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Cranfield Health, Cranfield University, Cranfield, Bedfordshire MK43 0AL, England, UK





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Abstract In this paper, we describe a peptide library designed by computational modelling and the selection of two peptide sequences showing affinity towards the mycotoxin, ochratoxin A OTA. A virtual library of 20 natural amino acids was used as building blocks to design a short peptide library against ochratoxin A template using the de novo design program, LeapFrog, and the dynamic modelling software, FlexiDock. Peptide sequences were ranked according to calculated binding scores in their capacity to bind to ochratoxin A. Two high scoring peptides with the sequences N-Cys-Ser-Ile-Val-Glu-Asp-Gly-Lys-C octapeptide and N-Gly-Pro-Ala-Gly-Ile-Asp-Gly-Pro-Ala-Gly-Ile-Arg-Cys-C 13-mer were selected for synthesis from the resulting database. These synthesized peptides were characterized using a microtitre plate-based binding assay and a surface plasmon resonance biosensor Biacore 3000. The binding assay confirmed that both de novo designed peptides did bind to ochratoxin A in vitro. SPR analysis confirmed that the peptides bind to ochratoxin A, with calculated KD values of ~15.7 μM 13-mer and ~11.8 μM octamer. The affinity of the peptides corresponds well with the molecular modelling results, as the 13-mer peptide affinity is about 1.3-times weaker than the octapeptide; this is in accordance with the binding energy values modelled by FlexiDock. This work illustrates the potential of using computational modelling to design a peptide sequence that exhibits in vitro binding affinity for a small molecular weight toxin. View Full-Text

Keywords: ochratoxin A; mycotoxins; peptide; computational modelling; surface plasmon resonance; biosensor ochratoxin A; mycotoxins; peptide; computational modelling; surface plasmon resonance; biosensor





Autor: Meike Heurich, Zeynep Altintas and Ibtisam E. Tothill *

Fuente: http://mdpi.com/



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