39 citations · 47 across the 8 of their papers we have counts for
4 papers · 1 filter
VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data
Liang Zhang, Hua Pang, Chenghao Zhang +11
In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (…
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks
Bingxin Zhou, Outongyi Lv, Kai Yi +4
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 le…
TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling
Pan Tan, Mingchen Li, Liang Zhang +2
We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling. By assembling an ext…
SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Mingchen Li, Liqi Kang, Yi Xiong +4
Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functio…