Fig. 2 | Scientific Reports

Fig. 2

From: Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2

Fig. 2The alternative text for this image may have been generated using AI.

2D-RMSD analysis. (a) Root-mean-square fluctuation (RMSF) for the entire receptor binding domain (RBD). (b) Two-dimensional root-mean-square deviation (2D-RMSD) graph for the receptor binding motif (RBM). (c) RMSF of the RBM region (residues 438–508), aligned and analyzed exclusively within this region. RMSF values are presented as average (lines) and deviation (shading) across three independent replicates, including an aggregated result of all three for each mutation. (d) 2D-RMSD graph for the \(\beta _{1}^{\prime }/\beta _{2}^{\prime }-C\) loop. The color scheme, ranging from blue to red, represents changes in mobility for the \(C\alpha\) atoms of the most mobile ensembles. This coding helps to distinguish the alignment of mutant poses designated as “\({-}{-}\)”, “\(++\)”, “\(+-\)” (mutations more infectious and less resistant to the antibody), and “\(-+\)”(mutations less infectious and more resistant to the antibody) across all systems and simulations. Each position on the x-axis represents a frame, compared to each frame on the y-axis; the diagonal, always zero, indicates a frame compared against itself.

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