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Related Experiment Videos

Molecular modeling of protein function regions.

Carol DeWeese-Scott1, John Moult

  • 1Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, 9600 Gudelsky Drive, Rockville, Maryland, USA.

Proteins
|May 18, 2004
PubMed
Summary

Computer models of protein structures can predict small molecule binding, but accuracy depends on sequence identity and alignment quality. Reliable insights are possible with models based on >30% sequence identity and correct alignments.

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Area of Science:

  • Structural biology
  • Computational chemistry
  • Drug discovery

Background:

  • Experimental protein structures offer crucial insights into small molecule binding, aiding protein function understanding and drug design.
  • Assessing the reliability of computer-generated protein models for predicting ligand interactions is essential.

Purpose of the Study:

  • To evaluate the accuracy of ligand-binding information derived from comparative protein models versus experimental structures.
  • To identify factors influencing the reliability of ligand-binding predictions from protein models.

Main Methods:

  • Analysis of comparative models from CASP (Critical Assessment of protein Structure Prediction) experiments.
  • Assessment of atomic contacts between protein model atoms and experimentally determined ligand atom positions.

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  • Evaluation of error sources, focusing on sequence alignment accuracy.

    • Main Results:

    • Ligand-binding prediction accuracy from comparative models correlates with sequence identity to the template structure.
    • Sequence alignment errors between the model and template significantly degrade prediction accuracy.
    • Models based on >30% sequence identity can provide good, though not perfect, ligand-binding insights if alignments are accurate.

    Conclusions:

    • Comparative protein models can be valuable for understanding ligand binding, provided sequence identity is sufficient and alignment is correct.
    • Supports a structural genomics approach utilizing experimental structure sampling to enable reliable modeling (>30% sequence identity).