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A Protocol for Computer-Based Protein Structure and Function Prediction
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Improved pose and affinity predictions using different protocols tailored on the basis of data availability.

Philip Prathipati1, Chioko Nagao2, Shandar Ahmad2

  • 1National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki City, Osaka, 567-0085, Japan. philip@nibiohn.go.jp.

Journal of Computer-Aided Molecular Design
|October 8, 2016
PubMed
Summary

We evaluated drug design protocols for HSP90 and MAP4K4 targets in the D3R challenge. Our methods achieved reasonable pose and affinity predictions for HSP90 and top-ranked affinity predictions for MAP4K4.

Keywords:
Data-optimal protocolDockingGLMNETKinetic stabilityQSARScoringThermodynamic stability

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Structural biology

Background:

  • The D3R 2015 grand drug design challenge provided blinded datasets for evaluating pose and affinity prediction protocols.
  • Two protein targets, HSP90 and MAP4K4, were selected for this study, representing systems with varying degrees of available structural and SAR data.

Purpose of the Study:

  • To assess the efficacy of distinct computational strategies for molecular pose and binding affinity prediction.
  • To benchmark prediction performance on well-characterized (HSP90) and novel (MAP4K4) protein targets.

Main Methods:

  • For HSP90, an integrated docking and scoring approach combining pharmacophoric and heavy atom similarity, local minimization, and QSAR modeling was employed.
  • For MAP4K4, an exhaustive pose and affinity prediction protocol using PLANTS software, incorporating side-chain flexibility and protein-ligand fingerprints, was implemented.

Main Results:

  • HSP90: Reasonable pose prediction (RMSD ~1.4-1.8 Å) and affinity prediction (AUC=0.702, R=0.45).
  • MAP4K4: Poor pose prediction (RMSD ~4.3-4.7 Å) but strong affinity prediction (AUC=0.728, R=0.67), ranking 1st among 80 submissions.

Conclusions:

  • The integrated approach demonstrated effectiveness for well-studied targets like HSP90.
  • The PLANTS-based protocol showed promise for novel targets like MAP4K4, particularly in affinity prediction, despite challenges in pose prediction.