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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

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Updated: May 9, 2026

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
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Published on: October 15, 2018

Enabling Automatic Generation of Protein-Ligand Complex Data Sets with Atomistic Detail.

Torben Gutermuth1, Emanuel S R Ehmki1, Florian Flachsenberg1

  • 1University of Hamburg, ZBH─Center for Bioinformatics, Albert Einstein Ring 8-10, 22761 Hamburg, Germany.

Journal of Chemical Information and Modeling
|May 8, 2026
PubMed
Summary

We developed StrAcTable, an automated method to create annotated protein-ligand complex datasets. This resource aids in developing structure-based drug design methods by providing high-quality data for machine learning model training.

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Last Updated: May 9, 2026

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
07:33

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Published on: October 15, 2018

Modeling Ligands into Maps Derived from Electron Cryomicroscopy
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Computational chemistry and cheminformatics
  • Structural biology
  • Drug discovery and development

Background:

  • Predicting protein-ligand bioactivities is critical for drug discovery but challenging.
  • Supervised machine learning models are competitive in structure-based drug design but require high-quality data.
  • Integrating bioactivity and protein structure data from repositories like ChEMBL and PDB is difficult.

Purpose of the Study:

  • To create an automated and continuously growing dataset of annotated protein-ligand complexes.
  • To facilitate the development of structure-based molecular design methods.
  • To provide a foundation for novel datasets for training and testing machine learning models.

Main Methods:

  • Utilized ActivityFinder for automated linking of protein sequence/structure with bioactivity data.
  • Integrated ActivityFinder with tools for structure quality estimation and property calculation.
  • Constructed the StrAcTable dataset based on ChEMBL (Version 35) and PDB data.

Main Results:

  • Created StrAcTable, an automatically generated dataset of 20,063 annotated protein-ligand complexes.
  • StrAcTable includes detailed quality metrics for data matching, macromolecular structure, ligands, and bioactivity.
  • The automated approach ensures sustainable growth and accessibility of the dataset.

Conclusions:

  • StrAcTable offers a valuable resource for constructing training and validation datasets in structure-based drug design.
  • The dataset supports the development and testing of both machine learning and traditional molecular design methods.
  • Automated data generation is key for building comprehensive and up-to-date resources for computational drug discovery.