Cited by 16Protein Structure and DynamicsAdvanced Physical and Chemical Molecular InteractionsFractional Differential Equations Solutions

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Hydrodynamic Radii of Intrinsically Disordered Proteins: Fast Prediction by Minimum Dissipation Approximation and Experimental Validation

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Waszkiewicz, Radost, Michaś, Agnieszka, Białobrzewski, Michał K., Klepka, Barbara P., Cieplak-Rotowska, Maja K., Staszałek, Zuzanna, Cichocki, Bogdan, Lisicki, Maciej, Szymczak, Piotr, Niedzwiecka, Anna·2024·The Journal of Physical Chemistry Letters

Published version of a preprint

Abstract

The diffusion coefficients of globular and fully unfolded proteins can be predicted with high accuracy solely from their mass or chain length. However, this approach fails for intrinsically disordered proteins (IDPs) containing structural domains. We propose a rapid predictive methodology for estimating the diffusion coefficients of IDPs. The methodology uses accelerated conformational sampling based on self-avoiding random walks and includes hydrodynamic interactions between coarse-grained protein subunits, modeled using the generalized Rotne-Prager-Yamakawa approximation. To estimate the hydrodynamic radius, we rely on the minimum dissipation approximation recently introduced by Cichocki et al. Using a large set of experimentally measured hydrodynamic radii of IDPs over a wide range of chain lengths and domain contributions, we demonstrate that our predictions are more accurate than the Kirkwood approximation and phenomenological approaches. Our technique may prove to be valuable in predicting the hydrodynamic properties of both fully unstructured and multidomain disordered proteins.

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Figure 1 Construction of the coarse-grained globule-linker model (GLM) for an illustratory IDP, H6–SUMO–CNOT1(800–999), containing three ordered domains of different sizes (no. 28 in Table S1). (A) Sequence with highlighted ordered (orange) and disordered (blue) segments, and domain boundaries marked by square brackets. (B) Representative full atom conformation generated by AlphaFold2 (for visualization purposes only;69,70 beads with van der Waals radii; hydrogen atoms omitted for the sake of clarity). Ordered clusters (orange) form dense blobs connected with linkers (blue). (C) Visualization of a representative configuration generated using the GLM method in which beads are displayed with their hydrodynamic radii.
Figure 1 Construction of the coarse-grained globule-linker model (GLM) for an illustratory IDP, H6–SUMO–CNOT1(800–999), containing three ordered domains of different sizes (no. 28 in Table S1). (A) Sequence with highlighted ordered (orange) and disordered (blue) segments, and domain boundaries marked by square brackets. (B) Representative full atom conformation generated by AlphaFold2 (for visualization purposes only;69,70 beads with van der Waals radii; hydrogen atoms omitted for the sake of clarity). Ordered clusters (orange) form dense blobs connected with linkers (blue). (C) Visualization of a representative configuration generated using the GLM method in which beads are displayed with their hydrodynamic radii.CC BY · PubMed Central
Figure 2 Examples of normalized FCS autocorrelation curves with raw fitting residuals for an intrinsically disordered H6–SUMO–GW182SD–mCherry (N = 809; Rh = 66 ± 6 Å) (green) in comparison with apoferritin (N = 4200; Rh = 58 ± 3 Å) (black). The crystal structure of apoferritin (Protein Data Bank entry 2w0o(93)) and the putative conformation of H6–SUMO–GW182SD–mCherry predicted by AlphaFold69 are shown for the purpose of illustration, preserving the relative sizes of the solvent accessible surfaces of atoms.
Figure 2 Examples of normalized FCS autocorrelation curves with raw fitting residuals for an intrinsically disordered H6–SUMO–GW182SD–mCherry (N = 809; Rh = 66 ± 6 Å) (green) in comparison with apoferritin (N = 4200; Rh = 58 ± 3 Å) (black). The crystal structure of apoferritin (Protein Data Bank entry 2w0o(93)) and the putative conformation of H6–SUMO–GW182SD–mCherry predicted by AlphaFold69 are shown for the purpose of illustration, preserving the relative sizes of the solvent accessible surfaces of atoms.CC BY · PubMed Central
Figure 3 (A) Experimental Rh values plotted vs the number of amino acid residues in the protein chain, N, and power law curve fitted to Rh values of folded proteins (FPs) together with the 95% confidence band. (B) Direct comparison of the predicted vs measured Rh values for all of the proteins modeled using the MDA+GLM approach.
Figure 3 (A) Experimental Rh values plotted vs the number of amino acid residues in the protein chain, N, and power law curve fitted to Rh values of folded proteins (FPs) together with the 95% confidence band. (B) Direct comparison of the predicted vs measured Rh values for all of the proteins modeled using the MDA+GLM approach.CC BY · PubMed Central
Figure 4 Comparison of different methods of estimation of Rh. Boxes show interquartile ranges with median confidence bands marked by notches. MDA with GLM ensemble generation (A) performs best on the IDP benchmark set with standard errors of 18.15% and 7.09 Å (compared to 24.80% and 8.46 Å for a simple power law). Methods based on the Kirkwood–Riseman Rh estimation (C and D) typically underestimate hydrodynamic size of the molecule. Power law fits with one free parameter (E) and two free parameters (F) evaluated using leave-one-out cross-validation are compared with the formerly reported power law112 (G) and models based on polyproline II structure propensities without30 (H) and with31 (I) regard to the charge, and a sequence-based model112 (J) that takes into account the total charge of the molecule. Theoretical methods with no knowledge of the presence of domains in the IDP (ND; B and D) significantly overestimate the hydrodynamic size of the molecule. Domain data can be incorporated into our ensemble generation engine leading to more accurate estimates of Rh (A). Note that experimental uncertainty also contributes to the errors presented here and in Table 1.
Figure 4 Comparison of different methods of estimation of Rh. Boxes show interquartile ranges with median confidence bands marked by notches. MDA with GLM ensemble generation (A) performs best on the IDP benchmark set with standard errors of 18.15% and 7.09 Å (compared to 24.80% and 8.46 Å for a simple power law). Methods based on the Kirkwood–Riseman Rh estimation (C and D) typically underestimate hydrodynamic size of the molecule. Power law fits with one free parameter (E) and two free parameters (F) evaluated using leave-one-out cross-validation are compared with the formerly reported power law112 (G) and models based on polyproline II structure propensities without30 (H) and with31 (I) regard to the charge, and a sequence-based model112 (J) that takes into account the total charge of the molecule. Theoretical methods with no knowledge of the presence of domains in the IDP (ND; B and D) significantly overestimate the hydrodynamic size of the molecule. Domain data can be incorporated into our ensemble generation engine leading to more accurate estimates of Rh (A). Note that experimental uncertainty also contributes to the errors presented here and in Table 1.CC BY · PubMed Central

