# Computational Framework for the Mechanistic Studies and Physics-Informed Prediction and Design of Photoenzymes

> **NIH NIH R35** · NORTHEASTERN UNIVERSITY · 2024 · $400,000

## Abstract

A robust enzyme design strategy will profoundly impact human health, such as by achieving asymmetric
synthesis of pharmaceuticals not easy to be achieved by small-molecule catalysts. However, photoexcitation
has rarely been considered in enzyme design. In recent years, it has been found that certain enzymes can be
repurposed by photoexcitation for non-natural chemical reactions that cannot be easily achieved by small-
molecule catalysts or traditionally engineered enzymes. These enzymes, termed photoenzymes, are believed to
utilize photoactivatable cofactors, combined with a natural or mutated enzyme scaffold, to reach new reaction
spaces. Photoenzymes have emerged as a promising new class of catalysts for non-natural reactions important
in pharmaceutical synthesis, such as asymmetric radical reactions important in late-stage functionalization of
drug-like molecules. However, the mechanisms of photoenzymes have not been studied well and there has not
been a clear rational discovery and design strategy for photoenzymes, not only because these are emergent
systems, but also because existing computational methods are not adequate. In this research program, we will
develop an integrated computational framework to predict the combined effect of light, cofactor, substrate(s),
and protein sequences on photoenzyme reactivity and the mechanisms that lead to this effect, and will develop
a physics-informed design strategy that makes use of descriptors derived from both ground and excited
electronic states to control the activity and selectivity of photoenzymatic reactions. This will fill the gap in
computational enzyme design where the excited electronic states are not normally considered. In specific, we
will 1) develop machine learning-enhanced simulation methods to efficiently simulate both the ground and
excited electronic states of photoenzymes to assist mechanistic studies and to inform the prediction and design
of photoenzymes, and 2) develop a photoenzyme design strategy centered on descriptors derived from both
ground and excited electronic states computed by molecular simulations. We will use flavin-dependent “ene”-
reductases (EREDs) as the prototype system for the computational tool development and testing since there
have already been a collection of computational and experimental data for EREDs, where the computational
data are from the PI’s group. This research program will not only deepen our understanding of photoenzyme
mechanisms, but will also greatly facilitate the design and prediction of photoenzymes for non-natural reactions
important in pharmaceutical synthesis. In the long term, it will also facilitate the identification of natural enzymes
that may have previously unknown photo-driven reactivity, which may become new protein scaffolds for
developing novel photoenzymatic reactions or become new drug targets.

## Key facts

- **NIH application ID:** 10940408
- **Project number:** 1R35GM155112-01
- **Recipient organization:** NORTHEASTERN UNIVERSITY
- **Principal Investigator:** Sijia Dong
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $400,000
- **Award type:** 1
- **Project period:** 2024-09-15 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10940408

## Citation

> US National Institutes of Health, RePORTER application 10940408, Computational Framework for the Mechanistic Studies and Physics-Informed Prediction and Design of Photoenzymes (1R35GM155112-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10940408. Licensed CC0.

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