Towards Rational Design of Self-Aggregating Peptides
Conference Year
January 2020
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
Faculty/Staff Collaborators
Prof. Jianing Li (PI), Jonathon Ferrell (Graduate Student Mentor)
Status
Undergraduate
Student College
College of Arts and Sciences
Program/Major
Chemistry
Primary Research Category
Engineering & Physical Sciences
Secondary Research Category
Biological Sciences
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.