A unified model for de novo design of elastin-like polypeptides with tunable inverse transition temperatures

Elastin-like polypeptides (ELPs) are stimulus-responsive peptide polymers that exhibit inverse temperature phase transition behavior, causing an ELP to aggregate above its inverse transition temperature (T_t). Although this property has been exploited in a variety of biotechnological applications, de novo design of ELPs that display a specific T_t is not trivial because the T_t of an ELP is a complex function of several variables, including its sequence, chain length, polypeptide concentration, and the type and concentration of cosolutes in solution. This paper provides a quantitative model that predicts the T_t of a family of ELPs (Val-Pro-Gly-Xaa-Gly, where Xaa = Ala and/or Val) from their composition, chain length, and concentration in phosphate buffered saline. This model will enable de novo prediction of the amino acid sequence and chain length of ELPs that will display a predetermined T_t in physiological buffer within a specified concentration regime, thereby greatly facilitating the design of new ELPs for applications in medicine and biotechnology.