Rxivist logo

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.

Download data

  • Downloaded 1,077 times
  • Download rankings, all-time:
    • Site-wide: 20,411
    • In biophysics: 537
  • Year to date:
    • Site-wide: 1,888
  • Since beginning of last month:
    • Site-wide: 4,852

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide