Evaluation of Ochratoxin Recognition by Peptides Using Explicit Solvent Molecular Dynamics.

Area of Science:

• Biochemistry
• Molecular Biology
• Analytical Chemistry

Background:

• Peptide recognition biosensors are cost-effective alternatives to antibody-based methods for detecting mycotoxins like ochratoxin-A (OTA) and ochratoxin-B (OTB).
• Engineering peptides for enhanced recognition efficacy necessitates detailed structural and thermodynamic characterization of their binding conformations.
• Traditional molecular dynamics (MD) simulations alone are insufficient for comprehensively assessing peptide recognition performance.

Purpose of the Study:

• To evaluate the in-solution binding properties of four peptides previously designed for OTA recognition.
• To identify peptides with improved sensing capabilities and selectivity for OTA over OTB.
• To provide structural and energetic insights for engineering more effective peptide biosensors.

Main Methods:

• Utilized advanced MD simulations incorporating accelerated configurational search and predictive modeling.
• Employed biased exchange metadynamics to generate peptide configurations relevant to ochratoxin binding.
• Derived dynamic binding properties using Markov State Models to analyze in-solution peptide-ochratoxin interactions.

Main Results:

• The NFO4 peptide demonstrated superior in-solution sensing performance for OTA.
• NFO4 exhibited enhanced selectivity for OTA compared to OTB, attributed to a lower energetic penalty in its bound complex solvation.
• Advanced MD simulations provided critical structural and energetic data for hapten-specific recognition.

Conclusions:

• Advanced MD simulations are crucial for understanding peptide binding dynamics and improving biosensor design.
• The NFO4 peptide shows significant promise for developing more effective ochratoxin detection assays.
• These findings support the engineering of peptides with superior sensing efficacies through detailed computational analysis.