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Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

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...

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

The Equilibrium Binding Constant and Binding Strength

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:

Cooperative Allosteric Transitions

Cooperative Allosteric Transitions

Ligand Binding and Linkage

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked. In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...

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Related Experiment Video

Updated: Oct 15, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

End-to-end learning for compound activity prediction based on binding pocket information.

Toshitaka Tanebe¹, Takashi Ishida²

¹Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-85 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.

BMC Bioinformatics

|October 30, 2021

Summary

This study introduces a novel machine learning approach for predicting drug compound activity on new target proteins. By utilizing protein binding pocket structures, the method achieves higher accuracy than sequence-based models and matches docking simulation speed.

Keywords:

Deep neural network Docking simulation Drug discovery Graph convolution Virtual screening

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Area of Science:

Computational chemistry
Drug discovery
Machine learning

Background:

Machine learning for ligand activity prediction is advancing but requires known active compounds.
Docking simulations are used for novel targets but are computationally intensive.
Existing machine learning methods for novel targets lack sufficient accuracy due to limited input features.

Purpose of the Study:

To develop an accurate and efficient machine learning method for predicting compound activity on novel target proteins.
To leverage protein structure information for improved predictive performance.
To reduce the computational burden associated with traditional docking simulations.

Main Methods:

Developed an end-to-end learning framework for compound activity prediction.
Utilized graph neural networks to learn features from both compound structures and protein binding pocket structures.
Incorporated 3D structural information of the protein's binding pocket.

Main Results:

The proposed method demonstrated higher prediction accuracy compared to existing methods relying solely on amino acid sequence information.
The model effectively learned relevant features from the integrated structural data.
Achieved accuracy comparable to AutoDock Vina docking simulations.

Conclusions:

Machine learning approaches are viable for activity prediction even with novel target proteins.
The proposed structure-based machine learning method offers a computationally efficient alternative to traditional docking simulations.
This advancement holds promise for accelerating drug discovery pipelines.