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Ligand Binding Sites

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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Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....

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Related Experiment Video

Updated: Oct 16, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.

Joel Ricci-Lopez^1,2, Sergio A Aguila², Michael K Gilson³

¹Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California C.P. 22860, Mexico.

Journal of Chemical Information and Modeling

|October 15, 2021

Summary

This summary is machine-generated.

Machine learning (ML) improves structure-based virtual screening (SBVS) by enhancing ensemble docking results. This approach significantly outperforms traditional methods for predicting ligand binding, offering better predictive power in drug discovery.

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Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

Structure-based virtual screening (SBVS) faces challenges incorporating receptor flexibility due to high computational costs.
Ensemble docking, using multiple receptor conformations, is a common alternative but lacks optimal result aggregation strategies.
Traditional consensus strategies for ensemble docking show limited improvement over single-structure approaches.

Purpose of the Study:

To investigate the potential of machine learning (ML) methodologies to enhance the predictive power of ensemble docking in SBVS.
To compare the performance of ML classifiers against traditional consensus strategies and single-structure docking.
To provide statistical evidence for the effectiveness of ML in improving SBVS outcomes.

Main Methods:

Ensemble docking was performed using protein conformational ensembles built from crystallographic structures for CDK2, FXa, EGFR, and HSP90.
Evaluated compound libraries included DUD, DEKOIS 2.0, CSAR-2012 datasets, and cocrystallized molecules.
Two ML classifiers, logistic regression and gradient boosting trees, were trained and validated using 30 repetitions of 4-fold cross-validation on ensemble docking results.

Main Results:

ML classifiers significantly outperformed traditional consensus strategies in predicting ligand binding.
The ML-enhanced ensemble docking approach surpassed the performance of single-structure docking methods.
Statistical analysis confirmed the effectiveness of ML in improving the predictive accuracy of SBVS.

Conclusions:

Machine learning significantly enhances the performance of ensemble docking in structure-based virtual screening.
ML-based approaches offer a superior alternative to traditional consensus strategies for aggregating ensemble docking results.
This study provides strong evidence for the utility of ML in improving the efficiency and accuracy of drug discovery pipelines.