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Research Article

Direct inference of protein–DNA interactions using compressed sensing methods

Mohammed AlQuraishi and Harley H. McAdams
  1. aDepartment of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305;
  2. bDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305; and
  3. cDepartment of Statistics, Stanford University, Stanford, CA 94305

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PNAS September 6, 2011 108 (36) 14819-14824; https://doi.org/10.1073/pnas.1106460108
Mohammed AlQuraishi
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Harley H. McAdams
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  • For correspondence: hmcadams@stanford.edu
  1. Edited* by Stephen J. Benkovic, Pennsylvania State University, University Park, PA, and approved July 6, 2011 (received for review April 25, 2011)

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Abstract

Compressed sensing has revolutionized signal acquisition, by enabling complex signals to be measured with remarkable fidelity using a small number of so-called incoherent sensors. We show that molecular interactions, e.g., protein–DNA interactions, can be analyzed in a directly analogous manner and with similarly remarkable results. Specifically, mesoscopic molecular interactions act as incoherent sensors that measure the energies of microscopic interactions between atoms. We combine concepts from compressed sensing and statistical mechanics to determine the interatomic interaction energies of a molecular system exclusively from experimental measurements, resulting in a “de novo” energy potential. In contrast, conventional methods for estimating energy potentials are based on theoretical models premised on a priori assumptions and extensive domain knowledge. We determine the de novo energy potential for pairwise interactions between protein and DNA atoms from (i) experimental measurements of the binding affinity of protein–DNA complexes and (ii) crystal structures of the complexes. We show that the de novo energy potential can be used to predict the binding specificity of proteins to DNA with approximately 90% accuracy, compared to approximately 60% for the best performing alternative computational methods applied to this fundamental problem. This de novo potential method is directly extendable to other biomolecule interaction domains (enzymes and signaling molecule interactions) and to other classes of molecular interactions.

  • DNA motifs
  • structural biology
  • machine learning
  • protein–DNA binding
  • DNA binding sites

Footnotes

  • ↵1To whom correspondence should be addressed. E-mail: hmcadams{at}stanford.edu.
  • Author contributions: M.A. designed research; M.A. performed research; M.A. analyzed data; and M.A. and H.H.M. wrote the paper.

  • The authors declare no conflict of interest.

  • *This Direct Submission article had a prearranged editor.

  • See Commentary on page 14713.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1106460108/-/DCSupplemental.

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Direct inference of protein–DNA interactions using compressed sensing methods
Mohammed AlQuraishi, Harley H. McAdams
Proceedings of the National Academy of Sciences Sep 2011, 108 (36) 14819-14824; DOI: 10.1073/pnas.1106460108

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Direct inference of protein–DNA interactions using compressed sensing methods
Mohammed AlQuraishi, Harley H. McAdams
Proceedings of the National Academy of Sciences Sep 2011, 108 (36) 14819-14824; DOI: 10.1073/pnas.1106460108
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  • Biological Sciences
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