1932

Abstract

The structure and interactions of proteins play a critical role in determining the quality attributes of many foods, beverages, and pharmaceutical products. Incorporating a multiscale understanding of the structure–function relationships of proteins can provide greater insight into, and control of, the relevant processes at play. Combining data from experimental measurements, human sensory panels, and computer simulations through machine learning allows the construction of statistical models relating nanoscale properties of proteins to the physicochemical properties, physiological outcomes, and tastes of foods. This review highlights several examples of advanced computer simulations at molecular, mesoscale, and multiscale levels that shed light on the mechanisms at play in foods, thereby facilitating their control. It includes a practical simulation toolbox for those new to in silico modeling.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-food-032519-051640
2020-03-25
2024-06-12
Loading full text...

Full text loading...

/deliver/fulltext/food/11/1/annurev-food-032519-051640.html?itemId=/content/journals/10.1146/annurev-food-032519-051640&mimeType=html&fmt=ahah

Literature Cited

  1. Abrams CF, Vanden-Eijnden E. 2010. Large-scale conformational sampling of proteins using temperature-accelerated molecular dynamics. PNAS 107:4961–66
    [Google Scholar]
  2. Alfonso-Prieto M, Giorgetti A, Carloni P 2019. Multiscale simulations on human Frizzled and Taste2 GPCRs. Curr. Opin. Struct. Biol. 55:8–16
    [Google Scholar]
  3. Ayyaswamy PS, Muzykantov V, Eckmann DM, Radhakrishnan R 2013. Nanocarrier hydrodynamics and binding in targeted drug delivery: challenges in numerical modeling and experimental validation. J. Nanotechnol. Eng. Med. 4:1011001
    [Google Scholar]
  4. Bansal B, Chen XD. 2006. A critical review of milk fouling in heat exchangers. Compr. Rev. Food Sci. Food Saf. 5:27–33
    [Google Scholar]
  5. Barducci A, Bussi G, Parrinello M 2008. Well-tempered metadynamics: a smoothly converging and tunable free-energy method. Phys. Rev. Lett. 100:020603
    [Google Scholar]
  6. Barroso da Silva FL, Boström M, Persson C 2014. Effect of charge regulation and ion-dipole interactions on the selectivity of protein-nanoparticle binding. Langmuir 30:4078–83
    [Google Scholar]
  7. Barroso da Silva FL, Dias LG 2017. Development of constant-pH simulation methods in implicit solvent and applications in biomolecular systems. Biophys. Rev. 9:699–728
    [Google Scholar]
  8. Barroso da Silva FL, Jönsson B 2009. Polyelectrolyte-protein complexation driven by charge regulation. Soft Matter 5:2862–68
    [Google Scholar]
  9. Barroso da Silva FL, Lund M, Jönsson B, Akesson T 2006. On the complexation of proteins and polyelectrolytes. J. Phys. Chem. B 110:4459–64
    [Google Scholar]
  10. Barroso da Silva FL, MacKernan D 2017. Benchmarking a fast proton titration scheme in implicit solvent for biomolecular simulations. J. Chem. Theory Comput. 13:2915–29
    [Google Scholar]
  11. Barroso da Silva FL, Pasquali S, Derreumaux P, Dias LG 2016. Electrostatics analysis of the mutational and pH effects of the N-terminal domain self-association of the major ampullate spidroin. Soft Matter 12:5600–12
    [Google Scholar]
  12. Barroso da Silva FL, Sterpone F, Derreumaux P 2019. OPEP6: a new constant-pH molecular dynamics simulation scheme with OPEP coarse-grained force field. J. Chem. Theory Comput. 15:63875–88
    [Google Scholar]
  13. Bellion M, Santen L, Mantz H, Hähl H, Quinn A et al. 2008. Protein adsorption on tailored substrates: long-range forces and conformational changes. J. Phys. Condens. Matter 20:404226
    [Google Scholar]
  14. Belton PS, Gil AM. 1994. IR and Raman spectroscopic studies of the interaction of trehalose with hen egg white lysozyme. Biopolymers 34:957–61
    [Google Scholar]
  15. Bennett WFD, Chen AW, Donnini S, Groenhof G, Tieleman DP 2013. Constant pH simulations with the coarse-grained MARTINI model: application to oleic acid aggregates. Can. J. Chem. 91:839–46
    [Google Scholar]
