The BindingNet aims at modeling high-quality binding poses for protein-ligand complexes with experimentally determined binding affinity data. BindingNet provides valuable insights into investigating protein-ligand interactions, allowing visual inspection and interpretation of structural analogs' structure-activity relationships (SARs). It can also be used for evaluating machine learning-based scoring functions and has the potential utilization for benchmarking the molecular docking methods and ligand binding free energy calculation approaches.
By comparative complex stricture modeling, it now contains 69,816 modeled high-quality protein-ligand complex structures with experimental binding affinity data from ChEMBL_v28 and template structures from PDBbind_v2019.
BindingNet is available at http://bindingnet.huanglab.org.cn/ under a CC BY-NC 4.0 license.
conda env create -f bindingnet_generate.yml
cd 1-extract_PDBid
bash extractPDBid.sh
-> PDBIDs_INDEX_general_PL_data.2019
PDBIDs_INDEX_general_PL_data.2019
PDB
to UniPortKB
in Select options and click Submit
Your list...(PDB ID)
, Entry
, ChEMBL
, (BindingDB
), Protein names
Tab-seperated
-> 2-query_target_ChEMBLid/converted_PDBIDs_INDEX_general_PL_data.2019.tab
PDB ID
select * where a3 !== ""
in RBQL Console (VSCode extension Rainbow CSV)
Ctrl
+ Shift
+ P
at VSCodeRainbow CSV: RBQL
select * where a3 !== ""
-> 2-query_target_ChEMBLid/converted_PDBIDs_INDEX_general_PL_data.2019.tab.tsv
UniPort ID
from ChEMBLcd 3-query_ChEMBL
python query_chembl_v2019_x019.py
ChEMBL webresource client
cd 4-extract_similar_compnds
bash extract_simi_compounds.sh
cd 5-pipeline_after_4-extract_simi_compnds
cd 1-obtain_list
bash obtain_target_pdbid_list.sh
-> all_target_pdbid.list
-> for rec_optbash obtain_target_pdbid_compound_list.sh
-> all_target_pdbid_compound.list
opt yes
in plop6.0cd 2-rec_opt
bash rec_opt_qsub_anywhere.sh
cd 3-align-filter_clash-rescore-final_filter
qsub -p -100 run_for_each_compound.sh
-t start-end
${SGE_TASK_ID}
: "target pdbid_compound_id"cd 5-pipeline_after_4-extract_simi_compnds/4-deal_with_result
cd 5-pipeline_after_4-extract_simi_compnds/5-Requery_And_Obtain_all_affinity_for_SAR
_final.pdb
to compound.sdf
, and Extract pocket for machine learningcd 5-pipeline_after_4-extract_simi_compnds/6-convert_sdf_AND_extract_pocket
cd 5-pipeline_after_4-extract_simi_compnds/7-PDBbind_v2019_minimize
cd 6-deep_learning/2-FAST/
cd 7-web_server/
cd 10-analysis
Li, X.; Shen, C.; Zhu, H.; Yang, Y.; Wang, Q.; Yang, J.; Huang, N. A High-Quality Data Set of Protein–Ligand Binding Interactions Via Comparative Complex Structure Modeling. J. Chem. Inf. Model. 2024. https://doi.org/10.1021/acs.jcim.3c01170 .