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Modeling protein-protein interactions requires accurate scoring functions that can rank potential poses (conformations) of a protein-protein complex to differentiate near-native poses from incorrect ones. Here, we propose a general framework for improving protein-protein pose ranking and other biomolecular interaction...
Protein engineering appears to be entering a golden age, defined by a rapidly accelerating pace of progress, even as significant challenges in design, screening, and real-world application remain.
The AIntibody challenge shows that AI can optimize antibodies in defined, biologically grounded regimes, in addition to highlighting critical gaps including affinity prediction and library-inspired antibody design and cross-task generalization.
Deep Learning for Proteins: a series of 10 interactive notebook modules that introduce fundamental machine-learning concepts, guide users through training machine-learning models for protein-related tasks, and ultimately present cutting-edge protein structure prediction and design pipelines are developed.
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