Structure-Based Computational Scanning of Chemical Modification Sites in Biologics

To address the challenges of short half-life, immunogenicity, and nonspecific distribution, chemical modifications of peptide and protein-based drugs have emerged as a versatile strategy for improving their therapeutic efficacy. One such modification involves the derivatization of peptides and proteins with fatty acids, which can protract their half-life, modify their biodistribution, and potentially enable targeted delivery to specific tissues or disease sites of interest. However, the present strategies for the synthesis of such synthetically modified biologics require numerous rounds of experimental testing and often yield unstable, inactive, or heterogeneous products. To address the inefficiencies in designing modified biologics, we developed a hybrid computational workflow that integrates RosettaMatch from the Rosetta suite of protein modeling tools with molecular dynamics (MD) simulations. This approach not only reduces the number of amino acid positions that need to be experimentally tested by targeting only the most promising candidates for modification but also expedites the design of chemically modified biologics with the desired properties, ensuring a rapid and cost-effective development cycle. Although we demonstrate the utility of our method on a peptide therapeutic, GLP-1, with different fatty acid derivatizations, this straightforward approach has the potential to streamline the design process of a diverse range of chemically modified therapeutics, enabling tailored enhancements to their pharmacokinetic properties.