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Cites

Research works printed but not linked: 114

  1. 10.1146/annurev-biochem-072711-164947 · DOI · Paper page
  2. 10.1038/nrm3920 · DOI
  3. 10.1016/j.jmb.2004.02.002 · DOI
  4. 10.1016/j.sbi.2020.12.012 · DOI
  5. 10.1007/s10930-009-9201-4 · DOI
  6. 10.1002/prot.24449 · DOI
  7. 10.1073/pnas.1001743107 · DOI
  8. 10.1021/acs.jpclett.1c01607 · DOI
  9. 10.1021/acs.jpclett.0c02822 · DOI
  10. 10.1021/jz3010915 · DOI
  11. 10.1038/nature13999 · DOI
  12. 10.1073/pnas.1904997116 · DOI
  13. 10.1186/s12964-020-00626-6 · DOI
  14. 10.1002/pmic.201900036 · DOI
  15. 10.1073/pnas.1814051115 · DOI
  16. 10.1021/bi972494r · DOI
  17. 10.1002/pro.5560070720 · DOI
  18. 10.1101/gad.912401 · DOI · Paper page
  19. 10.1016/j.cell.2018.02.051 · DOI
  20. 10.1007/s13361-017-1830-9 · DOI
  21. 10.1016/j.sbi.2022.102460 · DOI
  22. 10.1038/s41580-023-00647-2 · DOI
  23. 10.1016/j.sbi.2023.102678 · DOI
  24. 10.1016/j.cell.2016.06.010 · DOI
  25. 10.1261/rna.079026.121 · DOI
  26. 10.1007/s00249-023-01683-8 · DOI · Paper page
  27. 10.1016/j.bpj.2017.06.042 · DOI
  28. 10.1016/j.bpj.2022.12.013 · DOI
  29. 10.1371/journal.pcbi.1004686 · DOI
  30. 10.1002/prot.25222 · DOI
  31. 10.1021/acs.jpcb.1c02453 · DOI
  32. 10.1021/acs.jpcb.5b09133 · DOI
  33. 10.1021/jp500776v · DOI
  34. 10.1073/pnas.0911107107 · DOI
  35. 10.1016/j.str.2010.10.006 · DOI
  36. 10.1093/bioinformatics/bts172 · DOI
  37. 10.1021/jp308984e · DOI
  38. 10.1016/j.jmb.2018.05.012 · DOI · Paper page
  39. 10.1021/acscentsci.7b00626 · DOI
  40. 10.1016/j.str.2018.10.016 · DOI
  41. 10.1021/acs.jpcb.9b02575 · DOI
  42. 10.1016/j.jmb.2019.12.027 · DOI
  43. 10.1021/jacs.0c02088 · DOI
  44. 10.1073/pnas.2111696118 · DOI
  45. 10.1038/s41586-023-07004-5 · DOI
  46. 10.1038/s41592-023-02159-5 · DOI
  47. 10.1021/ma60075a022 · DOI
  48. 10.1021/ma00230a059 · DOI
  49. 10.1140/epje/i2012-12130-x · DOI
  50. 10.1007/978-4-431-55985-6_11 · DOI
  51. 10.1021/ma50007a019 · DOI
  52. 10.1063/1.444849 · DOI
  53. Happel J., 1973, Low Reynolds Number Hydrodynamics
  54. 10.1063/1.3689842 · DOI
  55. 10.1063/1.442999 · DOI
  56. 10.1063/1.444917 · DOI
  57. 10.1021/ma00154a029 · DOI
  58. 10.1017/jfm.2019.652 · DOI
  59. 10.1038/347631a0 · DOI
  60. Doi M., 1988, The Theory of Polymer Dynamics, 73
  61. 10.1016/S0169-3107(99)80039-2 · DOI
  62. 10.1088/0953-8984/23/3/033102 · DOI
  63. 10.1063/1.4962258 · DOI
  64. Frenkel D., 2001, Understanding molecular simulation: from algorithms to applications, 1
  65. 10.1063/1.1699180 · DOI · Paper page
  66. 10.1063/1.1742462 · DOI
  67. 10.1063/1.1746947 · DOI
  68. 10.1093/bioinformatics/btu744 · DOI
  69. 10.7717/peerj.1 · DOI
  70. 10.1016/S1359-0278(98)00016-9 · DOI
  71. 10.1002/1097-0134(20010101)42:1<38::AID-PROT50>3.0.CO;2-3 · DOI
  72. 10.1016/j.sbi.2015.03.008 · DOI
  73. 10.1016/j.bpj.2017.04.021 · DOI
