Roaming leads to amino acid photodamage: A deep learning study of tyrosine

TL;DR

This study introduces a deep learning approach to investigate tyrosine's photochemistry, revealing roaming atoms as radicals that influence peptide and protein photochemistry, surpassing traditional quantum methods in accuracy and efficiency.

Contribution

A novel deep learning-based method was developed to study amino acid photochemistry, discovering roaming atoms in tyrosine for the first time in biological molecules.

Findings

Discovered roaming atoms in tyrosine, a first in biology.

Roaming atoms may significantly impact peptide and protein photochemistry.

Proposed method improves accuracy and efficiency over conventional quantum chemical techniques.

Abstract

Although the amino acid tyrosine is among the main building blocks of life, its photochemistry is not fully understood. Traditional theoretical simulations are neither accurate enough, nor computationally efficient to provide the missing puzzle pieces to the experimentally observed signatures obtained via time-resolved pump-probe spectroscopy or mass spectroscopy. In this work, we go beyond the realms of possibility with conventional quantum chemical methods and develop as well as apply a new technique to shed light on the photochemistry of tyrosine. By doing so, we discover roaming atoms in tyrosine, which is the first time such a reaction is discovered in biology. Our findings suggest that roaming atoms are radicals that could play a fundamental role in the photochemistry of peptides and proteins, offering a new perspective. Our novel method is based on deep learning, leverages the physics underlying the data, and combines different levels of theory. This combination of methods to obtain an accurate picture of excited molecules could shape how we study photochemical systems in the future and how we can overcome the current limitations that we face when applying quantum chemical methods.