The problem of designing protein sequences with desired properties is
challenging, as it requires to explore a high-dimensional protein sequence
space with extremely sparse meaningful regions. This has led to the development
of model-based optimization (MBO) techniques that aid in the design, by using
effective search models guided by the properties over the sequence space.
However, the intrinsic imbalanced nature of experimentally derived datasets
causes existing MBO approaches to struggle or outright fail. We propose a
property-guided variational auto-encoder (PGVAE) whose latent space is
explicitly structured by the property values such that samples are prioritized
according to these properties. Through extensive benchmarking on real and
semi-synthetic protein datasets, we demonstrate that MBO with PGVAE robustly
finds sequences with improved properties despite significant dataset
imbalances. We further showcase the generality of our approach to continuous
design spaces, and its robustness to dataset imbalance in an application to
physics-informed neural networks.

该研究提出了一种基于属性引导变分自编码器的模型优化技术来设计具有期望性质的蛋白序列，并在实验与半合成蛋白质数据集上进行了全面的基准测试，表明该技术具有鲁棒性，能够有效地应对实验结果不够均衡的问题。