(494d) Modeling the Formation and Function of Responsive Peptide Systems | AIChE

(494d) Modeling the Formation and Function of Responsive Peptide Systems


Hsieh, M. C. - Presenter, Georgia Institute of Technology
Chen, C., Emory University
Tan, J., Emory University
Lynn, D., Emory University
Grover, M., Georgia Institute of Technology

The formation and function of responsive peptide-based biopolymers are analyzed and modeled in this research. To selectively control the formation of functional biopolymers, we envision three interdependent components that must be optimized for the generation of a functional biopolymer: 1) reversible monomer olgomerization, 2) environmentally responsive peptide assembly and 3) chemical reactions catalyzed and templated by the macromolecular assemblies.

    Our approach employs a dynamic combinatorial network (DCN) with peptides modified to access reversible linkages. Mathematical models were constructed to elucidate the interactions within the thermodynamically-controlled network. Peptide assembly of the pH-responsive Ac-KLVFFAE-NH2peptide is selected as the model system for responsive peptide self-assembly. KLVFFAE self-assembles into peptide nanotubes and fibers that are reversibly interchangeable, under different pH conditions. Finally, unlike isolated peptides which do not have function, peptide self-assemblies can serve as catalysts for the retro-aldol reaction. The enzyme kinetics model elucidates the critical enantioselectivity step for the reaction with a racemic substrate.

    In the first dimension, reversible monomer olgomerization, the kinetics of a peptide DCN was analyzed by HPLC., based on which a model was constructed and used to fit the DCN kinetics behavior. The critical reactions in the DCN are equilibrium of linear molecules, cyclization of linear molecules and peptide self-assembly. Although peptide self-assemblies were observable at later times by transmission electron microscopy (TEM) images, to start with a simpler model, we first exclude peptide self-assembly from the model. The rate constants were related with chain length of the species to reduce the total number of parameters. This simple model fit the data well at early times but later deviated, suggesting unmodeled events at later times. With this information, we proposed and confirmed the existence of the phase transition through EM measurements.

    In the second dimension we study how environmentally-responsive peptides self-assemble and change their macrostructures under different conditions. Here the peptide KLVFFAE is used as a model peptide because it assembles into multiple assembly structures: fibers and nanotubes. The β-sheet signature of the peptide solution was kinetically-recorded from circular dichroism, which was combined with ultrafiltration and BCA assay to determine the assembly concentration. We then use the peptide self-assembly models to fit the data. The assembly concentrations were well described from the model and the self-catalytic characteristics of the self-assembling process was well defined with clear lag and exponential phases.

    In the third dimension we study the functionalities of peptide self-assemblies. A model is established to clarify the catalytic mechanism of OrnLVFFAL tubes and to estimate the number of binding sites. Unlike native enzymes, the number of binding sites of a peptide self-assembly cannot be related to the peptide concentration directly but rather as a function of the assembly structure and the size of the substrate. The substrate consumption rates were followed by chiral HPLC and the product release rate was followed by fluorescence for a racemic R/S-methodol substrate. The HPLC analysis showed that the S-methodol was consumed faster than the R-methodol, suggesting that the enzyme is enantioselective. Also, the product releasing rate was slower than the total substrate consumption rate, because of product rebinding. An enzyme kinetics model was established to clarify the critical enantioselective step. Here the classical Michaelis-Menton mechanism was modified for a racemic substrate with product rebinding. With seven peptides per binding site providing the best fit, the dissociation constants of the two substrates showed that the R-methodol had the higher affinity to the enzyme. However, the rate-determining step was the chemical step and since the chemical step constant for S-methodol was larger than that for R-methodol, the S-methodol was consumed faster even though it bound less strongly to the enzyme.

    In all, we studied and modeled the equilibrium within the dynamic combinatorial network, the responsive peptide self-assembly and the functionality of peptide assemblies. The long term goal of this study is to employ feedback from the assemblies to affect the dynamic network to generate more species, thus further diversifying the structures and functions of peptide self-assemblies.