Are Current Atomistic Force Fields Accurate Enough to Study Proteins in Crowded Environments?

The high concentration of macromolecules in the crowded cellular interior influences different thermodynamic and kinetic properties of proteins, including their structural stabilities, intermolecular binding affinities and enzymatic rates. Moreover, various structural biology methods, such as NMR or different spectroscopies, typically involve samples with relatively high protein concentration. Due to large sampling requirements, however, the accuracy of classical molecular dynamics (MD) simulations in capturing protein behavior at high concentration still remains largely untested. Here, we use explicit-solvent MD simulations and a total of 6.4 µs of simulated time to study wild-type (folded) and oxidatively damaged (unfolded) forms of villin headpiece at 6 mM and 9.2 mM protein concentration. We first perform an exhaustive set of simulations with multiple protein molecules in the simulation box using GROMOS 45a3 and 54a7 force fields together with different types of electrostatics treatment and solution ionic strengths. Surprisingly, the two villin headpiece variants exhibit similar aggregation behavior, despite the fact that their estimated aggregation propensities markedly differ. Importantly, regardless of the simulation protocol applied, wild-type villin headpiece consistently aggregates even under conditions at which it is experimentally known to be soluble. We demonstrate that aggregation is accompanied by a large decrease in the total potential energy, with not only hydrophobic, but also polar residues and backbone contributing substantially. The same effect is directly observed for two other major atomistic force fields (AMBER99SB-ILDN and CHARMM22-CMAP) as well as indirectly shown for additional two (AMBER94, OPLS-AAL), and is possibly due to a general overestimation of the potential energy of protein-protein interactions at the expense of water-water and water-protein interactions. Overall, our results suggest that current MD force fields may distort the picture of protein behavior in biologically relevant crowded environments.

[1]  Pedro M. Valero-Mora,et al.  ggplot2: Elegant Graphics for Data Analysis , 2010 .

[2]  T. Head-Gordon,et al.  Optimizing solute-water van der Waals interactions to reproduce solvation free energies. , 2012, The journal of physical chemistry. B.

[3]  C. Dobson,et al.  Protein misfolding, functional amyloid, and human disease. , 2006, Annual review of biochemistry.

[4]  Carsten Kutzner,et al.  GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. , 2008, Journal of chemical theory and computation.

[5]  M. Thielges,et al.  Dynamics of the folded and unfolded villin headpiece (HP35) measured with ultrafast 2D IR vibrational echo spectroscopy , 2011, Proceedings of the National Academy of Sciences.

[6]  Hai-Meng Zhou,et al.  Two-dimensional infrared correlation spectroscopy study of sequential events in the heat-induced unfolding and aggregation process of myoglobin. , 2003, Biophysical journal.

[7]  K. Lindorff-Larsen,et al.  How robust are protein folding simulations with respect to force field parameterization? , 2011, Biophysical journal.

[8]  Michele Vendruscolo,et al.  Prediction of "aggregation-prone" and "aggregation-susceptible" regions in proteins associated with neurodegenerative diseases. , 2005, Journal of molecular biology.

[9]  Yuji Sugita,et al.  Reduced native state stability in crowded cellular environment due to protein-protein interactions. , 2013, Journal of the American Chemical Society.

[10]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[11]  E. Stadtman,et al.  Oxidative modification of proteins during aging , 2001, Experimental Gerontology.

[12]  Bernhardt L Trout,et al.  Aggregation in protein-based biotherapeutics: computational studies and tools to identify aggregation-prone regions. , 2011, Journal of pharmaceutical sciences.

[13]  E. Goormaghtigh,et al.  ATR-FTIR: a "rejuvenated" tool to investigate amyloid proteins. , 2013, Biochimica et biophysica acta.

[14]  Robert A. Grothe,et al.  Structure of the cross-beta spine of amyloid-like fibrils. , 2005, Nature.

[15]  R. Tycko,et al.  Probing site-specific conformational distributions in protein folding with solid-state NMR. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[16]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules , 1995 .

[17]  Roger D Kamm,et al.  Kinetic control of dimer structure formation in amyloid fibrillogenesis. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  R. Dror,et al.  Systematic Validation of Protein Force Fields against Experimental Data , 2012, PloS one.

[19]  Chris Oostenbrink,et al.  A Systematic Framework for Molecular Dynamics Simulations of Protein Post-Translational Modifications , 2013, PLoS Comput. Biol..

[20]  Fabrizio Chiti,et al.  Amyloid formation by globular proteins under native conditions. , 2009, Nature chemical biology.

[21]  Multimolecule test-tube simulations of protein unfolding and aggregation , 2012, Proceedings of the National Academy of Sciences.

[22]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[23]  R. Dror,et al.  Improved side-chain torsion potentials for the Amber ff99SB protein force field , 2010, Proteins.

[24]  Michael R. Shirts,et al.  Extremely precise free energy calculations of amino acid side chain analogs: Comparison of common molecular mechanics force fields for proteins , 2003 .

[25]  Wilfred F van Gunsteren,et al.  Biomolecular modeling: Goals, problems, perspectives. , 2006, Angewandte Chemie.

[26]  C. Dobson,et al.  Rationalization of the effects of mutations on peptide andprotein aggregation rates , 2003, Nature.

[27]  Michele Vendruscolo,et al.  Theoretical approaches to protein aggregation. , 2006, Protein and peptide letters.

[28]  C. Hall,et al.  Molecular dynamics simulations of spontaneous fibril formation by random-coil peptides. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Wilfred F van Gunsteren,et al.  Molecular simulation as an aid to experimentalists. , 2008, Current opinion in structural biology.

[30]  Adrian H Elcock,et al.  Models of macromolecular crowding effects and the need for quantitative comparisons with experiment. , 2010, Current opinion in structural biology.

