ProteinDJ: A high-performance and modular protein design pipeline

Abstract

Leveraging artificial intelligence and deep learning to generate proteins de novo (a.k.a. ‘synthetic proteins’) has unlocked new frontiers of protein design. Deep learning models trained on protein structures can generate novel protein designs that explore structural landscapes unseen by evolution. This approach enables the development of bespoke binders that target specific proteins and domains through new protein–protein interactions. However, successful binder generation can suffer from low in silico success rates, often requiring thousands of designs and hundreds of GPU hours to obtain enough hits for experimental testing. While workstation implementations are available for binder design..