  16. Berendsen HJC, van der Spoel D, van Drunen R 1995. GROMACS: a message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91:43–56
    [Google Scholar]
  17. Berman H, Kleywegt G, Nakamura H, Markley J 2014. The Protein Data Bank archive as an open data resource. J. Comput. Aided Mol. Des. 28:101009–14
    [Google Scholar]
  18. Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G et al. 2014. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42:W252–58
    [Google Scholar]
  19. Binder K. 1997. Applications of the Monte Carlo methods to statistical physics. Rep. Prog. Phys. 60:487–559
    [Google Scholar]
  20. Blanco E, Shen H, Ferrari M 2015. Principles of nanoparticle design for overcoming biological barriers to drug delivery. Nat. Biotechnol. 33:941–51
    [Google Scholar]
  21. Boire A, Renard D, Bouchoux A, Pezennec S, Croguennec T et al. 2019. Soft-matter approaches for controlling food protein interactions and assembly. Annu. Rev. Food Sci. Technol. 10:521–39
    [Google Scholar]
  22. Bolnykh V, Olsen JMH, Meloni S, Bircher MP, Ippoliti E et al. 2019. Extreme scalability of DFT-based QM/MM MD simulations using MiMiC. J. Chem. Theory Comput. 15:105601–13
    [Google Scholar]
  23. Bonomi M, Branduardi D, Bussi G, Camilloni C, Provasi D et al. 2009. PLUMED: a portable plugin for free-energy calculations with molecular dynamics. Comput. Phys. Commun. 180:1961–72
    [Google Scholar]
  24. Brandt EG, Lyubartsev AP. 2015. Molecular dynamics simulations of adsorption of amino acid side chain analogues and a titanium binding peptide on the TiO2 (100) surface. J. Phys. Chem. C 119:18126–39
    [Google Scholar]
  25. Carpenter JF, Crowe JH. 1989. An infrared spectroscopic study of the interactions of carbohydrates with dried proteins. Biochemistry 28:3916–22
    [Google Scholar]
  26. Casasnovas R, Limongelli V, Tiwary P, Carloni P, Parrinello M 2017. Unbinding kinetics of a p38 MAP kinase type II inhibitor from metadynamics simulations. J. Am. Chem. Soc. 139:4780–88
    [Google Scholar]
  27. Case DA, Cheatham TE, Darden T, Gohlke H, Luo R et al. 2005. The Amber biomolecular simulation programs. J. Comput. Chem. 26:1668–88
    [Google Scholar]
  28. Chellaram C, Murugaboopathi G, John AA, Sivakumar R, Ganesan S et al. 2014. Significance of nanotechnology in food industry. APCBEE Procedia 8:109–13
    [Google Scholar]
  29. Chen W, Morrow BH, Shi C, Shen JK 2014. Recent development and application of constant pH molecular dynamics. Mol. Simul. 40:830–38
    [Google Scholar]
  30. Chen W, Wallace JA, Yue Z, Shen JK 2013. Introducing titratable water to all-atom molecular dynamics at constant pH. Biophys. J. 105:L15–17
    [Google Scholar]
  31. Chen Y, Roux B. 2015. Constant-pH hybrid nonequilibrium molecular dynamics–Monte Carlo simulation method. J. Chem. Theory Comput. 11:3919–31
    [Google Scholar]
  32. Cheung DL. 2012. Molecular simulation of hydrophobin adsorption at an oil-water interface. Langmuir 28:8730–36
    [Google Scholar]
  33. Cheung DL. 2016. Conformations of myoglobin-derived peptides at the air-water interface. Langmuir 32:4405–14
    [Google Scholar]
  34. Cheung DL. 2017. Adsorption and conformations of lysozyme and α-lactalbumin at a water-octane interface. J. Chem. Phys. 147:195101
    [Google Scholar]
  35. Chopade PD, Sarma B, Santiso EE, Simpson J, Fry JC et al. 2015. On the connection between nonmonotonic taste behavior and molecular conformation in solution: the case of rebaudioside-A. J. Chem. Phys. 143:24244301
    [Google Scholar]
  36. Cordone L, Cupane A, Emanuele A, Giuffrida S, Cottone G, Levantino M 2015. Proteins in saccharides matrices and the trehalose peculiarity: biochemical and biophysical properties. Curr. Org. Chem. 19:171684–706
    [Google Scholar]
  37. Cottone G. 2007. A comparative study of carboxy myoglobin in saccharide–water systems by molecular dynamics simulation. J. Phys. Chem. B 111:3563–69
    [Google Scholar]
  38. Delboni L, Barroso da Silva FL 2016. On the complexation of whey proteins. Food Hydrocoll 55:89–99
    [Google Scholar]
