Structural Analysis of Non-native Peptide-Based Catalysts Using 2D NMR-Guided MD Simulations

Area of Science:

• Biochemistry
• Supramolecular Chemistry
• Catalysis

Background:

• Proteins and enzymes utilize well-defined 3D structures for pre-organization of reactive groups.
• Mimicking this supramolecular pre-organization leads to advanced biomaterials, medicines, and artificial enzymes.
• Helical and β-turn motifs precisely position functional groups, enabling enzyme-like substrate activation and selectivity.

Purpose of the Study:

• To computationally determine how peptide catalyst structure influences reactivity enhancements.
• To investigate the role of bifunctional design in achieving enzyme-like catalysis.
• To understand the reasons behind the low reactivity observed in monofunctional peptides.

Main Methods:

• Utilized computational tools CYANA and AmberTools for structural analysis.
• Developed a computational approach to link peptide structure to catalytic reactivity.
• Compared the behavior of bifunctional and monofunctional peptide catalysts.

Main Results:

• Bifunctional helical peptides enhance reactivity by pre-organizing two catalysts in proximity.
• Computational modeling supports the hypothesis that proximity accelerates catalysis.
• Monofunctional peptides exhibit low reactivity due to catalyst-backbone interactions, unlike bifunctional variants.

Conclusions:

• Helical peptides serve as effective scaffolds for enzyme-like catalysis through pre-organization.
• Bifunctional design is crucial for overcoming inhibitory backbone interactions and enhancing catalytic efficiency.
• Computational approaches are valuable for understanding structure-activity relationships in peptide catalysts.