Structure-Based\nComputational Scanning of Chemical\nModification Sites in Biologics

To address the challenges of short half-life, immunogenicity,\nand\nnonspecific distribution, chemical modifications of peptide and protein-based\ndrugs have emerged as a versatile strategy for improving their therapeutic\nefficacy. One such modification involves the derivatization of peptides\nand proteins with fatty acids, which can protract their half-life,\nmodify their biodistribution, and potentially enable targeted delivery\nto specific tissues or disease sites of interest. However, the present\nstrategies for the synthesis of such synthetically modified biologics\nrequire numerous rounds of experimental testing and often yield unstable,\ninactive, or heterogeneous products. To address the inefficiencies\nin designing modified biologics, we developed a hybrid computational\nworkflow that integrates RosettaMatch from the Rosetta suite of protein\nmodeling tools with molecular dynamics (MD) simulations. This approach\nnot only reduces the number of amino acid positions that need to be\nexperimentally tested by targeting only the most promising candidates\nfor modification but also expedites the design of chemically modified\nbiologics with the desired properties, ensuring a rapid and cost-effective\ndevelopment cycle. Although we demonstrate the utility of our method\non a peptide therapeutic, GLP-1, with different fatty acid derivatizations,\nthis straightforward approach has the potential to streamline the\ndesign process of a diverse range of chemically modified therapeutics,\nenabling tailored enhancements to their pharmacokinetic properties.