Artificial intelligence (AI) methods for proteins have advanced rapidly, improving structure prediction and design, particularly for de novo binders. However, most evaluations emphasize binding affinity rather than higher-order biological function. We present Bits to Binders, a global competition evaluating de novo bin...
Clayton W. Kosonocky, Alex M. Abel, Aaron L. Feller et al.· Molecular Systems Biology· 6 citations· ⚡2
Hydrogen bonding and hydrophobic stacking are the primary interactions that control the structure and functionality of DNA systems, including those of most synthetic variants. These interactions are weak and are highly dynamic. Tunable Orthogonal Reversible Covalent (TORC) bonding utilizes dynamic covalent interactions...
Phuoc H. T. Ngo, Wesley Oliver, Priyalatha M. Kirisenage et al.· Journal of the American Chem...· 0 citations
This work evolves enantioselective transcription factors that convert enzyme-catalyzed enantiomer concentrations into programmable gene expression outputs, focusing on imine reductases, and designs a genetic circuit that enables TF generation within weeks.
Simon d’Oelsnitz, Wantae Kim, Nicole N Zhao et al.· Nature Chemical Biology· 7 citations
A systematic comparison of zero-shot ML models is provided and an iterative framework for integrating machine learning with directed evolution to accelerate enzyme engineering is established to accelerate enzyme engineering.
Daniel Gutierrez, Isa Madrigal Harrison, Aaron L. Feller et al.· bioRxiv· 0 citations
PubCheF-1, a deep learning model that predicts literature-derived biological function directly from chemical structure, establishes that machine learning-based prediction of biological function derived from the language of scientific literature allows the identification of bioactive molecules at high hit rates, thereby...
Clayton W. Kosonocky, Nikol Kadeřábková, Kangsan Kim et al.· bioRxiv· 0 citations
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