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Proteus: a random forest classifier to predict disorder-to-order transitioning binding regions in intrinsically disordered proteins
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. University of Calcutta, India.
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-3772-8279
2017 (English)In: Journal of Computer-Aided Molecular Design, ISSN 0920-654X, E-ISSN 1573-4951, Vol. 31, no 5, 453-466 p.Article in journal (Refereed) Published
Abstract [en]

The focus of the computational structural biology community has taken a dramatic shift over the past one-and-a-half decades from the classical protein structure prediction problem to the possible understanding of intrinsically disordered proteins (IDP) or proteins containing regions of disorder (IDPR). The current interest lies in the unraveling of a disorder-to-order transitioning code embedded in the amino acid sequences of IDPs/ IDPRs. Disordered proteins are characterized by an enormous amount of structural plasticity which makes them promiscuous in binding to different partners, multi-functional in cellular activity and atypical in folding energy landscapes resembling partially folded molten globules. Also, their involvement in several deadly human diseases (e.g. cancer, cardiovascular and neurodegenerative diseases) makes them attractive drug targets, and important for a biochemical understanding of the disease(s). The study of the structural ensemble of IDPs is rather difficult, in particular for transient interactions. When bound to a structured partner, an IDPR adapts an ordered conformation in the complex. The residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus operandi of an IDP/IDPR would be to predict these residues. There are a few available methods which predict these protean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents Proteus, a random forest classifier that predicts the likelihood of a residue undergoing a disorder-toorder transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55 vs. 27%) with a much higher precision on an independent data set. The current study also sheds some light on a possible disorderto-order transitioning consensus, untangled, yet embedded in the amino acid sequence of IDPs. Some guidelines have also been suggested for proceeding with a real-life structural modeling involving an IDPR using Proteus.

Place, publisher, year, edition, pages
SPRINGER , 2017. Vol. 31, no 5, 453-466 p.
Keyword [en]
Intrinsic disorder; Protean; Random forest; Disorder-to-order transition; Topography length
National Category
Medicinal Chemistry
Identifiers
URN: urn:nbn:se:liu:diva-138275DOI: 10.1007/s10822-017-0020-yISI: 000401265900003PubMedID: 28365882OAI: oai:DiVA.org:liu-138275DiVA: diva2:1109100
Note

Funding Agencies|Swedish Research Council [VR-NT 2012-5270]; Swedish e-Science Research Center (SeRC); Department of Science and Technology - Science and Engineering Research Board, India (DST-SERB) [PDF/2015/001079]

Available from: 2017-06-13 Created: 2017-06-13 Last updated: 2017-06-13

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