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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.
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Ligand-gated ion channels are transmembrane proteins that play a vital role in intercellular communication and functions of the nervous system. They allow the influx of ions across the membrane once the neurotransmitter binds, allowing the subsequent transmission of electrical excitation across the neurons. Other ligand-gated ion channels, like the γ-aminobutyric acid (GABA) receptor, permit anions like chloride into the cells on the binding of the GABA molecule. Their entry into the cell...
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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 the...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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G Protein-coupled Receptors01:15

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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
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Modeling Ligands into Maps Derived from Electron Cryomicroscopy
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Deep learning-guided ligand generation for the strigolactone receptor ShHTL7.

Yu Li1, Xing Wang1, Yongxin Shuai2

  • 1State Key Laboratory for Development and Utilization of Forest Food Resources, Zhejiang A&F University, Hangzhou 311300, PR China; College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, PR China.

Computational Biology and Chemistry
|June 15, 2026
PubMed
Summary

Researchers developed a computational method to find new ShHTL7-targeting molecules for controlling the parasitic weed Striga hermonthica. This approach aids in discovering selective chemical regulators to protect crops in sub-Saharan Africa.

Keywords:
Deep learningMolecular dockingMolecular dynamics simulationREINVENT4ShHTL7Striga hermonthicaStrigolactone receptor

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Area of Science:

  • Agricultural Science
  • Computational Chemistry
  • Molecular Biology

Background:

  • Striga hermonthica is a major parasitic weed impacting crop yields in sub-Saharan Africa.
  • The strigolactone receptor ShHTL7 is crucial for Striga seed germination and a potential target for weed control.

Purpose of the Study:

  • To develop a computational workflow for discovering ShHTL7-targeted ligands.
  • To identify potential selective chemical regulators for Striga hermonthica.

Main Methods:

  • Integrated deep learning (REINVENT4) and transfer learning for ligand generation.
  • Multi-parameter screening including physicochemical properties, ADMET, molecular docking, and simulations.
  • Molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) analysis for binding energetics.

Main Results:

  • A computational framework successfully generated and prioritized ShHTL7-targeted ligand candidates.
  • Docking and molecular dynamics simulations indicated favorable interactions and stable complexes for selected ligands.
  • The compound inh-117 showed promising binding energetics and residue contributions compared to a known ligand.

Conclusions:

  • The study presents an effective computational strategy for prioritizing ShHTL7 ligands.
  • This framework can guide experimental research towards developing selective Striga hermonthica regulators.
  • The findings contribute to sustainable agriculture by offering new avenues for parasitic weed management.