Machine Learning Models for Local Optimization of Red Fluorescent Protein Variants in a Low-Data Setting.

Authors

Ran Ji, Jean Jung, Howard Cheng, Ella Y Xu, Audrey Wang, Victor Mun-Sing Sit, Keith Pardee, Yufeng Zhao

Published in

Journal of chemical information and modeling. Jul 13, 2026. Epub Jul 13, 2026.

Abstract

Fluorescent proteins (FPs) are widely used reporters for visualizing cellular structures and processes. Traditional wet-lab strategies for FP engineering (rational design and directed evolution) have enabled substantial improvements in photophysical performance but are limited by their requirement for deep expert knowledge or labor-intensive screening. AI-driven approaches have recently gained traction for engineering variants of green FPs, yet applications to red fluorescent proteins (RFPs) remain scarce. Here, we demonstrate an application-focused approach using machine learning (ML) for the local optimization of RFP variants in a low-data setting. Using a data set of over 150 reported RFP sequences, we trained lightweight descriptor-based ML models to prioritize variants within the local sequence space of the state-of-the-art RFP mScarlet-I3. Guided by model predictions, we identified variants exhibiting red-shifted emission peaks, large Stokes shifts, or brightness comparable to the parental protein. Our findings show that these interpretable, data-efficient models can serve as effective auxiliary guides for targeted local engineering, and provide a practical framework for protein optimization.

PMID:
42443100
Bibliographic data and abstract were imported from PubMed on 14 Jul 2026.

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