ProDyn0: Inferring calponin homology domain stretching behavior using graph neural networks
This work addresses the challenge of understanding protein dynamics for biophysics researchers, but it is incremental as it applies existing graph neural network methods to a new protein dataset.
The researchers tackled the problem of predicting the mechanosensitive force response of mutated calponin homology domains by generating a database of 2020 molecular dynamics simulations and using graph neural networks, achieving 86.63% accuracy for force mode classification and specific MAE values for force magnitude and time.
Graph neural networks are a quickly emerging field for non-Euclidean data that leverage the inherent graphical structure to predict node, edge, and global-level properties of a system. Protein properties can not easily be understood as a simple sum of their parts (i.e. amino acids), therefore, understanding their dynamical properties in the context of graphs is attractive for revealing how perturbations to their structure can affect their global function. To tackle this problem, we generate a database of 2020 mutated calponin homology (CH) domains undergoing large-scale separation in molecular dynamics. To predict the mechanosensitive force response, we develop neural message passing networks and residual gated graph convnets which predict the protein dependent force separation at 86.63 percent, 81.59 kJ/mol/nm MAE, 76.99 psec MAE for force mode classification, max force magnitude, max force time respectively-- significantly better than non-graph-based deep learning techniques. Towards uniting geometric learning techniques and biophysical observables, we premiere our simulation database as a benchmark dataset for further development/evaluation of graph neural network architectures.