An Algorithm to Predict the Possible SARS-CoV-2 Mutations - Raúl, Isea (International Journal of Coronaviruses, 2021)
An algorithm to determine the possible mutations that can occur in the S protein responsible of the Covid-19 in humans is designed. To do that, nine tridimensional sequences available in the Protein Data Bank similar to the initial strain sequenced in Wuhan (December 2019) are identified. The conditions driving this potential mutation are: (1) an accumulated number of mutations greater than (or equal to) 5 in each position; (2), a cumulative value of the different variations of Gibbs free energy less than -2.0 Kcal/mol; and (3), a squared fluctuation greater than 1.6 Å obtained according to calculations for normal mode analysis based on anisotropic network models (ANM) after averaging the first 20 vibration modes. The result is that 491 positions can mutate, while 424 positions did not provide any mutation. Finally, the results reveal that there are mutations that cannot be predicted, so more studies are needed to determine why they are present in the human population.
Keywords: Covid-19; SARS-CoV-19; Mutation; ∆∆G; Gibbs; ANM; Python