  74. 10.1021/ja01118a065 · DOI
  75. 10.1017/jfm.2013.668 · DOI
  76. 10.1017/S002211207000215X · DOI
  77. 10.1017/S0022112079001464 · DOI
  78. 10.1007/BF01601334 · DOI
  79. 10.1017/jfm.2021.350 · DOI
  80. 10.1002/pol.1954.120120102 · DOI
  81. 10.1063/1.1564047 · DOI
  82. 10.3390/ijms24021444 · DOI
  83. Kim S., 1991, Microhydrodynamics: Principles and Selected Applications
  84. 10.1016/0378-4371(82)90127-3 · DOI
  85. 10.1063/1.466366 · DOI
  86. 10.1063/1.1670977 · DOI
  87. 10.1063/1.1673799 · DOI
  88. 10.1016/j.bpj.2018.07.015 · DOI
  89. 10.21468/SciPostPhysCodeb.11 · DOI
  90. 10.1016/j.jinorgbio.2012.02.031 · DOI
  91. 10.1139/o85-102 · DOI
  92. 10.1021/bi992382v · DOI
  93. 10.1021/bi0019545 · DOI
  94. 10.1002/mrc.1132 · DOI
  95. 10.1006/viro.2001.1296 · DOI
  96. 10.1074/jbc.M300518200 · DOI
  97. 10.1110/ps.051368005 · DOI
  98. 10.1016/j.molcel.2004.11.019 · DOI
  99. 10.1021/pr070237v · DOI
  100. 10.1016/j.jmb.2008.03.039 · DOI
  101. 10.1073/pnas.0809222105 · DOI
  102. 10.1529/biophysj.107.126995 · DOI
  103. 10.1371/journal.pone.0011684 · DOI
  104. 10.1016/j.str.2011.01.011 · DOI
  105. 10.1002/prot.24692 · DOI
  106. 10.1016/j.jmb.2016.11.017 · DOI
  107. 10.1016/j.bbapap.2017.08.019 · DOI
  108. 10.1016/j.jsbmb.2018.09.008 · DOI
  109. 10.1016/j.bpj.2010.02.006 · DOI
  110. 10.1103/PhysRevLett.39.95 · DOI
  111. 10.1016/j.str.2005.04.009 · DOI
  112. 10.1007/s00249-023-01629-0 · DOI
  113. 10.1021/bi991765q · DOI
  114. 10.1093/nar/gkad947 · DOI

Metadata

Authors
Waszkiewicz, R., Michaś, A., Białobrzewski, M. K., Klepka, B. P., Cieplak-Rotowska, M. K., Staszałek, Z., Cichocki, B., Lisicki, M., Szymczak, P., & Niedzwiecka, A.
Affiliations
  • University of Warsaw (PL)
  • Institute of Physics (PL)
  • Institute of Physics, Polish Academy of Sciences, Aleja Lotnikow 32/46, PL-02668 Warsaw, Poland
Corresponding author
Szymczak, Piotr
Venue
The Journal of Physical Chemistry Letters
Published
2024-05-02
Volume
15
Issue
19
Pages
5024-5033
ISSN
1948-7185 (ISSN-L)
OpenAlex
W4396593132
Cited by
16
References
3
Citations by year
2026: 4 · 2025: 10 · 2024: 2
FWCI
2.18
Type
journal article
Language
en
Open access
Locations
  • The Journal of Physical Chemistry Letters (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • European Commission (POIG.02.02.00-00-025/09)
  • Narodowe Centrum Nauki (2016/22/E/NZ1/00656, 2018/31/D/ST3/02408)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
  • Physical Sciences › Mathematics › Modeling and Simulation › Fractional Differential Equations Solutions
Keywords
intrinsically disordered proteins, hydrodynamic radius, diffusion coefficient prediction, hydrodynamic interactions, self-avoiding walks, conformational sampling
MeSH terms
Diffusion, Protein Conformation, Hydrodynamics, Intrinsically Disordered Proteins

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