The Role of Force Fields and Water Models in Protein Folding and Unfolding Dynamics

Protein folding is a fascinating, not fully understood phenomenon in biology. Molecular dynamics (MD) simulations are an invaluable tool to study conformational changes in atomistic detail, including folding and unfolding processes of proteins. However, the accuracy of the conformational ensembles derived from MD simulations inevitably relies on the quality of the underlying force field in combination with the respective water model. Here, we investigate protein folding, unfolding, and misfolding of fast-folding proteins by examining different force fields with their recommended water models, i.e., ff14SB with the TIP3P model and ff19SB with the OPC model. To this end, we generated long conventional MD simulations highlighting the perks and pitfalls of these setups. Using Markov state models, we defined kinetically independent conformational substates and emphasized their distinct characteristics, as well as their corresponding state probabilities. Surprisingly, we found substantial differences in thermodynamics and kinetics of protein folding, depending on the combination of the protein force field and water model, originating primarily from the different water models. These results emphasize the importance of carefully choosing the force field and the respective water model as they determine the accuracy of the observed dynamics of folding events. Thus, the findings support the hypothesis that the water model is at least equally important as the force field and hence needs to be considered in future studies investigating protein dynamics and folding in all areas of biophysics.

[1]  V. Greiff,et al.  Defining and Studying B Cell Receptor and TCR Interactions. , 2023, Journal of immunology.

[2]  S. Ovchinnikov,et al.  Mega-scale experimental analysis of protein folding stability in biology and design , 2023, Nature.

[3]  Patrick K. Quoika,et al.  Water model determines thermosensitive and physicochemical properties of poly(N-isopropylacrylamide) in molecular simulations , 2023, Frontiers in Materials.

[4]  M. Zacharias,et al.  Mechanism of β‐hairpin formation in AzoChignolin and Chignolin , 2022, J. Comput. Chem..

[5]  J. Reichert,et al.  Antibodies to watch in 2023 , 2022, mAbs.

[6]  K. Liedl,et al.  The influence of antibody humanization on shark variable domain (VNAR) binding site ensembles , 2022, Frontiers in Immunology.

[7]  P. Doherty,et al.  A Single Domain Shark Antibody Targeting the Transferrin Receptor 1 Delivers a TrkB Agonist Antibody to the Brain and Provides Full Neuroprotection in a Mouse Model of Parkinson’s Disease , 2022, Pharmaceutics.

[8]  K. Liedl,et al.  Grid inhomogeneous solvation theory for cross-solvation in rigid solvents. , 2022, The Journal of chemical physics.

[9]  C. Klein,et al.  Prodrug-Activating Chain Exchange (PACE) converts targeted prodrug derivatives to functional bi- or multispecific antibodies , 2022, Biological chemistry.

[10]  Henry A. Utset,et al.  Broadly neutralizing antibodies target a haemagglutinin anchor epitope , 2021, Nature.

[11]  B. Flucher,et al.  Ion-pair interactions between voltage-sensing domain IV and pore domain I regulate CaV1.1 gating , 2021, Biophysical journal.

[12]  C. Camilloni,et al.  How to Determine Accurate Conformational Ensembles by Metadynamics Metainference: A Chignolin Study Case , 2021, Frontiers in Molecular Biosciences.

[13]  Luciano A. Abriata,et al.  Assessment of transferable forcefields for protein simulations attests improved description of disordered states and secondary structure propensities, and hints at multi-protein systems as the next challenge for optimization , 2021, Computational and structural biotechnology journal.

[14]  K. Liedl,et al.  Mutation of Framework Residue H71 Results in Different Antibody Paratope States in Solution , 2021, Frontiers in Immunology.

[15]  G. Georges,et al.  Ensembles in solution as a new paradigm for antibody structure prediction and design , 2021, mAbs.

[16]  H. Kettenberger,et al.  Conformational Ensembles of Antibodies Determine Their Hydrophobicity , 2020, Biophysical journal.

[17]  K. Liedl,et al.  Polarizable and non-polarizable force fields: Protein folding, unfolding, and misfolding. , 2020, The Journal of chemical physics.

[18]  Andrew C. Simmonett,et al.  Thermodynamic Decomposition of Solvation Free Energies with Particle Mesh Ewald and Long-Range Lennard-Jones Interactions in Grid Inhomogeneous Solvation Theory. , 2020, Journal of chemical theory and computation.

