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PLIC: protein-ligand interaction clusters.

Praveen Anand¹, Deepesh Nagarajan, Sumanta Mukherjee

¹Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India and IISc Mathematics Initiative, Indian Institute of Science, Banglaore 560012, Karnataka, India.

Database : the Journal of Biological Databases and Curation

|April 26, 2014

Summary

This summary is machine-generated.

This study introduces the PLIC database, classifying over 84,000 protein-ligand binding sites using the PocketMatch algorithm and Markov clustering to reveal patterns in molecular recognition for drug design and protein engineering.

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Identifying Protein-protein Interaction Sites Using Peptide Arrays

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Published on: November 18, 2014

Area of Science:

Structural Biology
Computational Biology
Bioinformatics

Background:

Protein-ligand interactions are fundamental to biological processes.
Understanding these interactions aids drug design and protein engineering.
Existing databases lack comprehensive annotation of protein-ligand interaction attributes.

Purpose of the Study:

To develop a comprehensive database classifying protein-ligand binding sites.
To analyze and categorize binding site similarities and attributes.
To provide a resource for understanding molecular recognition.

Main Methods:

Utilized the PocketMatch algorithm for rapid, in-house comparison of 84,846 ligand-binding sites.
Constructed a similarity network and applied Markov Clustering (MCL) to group similar sites.
Analyzed binding site characteristics, atomic contacts, and energetics within clusters.

Main Results:

Identified and clustered discrete sets of similar binding sites based on structural and residue attributes.
Characterized interactions within clusters, including pocket shape, residue nature, atomic probes, contacts, and water molecules.
Developed the Protein-Ligand Interaction Clusters (PLIC) database.

Conclusions:

The PLIC database offers a novel resource for studying protein-ligand interactions.
The classification provides insights into molecular recognition principles.
This resource can advance drug design, protein engineering, and understanding protein function.