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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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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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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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Protein-Drug Binding: Mechanism and Kinetics01:16

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Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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Protein Binding Pocket Optimization for Virtual High-Throughput Screening (vHTS) Drug Discovery.

Dimitris Gazgalis1, Mehreen Zaka1,2, Bilal Haider Abbasi2

  • 1Department of Pharmaceutical Sciences, Northeastern University School of Pharmacy, Boston, Massachusetts 02115, United States.

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Virtual high-throughput screening (vHTS) can miss drug candidates. A new Monte Carlo Pocket Optimization (MCPO) method refines protein targets to reduce false negatives and improve drug discovery success rates.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Structural biology

Background:

  • Virtual high-throughput screening (vHTS) is vital for identifying drug leads but faces challenges with receptor flexibility and false negatives.
  • Inaccurate protein target conformations, especially in apo structures or homology models, lead to small binding pockets and high false negative rates.
  • Ligand-induced fit effects in holo structures also contribute to false negatives, as binding pocket shape is ligand-dependent.

Purpose of the Study:

  • To develop a novel computational approach to optimize protein target conformations for virtual screening.
  • To reduce false negative rates in virtual high-throughput screening (vHTS) to improve the efficiency of drug discovery.
  • To enhance the reliability of vHTS, particularly when using apo structures or homology models.

Main Methods:

  • Developed a Monte Carlo-based approach named Monte Carlo Pocket Optimization (MCPO).
  • MCPO systematically optimizes the binding pocket of protein targets to better represent biologically relevant conformations.
  • The method was evaluated on multiple datasets to assess its performance in improving vHTS accuracy.

Main Results:

  • The Monte Carlo Pocket Optimization (MCPO) approach demonstrated promising results in optimizing protein binding pockets.
  • The method showed potential in reducing false negative rates commonly encountered in virtual screening.
  • MCPO enhances the accuracy of identifying potential drug lead compounds.

Conclusions:

  • The developed Monte Carlo Pocket Optimization (MCPO) approach is a valuable tool for improving virtual high-throughput screening (vHTS).
  • This method is particularly beneficial when working with limited or potentially inaccurate protein target structures like apo forms or homology models.
  • MCPO can significantly enhance the success rate of drug discovery pipelines by reducing false negatives.