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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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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...
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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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Mismatch Repair01:20

Mismatch Repair

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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Related Experiment Video

Updated: Mar 11, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design.

Adegoke Ojewole1, Anna Lowegard1, Pablo Gainza2

  • 1Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA.

Methods in Molecular Biology (Clifton, N.J.)
|December 4, 2016
PubMed
Summary

Computational protein design can predict future antibiotic resistance mutations. A new protocol using OSPREY software identifies novel mutations to create more effective antibiotics against drug-resistant bacteria.

Keywords:
Antibiotic resistance predictionComputational protein designOSPREYPositive and negative design

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Area of Science:

  • Computational biology and structural bioinformatics.
  • Drug discovery and development.
  • Antimicrobial resistance research.

Background:

  • Antibiotic resistance is a growing threat, reducing drug efficacy.
  • Predicting future resistance mutations is crucial for designing robust antibiotics.
  • Computational structure-based protein design (CSPD) offers a pathway for such predictions.

Purpose of the Study:

  • To present an updated protocol for predicting unseen antibiotic resistance mutations using CSPD.
  • To demonstrate the application of the OSPREY software suite for prospective resistance prediction.
  • To identify active site mutations that confer resistance while preserving enzyme function.

Main Methods:

  • Utilized the OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms.
  • Employed a combination of positive and negative design strategies.
  • Focused on the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) as a model system.

Main Results:

  • Successfully predicted viable resistance mutations in SaDHFR.
  • Demonstrated that predicted mutations confer resistance to an experimental inhibitor.
  • Showcased the enhanced capabilities of the latest OSPREY version, including improved flexibility modeling and efficient multi-state design.

Conclusions:

  • CSPD, particularly with advanced tools like OSPREY, can prospectively identify novel resistance mutations.
  • This predictive capability enables the rational design of next-generation antibiotics effective against resistant pathogens.
  • The presented protocol offers a valuable approach for pre-clinical drug development to combat antimicrobial resistance.