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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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Interacting with GPCRs: Using Interaction Fingerprints for Virtual Screening.

Eelke B Lenselink1, Willem Jespers1, Herman W T van Vlijmen1

  • 1Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University , 2333 CC Leiden, The Netherlands.

Journal of Chemical Information and Modeling
|September 15, 2016
PubMed
Summary
This summary is machine-generated.

Interaction fingerprints (IFPs) like SPLIF improve virtual screening for G protein-coupled receptors (GPCRs). These methods offer better ligand affinity prediction than traditional docking, especially for large GPCR binding pockets.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Pharmacology

Background:

  • G protein-coupled receptors (GPCRs) structural data is expanding, enhancing understanding of receptor function and ligand recognition.
  • Structure-based virtual screening (SBVS) is successful for GPCRs, but docking scores often lack correlation with ligand affinity.
  • Interaction fingerprints (IFPs) offer explicit protein-ligand interaction analysis, serving as alternatives or complements to docking.

Purpose of the Study:

  • To compare the performance of five different interaction fingerprints (IFPs) for G protein-coupled receptors (GPCRs).
  • To determine the optimal IFP and its application strategy for virtual screening of GPCRs.
  • To evaluate the enhancement of virtual screening enrichment using IFPs compared to traditional methods.

Main Methods:

  • Comparative analysis of five IFPs (Deng, Credo, Elements, SYBYL, SPLIF) on five representative GPCRs.
  • Implementation of extensions to original IFP methods.
  • Tuning of the SPLIF method using parameters like number of poses, fingerprint similarity coefficient, and structural ensembles.

Main Results:

  • More detailed IFPs, specifically SYBYL and SPLIF, demonstrated superior performance over Deng, Credo, and Elements.
  • Tuned SPLIF achieved significantly higher virtual screening enrichments compared to initial results and 2D-similarity methods.
  • The study identified SPLIF as a promising IFP for GPCR virtual screening.

Conclusions:

  • Detailed interaction fingerprints, particularly SPLIF, enhance virtual screening enrichment for GPCRs.
  • IFPs are anticipated to be increasingly utilized alongside docking, especially for GPCRs with large binding pockets.
  • The findings provide guidance on selecting and applying IFPs for GPCR drug discovery.