Improving de novo Protein Binder Design with Deep Learning

Bennett, N.; Coventry, B.; Goreshnik, I.; Huang, B.; Allen, A.; Vafeados, D.; Peng, Y. P.; Dauparas, J.; Baek, M.; Stewart, L.; DiMaio, F.; De Munck, S.; Savvides, S.; Baker, D.

doi:10.1101/2022.06.15.495993

bioRxiv · 10.1101/2022.06.15.495993

Improving de novo Protein Binder Design with Deep Learning

Bennett, N.·Coventry, B.·Goreshnik, I.·Huang, B.·Allen, A.·Vafeados, D.·Peng, Y. P.·Dauparas, J.·Baek, M.·Stewart, L.·DiMaio, F.·De Munck, S.·Savvides, S.·Baker, D.

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Abstract

We explore the improvement of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.

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Bennett, N., Coventry, B., Goreshnik, I., Huang, B., Allen, A., Vafeados, D., Peng, Y. P., Dauparas, J., Baek, M., Stewart, L., DiMaio, F., De Munck, S., Savvides, S., Baker, D.. 2022-06-17. Improving de novo Protein Binder Design with Deep Learning. https://doi.org/10.1101/2022.06.15.495993

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