We then introduced a simple strategy to generate novel sequences based on the geometry of the latent space. We achieved this by reconstructing the embeddings along the trajectory towards the origin of the latent space. Employing this strategy, we systematically executed the pipeline in three rounds of laboratory experiments and optimization, leading to four VAE models producing 20 designs in total. Similarly to the study of computational filter evaluation for synthetical protein designs from generative models , each iteration revealed new insights, allowing for iterative refinement and improvement of our approach. Notably, we increased the success rate of soluble designs from 11% in the first round and 66% in the second round to 75% in the third round, illustrating the effectiveness of applying accumulated knowledge for improvement (Section 1 Workflow overview of SI).