[31]  Chris Oostenbrink,et al.  A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force‐field parameter sets 53A5 and 53A6 , 2004, J. Comput. Chem..

[32]  Ruth Nussinov,et al.  Energy landscape of amyloidogenic peptide oligomerization by parallel-tempering molecular dynamics simulation: significant role of Asn ladder. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[33]  C. Ross,et al.  Protein aggregation and neurodegenerative disease , 2004, Nature Medicine.

[34]  Syma Khalid,et al.  Coarse-grained MD simulations of membrane protein-bilayer self-assembly. , 2008, Structure.

[35]  R. Friesner,et al.  Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides† , 2001 .

[36]  Vijay S. Pande,et al.  A Role for Confined Water in Chaperonin Function , 2008, Journal of the American Chemical Society.

[37]  Y. Sugita,et al.  Variable interactions between protein crowders and biomolecular solutes are important in understanding cellular crowding. , 2012, The journal of physical chemistry. B.

[38]  T. Darden,et al.  A smooth particle mesh Ewald method , 1995 .

[39]  Paul T. Matsudaira,et al.  NMR structure of the 35-residue villin headpiece subdomain , 1997, Nature Structural Biology.

[40]  Thomas Huber,et al.  G protein-coupled receptors self-assemble in dynamics simulations of model bilayers. , 2007, Journal of the American Chemical Society.

[41]  Drazen Petrov,et al.  Vienna-PTM web server: a toolkit for MD simulations of protein post-translational modifications , 2013, Nucleic Acids Res..

[42]  M. Auger,et al.  Two-dimensional infrared correlation spectroscopy study of the aggregation of cytochrome c in the presence of dimyristoylphosphatidylglycerol. , 2001, Biophysical journal.

[43]  A. Elcock,et al.  Molecular dynamics simulations of highly crowded amino acid solutions: comparisons of eight different force field combinations with experiment and with each other. , 2013, Journal of chemical theory and computation.

[44]  Andreas P. Eichenberger,et al.  Definition and testing of the GROMOS force-field versions 54A7 and 54B7 , 2011, European Biophysics Journal.

[45]  Huan‐Xiang Zhou,et al.  Macromolecular crowding and confinement: biochemical, biophysical, and potential physiological consequences. , 2008, Annual review of biophysics.

[46]  K. Lindorff-Larsen,et al.  Structure and dynamics of an unfolded protein examined by molecular dynamics simulation. , 2012, Journal of the American Chemical Society.

[47]  A. Caflisch,et al.  The role of side-chain interactions in the early steps of aggregation: Molecular dynamics simulations of an amyloid-forming peptide from the yeast prion Sup35 , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[48]  W. Delano The PyMOL Molecular Graphics System , 2002 .

[49]  Y. Sugita,et al.  Protein crowding affects hydration structure and dynamics. , 2012, Journal of the American Chemical Society.

[50]  Adrian H. Elcock,et al.  Diffusion, Crowding & Protein Stability in a Dynamic Molecular Model of the Bacterial Cytoplasm , 2010, PLoS Comput. Biol..

[51]  C. Brooks,et al.  Balancing solvation and intramolecular interactions: toward a consistent generalized Born force field. , 2006, Journal of the American Chemical Society.

[52]  R. Ellis Macromolecular crowding : obvious but underappreciated , 2022 .

[53]  D. Raleigh,et al.  Effect of modulating unfolded state structure on the folding kinetics of the villin headpiece subdomain. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[54]  H. Berendsen,et al.  Interaction Models for Water in Relation to Protein Hydration , 1981 .

[55]  J. Kelly,et al.  Probing the folding transition state structure of the villin headpiece subdomain via side chain and backbone mutagenesis. , 2009, Journal of the American Chemical Society.

[56]  A. Pepe,et al.  Molecular and supramolecular studies on polyglycine and poly-L-proline , 2011 .

[57]  M. Smoluchowski Versuch einer mathematischen Theorie der Koagulationskinetik kolloider Lösungen , 1918 .

[58]  Wilfred F. van Gunsteren,et al.  An improved GROMOS96 force field for aliphatic hydrocarbons in the condensed phase , 2001, J. Comput. Chem..

[59]  A. Tokmakoff,et al.  Insulin dimer dissociation and unfolding revealed by amide I two-dimensional infrared spectroscopy. , 2010, Physical chemistry chemical physics : PCCP.

[60]  H. Berendsen,et al.  Molecular dynamics with coupling to an external bath , 1984 .

[61]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[62]  G. Hummer,et al.  Are current molecular dynamics force fields too helical? , 2008, Biophysical journal.

[63]  Ruth Nussinov,et al.  Simulations as analytical tools to understand protein aggregation and predict amyloid conformation. , 2006, Current opinion in chemical biology.

[64]  L. Miller,et al.  FTIR spectroscopic imaging of protein aggregation in living cells. , 2013, Biochimica et biophysica acta.

[65]  Wilfred F. van Gunsteren,et al.  A generalized reaction field method for molecular dynamics simulations , 1995 .

[66]  Tomasz Walski,et al.  Spectroscopic techniques in the study of human tissues and their components. Part I: IR spectroscopy. , 2012, Acta of bioengineering and biomechanics.

[67]  A. Minton,et al.  Models for excluded volume interaction between an unfolded protein and rigid macromolecular cosolutes: macromolecular crowding and protein stability revisited. , 2005, Biophysical journal.

[68]  D. Giustarini,et al.  Biomarkers of oxidative damage in human disease. , 2006, Clinical chemistry.

[69]  B. Zagrovic,et al.  Conformational averaging in structural biology: issues, challenges and computational solutions. , 2009, Molecular bioSystems.

[70]  Allen P. Minton,et al.  Cell biology: Join the crowd , 2003, Nature.