  39. Dimitrijevic M, Karabasil N, Boskovic M, Teodorovic V, Vasilev D et al. 2015. Safety aspects of nanotechnology applications in food packaging. Procedia Food Sci 5:57–60
    [Google Scholar]
  40. Dobrev P, Donnini S, Groenhof G, Grubmüller H 2017. Accurate three states model for amino acids with two chemically coupled titrating sites in explicit solvent atomistic constant pH simulations and pKa calculations. J. Chem. Theory Comput. 13:147–60
    [Google Scholar]
  41. Donnini S, Tegeler F, Groenhof G, Grubmüller H 2011. Constant pH molecular dynamics in explicit solvent with λ-dynamics. J. Chem. Theory Comput. 7:1962–78
    [Google Scholar]
  42. Donnini S, Ullmann RT, Groenhof G, Grubmüller H 2016. Charge-neutral constant pH molecular dynamics simulations using a parsimonious proton buffer. J. Chem. Theory Comput. 12:31040–51
    [Google Scholar]
  43. Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y et al. 2017. OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLOS Comput. Biol. 13:e1005659
    [Google Scholar]
  44. Egan T, O'Riordan D, O'Sullivan M, Jacquier J-C 2014. Cold-set whey protein microgels as pH modulated immobilisation matrices for charged bioactives. Food Chem 156:197–203
    [Google Scholar]
  45. Farokhirad S, Bradley RP, Sarkar A, Shih A, Telesco S et al. 2017. Computational methods related to molecular structure and reaction chemistry of biomaterials. Comprehensive Biomaterials II P Ducheyne 245–67 Oxford, UK: Elsevier
    [Google Scholar]
  46. Fierro F, Suku E, Alfonso-Prieto M, Giorgetti A, Cichon S, Carloni P 2017. Agonist binding to chemosensory receptors: a systematic bioinformatics analysis. Front. Mol. Biosci. 4:63
    [Google Scholar]
  47. Frenkel D, Frenkel D, Smit B 2001. Understanding Molecular Simulation: From Algorithms to Applications San Diego, CA: Academic. , 2nd ed..
    [Google Scholar]
  48. Gallocchio F, Belluco S, Ricci A 2015. Nanotechnology and food: brief overview of the current scenario. Procedia Food Sci 5:85–88
    [Google Scholar]
  49. Giuffrida S, Cordone L, Cottone G 2018. Bioprotection can be tuned with a proper protein/saccharide ratio: the case of solid amorphous matrices. J. Phys. Chem. B 122:8642–53
    [Google Scholar]
  50. Green JL, Angell CA. 1989. Phase relations and vitrification in saccharide-water solutions and the trehalose anomaly. J. Phys. Chem. 93:2880–82
    [Google Scholar]
  51. Guest M. 2012. Prace: The Scientific Case for HPC in Europe Bristol, UK: Insight Publ.
    [Google Scholar]
  52. Gulzar M, Bouhallab S, Jeantet R, Schuck P, Croguennec T 2011. Influence of pH on the dry heat-induced denaturation/aggregation of whey proteins. Food Chem 129:1110–16
    [Google Scholar]
  53. Gulzar M, Jacquier J-C. 2018. Impact of residual lactose on dry heat-induced pre-texturization of whey proteins. Food Bioproc. Technol. 11:1985–94
    [Google Scholar]
  54. Guyomarc'h F, Famelart M-H, Henry G, Gulzar M, Leonil J et al. 2015. Current ways to modify the structure of whey proteins for specific functionalities: a review. Dairy Sci. Technol. 95:6795–814
    [Google Scholar]
  55. He W, Lu Y, Qi J, Chen L, Hu F, Wu W 2013. Food proteins as novel nanosuspension stabilizers for poorly water-soluble drugs. Int. J. Pharm. 441:269–78
    [Google Scholar]
  56. He X, Hwang H-M. 2016. Nanotechnology in food science: functionality, applicability, and safety assessment. J. Food Drug Anal. 24:671–81
    [Google Scholar]
  57. Hiller C, Kühhorn J, Gmeiner P 2013. Class A G-protein-coupled receptor (GPCR) dimers and bivalent ligands. J. Med. Chem. 56:6542–59
    [Google Scholar]
  58. Hub JS, de Groot BL, Grubmüller H, Groenhof G 2014. Quantifying artifacts in Ewald simulations of inhomogeneous systems with a net charge. J. Chem. Theory Comput. 10:381–90
    [Google Scholar]
  59. Jo S, Kim T, Iyer VG, Im W 2008. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29:1859–65
    [Google Scholar]
  60. Jönsson B, Lund M, Barroso da Silva FL 2007. Electrostatics in macromolecular solution. Food Colloids: Self-Assembly and Material Science E Dickinson, ME Leser 129–54 Cambridge, UK: RSC Publ.
