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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...

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:

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...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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

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ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring

Xujun Zhang¹, Chao Shen^1,2, Tianyue Wang¹

¹Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.

Briefings in Bioinformatics

|September 22, 2023

Summary

This summary is machine-generated.

We developed ML-PLIC, a web platform for characterizing protein-ligand interactions (PLI) and generating machine learning-based scoring functions (MLSFs) for drug discovery via virtual screening.

Keywords:

machine learning scoring function virtual screening web platform

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Area of Science:

Computational chemistry and cheminformatics
Drug discovery and development
Machine learning in bioinformatics

Background:

Protein-ligand interactions (PLI) are crucial for structure-based drug design.
Machine learning-based scoring functions (MLSFs) offer a promising approach to analyze PLI.
Existing methods require robust platforms for automated PLI characterization and MLSF generation.

Purpose of the Study:

To introduce ML-PLIC, a novel web platform for automated protein-ligand interaction (PLI) characterization.
To enable the generation of machine learning-based scoring functions (MLSFs) for virtual screening (VS).
To facilitate structure-based drug design by identifying potential protein binders.

Main Methods:

ML-PLIC integrates five modules: Docking, Descriptors, Modeling, Screening, and Pipeline.
The platform automates the generation of physical and biochemical representations of PLI.
MLSFs are trained using these descriptors for subsequent VS.
Validation was performed on benchmark datasets and a case study involving Serine/threonine-protein kinase WEE1.

Main Results:

MLSFs generated by ML-PLIC demonstrated superior accuracy compared to traditional docking tools.
The platform achieved competitive performance against deep learning-based scoring functions.
A successful case study highlighted the utility of ML-PLIC in developing MLSFs for WEE1 kinase.

Conclusions:

ML-PLIC provides a powerful, integrated platform for PLI characterization and MLSF generation.
The platform enhances the design of structure-based virtual screening pipelines.
ML-PLIC is freely available, promoting advancements in drug discovery and design.