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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.
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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.
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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...

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The Equilibrium Binding Constant and Binding Strength

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Related Experiment Video

Updated: Sep 22, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning.

H Tomas Rube^1,2, Chaitanya Rastogi², Siqian Feng³

¹Department of Bioengineering, University of California, Merced, Merced, CA, USA.

Nature Biotechnology

|May 23, 2022

Summary

ProBound is a new machine learning method that accurately quantifies protein-ligand interactions, providing precise binding affinity and kinetic rates for transcription factors and kinases.

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Last Updated: Sep 22, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Published on: January 26, 2024

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

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Published on: February 23, 2024

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Computational biology
Biophysics
Machine learning

Background:

High-throughput assays profile protein-ligand interactions but lack rigorous biophysical parameters.
Accurate quantification of molecular interactions is crucial for understanding biological networks.

Purpose of the Study:

To develop a flexible machine learning method, ProBound, for accurately quantifying protein-ligand interactions.
To model molecular interactions and data generation for precise binding affinity and kinetic rate determination.

Main Methods:

ProBound utilizes a multi-layered maximum-likelihood framework.
Models both molecular interactions and the data generation process.
Integrates with assays like K_D-seq and in vivo data (ChIP-seq).

Main Results:

ProBound predicts binding affinity over a wide range, surpassing previous methods.
It captures the effects of DNA modifications and multi-TF complex flexibility.
Infers specificity from ChIP-seq data without peak calling and determines absolute binding affinity.

Conclusions:

ProBound offers a robust method for quantifying protein-ligand interactions, including transcription factor and kinase kinetics.
Enables decoding of biological networks and rational engineering of protein-ligand interactions.