APOLLO: A quality assessment service for single and multiple protein models

Zheng Wang, Jesse Eickholt, Jianlin Cheng

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure.

Original languageEnglish (US)
Article numberbtr268
Pages (from-to)1715-1716
Number of pages2
Issue number12
StatePublished - Jun 2011
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


Dive into the research topics of 'APOLLO: A quality assessment service for single and multiple protein models'. Together they form a unique fingerprint.

Cite this