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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
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Locating Large, Flexible Ligands on Proteins.

Jean-Noël Grad1, Alba Gigante2, Christoph Wilms1

  • 1Bioinformatics and Computational Biophysics, Faculty of Biology, University of Duisburg-Essen , Universitätstraße 7, 45141 Essen, Germany.

Journal of Chemical Information and Modeling
|December 22, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel energy grid (EG) method for efficiently mapping large, flexible protein ligands. The approach accurately predicts ligand binding sites, complementing traditional methods like molecular dynamics simulations.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • Biologically significant ligands are often large, flexible, and charged, binding to extensive protein surface regions.
  • Standard methods like docking and molecular dynamics (MD) simulations are often infeasible or costly for these complex interactions.

Purpose of the Study:

  • To develop an efficient computational method for locating large, flexible ligands on protein surfaces.
  • To provide an alternative to standard docking and MD simulations for analyzing complex protein-ligand interactions.

Main Methods:

  • Scanning protein surfaces with smaller ligand fragments on a spatial and angular grid.
  • Utilizing fast Fourier transform-accelerated algorithms for efficient energy grid (EG) computation.
  • Incorporating protein and ligand flexibility into the energy calculations.

Main Results:

  • The energy grid (EG) approach successfully mapped diverse protein-large ligand complexes.
  • Results showed good agreement with experimental data and molecular dynamics (MD) simulations.
  • The method demonstrated efficacy across various systems, including protein-heparin and protein-ATP complexes.

Conclusions:

  • The energy grid (EG) method offers an efficient and accurate alternative for studying large, flexible ligand-protein interactions.
  • This approach can be transformed into probability distributions for direct comparison with experimental and simulation data.
  • The EG method shows broad applicability in structural biology and drug discovery.