The ability to map protein interactomes and organelle proteomes is foundational for achieving a molecular understanding of living cells. Proximity labeling (PL) provides a powerful strategy for this, but existing enzymes and photocatalysts are limited by their spatial resolution, reliance on biotin, and/or in vivo comp...
Shi-Zhong A. Dai, Peter E. Cavanagh, Albert Qiang et al.· bioRxiv· 1 citation
This work introduces antiGen, a machine learning model that forecasts the evolutionary future of viruses by learning from their evolutionary past, and achieves state-of-the-art performance for predicting mutations to the SARS-CoV-2 spike protein.
I. Specht, Soyoon Park, Seyone Chithrananda et al.· bioRxiv· 1 citation
This work demonstrates how to provide task-specific information without losing the general knowledge learned during pretraining by using direct preference optimization to align a structure-conditioned protein language model to preferentially generate stable protein sequences.
Talal Widatalla, Ashir Borah, Samuel H. King et al.· Nature Methods· 3 citations
The utility of ML-assisted evolution for engineering Rubisco with improved carboxylation efficiency and potential for enhancing crop productivity is demonstrated.
Julie L. McDonald, Jiachen Lin, Yunlong Zhao et al.· bioRxiv· 0 citations
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