The design of novel proteins has many applications but remains an attritional
process with success in isolated cases. Meanwhile, deep learning technologies
have exploded in popularity in recent years and are increasingly applicable to
biology due to the rise in available data. We attempt to link protein design
and deep learning by using variational autoencoders to generate protein
sequences conditioned on desired properties. Potential copper and calcium
binding sites are added to non-metal binding proteins without human
intervention and compared to a hidden Markov model. In another use case, a
grammar of protein structures is developed and used to produce sequences for a
novel protein topology. One candidate structure is found to be stable by
molecular dynamics simulation. The ability of our model to confine the vast
search space of protein sequences and to scale easily has the potential to
assist in a variety of protein design tasks.

利用变分自编码器生成所需属性的蛋白质序列，不需要人为干预，可添加潜在的铜和钙结合位点并与隐马尔可夫模型进行比较，同时开发了蛋白质结构语法用来产生一种新型蛋白质拓扑结构。该模型能够限制蛋白质序列的搜索空间，易于扩展，有助于各种蛋白质设计任务。