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Descriptor Data Bank (DDB): A Cloud Platform for Multiperspective Modeling of Protein-Ligand Interactions.

Hossam M Ashtawy1, Nihar R Mahapatra1

  • 1Department of Electrical and Computer Engineering, Michigan State University , East Lansing, Michigan 48824-1226, United States.

Journal of Chemical Information and Modeling
|December 1, 2017
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Summary

We developed the Descriptor Data Bank (DDB), a cloud platform for protein-ligand (PL) interaction modeling. Multiperspective modeling using DDB significantly improves binding affinity prediction accuracy by over 15%.

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Area of Science:

  • Computational biology
  • Drug discovery
  • Bioinformatics

Background:

  • Protein-ligand (PL) interactions are crucial for biological processes, involving forces like hydrogen bonding and hydrophobic effects.
  • Existing models for these interactions are fragmented, limiting accurate prediction of binding affinity.
  • A unified approach is needed to leverage diverse hypotheses for improved PL interaction modeling.

Purpose of the Study:

  • To introduce the Descriptor Data Bank (DDB), a cloud-based platform for multiperspective modeling of PL interactions.
  • To integrate diverse descriptor extraction tools and machine learning for enhanced prediction of binding affinity.
  • To develop and validate novel protein-specific descriptors for improved interaction modeling.

Main Methods:

  • Developed DDB, an open-access platform hosting descriptor extraction tools and data for PL interaction hypotheses.
  • Implemented a machine learning (ML) toolbox for descriptor filtering, analysis, and scoring function (SF) fitting.
  • Generated over 2700 descriptors from 16 tools, including novel protein-specific descriptors based on structural alignments.
  • Fit SFs using DDB's ML library and evaluated them on drug screening-relevant datasets.

Main Results:

  • Multiperspective SFs built with diverse DDB descriptors outperformed single-perspective models by over 15% on average.
  • The descriptor filtering module successfully reduced irrelevant and noisy features.
  • Novel protein-specific descriptors demonstrably improved the accuracy of SFs.
  • DDB facilitates collaborative development and sharing of PL interaction modeling tools and data.

Conclusions:

  • Multiperspective modeling using the DDB platform significantly enhances the accuracy of protein-ligand binding affinity prediction.
  • The DDB platform provides a valuable resource for the computational biology and drug discovery communities.
  • Protein-specific descriptors represent a promising avenue for improving the precision of molecular interaction modeling.