Functional alignment of protein language models via reinforcement learning
6 affiliations
- 1Department of Biomedical Engineering, Duke University, Durham, NC, USA
- 2Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
- 3Modality Research Laboratories II, Daiichi Sankyo Co., Ltd., Tokyo, Japan
- 4Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- 5Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA. [email protected]
- 6Department of Biomedical Engineering, Duke University, Durham, NC, USA. [email protected]
Abstract
Protein language models (pLMs) enable generative design of novel protein sequences but remain fundamentally misaligned with protein engineering goals, as they lack explicit understanding of function and often fail to improve properties beyond those found in nature. We introduce Reinforcement Learning from eXperimental Feedback (RLXF), a general framework that aligns protein language models with experimentally measured functional objectives, drawing inspiration from the methods used to align large language models like ChatGPT. Applied across five diverse protein families, RLXF improves generation of high-functioning variants beyond pre-trained baselines. We demonstrate this with CreiLOV, an oxygen-independent fluorescent protein, where RLXF-aligned models generate sequences with significantly enhanced fluorescence, including the most fluorescent CreiLOV variants reported to date. Our results indicate that RLXF-aligned models effectively integrate the evolutionary knowledge encoded in pre-trained pLMs with experimental observations, improving the success rate of generated sequences and enabling the discovery of synergistic mutation combinations that are difficult to identify through zero-shot or evolutionary approaches. RLXF provides a scalable and accessible approach to steer generative models toward desired biochemical properties, enabling function-driven protein design beyond the limits of natural evolution. Protein language models are powerful tools for generative protein design, but because they are trained solely on natural sequences, they often struggle to generate proteins with enhanced or non-natural functions. This work a general framework that aligns generative protein language models with experimentally defined functional objectives.
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BibTeX
@article{blalock2026functional,
title = {{Functional alignment of protein language models via reinforcement learning}},
author = {Nathaniel Blalock and Srinath Seshadri and Kensuke Nakamura and Agrim Babbar and Sarah A. Fahlberg and Ameya Kulkarni and Philip A. Romero},
journal = {Nature Communications},
year = {2026},
month = sep,
doi = {10.1038/s41467-026-77557-2},
url = {https://doi.org/10.1038/s41467-026-77557-2},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Functional alignment of protein language models via reinforcement learning
AU - Nathaniel Blalock
AU - Srinath Seshadri
AU - Kensuke Nakamura
AU - Agrim Babbar
AU - Sarah A. Fahlberg
AU - Ameya Kulkarni
AU - Philip A. Romero
PY - 2026
DA - 2026/09/12
JO - Nature Communications
DO - 10.1038/s41467-026-77557-2
UR - https://doi.org/10.1038/s41467-026-77557-2
AB - Protein language models (pLMs) enable generative design of novel protein sequences but remain fundamentally misaligned with protein engineering goals, as they lack explicit understanding of function and often fail to improve properties beyond those found in nature. We introduce Reinforcement Learning from eXperimental Feedback (RLXF), a general framework that aligns protein language models with experimentally measured functional objectives, drawing inspiration from the methods used to align large language models like ChatGPT. Applied across five diverse protein families, RLXF improves generation of high-functioning variants beyond pre-trained baselines. We demonstrate this with CreiLOV, an oxygen-independent fluorescent protein, where RLXF-aligned models generate sequences with significantly enhanced fluorescence, including the most fluorescent CreiLOV variants reported to date. Our results indicate that RLXF-aligned models effectively integrate the evolutionary knowledge encoded in pre-trained pLMs with experimental observations, improving the success rate of generated sequences and enabling the discovery of synergistic mutation combinations that are difficult to identify through zero-shot or evolutionary approaches. RLXF provides a scalable and accessible approach to steer generative models toward desired biochemical properties, enabling function-driven protein design beyond the limits of natural evolution. Protein language models are powerful tools for generative protein design, but because they are trained solely on natural sequences, they often struggle to generate proteins with enhanced or non-natural functions. This work a general framework that aligns generative protein language models with experimentally defined functional objectives.
SN - 2041-1723
ER -