Protein engineering by highly parallel screening of computationally designed variants

By Mark G. F. Sun, M. Seo, Satra Nim, Carles Corbi-Verge, Philip M. Kim. 2016

42 citations recorded by the source corpus.

Combining the power of combinatorial screening and computational design for optimal protein engineering. Current combinatorial selection strategies for protein engineering have been successful at generating binders against a range of targets; however, the combinatorial nature of the libraries and their vast undersampling of sequence space inherently limit these methods due to the difficulty in finely controlling protein properties of the engineered region. Meanwhile, great advances in computational protein design that can address these issues have largely been underutilized. We describe an integrated approach that computationally designs thousands of individual protein binders for high-throughput synthesis and selection to engineer high-affinity binders. We show that a computationally designed library enriches for tight-binding variants by many orders of magnitude as compared to conventional randomization strategies. We thus demonstrate the feasibility of our approach in a proof-of-concept study and successfully obtain low-nanomolar binders using in vitro and in vivo selection systems.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

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In Molecular Biology

1. Alexey Strokach
2. Carles Corbi-Verge
3. David Becerra
4. Albert Perez-Riba
5. Philip M. Kim

Citations in the corpus

• Fast and flexible design of novel proteins using graph neural networks (2019, background): “While there have been successful approaches that screen many thousands of individual designs using in vitro selection techniques ( 8 , 9 ), those approaches remain reliant on labor-intensive experiments.”