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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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Structure-based protein-ligand interaction fingerprints for binding affinity prediction.

Debby D Wang1, Moon-Tong Chan2, Hong Yan3

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd, Shanghai 200093, China.

Computational and Structural Biotechnology Journal
|December 13, 2021
PubMed
Summary
This summary is machine-generated.

Binding affinity prediction (BAP) is vital for drug design. This study reviews interaction fingerprints (IFPs) and finds atom-pair-counts and substructure IFPs show great potential for accurate BAP using machine learning.

Keywords:
Computer-aided drug designInteraction fingerprintMachine learningProtein–ligand binding affinityScoring function

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Area of Science:

  • Computational chemistry
  • Structural bioinformatics
  • Drug discovery

Background:

  • Binding affinity prediction (BAP) is critical for computer-aided drug design.
  • Accurate BAP remains a significant challenge in the field.
  • Machine-learning scoring functions (SFs) utilize various descriptors for BAP.

Purpose of the Study:

  • To review a wide range of protein-ligand interaction fingerprint (IFP) models.
  • To compare representative IFP-based SFs for BAP.
  • To identify promising IFP types for efficient and accurate BAP.

Main Methods:

  • Adopted a building-block-based taxonomy to categorize IFP models.
  • Reviewed existing literature on IFP development and application.
  • Compared performance of IFP-based SFs in target-specific and generic scoring tasks.

Main Results:

  • Protein-ligand interaction fingerprints (IFPs) offer competitive advantages due to simple representations and detailed interaction profiles.
  • Atom-pair-counts-based IFPs demonstrated strong potential in scoring tasks.
  • Substructure-based IFPs also showed significant promise for BAP.

Conclusions:

  • IFPs are valuable descriptors for machine-learning-based BAP.
  • Atom-pair-counts and substructure IFPs are particularly effective for improving BAP accuracy.
  • This review provides insights into IFP models for advancing drug design.