The official code of ["Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence"].
Predicting the protein stability changes upon mutations is one of the effective ways to improve the efficiency of protein engineering. Here, we propose a dual-view ensemble learning-based framework, DVE-stability, for mutation-induced protein stability change prediction from single sequence. DVE-stability integrates the global and local dependencies of mutations to capture the intramolecular interactions from two views through ensemble learning, in which a structural microenvironment simulation module is designed to indirectly introduce the information of structural microenvironment at the sequence level. DVE-stability achieved state-of-the-art prediction performance on 7 single-point mutation benchmark datasets, and comprehensively surpassed other methods on 5 of them. Furthermore, DVE-stability outperformed other methods comprehensively through zero-shot inference on multiple-point mutation prediction task, demonstrating superior model generalizability to capture the epistasis of multiple-point mutations. More importantly, DVE-stability exhibited superior generalization performance in predicting rare beneficial mutations that are crucial for practical protein directed evolution scenarios. In addition, DVE-stability identified important intramolecular interactions via attention scores, demonstrating interpretable. Overall, DVE-stability provides a flexible and efficient tool for mutation-induced protein stability change prediction in an interpretable ensemble learning manner.
The experiments are tested on one Tesla V100 (32GB).
Build the environment.
pip install -r requirements.txtPut the training and testing datasets in the "data" folder. Download high-throughput fitness dataset at mega-scale repo. Download C2878 and T2837 dataset at Stability-oracle repo. Download S8754, M1261, S461 and S783 dataset at GeoStab repo.
To train this model on downstream tasks from scratch, please run
python train.pyTo test this model on different test datas, please run
python test.pyThis project is licensed under the MIT License.