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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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PSnpBind: a database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual

Ammar Ammar1, Rachel Cavill2, Chris Evelo3

  • 1Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands. a.ammar@maastrichtuniversity.nl.

Journal of Cheminformatics
|March 1, 2022
PubMed
Summary

A new database, PSnpBind, was created to study how genetic variations in drug targets affect drug efficacy. This resource contains 0.6 million mutated protein-ligand complexes to aid drug design and personalized medicine research.

Keywords:
AutoDock VinaBinding affinityBinding pocketDatabaseMutation effectREST APISNPVirtual screening

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Area of Science:

  • Pharmacogenomics
  • Computational Chemistry
  • Drug Discovery

Background:

  • Understanding how genetic variations (single-nucleotide polymorphisms or SNPs) in drug targets influence drug response is crucial for personalized medicine.
  • Existing datasets for studying these effects are limited, hindering the development of predictive models.
  • A comprehensive resource is needed to analyze the impact of binding site mutations on protein-ligand binding affinity.

Purpose of the Study:

  • To construct a large-scale reference dataset of protein-ligand complexes with mutated binding sites.
  • To provide a resource for developing and validating machine learning models for predicting drug-target interactions and SNP effects.
  • To facilitate research into inter-individual variability in drug response.

Main Methods:

  • A molecular docking approach was employed to generate a dataset of protein-ligand complexes.
  • A multithreaded virtual screening workflow was utilized for efficient data generation.
  • The database, PSnpBind, was constructed containing 0.6 million mutated binding site protein-ligand complexes.

Main Results:

  • PSnpBind comprises 0.6 million mutated protein-ligand complexes, representing a significant expansion of available data.
  • The database covers a wide range of binding pocket mutations and small molecules.
  • A web interface and REST API were developed for data exploration and programmatic access.

Conclusions:

  • PSnpBind provides an unprecedented resource for studying the impact of genetic variations on drug binding affinity.
  • The database is valuable for advancing machine learning applications in drug discovery and pharmacogenomics.
  • PSnpBind is open-source and freely available, promoting wider research accessibility.