Computational Design of miniprotein binders
Abstract
Miniprotein binders hold a great interest as a class of drugs that bridges the gap between
monoclonal antibodies and small molecule drugs [1]. Like monoclonal antibodies, they can be
designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer [2]. Thus, efficient and robust methods are expected to reduce the cost and time required for tailor-made inhibitor design. Here, we present a structure-based computational generic approach for de novo miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2 [3]. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods [4,5], with some recent additions in terms of binding energy estimation, multistate design and diverse library generation [6-8].
References
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https://doi.org/10.1093/bioinformatics/bty092