Conformation-specific Design: a New Benchmark and Algorithm with Application to Engineer a Constitutively Active MAP Kinase

Jacob Stern, Siba Alharbi, Anandsukeerthi Sandholu, Stefan T. Arold, Dennis Della Corte

Track: Biology: datasets and/or experimental results

Nature Biotechnology: Yes

Keywords: deep learning, protein design, inverse folding, conformational specificity, kinase

TL;DR: We introduce a benchmark, an algorithm, and an in vitro case study validating conformation-specific protein design.

Abstract: A general method for designing proteins with high conformational specificity is desirable for a variety of applications, including enzyme design and drug target redesign. To assess the ability of algorithms to design for conformational specificity, we introduce MotifDiv, a benchmark dataset of 200 conformational specificity design challenges. We also introduce CSDesign, an algorithm for designing proteins with high preference for a target conformation over an alternate conformation. On the MotifDiv benchmark, CSDesign designs protein sequences that are predicted to prefer the target conformation. We apply this method in vitro to redesign human MAP kinase ERK2, an enzyme with active and inactive conformations. Out of two designs for the active conformation, one increased activity sufficiently to retain activity in the absence of activating phosphorylations, a property not present in the wild type protein.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Jacob_Stern1

Format: Maybe: the presenting author will attend in person, contingent on other factors that still need to be determined (e.g., visa, funding).

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Submission Number: 107