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Functional protein mining with conformal guarantees
Boger, Ron S. Chithrananda, Seyone Angelopoulos, Anastasios N. Yoon, Peter H. Jordan, Michael I. Doudna, Jennifer A.
Boger, Ron S.
Chithrananda, Seyone
Angelopoulos, Anastasios N.
Yoon, Peter H.
Jordan, Michael I.
Doudna, Jennifer A.
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Molecular structure prediction and homology detection offer promising paths to discovering protein function and evolutionary relationships. However, current approaches lack statistical reliability assurances, limiting their practical utility for selecting proteins for further experimental and in-silico characterization. To address this challenge, we introduce a statistically principled approach to protein search leveraging principles from conformal prediction, offering a framework that ensures statistical guarantees with user-specified risk and provides calibrated probabilities (rather than raw ML scores) for any protein search model. Our method (1) lets users select many biologically-relevant loss metrics (i.e. false discovery rate) and assigns reliable functional probabilities for annotating genes of unknown function; (2) achieves state-of-the-art performance in enzyme classification without training new models; and (3) robustly and rapidly pre-filters proteins for computationally intensive structural alignment algorithms. Our framework enhances the reliability of protein homology detection and enables the discovery of uncharacterized proteins with likely desirable functional properties.
Citation
R. S. Boger, S. Chithrananda, A. N. Angelopoulos, P. H. Yoon, M. I. Jordan, and J. A. Doudna, “Functional protein mining with conformal guarantees,” Nature Communications 2025 16:1, vol. 16, no. 1, pp. 1–13, Jan. 2025, doi: 10.1038/s41467-024-55676-y.
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Nature Communications
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Keywords
Functional protein mining, Conformal prediction, Molecular structure prediction, Homology detection, Protein function discovery
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Publisher
Springer Nature