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Related Concept Videos

Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Protein glycosylation starts in the ER lumen and continues in the Golgi apparatus. Glycosyltransferases catalyze the addition of sugar molecules or glycosylation of proteins. Usually, these enzymes add sugars to the hydroxyl groups of selected serine or threonine residues to form O-linked glycans or the amino groups of asparagine residues to form N-linked glycans. Different positions on the same polypeptide chain can contain differently linked glycans.
Multiple sugar molecules that may or may...

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Targeting Selectivity: Improving Golgi α-Mannosidase II (GMII) Inhibitors Through In Silico Studies.

Nieves G Ledesma1, Carlos T Nieto1, Alejandro Manchado1

  • 1Departamento de Química Orgánica, Facultad de Ciencias Químicas, Universidad de Salamanca, 37008 Salamanca, Spain.

Biomolecules
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Developing safer cancer therapies requires precise targeting of Golgi α-mannosidase II (GMII). Computational methods enable the design of selective GMII inhibitors, overcoming toxicity issues associated with earlier compounds.

Keywords:
Golgi α-mannosidase IIglycosidase inhibitorsmolecular dockingmolecular dynamicsquantum mechanicsselectivitystructure-based drug designswainsoninevirtual screening

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Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Aberrant glycosylation is a cancer hallmark, making Golgi α-mannosidase II (GMII) a therapeutic target.
  • Swainsonine shows anticancer effects but causes toxicity due to off-target inhibition of lysosomal α-mannosidase (LMan).

Purpose of the Study:

  • To review computational methodologies for developing selective GMII inhibitors.
  • To guide the design of safer anticancer drugs by optimizing inhibitor specificity.

Main Methods:

  • Survey of computational techniques from Molecular Docking to Quantum Mechanics (QM) and Molecular Dynamics (MD).
  • Analysis of metalloenzyme flexibility, energetics, and inhibitor-enzyme interactions.
  • Application of pKa predictions and electronic structure calculations for designing targeted inhibitors.

Main Results:

  • Selectivity can be achieved by exploiting structural differences, pH gradients, and substrate conformations.
  • Designing "electrostatic switches" ensures neutral binding at Golgi pH and repulsion in lysosomes.
  • Targeting specific enzyme sites and using conformationally restricted scaffolds are effective strategies.

Conclusions:

  • Advanced computational approaches are crucial for developing next-generation GMII inhibitors.
  • Integrating dynamic sampling and energetic profiling enhances the design of safe and selective drugs.
  • Precision optimization overcomes swainsonine's toxicity limitations for improved cancer therapy.