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Structural propensity database of proteins

Kamil Tamiola, Matthew M Heberling, Jan Domanski
doi: https://doi.org/10.1101/144840
Kamil Tamiola
1Peptone - The Protein Intelligence Company, Amsterdam, Hullenbergweg 280, 1101BV, The Netherlands
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  • For correspondence: kamil@peptone.io
Matthew M Heberling
1Peptone - The Protein Intelligence Company, Amsterdam, Hullenbergweg 280, 1101BV, The Netherlands
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Jan Domanski
1Peptone - The Protein Intelligence Company, Amsterdam, Hullenbergweg 280, 1101BV, The Netherlands
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Abstract

An overwhelming amount of experimental evidence suggests that elucidations of protein function, interactions, and pathology are incomplete without inclusion of intrinsic protein disorder and structural dynamics. Thus, to expand our understanding of intrinsic protein disorder, we have created a database of secondary structure (SS) propensities for proteins (dSPP) as a reference resource for experimental research and computational biophysics. The dSPP comprises SS propensities of 7,094 unrelated proteins, as gauged from NMR chemical shift measurements in solution and solid state. Here, we explain the concept of SS propensity and analyze dSPP entries of therapeutic relevance, α-synuclein, MOAG-4, and the ZIKA NS2B-NS3 complex to show: (1) how propensity mapping generates novel structural insights into intrinsically disordered regions of pathologically relevant proteins, (2) how computational biophysics tools can benefit from propensity mapping, and (3) how the residual disorder estimation based on NMR chemical shifts compares with sequence-based disorder predictors. This work demonstrates the benefit of propensity estimation as a method that reports both on protein structure, lability, and disorder.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted June 01, 2017.
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Structural propensity database of proteins
Kamil Tamiola, Matthew M Heberling, Jan Domanski
bioRxiv 144840; doi: https://doi.org/10.1101/144840
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Structural propensity database of proteins
Kamil Tamiola, Matthew M Heberling, Jan Domanski
bioRxiv 144840; doi: https://doi.org/10.1101/144840

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