Peptides are short stretches of amino acids that perform a multitude of roles in biology. In addition to their natural roles, they have attracted attention in recent years as binders to disease biomarkers as potential inhibitors or sensors. Their stability and ese of synthesis combined with their ability to strongly bind to molecules of interest puts them in a promising position as an alternative to antibody-based detection modules for the development of accessible sensors. Currently, library-based screening is the most widely used method for obtaining peptide binders. Yet, due to practical limitations of the screening and synthesis, many promising hits are never tested while a large number of non-binders are screened. Computational methods are proposed to provide a powerful way to narrow down the search space for library screening and to speed up the process of finding hits. However, such methods are currently underexplored and the available examples are often case-dependent. In this presentation, I will talk about a recently developed generalizable method for computational design of peptide-based binders. We showed that starting from an anchor group with IC<sub>50</sub> in micromolar range against histone deacetylase 2, cyclic peptides with potencies in low nanomolar range can be obtained. I will then discuss the limitations of the current method and the steps we are taking towards development of a robust and general pipeline for generation of peptide-based binders.

Computational methods for designing peptide-based detection modules for sensing applications

ACS Spring Meeting 2021

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