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Related Concept Videos

Ligand Binding Sites02:40

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...
Ligand Binding Sites02:40

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...
Conserved Binding Sites01:49

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 analyses the...
Ligand Binding and Linkage00:49

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 the...
Ligand Binding and Linkage00:49

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 the...
The Equilibrium Binding Constant and Binding Strength02:18

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: Jun 10, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Lead generation and optimization based on protein-ligand complementarity.

Koji Ogata1, Tetsu Isomura, Shinji Kawata

  • 1Centre for Computational Biology, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.

Molecules (Basel, Switzerland)
|July 27, 2010
PubMed
Summary
This summary is machine-generated.

This study presents a computational method for designing novel drug leads by analyzing protein-ligand interactions. The approach successfully identified potential inhibitors for key protein kinases, with one synthesized compound showing confirmed activity.

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
07:33

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

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Last Updated: Jun 10, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
07:33

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Published on: October 15, 2018

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Structural biology

Background:

  • Structure-based drug design is crucial for identifying novel therapeutic agents.
  • Understanding protein-ligand interactions guides the optimization of lead compounds.

Purpose of the Study:

  • To develop and validate a computational procedure for structure-based lead generation and optimization.
  • To design novel inhibitor molecules for serine/threonine protein kinases, including p38 MAP kinase, p42 MAP kinase (ERK2), and c-Jun N-terminal kinase 3 (JNK3).

Main Methods:

  • Utilized a computational procedure based on the complementarity of protein-ligand interactions.
  • Designed structurally similar compounds by considering combinations of atomic species around known ligand heavy atom positions.
  • Employed molecular mechanics force fields for energetic evaluations and compound ranking.

Main Results:

  • The computational method generated potential inhibitors for p38 MAP kinase, ERK2, and JNK3.
  • Predicted inhibitor scores correlated well with experimental IC50 and Ki values.
  • A synthesized JNK3 inhibitor candidate demonstrated confirmed inhibitory activity against ATP hydrolysis.

Conclusions:

  • The proposed computational procedure is an effective tool for generating chemically diverse molecules.
  • The method aids in identifying active compounds against known protein targets, facilitating drug discovery.