E3Docker: a docking server for potential E3 binder discovery

Targeted protein degradation (TPD) has emerged as a promising therapeutic strategy for modulating protein levels in cells. Proteolysis-targeting chimeras and molecular glues facilitate the formation of a complex between the protein of interest (POI) and a specific E3 ligase, leading to POI ubiquitination and subsequent degradation by the proteasome. Considering over 600 E3s in the human genome, it is of great potential to find novel E3 binders and recruit new E3 ligase for TPD related drug discovery. Here we introduce E3Docker, an online computational tool for E3 binder discovery. A total of 1075 Homo sapiens E3 ligases are collected from databases and literature, and 4474 three-dimensional structures of these E3 ligases, in either apo or complex forms, are integrated into the web server. The druggable pockets for each E3 ligase are defined by experimentally bound ligand from PDB or predicted by using DeepPocket. CoDock-Ligand is employed as docking engine for potential E3 binder estimation. With a user-friendly interface, E3Docker facilitates the generation of binding poses and affinity scores for compounds with over 1000 kinds of E3 ligases and may benefit for novel E3 binder discovery. The E3Docker server and tutorials are freely available at https://e3docker.schanglab.org.cn/.

Authors

Institutions

Publication Details

Journal
Nucleic Acids Research
Published
2025-05-08
DOI
https://doi.org/10.1093/nar/gkaf391
Citations
4
Primary Topic
Protein Degradation and Inhibitors
Type
article
Field-Weighted Citation Impact
3.22

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

E3Docker: a docking server for potential E3 binder discovery

Kejia Yan, Ren Kong, Xufeng Lu, Shan Chang et al.
4 citations
Nucleic Acids Research
Protein Degradation and Inhibitors
3.22
article

E3Docker: a docking server for potential E3 binder discovery

Kejia Yan, Ren Kong, Xufeng Lu, Shan Chang, Han Zhang, Yu Wang, Mingwei Pang, Zhou Chen, Lianhua Piao, Wangqiu He
article en
4 citations

Abstract

Targeted protein degradation (TPD) has emerged as a promising therapeutic strategy for modulating protein levels in cells. Proteolysis-targeting chimeras and molecular glues facilitate the formation of a complex between the protein of interest (POI) and a specific E3 ligase, leading to POI ubiquitination and subsequent degradation by the proteasome. Considering over 600 E3s in the human genome, it is of great potential to find novel E3 binders and recruit new E3 ligase for TPD related drug discovery. Here we introduce E3Docker, an online computational tool for E3 binder discovery. A total of 1075 Homo sapiens E3 ligases are collected from databases and literature, and 4474 three-dimensional structures of these E3 ligases, in either apo or complex forms, are integrated into the web server. The druggable pockets for each E3 ligase are defined by experimentally bound ligand from PDB or predicted by using DeepPocket. CoDock-Ligand is employed as docking engine for potential E3 binder estimation. With a user-friendly interface, E3Docker facilitates the generation of binding poses and affinity scores for compounds with over 1000 kinds of E3 ligases and may benefit for novel E3 binder discovery. The E3Docker server and tutorials are freely available at https://e3docker.schanglab.org.cn/.

Nucleic Acids ResearchVol. 53(W1)
Tianjin University of Traditional Chinese Medicine (CN), Jiangsu University of Technology (CN), Institute of Bioinformatics (IN)
University of Missouri, National Natural Science Foundation of China, Jiangsu University, Jiangsu University of Technology
Openalex Percentile: Top 8%
Protein Degradation and Inhibitors
3.22
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.