Presentation Title

Towards Rational Design of Self-Aggregating Peptides

Project Collaborators

Prof. Jianing Li (PI), Jonathon Ferrell (Graduate Student Mentor)

Abstract

Self-aggregating peptides — as growing class of programmable, biocompatible, and biodegradable materials — show great potential for many uses. However, good predictive power of these supramolecular formations has not yet been reached, and rational design remains elusive. In order to solve this challenge, we present a robust and novel tool, implemented in our software Bio-BACon, used to seek supramolecular peptide structures and their rules of aggregation via large-scale multiscale modeling. This method streamlines the process of building, simulating, and analyzing potential peptide sequences for aggregation propensity. Combining this software with a Monte Carlo search allows us to efficiently traverse the massive chemical space of peptides, where changes in sequences, capping groups, and other initial conditions are evaluated. With these new modeling methods, it is now possible to efficiently test potential rules of aggregation, as well as to find self-aggregating peptides comparable with conventional high-throughput screening. Analysis of search paths and new supramolecular structures will bring predictive power and with it the potential for rational design.

Primary Faculty Mentor Name

Jianing Li

Graduate Student Mentors

Jonathon Ferrell

Status

Undergraduate

Student College

College of Arts and Sciences

Program/Major

Chemistry

Primary Research Category

Engineering & Physical Sciences

Secondary Research Category

Biological Sciences

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Towards Rational Design of Self-Aggregating Peptides

Self-aggregating peptides — as growing class of programmable, biocompatible, and biodegradable materials — show great potential for many uses. However, good predictive power of these supramolecular formations has not yet been reached, and rational design remains elusive. In order to solve this challenge, we present a robust and novel tool, implemented in our software Bio-BACon, used to seek supramolecular peptide structures and their rules of aggregation via large-scale multiscale modeling. This method streamlines the process of building, simulating, and analyzing potential peptide sequences for aggregation propensity. Combining this software with a Monte Carlo search allows us to efficiently traverse the massive chemical space of peptides, where changes in sequences, capping groups, and other initial conditions are evaluated. With these new modeling methods, it is now possible to efficiently test potential rules of aggregation, as well as to find self-aggregating peptides comparable with conventional high-throughput screening. Analysis of search paths and new supramolecular structures will bring predictive power and with it the potential for rational design.