Probabilistic search and energy guidance for biased decoy sampling in Ab initio protein structure prediction

Kevin Molloy, Sameh Saleh, Amarda Shehu

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


Adequate sampling of the conformational space is a central challenge in ab initio protein structure prediction. In the absence of a template structure, a conformational search procedure guided by an energy function explores the conformational space, gathering an ensemble of low-energy decoy conformations. If the sampling is inadequate, the native structure may be missed altogether. Even if reproduced, a subsequent stage that selects a subset of decoys for further structural detail and energetic refinement may discard near-native decoys if they are high energy or insufficiently represented in the ensemble. Sampling should produce a decoy ensemble that facilitates the subsequent selection of near-native decoys. In this paper, we investigate a robotics-inspired framework that allows directly measuring the role of energy in guiding sampling. Testing demonstrates that a soft energy bias steers sampling toward a diverse decoy ensemble less prone to exploiting energetic artifacts and thus more likely to facilitate retainment of near-native conformations by selection techniques. We employ two different energy functions, the associative memory Hamiltonian with water and Rosetta. Results show that enhanced sampling provides a rigorous testing of energy functions and exposes different deficiencies in them, thus promising to guide development of more accurate representations and energy functions.

Original languageEnglish (US)
Article number6489975
Pages (from-to)1162-1175
Number of pages14
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number5
StatePublished - Sep 2013
Externally publishedYes


  • Protein structure prediction
  • energy bias
  • near-native conformations
  • probabilistic conformational search

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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