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Related Concept Videos

Factors Affecting Protein-Drug Binding: Protein-Related Factors01:20

Factors Affecting Protein-Drug Binding: Protein-Related Factors

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Drug binding to proteins is a key aspect of pharmacokinetics and can influence a drug's distribution, absorption, and elimination in the body. Several factors, including the drug's physiochemical properties, protein concentration, disease states, and the number of binding sites on the protein, influence this process.
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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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Related Experiment Video

Updated: Feb 15, 2026

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity
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Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation.

Kyle A Barlow1, Shane Ó Conchúir2,3, Samuel Thompson4

  • 1Graduate Program in Bioinformatics , University of California San Francisco , San Francisco , California , United States of America.

The Journal of Physical Chemistry. B
|February 6, 2018
PubMed
Summary

We developed flex ddG, a computational method to predict protein-protein interaction changes. This approach improves accuracy by modeling conformational flexibility, outperforming existing methods on diverse mutation types.

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Area of Science:

  • Computational Biology
  • Protein Engineering
  • Biophysics

Background:

  • Accurate prediction of protein-protein interaction (PPI) changes upon mutation is crucial for understanding biological processes and designing novel proteins.
  • Existing computational methods often struggle to capture the conformational plasticity inherent in protein interactions, limiting their predictive power.

Purpose of the Study:

  • To develop and validate a novel computational method, flex ddG, within the Rosetta suite to predict interface binding free energy changes (ΔΔG) by incorporating conformational sampling.
  • To assess the performance of flex ddG against existing methods using a large benchmark dataset.

Main Methods:

  • Developed flex ddG, a Rosetta-based method that samples conformational diversity using the 'backrub' algorithm.
  • Applied torsion minimization, side chain repacking, and ensemble averaging to estimate interface ΔΔG values.
  • Utilized a generalized additive model (GAM) for nonlinear reweighting of the Rosetta energy function.

Main Results:

  • flex ddG demonstrated superior performance compared to existing methods on a benchmark set of 1240 mutants.
  • Significant improvements were observed for challenging mutation types, including small-to-large side chain changes, multiple simultaneous mutations, stabilizing mutations, and antibody-antigen interfaces.
  • The GAM reweighting improved agreement with experimental data but underscored the need for further energy function refinement.

Conclusions:

  • Computational modeling of conformational plasticity, as implemented in flex ddG, significantly enhances the accuracy of predicting interface binding free energy changes.
  • flex ddG provides a robust tool for large-scale prediction and perturbation of protein-protein interactions, particularly for complex mutation scenarios.
  • Future advancements in protein energy functions are essential for further improving the accuracy of computational predictions.