    [Google Scholar]
  61. Kamath P, Fernandez A, Giralt F, Rallo R 2015. Predicting cell association of surface-modified nanoparticles using protein corona structure–activity relationships (PCSAR). Curr. Top. Med. Chem. 15:1930–37
    [Google Scholar]
  62. Kar S, Leszczynski J. 2019. Exploration of computational approaches to predict the toxicity of chemical mixtures. Toxics 7:15
    [Google Scholar]
  63. Kier LB. 1972. A molecular theory of sweet taste. J. Pharm. Sci. 61:1394–97
    [Google Scholar]
  64. Kong X, Brooks CL III 1996. λ-Dynamics: a new approach to free energy calculations. J. Chem. Phys. 105:128–41
    [Google Scholar]
  65. Krekeler C, Agarwal A, Junghans C, Praprotnik M, Delle Site L 2018. Adaptive resolution molecular dynamics technique: down to the essential. J. Chem. Phys. 149:024104
    [Google Scholar]
  66. Leach AR. 1996. Molecular Modelling: Principles and Applications Singapore: Longman. , 1st ed..
    [Google Scholar]
  67. Lee MS, Salisbury FR Jr., Brooks CL III 2004. Constant-pH molecular dynamics using continuous titration coordinates. Proteins 56:738–52
    [Google Scholar]
  68. Li M, Al-Jamal KT, Kostarelos K, Reineke J 2010. Physiologically based pharmacokinetic modeling of nanoparticles. ACS Nano 4:6303–17
    [Google Scholar]
  69. Liu R, Jiang W, Walkey CD, Chan WCW, Cohen Y 2015. Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties. Nanoscale 7:9664–75
    [Google Scholar]
  70. Lopez H, Lobaskin V. 2015. Coarse-grained model of adsorption of blood plasma proteins onto nanoparticles. J. Chem. Phys. 143:243138
    [Google Scholar]
  71. Lu X, Fang D, Ito S, Okamoto Y, Ovchinnikov V, Cui Q 2016. QM/MM free energy simulations: recent progress and challenges. Mol. Simul. 42:131056–78
    [Google Scholar]
  72. Lucid J, Meloni S, MacKernan D, Spohr E, Ciccotti G 2013. Probing the structures of hydrated nafion in different morphologies using temperature-accelerated molecular dynamics simulations. J. Phys. Chem. C 117:774–82
    [Google Scholar]
  73. Mackerell AD Jr 2004. Empirical force fields for biological macromolecules: overview and issues. J. Comput. Chem. 25:1584–604
    [Google Scholar]
  74. MacKerell AD, Brooks B, Brooks CL, Nilsson L, Roux B et al. 1998. CHARMM: the energy function and its parameterization. Encycl. Comput. Chem. https://doi.org/10.1002/0470845015.cfa007
    [Crossref] [Google Scholar]
  75. Maragliano L, Fischer A, Vanden-Eijnden E, Ciccotti G 2006. String method in collective variables: minimum free energy paths and isocommittor surfaces. J. Chem. Phys. 125:024106
    [Google Scholar]
  76. McClements DJ, Li F, Xiao H 2015. The nutraceutical bioavailability classification scheme: classifying nutraceuticals according to factors limiting their oral bioavailability. Annu. Rev. Food Sci. Technol. 6:299–327
    [Google Scholar]
  77. McGuffin LJ, Adiyaman R, Maghrabi AHA, Shuid AN, Brackenridge DA et al. 2019. IntFOLD: an integrated web resource for high performance protein structure and function prediction. Nucleic Acids Res 47:W408–13
    [Google Scholar]
  78. Montellano DN, Spelzini D, Wayllace N, Boeris V, Barroso da Silva FL 2018. A combined experimental and molecular simulation study of factors influencing interaction of quinoa proteins–carrageenan. Int. J. Biol. Macromol. 107:949–56
    [Google Scholar]
  79. Morris VJ, Grove KHM. 2013. Food Microstructures: Microscopy, Measurement and Modelling Cambridge, UK: Woodhead Publ.
