(2fe) Engineering Peptides through Molecular Simulation, Machine Learning and Optimization Methods for Biological and Clean Energy Applications

Polypeptides are one of the most common and essential biomacromolecules for living systems. They have also become excellent candidates for a variety of engineering and biological applications due to their unique physical and biochemical properties. This poster demonstrates how a chemical engineer approaches and overcomes the challenges in peptide engineering fields, which includes two essential aspects, fundamental and application. In the first part, we cover works on combining molecular simulation and statistical mechanics to understand the thermodynamics consequence (phase transition and self-assembly) arising from complex molecular interaction between various building blocks organized in certain sequence. Quantitative phase diagrams are calculated for Alzheimer’s disease-related amyloid-forming peptides. Results are validated by experimental measurements. The development of molecular models and investigation of fluid phase behavior for other common biomolecules such as phospholipid and small chiral molecules are also included. In the second part, we utilize molecular simulation, machine learning and optimization methods to engineer peptides for three multidisciplinary projects, 1). Design charge complementary co-assembly peptide pair (CATCH) that forms functional peptide nanofibers; 2). Design lanthanide-binding tag (LBT) peptides for green and efficient separation of rare earth elements; 3). Elucidate peptide-bacterial membrane interaction mechanism and discover new antimicrobial peptides (AMP) against gram-negative E. Coli strains.

Teaching Interests

My philosophy as a chemical engineering teacher is to (1) offer an effective learning scenario where students could get sufficient inspirations from the seminal and beautiful concepts, theories or equations of the subject, but not to be overwhelmed with much detailed and semi-repetitive information; (2) to make sure that the course content include certain practical applications so that the students have a direct sense of the usefulness of the course and are able to make use of what they learn from the course to tackle problems with a broader applied science context.

With holding both bachelor and PhD degrees in Chemical Engineering major, I have worked as teaching assistants for undergraduate and graduate level thermodynamics course (with lecturing on selected topics) and graduate-level transport phenomena. As a postdoc affiliated in both chemical engineering and bioengineering departments, I have mentored two PhD students for learning coarse-grained molecular simulation methods and coding in Fortran/C language. I have also mentored one MS student for learning atomistic simulation methods and machine learning models.

For the future position of being an assistant professor, I would like to teach both undergraduate and graduate level chemical engineering courses related to (but not limited to) the following topics: statistical mechanics and thermodynamics, transport phenomena, physical chemistry and research-related topics such as numerical (optimization) methods, molecular simulation and high performance computing, machine learning.