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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: Jan 9, 2026

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

Atom-level enzyme active site scaffolding using RFdiffusion2.

Woody Ahern^1,2,3, Jason Yim^4,5, Doug Tischer^1,2

¹Department of Biochemistry, University of Washington, Seattle, WA, USA.

|December 3, 2025

Summary

This summary is machine-generated.

A new AI model, RFdiffusion2, designs novel enzymes directly from chemical reactions. This advances de novo enzyme design by bypassing limitations of previous computational methods.

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Area of Science:

Biochemistry
Computational Biology
Protein Engineering

Background:

Traditional enzyme design relies on idealized functional group arrangements and complex protein structure generation.
Existing AI methods for enzyme design often require predefined residue positions and limited flexibility.

Purpose of the Study:

To introduce RoseTTAFold diffusion 2 (RFdiffusion2), a deep generative model for de novo enzyme design.
To overcome limitations of current AI-based enzyme design by enabling direct generation from functional group geometries.

Main Methods:

Developed RFdiffusion2, a deep generative model for enzyme design.
Utilized functional group geometries as direct input, bypassing residue order specification and inverse rotamer generation.
Benchmarked RFdiffusion2 against existing methods on a diverse set of active sites.

Main Results:

RFdiffusion2 successfully generated scaffolds for all 41 active sites in a benchmark set, significantly outperforming previous methods (16 active sites).
Designed novel enzymes for three distinct catalytic mechanisms.
Identified active enzyme candidates with fewer than 96 experimental tests per mechanism.

Conclusions:

RFdiffusion2 demonstrates a powerful new approach for de novo enzyme creation.
Atomic-level generative modeling offers a direct pathway from reaction mechanisms to functional enzyme designs.
This method significantly enhances the efficiency and scope of computational enzyme engineering.