POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins

Jakob Toudahl Nielsen; Frans A.A. Mulder

doi:10.1007/s10858-018-0166-5

POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins

Jakob Toudahl Nielsen^*, Frans A.A. Mulder

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

111 Citations (Scopus)

Abstract

Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25–78%, with the largest improvements observed for ¹Hα and ¹³C′. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URL http://www.protein-nmr.org.

Original language	English
Journal	Journal of Biomolecular NMR
Volume	70
Issue	3
Pages (from-to)	141-165
Number of pages	25
ISSN	0925-2738
DOIs	https://doi.org/10.1007/s10858-018-0166-5
Publication status	Published - 20 Mar 2018

Keywords

Chemical shift
Intrinsically disordered proteins
Random coil
Software

Access to Document

10.1007/s10858-018-0166-5

Cite this

@article{3139cd18e5b04fba9a596dad9cd734f5,

title = "POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins",

abstract = "Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25–78%, with the largest improvements observed for 1Hα and 13C′. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URL http://www.protein-nmr.org.",

keywords = "Chemical shift, Intrinsically disordered proteins, Random coil, Software",

author = "Nielsen, {Jakob Toudahl} and Mulder, {Frans A.A.}",

year = "2018",

month = mar,

day = "20",

doi = "10.1007/s10858-018-0166-5",

language = "English",

volume = "70",

pages = "141--165",

journal = "Journal of Biomolecular NMR",

issn = "0925-2738",

publisher = "Springer Science and Business Media B.V.",

number = "3",

}

POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins. / Nielsen, Jakob Toudahl; Mulder, Frans A.A.
In: Journal of Biomolecular NMR, Vol. 70, No. 3, 20.03.2018, p. 141-165.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

TY - JOUR

T1 - POTENCI

T2 - prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins

AU - Nielsen, Jakob Toudahl

AU - Mulder, Frans A.A.

PY - 2018/3/20

Y1 - 2018/3/20

N2 - Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25–78%, with the largest improvements observed for 1Hα and 13C′. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URL http://www.protein-nmr.org.

AB - Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25–78%, with the largest improvements observed for 1Hα and 13C′. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URL http://www.protein-nmr.org.

KW - Chemical shift

KW - Intrinsically disordered proteins

KW - Random coil

KW - Software

UR - http://www.scopus.com/inward/record.url?scp=85041601564&partnerID=8YFLogxK

U2 - 10.1007/s10858-018-0166-5

DO - 10.1007/s10858-018-0166-5

M3 - Journal article

C2 - 29399725

AN - SCOPUS:85041601564

SN - 0925-2738

VL - 70

SP - 141

EP - 165

JO - Journal of Biomolecular NMR

JF - Journal of Biomolecular NMR

IS - 3

ER -

POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this