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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

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Reducing docking score variations arising from input differences.

Miklos Feher1, Christopher I Williams

  • 1Campbell Family Institute for Breast Cancer Research, University Health Network, Toronto Medical Discovery Tower, Toronto, ON, M5G 1L7, Canada. mfeher@uhnres.utoronto.ca

Journal of Chemical Information and Modeling
|August 12, 2010
PubMed
Summary
This summary is machine-generated.

Molecular docking variability stems from conformational search and input sensitivity. Researchers suggest using fewer input conformations and selecting top poses for reproducible docking results.

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Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Drug discovery

Background:

  • Molecular docking is crucial for predicting ligand-target interactions.
  • Variability in docking outcomes can hinder reliable predictions.
  • Understanding sources of variability is key to improving docking accuracy.

Purpose of the Study:

  • To investigate the causes of variability in molecular docking results.
  • To compare the performance of different docking programs (GOLD, Glide, FlexX, Surflex).
  • To provide guidelines for enhancing the reproducibility of docking studies.

Main Methods:

  • Studied docking result variability across GOLD, Glide, FlexX, and Surflex.
  • Analyzed the impact of ligand input conformations on docking outcomes.
  • Assessed the contributions of conformational search adequacy and input sensitivity.

Main Results:

  • Two primary sources of variability identified: conformational search and sensitivity to input perturbations.
  • Both factors significantly impact docking results across most programs.
  • Magnitude of variability varies considerably between different target systems.

Conclusions:

  • Reproducible docking requires careful consideration of conformational search and input data.
  • Using a limited set of input conformations and selecting top-scoring poses can improve reliability.
  • Guidelines are provided to minimize variability and enhance docking reproducibility for specific software.