    [Google Scholar]
  80. Norwood E-A, Pezennec S, Burgain J, Briard-Bion V, Schuck P et al. 2017. Crucial role of remaining lactose in whey protein isolate powders during storage. J. Food Eng. 195:206–16
    [Google Scholar]
  81. Oberle M, Yigit C, Angioletti-Uberti S, Dzubiella J, Ballauff M 2015. Competitive protein adsorption to soft polymeric layers: binary mixtures and comparison to theory. J. Phys. Chem. B 119:3250–58
    [Google Scholar]
  82. Ohtake S, Wang YJ. 2011. Trehalose: current use and future applications. J. Pharm. Sci. 100:2020–53
    [Google Scholar]
  83. Olsson MHM, Søndergaard CR, Rostkowski M, Jensen JH 2011. PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J. Chem. Theory Comput. 7:525–37
    [Google Scholar]
  84. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E et al. 2005. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26:1781–802
    [Google Scholar]
  85. Pink DA, Razul MSG. 2014. Computer simulation techniques for food science and engineering: simulating atomic scale and coarse-grained models. Food Struct 1:71–90
    [Google Scholar]
  86. Plimpton S. 1995. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117:1–19
    [Google Scholar]
  87. Ponder JW, Case DA. 2003. Force fields for protein simulations. Adv. Protein Chem. 66:27–85
    [Google Scholar]
  88. Poon S, Clarke AE, Schultz CJ 1999. Structure-function analysis of the emulsifying and interfacial properties of apomyoglobin and derived peptides. J. Colloid Interface Sci. 213:193–203
    [Google Scholar]
  89. Power D, Poggio S, Lopez H, Lobaskin V 2020. Bionano interactions: a key to mechanistic understanding of nanoparticle toxicity. Computational Nanotoxicology: Challenges and Perspectives A Gajewicz, T Puzyn 189–215 Singapore: Jenny Stanford Publ.
    [Google Scholar]
  90. Power D, Rouse I, Poggio S, Brandt E, Lopez H et al. 2019. A multiscale model of protein adsorption on a nanoparticle surface. Model. Simul. Mater. Sci. Eng. 27:084003
    [Google Scholar]
  91. Pronk S, Páll S, Schulz R, Larsson P, Bjelkmar P et al. 2013. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:7845–54
    [Google Scholar]
  92. Rabe M, Verdes D, Seeger S 2011. Understanding protein adsorption phenomena at solid surfaces. Adv. Colloid Interface Sci. 162:87–106
    [Google Scholar]
  93. Rayan A. 2010. New vistas in GPCR 3D structure prediction. J. Mol. Model. 16:183–91
    [Google Scholar]
  94. Rostkowski M, Olsson MHM, Søndergaard CR, Jensen JH 2011. Graphical analysis of pH-dependent properties of proteins predicted using PROPKA. BMC Struct. Biol. 11:6
    [Google Scholar]
  95. Roy K, Kar S, Das RN 2015. A Primer on QSAR/QSPR Modeling: Fundamental Concepts Cham, Switz.: Springer
    [Google Scholar]
  96. Salomon‐Ferrer R, Case DA, Walker RC 2013. An overview of the Amber biomolecular simulation package. WIREs Comput. Mol. Sci. 3:198–210
    [Google Scholar]
  97. Salvia-Trujillo L, Martín-Belloso O, McClements DJ 2016. Excipient nanoemulsions for improving oral bioavailability of bioactives. Nanomaterials 6:E17
    [Google Scholar]
  98. Sampedro JG, Uribe S. 2004. Trehalose-enzyme interactions result in structure stabilization and activity inhibition. The role of viscosity. Mol. Cell. Biochem. 256:319–27
    [Google Scholar]
  99. Sandal M, Behrens M, Brockhoff A, Musiani F, Giorgetti A et al. 2015. Evidence for a transient additional ligand binding site in the TAS2R46 bitter taste receptor. J. Chem. Theory Comput. 11:94439–49
    [Google Scholar]
  100. Schlick T. 2010. Molecular Modeling and Simulation: An Interdisciplinary Guide New York: Springer-Verlag. , 2nd ed..
