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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...

Ligand Binding Sites

Ligand Binding Sites

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 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...

Ligand Binding and Linkage

Ligand Binding and Linkage

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DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks.

Rishal Aggarwal¹, Akash Gupta¹, Vineeth Chelur¹

¹International Institute of Information Technology, Hyderabad 500 032, India.

Journal of Chemical Information and Modeling

|August 10, 2021

Summary

This summary is machine-generated.

DeepPocket, a novel framework combining geometry and deep learning, accurately identifies and ranks protein binding sites for drug design. It outperforms existing methods, showing strong generalization for novel structures.

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Area of Science:

Computational biology
Structural biology
Drug discovery

Background:

Accurate identification of druggable binding sites on protein structures is crucial for structure-based drug design.
Existing binding site detection methods often struggle with accurate identification and ranking.
Deep learning shows promise for advancing structural biology tasks.

Purpose of the Study:

To develop a novel framework, DeepPocket, for accurate and reliable detection and ranking of ligand binding sites (LBS) on protein structures.
To leverage deep learning, specifically 3D convolutional neural networks, to improve binding site prediction.
To introduce a new dataset, SC6K, for evaluating LBS detection methods.

Main Methods:

DeepPocket integrates geometry-based pocket detection (Fpocket) with deep learning for pocket rescoring.
Utilizes 3D convolutional neural networks to analyze protein structures and identify relevant cavities.
Developed and utilized the SC6K dataset, comprising protein structures from PDB (Jan 2018 - Feb 2020), for LBS detection.

Main Results:

DeepPocket demonstrates superior performance in identifying and ranking binding sites compared to current state-of-the-art methods.
The framework shows excellent generalization ability on novel protein structures.
Validation performed on established binding site datasets and the new SC6K dataset.

Conclusions:

DeepPocket offers a significant advancement in binding site detection for structure-based drug design.
The combination of geometry and deep learning provides a robust approach for identifying druggable pockets.
The framework's performance and generalization capabilities make it a valuable tool for drug discovery pipelines.