VAE is a generative machine learning algorithm based on the classic autoencoder architecture composed of two parts denoted as encoder and decoder. To make the algorithm more suitable for the inference of unseen sequences, the VAE framework adds the regularization term into the loss function and Monte Carlo sampling to the learning process. The sampling parameters are determined by the encoder learning a mean and deviation for input sequences. We used the one hidden layer in the encoder and decoder, both composed of N neurons, where N is the width of preprocessed MSA (number of positions), the latent space dimensionality of 2, and zero weight decay. The final model had 3 million parameters. We used the Adam optimizer with a learning rate of 0.001 and stopped training after not improving the loss function for more than 3 consecutive rounds.