Data-Driven Discovery of the Origins of UV Absorption in Alpha-3C Protein

TL;DR

This study uses data-driven methods and simulations to uncover how specific hydrogen-bonding motifs in alpha-3C protein contribute to its UV absorption, revealing the importance of protein environment and solvation effects.

Contribution

It introduces an unsupervised learning approach to identify structural motifs responsible for UV absorption in proteins, advancing understanding of their photophysical properties.

Findings

Hydrogen bonds involving arginine and carboxylate groups are key to UV absorption.

The absorption features are highly sensitive to the size of the QM region and solvation.

Gas-phase models are insufficient to capture the full absorption behavior.

Abstract

Over the last decade, there has been a growing body of experimental work showing that proteins devoid of aromatic and conjugated groups can absorb light in the near-UV beyond 300 nm and emit visible light. Understanding the origins of this phenomena offers the possibility of designing non-invasive spectroscopic probes for local interactions in biological systems. It was recently found that the synthetic protein {\alpha}3C displays UV-vis absorption between 250-800 nm which was shown to arise from charge-transfer excitations between charged amino acids. In this work, we use data-driven discovery to revisit the origins of these features using molecular dynamics and excited-state simulations. Specifically, an unsupervised learning approach beginning with encoding protein environments with local atomic descriptors, is employed to automatically detect relevant structural motifs. We identify three main motifs corresponding to different hydrogen-bonding patterns that are subsequently used to perform QM/MM simulations including the entire protein and solvent bath with the density-functional tight-binding (DFTB) approach. Hydrogen-bonding structures involving arginine and carboxylate groups appear to be the most prone to near-UV absorption. We show that these features are highly sensitive to the size of the QM region employed as well as to the inclusion of explicit solvation underscoring the limitations of using gas-phase cluster models.