Ming-Chien Hsieh

Contact Information
Office: ES&T 132
6
Phone:
678-848-7911
Email 

Mailing Address
Georgia Institute of Technology
School
of Chemical & Biomolecular Engineering
311 Ferst Drive NW
Atlanta, Georgia
30332-0100

 

Research Summary

    Proteins and peptides play important roles in our daily lifes. In nature, the combination of 20 amino acids provides all kinds of possibilities and makes life possible. Thus, the complicated enzymes and functional proteins, composed of amino acids, have inspired people to make new materials based on their basic building blocks. The advantages of amino acid-based materials include but not limited to: easy systematic programming based on their sequence, well-developed posttranslational modification, and the robust diversity of their macrostructures.

    The aim of my study is to analyze and model the peptide-based biopolymers and networks. To selectively control the formation of functional biopolymers, our approach employs a dynamic combinatorial network (DCN) of modified peptides to access reversible linkages, which increase the diversity of the network kinetically. Mathematical models help us to elucidate and verify the interactions within the thermodynamically-controlled network in different stages, based on the species distribution and macrostructural evolution.

    We are also interested in environmentally-responsive peptide self-assemblies. Here the KLVFFAE peptide was selected as the model system as a responsive peptide self-assembly. KLVFFAE self-assembles into different macrostructures, which are reversibly interchangeable, under different pH and temperature conditions. The beta-sheet signature recorded from CD and the transmission electron microscope (TEM) images, together with the mathematical fits, tell us how the peptides self-assemble under different conditions and how the macrostructures evolve with time.

    The structural functions of peptide nanofibers and nanotubes have been widely studied, while people know little about their catalytic ability. To mimic the native protein enzyme, we used the assemblies of Ac-KLVFFAL-NH2 and Ac-(Orn)LVFFAL-NH2 to perform a retro-aldol reaction on the methodol which has two enantiomers. The chiral HPLC reveals that our assemblies selectively break the enantiomers, and the fluorescence indicates that the aldehyde product rebinds onto the catalyst surface. The above observations are verified by a modified Michaelis-Menten model which is able to describe and predict the behavior of the catalytic assemblies.

 

    My research would focus on using math model to predict the interaction between peptides or proteins, which is supported by Center for Chemical Evolution from NASA/NSF, and in collaboration with Dr. Lynn's group at Emory University.  

 

Education

B.S., Department of Chemical & Materials Engineering

M.S., Department of Chemical Engineering

 

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