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Related Concept Videos

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...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

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Updated: Jun 10, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

Predicting resistance mutations using protein design algorithms.

Kathleen M Frey1, Ivelin Georgiev, Bruce R Donald

  • 1Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA.

Proceedings of the National Academy of Sciences of the United States of America
|July 21, 2010
PubMed
Summary
This summary is machine-generated.

Protein design algorithms can predict drug resistance mutations. This approach helps develop more effective drugs by anticipating and overcoming mutations in targets like Staphylococcus aureus dihydrofolate reductase.

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Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

  • Biochemistry
  • Structural Biology
  • Drug Discovery

Background:

  • Drug resistance due to target mutations is a significant challenge, limiting the efficacy and lifespan of many pharmaceuticals.
  • Investigating resistance mutations post-clinical exposure is reactive; proactive strategies during drug development are needed to predict and mitigate resistance.

Purpose of the Study:

  • To prospectively apply an ensemble-based protein design algorithm (K*) to predict resistance mutations in Staphylococcus aureus dihydrofolate reductase (SA-DHFR).
  • To utilize positive design to preserve catalytic function and negative design to disrupt inhibitor binding for SA-DHFR mutants.
  • To validate the predictive power of protein design algorithms in anticipating drug resistance mechanisms.

Main Methods:

  • Employed K* ensemble-based protein design algorithm for prospective prediction of resistance mutations in SA-DHFR.
  • Incorporated positive design principles to maintain enzyme catalytic activity.
  • Utilized negative design principles to engineer reduced inhibitor binding affinity.
  • Conducted enzyme inhibition assays to quantify the activity and inhibitor affinity of predicted mutants.
  • Determined the crystal structure of a top-ranked mutant enzyme to validate predicted structural changes.

Main Results:

  • Three out of four highly-ranked predicted SA-DHFR mutants exhibited retained enzymatic activity.
  • These active mutants displayed significantly reduced binding affinity for the lead inhibitor, with 18-, 9-, and 13-fold decreases.
  • The crystal structure of the top-ranked mutant confirmed the predicted residue conformations and elucidated the structural basis for diminished inhibitor potency.

Conclusions:

  • Protein design algorithms, such as K*, can effectively predict clinically relevant drug resistance mutations.
  • This prospective approach can be integrated into early drug design strategies to preemptively address target-based resistance.
  • The methodology offers a powerful tool for developing more robust therapeutics against enzymes susceptible to mutational resistance.