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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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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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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Protein-Ligand Docking in the Machine-Learning Era.

Chao Yang1, Eric Anthony Chen1, Yingkai Zhang1,2

  • 1Department of Chemistry, New York University, New York, NY 10003, USA.

Molecules (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

Molecular docking is crucial for drug discovery, with scoring functions guiding its success. This review covers recent advances in protein-ligand scoring functions, including machine learning, and their application in structure-based virtual screening.

Keywords:
datasetsdeep learningmachine learningmolecular dockingprotein–ligand scoring functionvirtual screening

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Bioinformatics

Background:

  • Molecular docking is integral to early-stage drug discovery, impacting virtual screening and lead optimization.
  • The accuracy of molecular docking heavily relies on the performance of protein-ligand scoring functions.

Purpose of the Study:

  • To provide a comprehensive overview of recent developments in protein-ligand scoring functions.
  • To discuss the application of docking in structure-based virtual screening (VS) and hit-to-lead optimization.
  • To highlight the progress and impact of machine learning in scoring function development.

Main Methods:

  • Review of recent literature on scoring function development and docking applications.
  • Discussion of strategies and resources for structure-based VS.
  • Assessment and comparison of classical and machine learning-based scoring functions.
  • Analysis of machine learning approaches, including descriptor-based models and deep learning.

Main Results:

  • Recent scoring function development shows significant progress, particularly with machine learning approaches.
  • Structure-based VS workflows involve structure preparation, binding site detection, docking, and post-docking analysis.
  • Machine learning scoring functions offer improved predictive power compared to classical methods.

Conclusions:

  • Advancements in scoring functions, especially machine learning, enhance the reliability of molecular docking for drug discovery.
  • Effective implementation of structure-based VS protocols is essential for successful drug candidate identification.
  • Continued research in scoring function development is vital for accelerating the drug discovery pipeline.