Using temperature coefficients to support resonance assignment of intrinsically disordered proteins

The resonance assignment of large intrinsically disordered proteins (IDPs) is difficult due to the low dispersion of chemical shifts (CSs). Luckily, CSs are often specific for certain residue types, which makes the task easier. Our recent work showed that the CS-based spin-system classification can be improved by applying a linear discriminant analysis (LDA). In this paper, we extend a set of classification parameters by adding temperature coefficients (TCs), i.e., rates of change of chemical shifts with temperature. As demonstrated previously by other groups, the TCs in IDPs depend on a residue type, although the relation is often too complex to be predicted theoretically. Thus, we propose an approach based on experimental data; CSs and TCs values of residues assigned using conventional methods serve as a training set for LDA, which then classifies the remaining resonances. The method is demonstrated on a large fragment (1-239) of highly disordered protein Tau. We noticed that adding TCs to sets of chemical shifts significantly improves the recognition efficiency. For example, it allows distinguishing between lysine and glutamic acid, as well as valine and isoleucine residues based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup><mml:mrow><mml:mtext>H</mml:mtext></mml:mrow> <mml:mtext>N</mml:mtext></mml:msup> </mml:math> , N, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mtext>C</mml:mtext> <mml:mi>α</mml:mi></mml:msub> </mml:math> and C <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mrow></mml:mrow> <mml:mrow></mml:mrow> <mml:mo>'</mml:mo></mml:mmultiscripts> </mml:math> data. Moreover, adding TCs to CSs of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup><mml:mrow><mml:mtext>H</mml:mtext></mml:mrow> <mml:mtext>N</mml:mtext></mml:msup> </mml:math> , N, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mtext>C</mml:mtext> <mml:mi>α</mml:mi></mml:msub> </mml:math> , and C <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mrow></mml:mrow> <mml:mrow></mml:mrow> <mml:mo>'</mml:mo></mml:mmultiscripts> </mml:math> is more beneficial than adding <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mtext>C</mml:mtext> <mml:mi>β</mml:mi></mml:msub> </mml:math> CSs. Our program for LDA analysis is available at https://github.com/gugumatz/LDA-Temp-Coeff .

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Journal
Journal of Biomolecular NMR
Published
2024-12-07
DOI
https://doi.org/10.1007/s10858-024-00452-9
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
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article

Using temperature coefficients to support resonance assignment of intrinsically disordered proteins

Anna Zawadzka‐Kazimierczuk, Agustín Romero, Markus Zweckstetter, Krzysztof Kazimierczuk et al.
Journal of Biomolecular NMR
Protein Structure and Dynamics
article

Using temperature coefficients to support resonance assignment of intrinsically disordered proteins

Anna Zawadzka‐Kazimierczuk, Agustín Romero, Markus Zweckstetter, Krzysztof Kazimierczuk, Christian F. Pantoja, Paulina Putko
article en

Abstract

The resonance assignment of large intrinsically disordered proteins (IDPs) is difficult due to the low dispersion of chemical shifts (CSs). Luckily, CSs are often specific for certain residue types, which makes the task easier. Our recent work showed that the CS-based spin-system classification can be improved by applying a linear discriminant analysis (LDA). In this paper, we extend a set of classification parameters by adding temperature coefficients (TCs), i.e., rates of change of chemical shifts with temperature. As demonstrated previously by other groups, the TCs in IDPs depend on a residue type, although the relation is often too complex to be predicted theoretically. Thus, we propose an approach based on experimental data; CSs and TCs values of residues assigned using conventional methods serve as a training set for LDA, which then classifies the remaining resonances. The method is demonstrated on a large fragment (1-239) of highly disordered protein Tau. We noticed that adding TCs to sets of chemical shifts significantly improves the recognition efficiency. For example, it allows distinguishing between lysine and glutamic acid, as well as valine and isoleucine residues based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup><mml:mrow><mml:mtext>H</mml:mtext></mml:mrow> <mml:mtext>N</mml:mtext></mml:msup> </mml:math> , N, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mtext>C</mml:mtext> <mml:mi>α</mml:mi></mml:msub> </mml:math> and C <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mrow></mml:mrow> <mml:mrow></mml:mrow> <mml:mo>'</mml:mo></mml:mmultiscripts> </mml:math> data. Moreover, adding TCs to CSs of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup><mml:mrow><mml:mtext>H</mml:mtext></mml:mrow> <mml:mtext>N</mml:mtext></mml:msup> </mml:math> , N, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mtext>C</mml:mtext> <mml:mi>α</mml:mi></mml:msub> </mml:math> , and C <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mrow></mml:mrow> <mml:mrow></mml:mrow> <mml:mo>'</mml:mo></mml:mmultiscripts> </mml:math> is more beneficial than adding <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mtext>C</mml:mtext> <mml:mi>β</mml:mi></mml:msub> </mml:math> CSs. Our program for LDA analysis is available at https://github.com/gugumatz/LDA-Temp-Coeff .

Journal of Biomolecular NMRVol. 79(1)
Kazimierz Wielki University in Bydgoszcz (PL), German Center for Neurodegenerative Diseases (DE), Max Planck Institute for the Study of Religious and Ethnic Diversity (DE), Max Planck Institute for Biophysical Chemistry (DE), Max Planck Institute of Experimental Medicine (DE), University of Warsaw (PL)
Narodowe Centrum Nauki
Reduced inequalities
Openalex Percentile: Top 80%
Protein Structure and Dynamics
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