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Ligand Binding Sites02:40

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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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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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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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Protein Ligand Complex Guided Approach for Virtual Screening.

Muthukumarasamy Karthikeyan1, Deepak Pandit, Renu Vyas

  • 1Digital Information Resource Centre (DIRC) & Centre of Excellence in Scientific Computing (CoESC) CSIR-National Chemical Laboratory Pune - 411008 India. m.karthikeyan@ncl.res.in.

Combinatorial Chemistry & High Throughput Screening
|July 4, 2015
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Summary

A new toolkit, J-ProLiNE, analyzes protein-ligand networks for drug design. This enables knowledge-based virtual screening by identifying selective scaffolds for various protein targets.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Drug Discovery

Background:

  • Protein-ligand association data is underutilized in drug design and virtual screening.
  • Existing methods lack comprehensive analysis of complex protein-ligand interactions.
  • Developing advanced tools is crucial for exploiting this data.

Purpose of the Study:

  • To develop and present the Java-based open-source toolkit, J-ProLiNE (Protein Ligand Network Extraction).
  • To analyze protein-ligand complex data for drug design and virtual screening applications.
  • To identify promiscuous and selective scaffolds for multiple protein targets.

Main Methods:

  • Developed J-ProLiNE with automated sequence alignment and similarity search components.
  • Extracted and analyzed 10,000 proteins with co-crystallized ligands from PDB and MOAD databases.
  • Generated protein-ligand networks and interaction matrices, including kinase-related data from US patents.

Main Results:

  • Successfully generated protein-ligand networks revealing target-ligand-scaffold relationships.
  • Identified promiscuous and selective scaffolds across different protein target classes.
  • Constructed disease-gene-ligand-scaffold networks using kinase data.

Conclusions:

  • J-ProLiNE facilitates comprehensive protein-ligand complex analysis.
  • The toolkit enables knowledge-based virtual screening for inhibitor design.
  • Identified scaffolds can guide the development of selective drugs.