It is generally known that, unlike structured proteins, intrinsically disordered proteins, IDPs, exhibit various structures and conformers, the so-called conformational ensemble, CoE. This study aims to better understand the conformers that make up the IDP ensemble by decomposing the CoE into groups separated by their radius of gyration, Rg. A common approach to studying CoE for IDPs is to use low-resolution techniques, such as small-angle scattering, and combine those with computer simulations on different length scales. Herein, the well-studied antimicrobial saliva protein histatin 5 was utilized as a model peptide for an IDP; the average intensity curves were obtained from small-angle X-ray scattering; and compared with fully atomistic, explicit water, molecular dynamics simulations; then, the intensity curve was decomposed with respect to the different Rg values; and their secondary structure propensities were investigated. We foresee that this approach can provide important information on the CoE and the individual conformers within; in that case, it will serve as an additional tool for understanding the IDP structure-function relationship on a more detailed level.

Division of Computational Chemistry Department of Chemistry Lund University P O Box 124 SE 221 00 Lund Sweden

Faculty of Pharmacy in Hradec Králové Charles University Akademika Heyrovského 1203 8 500 05 Hradec Králové Czech Republic

NanoLund Lund University Box 118 22100 Lund Sweden

Zobrazit více v PubMed

Oldfield C. J.; Dunker A. K. Intrinsically disordered proteins and intrinsically disordered protein regions. Annu. Rev. Biochem. 2014, 83, 553–584. 10.1146/annurev-biochem-072711-164947. PubMed DOI

Tompa P. Intrinsically unstructured proteins. Trends Biochem. Sci. 2002, 27, 527–533. 10.1016/S0968-0004(02)02169-2. PubMed DOI

Eliezer D. Biophysical characterization of intrinsically disordered proteins. Curr. Opin. Struct. Biol. 2009, 19, 23–30. 10.1016/j.sbi.2008.12.004. PubMed DOI PMC

Uversky V. N. Unusual biophysics of intrinsically disordered proteins. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 2013, 1834, 932–951. 10.1016/j.bbapap.2012.12.008. PubMed DOI

Fisher C. K.; Stultz C. M. Constructing ensembles for intrinsically disordered proteins. Curr. Opin. Struct. Biol. 2011, 21, 426–431. 10.1016/j.sbi.2011.04.001. PubMed DOI PMC

Kachala M.; Valentini E.; Svergun D. I. In Intrinsically Disordered Proteins Studied by NMR Spectroscopy; Felli I. C., Pierattelli R., Eds.; Springer International Publishing: Cham, Switzerland, 2015; pp 261–289.

Liquid-Liquid Phase Coexistence and Membraneless Organelles; Keating C. D., Ed.; Methods in Enzymology, Vol. 646; Academic Press, 2021; pp 185–222.

Johansen D.; Jeffries C. M. J.; Hammouda B.; Trewhella J.; Goldenberg D. P. Effects of Macromolecular Crowding on an Intrinsically Disordered Protein Characterized by Small-Angle Neutron Scattering with Contrast Matching. Biophys. J. 2011, 100, 1120–1128. 10.1016/j.bpj.2011.01.020. PubMed DOI PMC

Bernado P.; Blackledge M. A Self-Consistent Description of the Conformational Behavior of Chemically Denatured Proteins from NMR and Small Angle Scattering. Biophys. J. 2009, 97, 2839–2845. 10.1016/j.bpj.2009.08.044. PubMed DOI PMC

Cragnell C.; Staby L.; Lenton S.; Kragelund B. B.; Skepö M. Dynamical Oligomerisation of Histidine Rich Intrinsically Disordered Proteins Is Regulated through Zinc-Histidine Interactions. Biomolecules 2019, 9, 168.10.3390/biom9050168. PubMed DOI PMC

Rieloff E.; Tully M. D.; Skepö M. Assessing the Intricate Balance of Intermolecular Interactions upon Self-Association of Intrinsically Disordered Proteins. J. Mol. Biol. 2019, 431, 511–523. 10.1016/j.jmb.2018.11.027. PubMed DOI

Henriques J.; Arleth L.; Lindorff-Larsen K.; Skepö M. On the calculation of SAXS profiles of folded and intrinsically disordered proteins from computer simulations. J. Mol. Biol. 2018, 430, 2521–2539. 10.1016/j.jmb.2018.03.002. PubMed DOI

Bernado P.; Svergun D. I. Structural analysis of intrinsically disordered proteins by small-angle X-ray scattering. Molecular Biosystems 2012, 8, 151–167. 10.1039/C1MB05275F. PubMed DOI

Abraham M. J.; Murtola T.; Schulz R.; Pall S.; Smith J. C.; Hess B.; Lindahl E. Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1–2, 19–25. 10.1016/j.softx.2015.06.001. DOI

Berendsen H. J. C.; Postma J. P. M.; van Gunsteren W. F.; DiNola A.; Haak J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984, 81, 3684.10.1063/1.448118. DOI

Lindorff-Larsen K.; Piana S.; Palmo K.; Maragakis P.; Klepeis J.; Dror R.; Shaw D. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 2010, 78, 1950–1958. 10.1002/prot.22711. PubMed DOI PMC

Piana S.; Donchev A. G.; Robustelli P.; Shaw D. E. Water Dispersion Interactions Strongly Influence Simulated Structural Properties of Disordered Protein States. J. Phys. Chem. B 2015, 119, 5113–5348. 10.1021/jp508971m. PubMed DOI

