DynaProt predicts per-residue 3x3 covariance matrices for local flexibility and scalar pairwise covariances for dynamic coupling from static protein structures using an SE(3)-invariant Gaussian framework, achieving high RMSF accuracy with far fewer parameters than prior methods.
Deep sparse rectifier neural networks
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Learning residue level protein dynamics with multiscale Gaussians
DynaProt predicts per-residue 3x3 covariance matrices for local flexibility and scalar pairwise covariances for dynamic coupling from static protein structures using an SE(3)-invariant Gaussian framework, achieving high RMSF accuracy with far fewer parameters than prior methods.