Directed evolution as a widely-used engineering strategy faces obstacles in
finding desired mutants from the massive size of candidate modifications. While
deep learning methods learn protein contexts to establish feasible searching
space, many existing models are computationally demanding and fail to predict
how specific mutational tests will affect a protein's sequence or function.
This research introduces a lightweight graph representation learning scheme
that efficiently analyzes the microenvironment of wild-type proteins and
recommends practical higher-order mutations exclusive to the user-specified
protein and function of interest. Our method enables continuous improvement of
the inference model by limited computational resources and a few hundred
mutational training samples, resulting in accurate prediction of variant
effects that exhibit near-perfect correlation with the ground truth across deep
mutational scanning assays of 19 proteins. With its affordability and
applicability to both computer scientists and biochemical laboratories, our
solution offers a wide range of benefits that make it an ideal choice for the
community.

通过引入轻量化图形表示学习方案，本研究建议一种有效分析野生型蛋白质微环境、推荐实用高阶突变，并使用有限的计算资源和少量的突变训练样本进行推断模型的连续改进的方法，从而准确预测蛋白质功能和变异效应。