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Graph neural networks are a quickly emerging field for non-Euclidean datathat leverage the inherent graphical structure to predict node, edge, andglobal-level properties of a system. Protein properties can not easily beunderstood as a simple sum of their parts (i.e. amino acids), therefore,understanding their dynamical properties in the context of graphs is attractivefor revealing how perturbations to their structure can affect their globalfunction. To tackle this problem, we generate a database of 2020 mutatedcalponin homology (CH) domains undergoing large-scale separation in moleculardynamics. To predict the mechanosensitive force response, we develop neuralmessage passing networks and residual gated graph convnets which predict theprotein dependent force separation at 86.63 percent, 81.59 kJ/mol/nm MAE, 76.99psec MAE for force mode classification, max force magnitude, max force timerespectively-- significantly better than non-graph-based deep learningtechniques. Towards uniting geometric learning techniques and biophysicalobservables, we premiere our simulation database as a benchmark dataset forfurther development/evaluation of graph neural network architectures.

ProDyn0: Inferring calponin homology domain stretching behavior using graph neural networks