(4iz) The Power of Randomness and Curiosity: Design of Bioinspired Polymer Scaffolds

My future research will focus on the computational design and engineering of both synthetic and biological polymeric systems. Specifically, I am interested in using synthetic polymers as model systems to understand and mimic biocondensates for enzyme stabilization and functional tuning, in close collaboration with experimentalists. Major goals of my research will be: (1) multiscale polymer design from submonomer (e.g. atomistic structure) to chain-level (e.g. sequence and chain architecture), (2) elucidation and rationalization of the effect of process parameters, such as temperature, on coacervate formation, (3) identification of mechanisms of small molecule/substrate transport across liquid-liquid interfaces, and (4) design and engineering of on-demand synthetic polymer and chaperone protein recipes for enzyme binding and stabilization. Importantly, my research will be driven by the pursuit of a fundamental understanding of the phenomena rather than specific computational techniques, and thus I will use necessary tools to investigate each subject. This includes employing hierarchical simulations ranging from density functional theory, atomistic to coarse grained molecular dynamics, and machine learning.

Research Experience:

Proteins are the workhorses of living organisms and have been engineered as one of the “unit operations” in industrial processes. Such units operate with near-perfect precision under native conditions, but their stability diminishes when subjected to subtle environmental perturbations. In contrast to proteins, their synthetic polymer analogs suffer from weak sequence and structural control. Recently, methacrylate-based random heteropolymers (RHPs) have demonstrated the ability to maintain biomimetic functions, including catalysis, transport and chaperoning. However, the physicochemical design rules for achieving such convergent functionality under a random ensemble remain largely unknown, limiting our ability to rationally engineer RHPs for optimality.

In my graduate research at MIT with Prof. Alfredo Alexander-Katz and Prof. Connor Coley, in close collaboration with Prof. Ting Xu at UC-Berkeley, I have applied machine learning and atomistic molecular dynamics simulations to study several biophysical phenomena of RHPs with unusual glass transitions [1-3], proteins, and their interactions. I have demonstrated how RHPs achieve population-based protein-mimetic functions. In particular, I have uncovered the design rules to endow synthetic polymers with bio-like hydration frustration (HF) properties [4]. HF is the magic behind the functionality of proteins that allows them to dehydrate and hydrate hydrophilic and hydrophobic residues at will. For methacrylate-based RHPs, I found that hydration frustration arises from the competition between monomer compatibility (e.g. Flory-Huggins interaction parameter) and solvent exposed surface energy. The repeated appearance of atomistic structural motifs, particularly the alpha-methyl group on the methacrylate backbone and the polyethylene glycol side groups, results in physicochemical synergies despite the inherent randomness of their sequences [5].

Random patterns not only can be generalized to achieve convergent outcomes, but also can serve as a starting point for structural and functional evolution of biopolymers. By in-silico folding of tandem random amino acid sequence, I have explored the sequence and structural space for curiosity-driven de novo protein design, as well as understanding fibril formation and stability [6]. This process created an initial dataset of diverse repeated structures. Through evolution and selection, this mosaic was fine-tuned to yield specifically targeted structures and functionalities. This strategy takes advantages of the low-probability, yet measurable events-in random experimentation to generate novel, functional designs through iterative, curiosity-driven refinement. Using this approach I have been able to generate many of the folds observed in nature without any prior knowledge.

Besides my graduate research experience, additional research experiences on interfacial physics [7, 8], and extensive collaboration with experimentalists on responsive materials [9] and microplastics [10] have well-equipped me with knowledge in polymer and soft matter physics, machine learning techniques, and hierarchical simulation tools to tackle my future research plans on multiscale polymer and soft matter design.

References:

[1] T Jin, CW Coley, and A Alexander-Katz. Molecular signatures of the glass transition in polymers. Physical Review E, 2022, 106, 014506.

[2] T Jin, SL Hilburg, and A Alexander-Katz. Glass transition of random heteropolymers: a molecular dynamics simulation study in melt, in water, and in vacuum. Polymer, 2023, 265, 125503.

[3] T Jin, CW Coley, and A Alexander-Katz. A computationally-informed unified view on the effect of polarity and sterics on the glass transition in vinyl-based polymeric melts. ACS Macro Letter, 2023, 12 (11), 1517-1522

[4] T Jin, CW Coley, and A Alexander-Katz. Designing single polymer chain nanoparticles to mimic biomolecular hydration frustration. Revised and resubmitted.

[5] T Jin, CW Coley, and A Alexander-Katz. Sequence-sensitivity in functional synthetic polymer properties. Submitted.

[6] T Jin, JI Sass, W Gao, CW Coley, and A Alexander-Katz. Curiosity-driven de novo protein designs via polymerized amyloids. Submitted.

[7] T Jin, SJ Patel, and RC Van Lehn. Molecular simulations of lipid membrane partitioning and translocation by bacterial quorum sensing modulators. PLoS One, 2021, 16 (2), e0246187

[8] T Jin, CW Coley, and A Alexander-Katz. Adsorption of biomimetic amphiphilic heteropolymers onto graphene and its derivatives. Macromolecules, 2023, 56 (5), 1798–1809

[9] Q He, SXL Luo, T Jin, M Liu, S Park, A Alexander-Katz, B McDonald, F Fornasiero, and TM Swager. Grafting-to and from for multiplexed chemical-warfare-gent responsive polymer brushes. Chemistry of Materials, 2023, 35 (4), 1674-1683.

[10] L Zhang, R Xiao, T Jin, X Pan, KA Fransen, SK Alsaiari, A Lau, R He, J Han, B Pedretti, JY Yeo, X Yang, BD Olsen, A Alexander-Katz, ZP Smith, RS Langer, and A Jaklenec. Degradable poly(β-amino ester) microparticle platform as an alternative to microplastics. Submitted.

Teaching Interests:

My teaching philosophy is centered on fostering a growth mindset within an active and inclusive learning environment. I have refined this approach through instructing courses on chemical process modeling during my undergraduate studies, and graduate-level thermodynamics during my doctorate. While I am equipped to teach all core courses in chemical engineering, my background also overlaps with material science and data science. Thus, beyond the traditional core curriculum, I hope to develop special topics courses relevant to interdisciplinary topics in polymer and soft matter physics, molecular simulations, and machine learning.

Service and Commitment to DEI:

I view diversity, equity, and inclusion (DEI) as vital sources of strength, creativity, and innovation. I am committed to fostering DEI and broadening access to STEM by infusing our scientific endeavors with diverse perspectives. This commitment will shape my research group management, teaching curricula, and community involvement. As a graduate mentor in the MIT Undergraduate Research Opportunities Program, I have had the opportunity to mentor ten undergraduates from different continents around the world, helping them develop the necessary skill sets for research and alignment with their individual career goals and interests. This experience has resulted in three publications where they contributed as first or co-authors, and one in receiving the annual Rising Black Scientists Awards. Additionally, through my four-year participation in the ChemE Application Mentorship Program, I have assisted a total of 13 applicants from communities historically underrepresented in graduate education to MIT's chemical engineering graduate program.