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Bioavailability is a critical factor in determining a drug's effectiveness. It refers to the proportion of a drug that enters the circulation when introduced into the body and is, as a result, able to have an active effect. Enhancing bioavailability is essential for drugs with poor solubility, as it can significantly impact their therapeutic efficacy. Various methods are employed to increase the solubility of drugs, thereby enhancing their bioavailability.Micronization and nanonization are...
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Solubis: optimize your protein.

Greet De Baets1, Joost Van Durme1, Rob van der Kant2

  • 1VIB Switch Laboratory, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium and Vrije Universiteit Brussel, 1050 Brussels, Belgium.

Bioinformatics (Oxford, England)
|March 21, 2015
PubMed
Summary
This summary is machine-generated.

Protein aggregation, linked to disease and therapeutic challenges, can be prevented by optimizing aggregation-prone regions. The Solubis method automates point mutations to reduce aggregation propensity and enhance protein stability.

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Area of Science:

  • Biochemistry
  • Protein Science
  • Biotechnology

Background:

  • Protein aggregation is a hallmark of misfolding diseases and a significant challenge for therapeutic protein development.
  • Aggregation arises from aggregation-prone regions (APRs) within a protein's amino acid sequence.
  • Preventing aggregation requires minimizing APRs' intrinsic propensity and enhancing their stability within the native protein structure.

Purpose of the Study:

  • To present an automated method for protein engineering aimed at preventing aggregation.
  • To identify and select point mutations that reduce the intrinsic aggregation propensity of APRs.
  • To improve the local thermodynamic stability of APRs within the folded protein structure.

Main Methods:

  • Development of the Solubis method for automated selection of point mutations.
  • In silico analysis of protein sequences to identify aggregation-prone regions.
  • Computational prediction of mutation effects on aggregation propensity and local stability.

Main Results:

  • The Solubis method successfully automates the identification of beneficial point mutations.
  • Selected mutations effectively minimize the intrinsic aggregation propensity of APRs.
  • The method also enhances the local thermodynamic stability of these critical regions.

Conclusions:

  • The Solubis method offers an efficient approach to engineer proteins with reduced aggregation risk.
  • Optimizing APRs' intrinsic properties and local stability is crucial for preventing protein aggregation.
  • This automated strategy holds promise for improving the developability of therapeutic proteins.