aspects of the structure-function relationship of the ECD of hFSHR. Ten peptides were selected from the ECD on the basis of their ability to be surface oriented, synthesized by the solid-phase method using fluorenylmethyloxycarbonyl chemistry, purified and characterized. They were further studied for their ability to modulate both human follicle-stimulating hormone (hFSH)-FSHR binding and cAMP generation. Competitive inhibition studies showed that, of all the peptides studied, peptides 285-300 and 297-310 hFSHR were able to inhibit hFSH binding to FSHR. Both peptides function as weak competitive inhibitors of hFSH-FSHR binding. Peptides 285-300 hFSHR, 216-235 hFSHR, 184-195 hFSHR, 79-89 hFSHR and 15-31 hFSHR were observed to inhibit FSH-induced cAMP production. In summary, this study suggests that discrete, functional domains of the ECD have a role in hormone binding and signal transduction. Region 285-300 has been identified as a novel region crucial for both FSH binding and cAMP generation.

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Section: Discussionmentioning

confidence: 85%

Section: Identification Of Potential Surface-oriented Regions Of Hfshrmentioning

confidence: 99%

Identification of the structural and functional determinants of the extracellular domain of the human follicle stimulating hormone receptor

Kene

Nalavadi²,

Dighe³

et al. 2004

Journal of Endocrinology

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“…Thus prediction of β-turns could improve the prediction of epitopes. Krchnak et al [24] found that the parts of a protein, which can induce protein-reactive anti-peptide anti-bodies, mostly reside in regions that have a high tendency to form β-turns. A more recent article by the same authors showed that peptide sequences including a β-turn conformation tended to induce antibodies that were able to cross-react with the parent protein [25].…”

Section: Introductionmentioning

confidence: 99%

NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

2010

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β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC = 0.50, Qtotal = 82.1%, sensitivity = 75.6%, PPV = 68.8% and AUC = 0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17 – 0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively.ConclusionThe NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

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“…Hydropathy plots showed that the region encompassing the epitopes is hydrophilic and accessible (Fig. 3d); these two parameters are key determinants of antigenicity (Bittle et al, 1982;Hopp & Woods, 1983;Atassi, 1984;Berzofsky, 1985;Krchnak et al, 1987).…”

Section: Sequence Analysesmentioning

confidence: 99%

“…Bioinformatic algorithms have also been employed; these predict B-cell epitopes and their antigenicity based on the atomic mobility, accessibility and hydrophilicity of the constituent amino acids and their propensities to be exposed and to form b-turns (Hopp & Woods, 1983;Westhof et al, 1984;Krchnak et al, 1987;Bhat et al, 1990;Nicholson, 1994). These methods have been superseded and/or complemented by the use of lambda (Young & Davis, 1983), phagemid vectors and filamentous phage as surrogates for surface display of antigenic sites which can then be identified with the relevant antibodies (Smith, 1985;Parmley & Smith, 1988).…”

Section: Introductionmentioning

confidence: 99%

Delineation of epitopes on the Py235 rhoptry antigen ofPlasmodium yoeliiYM

Bayele¹,

Brown²

2007

FEMS Immunol Med Microbiol

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The 235-kDa antigenic rhoptry protein Py235 of Plasmodium yoelii is encoded by a large, highly polymorphic gene family. Monoclonal antibodies to some of these antigens have been shown to attenuate the virulence of the lethal YM strain of the parasite, converting a potentially fatal YM infection to a fulminating one typical of the nonlethal 17X strain, by inducing a switch in target cell preference from mature red blood cells to reticulocytes. The reason for this is not known but would suggest that antigenic determinants of Py235 may be useful in or as subunit vaccines. To identify such determinants, we constructed an epitope expression library of one Py235 variant and screened the library with the antibodies. Thus, we mapped 5-and 12-amino acid epitopes to the C-terminus of the antigen. Both epitopes were more reactive with protective than with nonprotective monoclonal antibodies. This may explain the differential protection conferred by these antibodies upon their passive transfer into mice.

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Computer prediction of potential immunogenic determinants from protein amino acid sequence

Cited by 44 publications

References 38 publications

Identification of the structural and functional determinants of the extracellular domain of the human follicle stimulating hormone receptor

Identification of the structural and functional determinants of the extracellular domain of the human follicle stimulating hormone receptor

NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

Delineation of epitopes on the Py235 rhoptry antigen ofPlasmodium yoeliiYM

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