MetalNet

We developed a coevolution-based machine learning method named “MetalNet” to systematically predict metal-binding sites in proteomes. Our computational method provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.

Contact: chuwang@pku.edu.cn, yao.cheng69@pku.edu.cn, wendao@pku.edu.cn

Server

We have developed a server that can predict metal binding sites online. This is a novel implementation with enhanced performance and higher efficiency. We welcome you to try it out.

Requirements

Python == 3.6.13

autogluon ==0.2.0

numpy==1.19.5

pandas==1.1.5

networkx ==2.5.1

graphviz==0.8.4

Prediction

python  predict.py  [protein_name]  [MSA_file]  [esm_coevolution_profile]

run example:

python  predict.py  P0A6G5  P0A6G5.a3m  P0A6G5.csv

and standard output of the example can be found in P0A6G5_output/.