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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Conserved Binding Sites01:49

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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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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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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

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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.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

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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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Updated: Apr 30, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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AI-Driven De Novo Binder Design: From Structure Prediction to Closed-Loop Optimization.

Xinhao Li1, Zeyu Fan1, Jiaping Yang1

  • 1Department of Biochemistry and Molecular Biology, College of Basic Medical Sciences, Naval Medical University, Shanghai 200433, China.

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Summary

Artificial intelligence is revolutionizing protein binder design. AI-driven methods offer a faster, more efficient alternative to traditional screening for developing novel protein binders for various applications.

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Area of Science:

  • Computational biology and bioinformatics
  • Protein engineering and design
  • Artificial intelligence in life sciences

Background:

  • Protein binders, such as antibodies and scaffolds (monobodies, designed ankyrin repeat proteins), are crucial for targeted therapy, diagnostics, biosensing, and synthetic biology.
  • Conventional binder discovery relies on screening methods (immunization, in vitro display) which are limited by library size, cost, and optimization challenges.

Purpose of the Study:

  • To review the technology stack for artificial intelligence (AI)-driven de novo protein binder design.
  • To present a practical workflow integrating recent advances in AI for binder development.
  • To discuss challenges and future directions in AI-based protein binder design.

Main Methods:

  • Utilizing advances in protein structure prediction, protein language models, and diffusion-based generative modeling.
  • Outlining a workflow including data resources, complex structure prediction, generative backbone design, and structure-conditioned sequence optimization.
  • Evaluating computational metrics, uncertainty management, and integrating high-throughput experimental feedback for closed-loop optimization.

Main Results:

  • Establishment of a standardized AI technology stack for de novo binder design.
  • A practical workflow for AI-driven binder design is detailed, from data resources to optimization.
  • Identification of key computational metrics and strategies for uncertainty management.

Conclusions:

  • AI-driven de novo design offers a powerful alternative to conventional screening for protein binders.
  • Key challenges remain, including induced fit, negative design, and dataset bias.
  • Future directions point towards end-to-end complex generation for protein binders.