[19]  Michael Schauperl,et al.  Solvation Thermodynamics in Different Solvents: Water–Chloroform Partition Coefficients from Grid Inhomogeneous Solvation Theory , 2020, J. Chem. Inf. Model..

[20]  G. Gilliland,et al.  Antibody Structure and Function: The Basis for Engineering Therapeutics , 2019, Antibodies.

[21]  K. Liedl,et al.  Solvation Free Energy as a Measure of Hydrophobicity: Application to Serine Protease Binding Interfaces , 2019, Journal of chemical theory and computation.

[22]  He Huang,et al.  ff19SB: Amino-acid specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. , 2019, Journal of chemical theory and computation.

[23]  G. Georges,et al.  CDR-H3 loop ensemble in solution – conformational selection upon antibody binding , 2019, mAbs.

[24]  A. Onufriev,et al.  General Purpose Water Model Can Improve Atomistic Simulations of Intrinsically Disordered Proteins. , 2019, Journal of chemical theory and computation.

[25]  J. Richard Protein Flexibility and Stiffness Enable Efficient Enzymatic Catalysis , 2019, Journal of the American Chemical Society.

[26]  B. Melnik,et al.  Experimental approach to study the effect of mutations on the protein folding pathway , 2019, PloS one.

[27]  K. Liedl,et al.  Characterizing the Diversity of the CDR-H3 Loop Conformational Ensembles in Relationship to Antibody Binding Properties , 2019, Front. Immunol..

[28]  G. A. Lazar,et al.  Next generation antibody drugs: pursuit of the 'high-hanging fruit' , 2017, Nature Reviews Drug Discovery.

[29]  Hao Wu,et al.  Variational Approach for Learning Markov Processes from Time Series Data , 2017, Journal of Nonlinear Science.

[30]  D. Baker,et al.  Global analysis of protein folding using massively parallel design, synthesis, and testing , 2017, Science.

[31]  A. Onufriev,et al.  Accuracy limit of rigid 3-point water models. , 2016, The Journal of chemical physics.

[32]  Michael K. Gilson,et al.  Solvation thermodynamic mapping of molecular surfaces in AmberTools: GIST , 2016, J. Comput. Chem..

[33]  Alexander D. MacKerell,et al.  An Empirical Polarizable Force Field Based on the Classical Drude Oscillator Model: Development History and Recent Applications , 2016, Chemical reviews.

[34]  Martin K. Scherer,et al.  PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models. , 2015, Journal of chemical theory and computation.

[35]  C. Simmerling,et al.  ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. , 2015, Journal of chemical theory and computation.

[36]  Claudio Soto,et al.  Type 2 diabetes as a protein misfolding disease. , 2015, Trends in molecular medicine.

[37]  Saeed Izadi,et al.  Building Water Models: A Different Approach , 2014, The journal of physical chemistry letters.

[38]  Michael K. Gilson,et al.  Thermodynamics of Water in an Enzyme Active Site: Grid-Based Hydration Analysis of Coagulation Factor Xa , 2014, Journal of chemical theory and computation.

[39]  Frank Noé,et al.  Markov state models of biomolecular conformational dynamics. , 2014, Current opinion in structural biology.

[40]  B. L. de Groot,et al.  Quantifying Artifacts in Ewald Simulations of Inhomogeneous Systems with a Net Charge. , 2014, Journal of chemical theory and computation.

[41]  S. Le Grand,et al.  Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald. , 2013, Journal of chemical theory and computation.

[42]  Daniel R Roe,et al.  PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data. , 2013, Journal of chemical theory and computation.

[43]  J. Hirsch,et al.  Structural flexibility of CaV1.2 and CaV2.2 I-II proximal linker fragments in solution. , 2013, Biophysical journal.

[44]  Marcus Weber,et al.  Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification , 2013, Advances in Data Analysis and Classification.

[45]  K. Dill,et al.  The Protein-Folding Problem, 50 Years On , 2012, Science.

[46]  Michael K Gilson,et al.  Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril. , 2012, The Journal of chemical physics.

[47]  R. Best,et al.  Force-field dependence of chignolin folding and misfolding: comparison with experiment and redesign. , 2012, Biophysical journal.

