An ensemble 3D deep-learning model to predict protein metal-binding site

Ahmad Mohamadi, Tianfan Cheng, Lijian Jin, Junwen Wang, Hongzhe Sun, Mohamad Koohi-Moghadam

Research output: Contribution to journalArticlepeer-review

Abstract

Predicting metal-binding sites in proteins is critical for understanding the protein's biological function. Here, we develop an ensemble deep convolutional neural network (CNN) method for predicting metal-binding sites based on their three-dimensional (3D) structure. We build multi-channel 3D voxels based on biophysical characteristics obtained from raw atom coordinates of each protein-binding pocket. Then, we use these 3D voxels as the input of an ensemble 3D CNN model. We train and evaluate the model using a curated dataset of 3D protein structures. Our proposed model shows high performance in predicting metal-binding sites for Zn, Fe, Mg, Mn, Ca, and Na. Our approach offers a framework to use 3D spatial features to train 3D-CNN, which may be used to predict complicated metal-binding sites directly from their biophysical characteristics. The source code and webserver of the model are publicly available.

Original languageEnglish (US)
Article number101046
JournalCell Reports Physical Science
Volume3
Issue number9
DOIs
StatePublished - Sep 21 2022

Keywords

  • 3D voxels
  • ensemble 3D deep learning
  • metal-binding sites
  • metalloprotein
  • spatial features

ASJC Scopus subject areas

  • Chemistry(all)
  • Materials Science(all)
  • Engineering(all)
  • Energy(all)
  • Physics and Astronomy(all)

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