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Related Concept Videos

Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

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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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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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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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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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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

Updated: Jun 29, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity

Xihe Qiu1, Haoyu Wang1, Xiaoyu Tan2

  • 1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China.

Computers in Biology and Medicine
|March 29, 2024
PubMed
Summary

G-K BertDTA improves drug-target affinity prediction by integrating protein structure, molecular semantics, and graph topology. This novel framework enhances accuracy and generalization for drug discovery, outperforming existing methods.

Keywords:
DTA predictionDrug properties miningDrug-target affinityGraph attention networksMolecular semantics

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Drug development is expensive and risky, with drug-target affinity (DTA) being a key predictor.
  • Current computational DTA models often lack protein structural and molecular semantic information, limiting accuracy.
  • Existing methods like string-based and single graph neural networks show limitations in handling spatial context and semantic features.

Purpose of the Study:

  • To develop a novel computational framework, G-K BertDTA, for accurate drug-target affinity prediction.
  • To integrate protein structural features, molecular semantic information, and molecular topological data into a unified model.
  • To enhance the accuracy and generalization capabilities of DTA prediction for improved drug screening and development.

Main Methods:

  • Representing drug molecules as graphs using a Graph Isomorphism Network (GIN) for topological feature learning.
  • Utilizing a DenseNet architecture for extracting protein structural features.
  • Incorporating a knowledge-based BERT semantic model to obtain rich pre-trained semantic embeddings for enhanced feature representation.

Main Results:

  • G-K BertDTA demonstrates superior performance on benchmark datasets (KIBA and Davis) compared to existing state-of-the-art methods.
  • The integrated approach effectively addresses limitations of previous models regarding spatial context and semantic information.
  • Experimental results show significant improvements in prediction accuracy, reducing root mean square error (RMSE) and misclassifications.

Conclusions:

  • G-K BertDTA offers a more accurate and robust approach to drug-target affinity prediction.
  • The framework's ability to incorporate diverse feature types enhances its applicability in drug discovery pipelines.
  • This method holds promise for accelerating the identification and development of safe and effective medicines.