Complementary techniques for the analysis of mutation sites at flexible regions, in which the position of atoms could not be determined by cryo-electron microscopy (Cryo-EM) such as the furin cleavage site of SARS-CoV-2, are necessary. The prediction data from SSSCPreds, a deep neural network-based prediction software of conformational flexibility or rigidity in proteins, can give insight into the conformational variability of mutation sites. We find that although the conformation of G614 is rigid, which is assigned as a left-handed (LH) α-helix-type one, that of D614 is flexible without the hydrogen bonding latch to T859. The rigidity of glycine which stabilizes the local conformation more effectively than that of aspartic acid with the latch, thereby contributes to the reduction of S1 shedding and increase in infectivity. Further it is predicted that no other amino acid allows the same conformation and stability as the glycine mutation in D614. The individual mutations in B.1.1.7 and B.1.351 have a lower effect and are not comparable to the overwhelming effectiveness of the D614G mutation. SSSCPreds provides important conformational flexibility insights into the deep neural network-based understanding of the current mutation sites and the potential for new ones in future.