Hanwell M. D.; Curtis D. E.; Lonie D. C.; Vandermeersch T.; Zurek E.; Hutchison G. R. Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics 2012, 4, 17.10.1186/1758-2946-4-17. PubMed DOI PMC

Darden T.; York D.; Pedersen L. Particle mesh Ewald: AnNxlog(N) method for Ewald sums in large systems. J. Chem. Phys. 1993, 98, 10089.10.1063/1.464397. DOI

Hess B.; Bekker H.; Berendsen H. J. C.; Fraaije J. G. E. M. LINCS: A linear constraint solver for molecular simulations. Journal of Computantional Chemistry 1997, 18, 1463–1472. 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H. DOI

Evans D. J.; Holian B. L. The Noose-Hover thermostat. J. Chem. Phys. 1985, 83, 4069.10.1063/1.449071. DOI

Parrinello M.; Rahman A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 1981, 52, 7182.10.1063/1.328693. DOI

Daura X.; Gademann K.; Jaun B.; Seebach D.; van Gunsteren W. F.; Mark A. E. Peptide Folding: When Simulation Meets Experiment. Angew. Chem. 1999, 38, 236–240. 10.1002/(SICI)1521-3773(19990115)38:1/2<236::AID-ANIE236>3.0.CO;2-M. DOI

Svergun D.; Barberato C.; Koch M. H. J. CRYSOL– a Program to Evaluate X-ray Solution Scattering of Biological Macromolecules from Atomic Coordinates. J. Appl. Crystallogr. 1995, 28, 768–773. 10.1107/S0021889895007047. DOI

Manalastas-Cantos K.; Konarev P. V.; Hajizadeh N. R.; Kikhney A. G.; Petoukhov M. V.; Molodenskiy D. S.; Panjkovich A.; Mertens H. D. T.; Gruzinov A.; Borges C.; Jeffries C. M.; et al. ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis. J. Appl. Crystallogr. 2021, 54, 343–355. 10.1107/S1600576720013412. PubMed DOI PMC

Hoffmann M.; Scherer M.; Hempel T.; Mardt A.; de Silva B.; Husic B. E.; Klus S.; Wu H.; Kutz N.; Brunton S. L.; et al. Deeptime: a Python library for machine learning dynamical models from time series data. Machine Learning: Science and Technology 2022, 3, 01500910.1088/2632-2153/ac3de0. DOI

Scherer M. K.; Trendelkamp-Schroer B.; Paul F.; Pérez-Hernández G.; Hoffmann M.; Plattner N.; Wehmeyer C.; Prinz J.-H.; Noé F. PyEMMA 2: A software package for estimation, validation, and analysis of Markov models. J. Chem. Theory Comput. 2015, 11, 5525–5542. 10.1021/acs.jctc.5b00743. PubMed DOI

McGibbon R. T.; Beauchamp K. A.; Harrigan M. P.; Klein C.; Swails J. M.; Hernández C. X.; Schwantes C. R.; Wang L.-P.; Lane T. J.; Pande V. S. MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys. J. 2015, 109, 1528–1532. 10.1016/j.bpj.2015.08.015. PubMed DOI PMC

Bakker M. J.; Svensson O.; Sorensen H. V.; Skepo M. Exploring the Functional Landscape of the p53 Regulatory Domain: The Stabilizing Role of Post-Translational Modifications. J. Chem. Theory Comput. 2024, 20, 5842.10.1021/acs.jctc.4c00570. PubMed DOI PMC

Bakker M. J.; So̷rensen H. V.; Skepo M. Exploring the Role of Globular Domain Locations on an Intrinsically Disordered Region of p53: A Molecular Dynamics Investigation. J. Chem. Theory Comput. 2024, 20, 1423–1433. 10.1021/acs.jctc.3c00971. PubMed DOI PMC

Pearson K. X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 1900, 50, 157–175. 10.1080/14786440009463897. DOI

Henriques J.; Cragnell C.; Skepö M. Molecular Dynamics Simulations of Intrinsically Disordered Proteins: Force Field Evaluation and Comparison with Experiment. J. Chem. Theory Comput. 2015, 11, 3420–3431. 10.1021/ct501178z. PubMed DOI

Jephthah S.; Pesce F.; Lindorff-Larsen K.; Skepö M. Force field effects in simulations of flexible peptides with varying polyproline II propensity. J. Chem. Theory Comput. 2021, 17, 6634–6646. 10.1021/acs.jctc.1c00408. PubMed DOI PMC

Henriques J.; Skepo M. Molecular dynamics simulations of intrinsically disordered proteins: on the accuracy of the TIP4P-D water model and the representativeness of protein disorder models. J. Chem. Theory Comput. 2016, 12, 3407–3415. 10.1021/acs.jctc.6b00429. PubMed DOI

Jephthah S.; Staby L.; Kragelund B.; Skepö M. Temperature dependence of intrinsically disordered proteins in simulations: What are we missing?. J. Chem. Theory Comput. 2019, 15, 2672–2683. 10.1021/acs.jctc.8b01281. PubMed DOI

Cragnell C.; Durand D.; Cabane B.; Skepö M. Coarse-grained modeling of the intrinsically disordered protein Histatin 5 in solution: Monte Carlo simulations in combination with SAXS. Proteins: Struct., Funct., Bioinf. 2016, 84, 777–791. 10.1002/prot.25025. PubMed DOI

Singh H.; Ahmad S. Context dependent reference states of solvent accessibility derived from native protein structures and assessed by predictability analysis. BMC Structural Biology 2009, 9, 25.10.1186/1472-6807-9-25. PubMed DOI PMC