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
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.
Python == 3.6.13
autogluon ==0.2.0
numpy==1.19.5
pandas==1.1.5
networkx ==2.5.1
graphviz==0.8.4
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/.