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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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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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Graphlet signature-based scoring method to estimate protein-ligand binding affinity.

Omkar Singh1, Kunal Sawariya1, Polamarasetty Aparoy1

  • 1Centre for Computational Biology and Bioinformatics , Central University of Himachal Pradesh , Dharamshala, Himachal Pradesh 176215, India.

Royal Society Open Science
|June 12, 2015
PubMed
Summary

This study introduces a novel scoring method, graphlet signature uniqueness score (GSUS), to quantify protein-ligand interactions and predict ligand affinity. GSUS analyzes residue interaction networks, showing promise in drug discovery by correlating network properties with binding activity.

Keywords:
binding affinitydockinggraphlet signatureinteraction network

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Area of Science:

  • Computational chemistry
  • Biophysics
  • Drug discovery

Background:

  • Receptor-ligand interactions are crucial in biological processes and drug development.
  • Understanding these interactions computationally aids in designing effective therapeutics.
  • Existing methods for quantifying ligand affinity have limitations.

Purpose of the Study:

  • To investigate the relationship between induced sub-graphs in residue interaction networks and ligand activity.
  • To develop a novel computational method for quantifying protein-ligand interactions and predicting ligand affinity.
  • To assess the efficacy of the developed method using known drug targets.

Main Methods:

  • Analysis of protein-ligand interactions using residue interaction networks.
  • Application of graphlet signature-based analysis to quantify neighbourhood connectivity.
  • Development of the graphlet signature uniqueness score (GSUS) based on amino acid variability during inhibitor binding.
  • Validation using hydrogen bond networks of COX-2 and CA-II inhibitors.

Main Results:

  • The GSUS method demonstrated a consistent correlation with pIC50 values for both COX-2 and CA-II inhibitors.
  • GSUS outperformed Autodock results in predicting ligand activity.
  • The scoring method showed improved accuracy for molecules with similar structures and diverse activities, and vice versa.

Conclusions:

  • The GSUS scoring method provides a robust approach for quantifying protein-ligand interactions and predicting ligand affinity.
  • This method can be a valuable tool, used independently or in conjunction with existing techniques, for advancing drug discovery efforts.
  • The study highlights the potential of network analysis in understanding and predicting molecular interactions in biological systems.