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Toxins 2017, 9(5), 164; doi:10.3390/toxins9050164

Evaluation of Ochratoxin Recognition by Peptides Using Explicit Solvent Molecular Dynamics

1
Center for Bioelectronics, Biosensors and Biochips (C3B), The College of Engineering, Texas A&M University, College Station, TX 77843, USA
2
Department of Biomedical Engineering, 5045 ETB, The Dwight Look College of Engineering, Texas A&M University, College Station, TX 77843, USA
3
Ecole des mines d'Ales, institut Mines, Telecom, 6 avenue de Clavieres, Ales cedex30319, France
4
ABTECH Scientific, Inc., Biotechnology Research Park, 800 East Leigh Street, Richmond, VA 23219, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Michelangelo Pascale and Maria C. DeRosa
Received: 31 January 2017 / Revised: 9 April 2017 / Accepted: 9 May 2017 / Published: 13 May 2017
(This article belongs to the Collection Biorecognition Assays for Mycotoxins)
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Abstract

Biosensing platforms based on peptide recognition provide a cost-effective and stable alternative to antibody-based capture and discrimination of ochratoxin-A (OTA) vs. ochratoxin-B (OTB) in monitoring bioassays. Attempts to engineer peptides with improved recognition efficacy require thorough structural and thermodynamic characterization of the binding-competent conformations. Classical molecular dynamics (MD) approaches alone do not provide a thorough assessment of a peptide’s recognition efficacy. In this study, in-solution binding properties of four different peptides, a hexamer (SNLHPK), an octamer (CSIVEDGK), NFO4 (VYMNRKYYKCCK), and a 13-mer (GPAGIDGPAGIRC), which were previously generated for OTA-specific recognition, were evaluated using an advanced MD simulation approach involving accelerated configurational search and predictive modeling. Peptide configurations relevant to ochratoxin binding were initially generated using biased exchange metadynamics and the dynamic properties associated with the in-solution peptide–ochratoxin binding were derived from Markov State Models. Among the various peptides, NFO4 shows superior in-solution OTA sensing and also shows superior selectivity for OTA vs. OTB due to the lower penalty associated with solvating its bound complex. Advanced MD approaches provide structural and energetic insights critical to the hapten-specific recognition to aid the engineering of peptides with better sensing efficacies. View Full-Text
Keywords: mycotoxin recognition; ochratoxins; peptide; molecular dynamics; NFO4; toxins; Markov state model; solvation penalty; binding free energy; biased exchange metadynamics mycotoxin recognition; ochratoxins; peptide; molecular dynamics; NFO4; toxins; Markov state model; solvation penalty; binding free energy; biased exchange metadynamics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Thyparambil, A.A.; Bazin, I.; Guiseppi-Elie, A. Evaluation of Ochratoxin Recognition by Peptides Using Explicit Solvent Molecular Dynamics. Toxins 2017, 9, 164.

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