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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 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...

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.
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Protein-Drug Binding: Mechanism and Kinetics

Protein-Drug Binding: Mechanism and Kinetics

Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
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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:

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Related Experiment Video

Updated: Jul 12, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.

Roshni Bhatt^1,2, David Ryan Koes¹, Jacob D Durrant²

¹Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260.

Biorxiv : the Preprint Server for Biology

|October 31, 2023

Summary

This summary is machine-generated.

We developed CENsible, a new method using context explanation networks (CENs) to predict small-molecule binding affinities. This interpretable approach helps identify key factors in drug discovery and lead optimization.

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Published on: September 20, 2016

Area of Science:

Computational chemistry and cheminformatics.
Machine learning in drug discovery.
Structural bioinformatics.

Background:

Accurate prediction of small-molecule binding affinities is crucial for efficient drug discovery.
Existing machine learning scoring functions often lack interpretability.
Understanding the basis of binding affinity aids lead optimization.

Approach:

Developed CENsible, a novel scoring function utilizing context explanation networks (CENs).
Employs a deep convolutional neural network to predict contributions of pre-calculated terms to binding affinity.
Input is the 3D structure of a protein/ligand complex.

Key Points:

CENsible effectively distinguishes between active and inactive compounds across various systems.
The primary advantage is retained interpretability, unlike many black-box machine learning models.
Researchers can identify the specific contribution of each pre-calculated term to the predicted affinity.

Conclusions:

CENsible offers an interpretable alternative for predicting binding affinities.
Its interpretability facilitates understanding and guides lead optimization in drug development.
This approach has significant implications for accelerating the discovery of novel therapeutics.