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Showing 1–3 of 3 results

Advanced filters: Author: Qianmu Yuan Clear advanced filters

Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures

The Enzyme Commission (EC) number is a commonly used method for defining enzyme function. Here, authors propose GraphEC, a geometric graph learning-based EC number predictor to identify unannotated enzyme functions, predict their active sites, and determine their optimal pH.

Yidong Song
Qianmu Yuan
Yuedong Yang
ResearchOpen Access18 Sept 2024
Nature Communications

Volume: 15, P: 1-11
A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions

Multi-scale learning still struggles with imbalanced information and greedy characteristics. Here the authors present MUSE, an Expectation-Maximization-based multi-scale framework, improving predictions across molecular interactions and atomic interfaces.

Jiahua Rao
Jiancong Xie
Yuedong Yang
ResearchOpen Access25 May 2024
Nature Communications

Volume: 15, P: 1-12
Accurately predicting optimal conditions for microorganism proteins through geometric graph learning and language model

GeoPoc uses geometric graph learning and protein structure data to predict optimal temperature, pH, and salt concentration for microbial proteins. Achieving high accuracy, it outperforms existing methods and identifies key properties for enhancing thermostability.

Mingming Zhu
Yidong Song
Yuedong Yang
ResearchOpen Access29 Dec 2024
Communications Biology

Volume: 7, P: 1-10