[48]  R. Dror,et al.  How Fast-Folding Proteins Fold , 2011, Science.

[49]  Claudio Soto,et al.  Misfolded protein aggregates: mechanisms, structures and potential for disease transmission. , 2011, Seminars in cell & developmental biology.

[50]  Klaus R. Liedl,et al.  A challenging system: Free energy prediction for factor Xa , 2011, J. Comput. Chem..

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

[52]  Andrew L. Lee,et al.  Using NMR to study fast dynamics in proteins: methods and applications. , 2010, Current opinion in pharmacology.

[53]  Christopher B. Harrison,et al.  Challenges in protein folding simulations: Timescale, representation, and analysis. , 2010, Nature physics.

[54]  Klaus R Liedl,et al.  Stabilizing of a globular protein by a highly complex water network: a molecular dynamics simulation study on factor Xa. , 2010, The journal of physical chemistry. B.

[55]  A. Elofsson,et al.  Structure is three to ten times more conserved than sequence—A study of structural response in protein cores , 2009, Proteins.

[56]  F. Peale,et al.  Variants of the Antibody Herceptin That Interact with HER2 and VEGF at the Antigen Binding Site , 2009, Science.

[57]  K. Jurkat-Rott,et al.  A CaV1.1 Ca2+ channel splice variant with high conductance and voltage-sensitivity alters EC coupling in developing skeletal muscle. , 2009, Biophysical journal.

[58]  Shinya Honda,et al.  Crystal structure of a ten-amino acid protein. , 2008, Journal of the American Chemical Society.

[59]  P. Labute proteins STRUCTURE O FUNCTION O BIOINFORMATICS Protonate3D: Assignment of ionization , 2013 .

[60]  T. Weikl,et al.  The protein folding problem. , 2008, Annual review of biophysics.

[61]  D. Kern,et al.  Dynamic personalities of proteins , 2007, Nature.

[62]  Kentaro Shimizu,et al.  Folding free‐energy landscape of a 10‐residue mini‐protein, chignolin , 2006, FEBS letters.

[63]  J. Onuchic,et al.  Water mediation in protein folding and molecular recognition. , 2006, Annual review of biophysics and biomolecular structure.

[64]  Shinya Honda,et al.  10 residue folded peptide designed by segment statistics. , 2004, Structure.

[65]  Greg L. Hura,et al.  Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew. , 2004, The Journal of chemical physics.

[66]  A. Laio,et al.  Escaping free-energy minima , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[67]  W. L. Jorgensen Quantum and statistical mechanical studies of liquids. 10. Transferable intermolecular potential functions for water, alcohols, and ethers. Application to liquid water , 2002 .

[68]  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 .

[69]  T. Lazaridis Inhomogeneous Fluid Approach to Solvation Thermodynamics. 1. Theory , 1998 .

[70]  Themis Lazaridis,et al.  Inhomogeneous Fluid Approach to Solvation Thermodynamics. 2. Applications to Simple Fluids , 1998 .

[71]  D. Selkoe,et al.  Amyloid β-Protein and the Genetics of Alzheimer's Disease* , 1996, The Journal of Biological Chemistry.

[72]  W. V. Gunsteren,et al.  Force field parametrization by weak coupling. Re-engineering SPC water , 1995 .

[73]  P. Kollman,et al.  Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models , 1992 .

[74]  W. V. van Gunsteren,et al.  Protein structures from NMR. , 1988, Biochemistry.

[75]  T. Straatsma,et al.  THE MISSING TERM IN EFFECTIVE PAIR POTENTIALS , 1987 .

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

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

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

[79]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[80]  Richard R. Ernst,et al.  Investigation of exchange processes by two‐dimensional NMR spectroscopy , 1979 .

[81]  G. Torrie,et al.  Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling , 1977 .

[82]  J. D. Doll,et al.  Generalized Langevin equation approach for atom/solid-surface scattering: General formulation for classical scattering off harmonic solids , 1976 .

[83]  C. Anfinsen Principles that govern the folding of protein chains. , 1973, Science.

[84]  J. Karush On the Chapman-Kolmogorov Equation , 1961 .

[85]  T. Creighton,et al.  Experimental studies of protein folding and unfolding. , 1978, Progress in biophysics and molecular biology.