High throughput Exploration of Sequence Space of Peptide Polymers that Exhibit Aqueous Demixing Phase Behavior

Project Personnel

Ashutosh Chilkoti

Principal Investigator

Duke University

Email

Rohit Pappu

Washington University in St. Louis

Email

Stefan Zauscher

Duke University

Email

Funding Divisions

Division of Materials Research (DMR), Division of Mathematical Sciences (DMS), Office of Advanced Cyberinfrastructure (OAC)

Stimulus responsiveness is a striking feature of proteins in Nature, whereby responses to chemical stimuli such as ligand binding, phosphorylation, and methylation, and physical stimuli such as changes in temperature, pH, light, and salt concentration lead to sharp conformational or phase transitions. Unlike proteins, which encode diverse responses to numerous stimuli by richly sampling amino acid sequence space, current bioinspired designs of repetitivepolypeptides have focused on a tiny fraction of the vast conceivable expanse of sequence space. The primary goal of the proposed research is thus to develop generalized materials design rules, by combining experiments, fast and accurate physics-based computer simulations, and data science, to accelerate the discovery and development of a potentially huge class of thermally-responsive polypeptide materials by a systematic exploration of sequence space. This research will —for the first time— provide a complete atomistic understanding of the determinants of the lower critical solution temperature (LCST) and upper critical solution temperature (UCST) phase behavior, enable de novomolecular design of LCST and UCST peptide polymers and identify rules on how to combine them to create hierarchically-ordered, nanostructured polypeptide materials that exhibit unique morphologies that can be tuned as a function of their stimulus responsiveness. These materials could serve as nanostructured scaffolds and templates and enable a broad range of biocatalytic, bioelectronic, or assay devices. The PIs also plan to release the PIMMS modeling package, a set of tools for performing lattice-based simulations of polymers. PIMMS will provide support for the machine learning algorithms that enable the design of responsive protein-based polymers. The PIMMS codebase will be released as open source. A user community will be coalesced around the language by ensuring that interested researchers are able to contribute modules to or implement application-specific algorithms within the codebase. This is expected to allow a wider growth of the project. This aspect is of special interest to the software cluster in the Office of Advanced Cyberinfrastructure, which has provided co-funding for this award.