Fig. 2: Distribution and sampling results for protein conformations.
From: Predicting equilibrium distributions for molecular systems with deep learning
a, Structures generated by DiG resemble the diverse conformations of millisecond MD simulations. MD-simulated structures are projected onto the reduced space spanned by two time-lagged independent component analysis (TICA) coordinates (that is, independent component (IC) 1 and 2), and the probability densities are depicted using contour lines. Left: for the RBD protein, MD simulation reveals four highly populated regions in the 2D space spanned by TICA coordinates. DiG-generated structures are mapped to this 2D space (shown as orange dots), with a distribution reflected by the colour intensity. Under the distribution plot, structures generated by DiG (thin ribbons) are superposed on representative structures. AlphaFold-predicted structures (stars) are shown in the plot. Right: the results for the SARS-CoV-2 main protease, compared with MD simulation and AlphaFold prediction results. The contour map reveals three clusters, DiG-generated structures overlap with clusters II and III, whereas structures in cluster I are underrepresented. b, The performance of DiG on generating multiple conformations of proteins. Structures generated by DiG (thin ribbons) are compared with the experimentally determined structures (each structure is labelled by its PDB ID, except DEER-AF, which is an AlphaFold predicted model, shown as cylindrical cartoons). For the four proteins (adenylate kinase, LmrP membrane protein, human BRAF kinase and D-ribose binding protein), structures in two functional states (distinguished by cyan and brown) are well reproduced by DiG (ribbons).
