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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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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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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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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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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The Equilibrium Binding Constant and Binding Strength02:18

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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Related Experiment Video

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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A simple spatial extension to the extended connectivity interaction features for binding affinity prediction.

Oghenejokpeme I Orhobor1, Abbi Abdel Rehim1, Hang Lou2

  • 1Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.

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|May 16, 2022
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Summary

Improving machine learning for protein-ligand binding affinity prediction involves enhancing feature representations like Extended Connectivity Interaction Features (ECIF) with discretized distances. Careful selection of resampling methods is crucial for accurate hyperparameter tuning and reliable model benchmarking.

Keywords:
machine learningprotein binding affinity predictionscoring functions

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Area of Science:

  • Computational chemistry
  • Machine learning in drug discovery
  • Structural bioinformatics

Background:

  • Accurate prediction of protein-ligand binding affinity is essential for drug discovery.
  • Machine learning models require effective representations of protein-ligand complexes.
  • Extended Connectivity Interaction Features (ECIF) are a known representation method.

Purpose of the Study:

  • To investigate the impact of incorporating discretized distances into ECIF for binding affinity prediction.
  • To evaluate the influence of resampling methods on hyperparameter optimization and model performance.
  • To enhance the accuracy and reliability of machine learning models in this domain.

Main Methods:

  • Modification of the Extended Connectivity Interaction Features (ECIF) scheme to include discretized distances between protein-ligand atom pairs.
  • Utilizing gradient boosted trees for binding affinity prediction.
  • Employing various resampling techniques for hyperparameter selection and model evaluation.

Main Results:

  • Including discretized distances in the ECIF representation significantly improved predictive accuracy for binding affinity.
  • The choice of resampling method during hyperparameter optimization demonstrated a substantial impact on the predictive performance of the models.
  • These findings highlight the importance of both feature engineering and robust evaluation strategies.

Conclusions:

  • Enhanced ECIF with discretized distances offer a more accurate approach for machine learning-based binding affinity prediction.
  • Careful consideration and selection of resampling techniques are critical for reliable model benchmarking and development.
  • This work contributes to improving the computational tools used in rational drug design.