Protein dynamics inform protein structure: An interdisciplinary investigation of protein crystallization propensity

Abstract

The classical central paradigm of structural biology links a protein’s sequence to its structure and function but overlooks conformational fluctuation that is integral to protein function. We propose a graph neural network model based on gated attention that explicitly incorporates protein dynamics via physics-based models to predict protein crystallization propensity. We compare results to all-atom molecular dynamics simulations of flexible, disordered human tropoelastin and ordered, globular human lysyl oxidase-like protein. Our findings show that fluctuating residues correlate with locally maximal attention scores in the neural network. By methodically truncating the sequences, we establish correlations between dynamical and physicochemical molecular properties and protein crystallization propensity. Accounting for comprehensive biological mechanisms, our tool can facilitate the rational design of protein sequences that lead to diffraction-quality crystals. Our study showcases the integration of physics-based and machine learning models for structure and property prediction, expanding the classical paradigm of structural biology.