CAREER: Peptide-Functionalized Surfaces for Designing Tunable Nanoscale Patterns

NON-TECHNICAL SUMMARY

This CAREER award supports computational and theoretical research aimed at designing peptide-functionalized surfaces with tunable nano-scale patterns. Controlling how surfaces interact with water is critical for engineering many useful properties. For example, modulating surface stickiness plays an important role in designing surfaces to serve as artificial tissue scaffolds, inducing water evaporation is valuable for manufacturing electronic components, and controlling selective surface interactions is important purifying drugs.

A powerful strategy for altering surface properties is attaching molecules with desirable properties to the surface. A major challenge with designing such molecules, however, is that they often change their behavior when tethered to a surface. The goal of this project is to understand how molecules change their behaviors when tethered to a surface, and how these molecular behaviors control how the surface interacts with water.

By coupling molecular dynamics simulations with deep learning models, this project will explore these challenges both by asking fundamental scientific questions about molecular interactions and by developing a rapid predictive tool aimed at quantitatively predicting surface-water interactions. The fundamental understanding and predictive tools gained from this project will enable the design of new surfaces for applications ranging from semiconductor manufacturing to water desalination.

Alongside these scientific goals, the PI will implement an education plan aimed at giving undergraduate students cross-disciplinary training in both molecular and scientific skillsets. Specifically, a series of application-oriented case studies will be developed aimed at helping students practice computational skills in scientific and engineering classes.

These case studies will be designed based on research from the above scientific project as well as collaborations with industrial and academic experts. Additionally, the PI will create free, publicly available educational materials to help other educators adapt case studies to be implemented across diverse classroom settings. These case studies will serve as a valuable tool for developing undergraduate students with strong computational and scientific skills.

TECHNICAL SUMMARY

This CAREER award supports theoretical/computational studies of peptide-functionalized surfaces with tunable nano-scale patterns. Surface functionalization is a powerful way to control hydrophobicity, and selective adsorption and plays a central role in many engineering applications such as protein purification, tissue culture, drug delivery, semiconductor manufacturing, and water desalination.

Peptides offer a promising strategy for modulating surface properties because they are chemically diverse and straightforward to synthesize on solid supports. Previous research has shown that combinations of weak, noncovalent interactions between neighboring peptide-derived ligands on a surface can lead to the formation of ordered, nanoscale patterns, whose properties can be controlled through small changes to ligand structure.

In this way, peptide surface functionalization presents a promising opportunity for designing nanoscale, patterned surfaces with tunable interfacial properties.

This project focuses on developing the fundamental understanding and deep learning tools required to engineer peptide-functionalized surfaces with desirable properties. In Aim 1, a hypothesis-driven study of a library of peptide-functionalized surfaces will be performed to connect peptide characteristics with surface patterns. In Aim 2, a simulation study will be conducted to elucidate how surface patterns control hydrophobicity, focusing on patterns that combine charge, hydrophobicity, and uncharged hydrophilic groups.

While these methods will establish concrete molecular design rules for peptide-functionalized surfaces, they are too computationally expensive to be used to explore surface design space. Therefore, in Aim 3, a deep learning model will be developed for rapidly predicting the hydrophobicity of functionalized surfaces to enable the design of new surfaces with hydrophobicity as a design objective.

In parallel, the PI will pursue a synergistic education plan centered on repurposing our research efforts to give undergraduate students cross-disciplinary training in the computational and molecular sciences. Specifically, a repository of educational case studies will be developed that showcases how computational techniques can be used to solve molecular problems based on research from the PI’s lab, academic collaborators, and industrial collaborators.

These case studies will be made publicly available and easy to implement in a variety of settings by creating thorough documentation, testing the materials with other instructors, and disseminating the materials using established education networks. The PI will additionally develop an annual summer research program to enable one undergraduate student to perform a subset of the proposed research plan using the skills developed through these case studies.

STATEMENT OF MERIT REVIEW

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.