    [Google Scholar]
  101. Semeraro EF, Giuffrida S, Cottone G, Cupane A 2017. Biopreservation of myoglobin in crowded environment: a comparison between gelatin and trehalose matrixes. J. Phys. Chem. B 121:8731–41
    [Google Scholar]
  102. Shallenberger RS, Acree TE. 1967. Molecular theory of sweet taste. Nature 216:480–82
    [Google Scholar]
  103. Singraber A, Behler J, Dellago C 2019. Library-based LAMMPS implementation of high-dimensional neural network potentials. J. Chem. Theory Comput. 15:1827–40
    [Google Scholar]
  104. Srivastava D, Santiso E, Gubbins K, Barroso da Silva FL 2017. Computationally mapping pKa shifts due to the presence of a polyelectrolyte chain around whey proteins. Langmuir 33:11417–28
    [Google Scholar]
  105. Swenson DWH, Prinz J-H, Noe F, Chodera JD, Bolhuis PG 2019. OpenPathSampling: a Python framework for path sampling simulations. 2. Building and customizing path ensembles and sample schemes. J. Chem. Theory Comput. 15:2837–56
    [Google Scholar]
  106. Tarenzi T, Calandrini V, Potestio R, Carloni P 2019. Open-boundary molecular mechanics/coarse-grained framework for simulations of low-resolution G-protein-coupled receptor-ligand complexes. J. Chem. Theory Comput. 15:2101–9
    [Google Scholar]
  107. Tarenzi T, Calandrini V, Potestio R, Giorgetti A, Carloni P 2017. Open boundary simulations of proteins and their hydration shells by Hamiltonian adaptive resolution scheme. J. Chem. Theory Comput. 13:5647–57
    [Google Scholar]
  108. Teixeira AA, Lund M, Barroso da Silva FL 2010. Fast proton titration scheme for multiscale modeling of protein solutions. J. Chem. Theory Comput. 6:3259–66
    [Google Scholar]
  109. Ubbink J. 2012. Soft matter approaches to structured foods: from “cook-and-look” to rational food design. ? Faraday Discuss 158:9–35
    [Google Scholar]
  110. Vanden-Eijnden E, Venturoli M. 2009. Revisiting the finite temperature string method for the calculation of reaction tubes and free energies. J. Chem. Phys. 130:194103
    [Google Scholar]
  111. van Gunsteren WF, Berendsen HJC 1990. Computer simulation of molecular dynamics: methodology, applications, and perspective in chemistry. Angew. Chem. 29:992–1023
    [Google Scholar]
  112. Vilanova O, Mittag JJ, Kelly PM, Milani S, Dawson KA et al. 2016. Understanding the kinetics of protein-nanoparticle corona formation. ACS Nano 10:10842–50
    [Google Scholar]
  113. Vilaseca P, Dawson KA, Franzese G 2013. Understanding and modulating the competitive surface-adsorption of proteins through coarse-grained molecular dynamics simulations. Soft Matter 9:6978–85
    [Google Scholar]
  114. Vroman L, Adams AL. 1969. Findings with the recording ellipsometer suggesting rapid exchange of specific plasma proteins at liquid/solid interfaces. Surf. Sci. 6:438–46
    [Google Scholar]
  115. Weik F, Weeber R, Szuttor K, Breitsprecher K, de Graaf J et al. 2019. ESPResSo 4.0: an extensible software package for simulating soft matter systems. Eur. Phys. J. Spec. Top. 227:141789–816
    [Google Scholar]
  116. Weng L, Stott SL, Toner M 2019. Exploring dynamics and structure of biomolecules, cryoprotectants, and water using molecular dynamics simulations: implications for biostabilization and biopreservation. Annu. Rev. Biomed. Eng. 21:1–31
    [Google Scholar]
  117. Wilson DI. 2018. Fouling during food processing: progress in tackling this inconvenient truth. Curr. Opin. Food Sci. 23:105–12
    [Google Scholar]
  118. Wu EL, Cheng X, Jo S, Rui H, Song KC et al. 2014. CHARMM-GUI membrane builder toward realistic biological membrane simulations. J. Comput. Chem. 35:1997–2004
    [Google Scholar]
  119. Xia XR, Monteiro-Riviere NA, Mathur S, Song X, Xiao L et al. 2011. Mapping the surface adsorption forces of nanomaterials in biological systems. ACS Nano 5:9074–81
    [Google Scholar]
  120. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y 2014. The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12:7–8
    [Google Scholar]
/content/journals/10.1146/annurev-food-032519-051640
Loading
/content/journals/10.1146/annurev-food-032519-051640
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error