1932

Abstract

Structural bioinformatics analyzes protein structural models with the goal of uncovering molecular drivers of food functionality. This field aims to develop tools that can rapidly extract relevant information from protein databases as well as organize this information for researchers interested in studying protein functionality. Food bioinformaticians take advantage of millions of protein amino acid sequences and structures contained within these databases, extracting features such as surface hydrophobicity that are then used to model functionality, including solubility, thermostability, and emulsification. This work is aided by a protein structure–function relationship framework, in which bioinformatic properties are linked to physicochemical experimentation. Strong bioinformatic correlations exist for protein secondary structure, electrostatic potential, and surface hydrophobicity. Modeling changes in protein structures through molecular mechanics is an increasingly accessible field that will continue to propel food science research.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-food-060721-022222
2023-03-27
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/food/14/1/annurev-food-060721-022222.html?itemId=/content/journals/10.1146/annurev-food-060721-022222&mimeType=html&fmt=ahah

Literature Cited

  1. Acland A, Agarwala R, Barrett T, Beck J, Benson DA et al. 2013. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 41:D1D8–20
    [Google Scholar]
  2. Aguilar-Toalá JE, Hall FG, Urbizo-Reyes UC, Garcia HS, Vallejo-Cordoba B et al. 2019. In silico prediction and in vitro assessment of multifunctional properties of postbiotics obtained from two probiotic bacteria. Probiotics Antimicrob. Proteins 12:2608–22
    [Google Scholar]
  3. Ainis WN, Boire A, Solé-Jamault V, Nicolas A, Bouhallab S, Ipsen R 2019. Contrasting assemblies of oppositely charged proteins. Langmuir 35:309923–33
    [Google Scholar]
  4. Alford RF, Leaver-Fay A, Jeliazkov JR, O'Meara MJ, DiMaio FP et al. 2017. The Rosetta all-atom energy function for macromolecular modeling and design. J. Chem. Theory Comput. 13:63031–48
    [Google Scholar]
  5. Alizadeh-Pasdar N, Li-Chan ECY. 2000. Comparison of protein surface hydrophobicity measured at various pH values using three different fluorescent probes. J. Agric. Food Chem. 48:2328–34
    [Google Scholar]
  6. Anfinsen CB. 1973. Principles that govern the folding of protein chains. Science 181:4096223–30
    [Google Scholar]
  7. Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S et al. 2021. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373:6557871–76
    [Google Scholar]
  8. Baler K, Martin OA, Carignano MA, Ameer GA, Vila JA, Szleifer I. 2014. Electrostatic unfolding and interactions of albumin driven by pH changes: a molecular dynamics study. J. Phys. Chem. B 118:4921–30
    [Google Scholar]
  9. Barth A. 2007. Infrared spectroscopy of proteins. Biochim. Biophys. Acta 1767:91073–101
    [Google Scholar]
  10. Bava KA, Gromiha MM, Uedaira H, Kitajima K, Sarai A 2004. ProTherm, version 4.0: thermodynamic database for proteins and mutants. Nucleic Acids Res 32:Suppl. 1D120–21
    [Google Scholar]
  11. Beck S, Knoerzer K, Sellahewa J, Emin A, Arcot J. 2016. Effect of different heat-treatment times and applied shear on secondary structure, molecular weight distribution, solubility and rheological properties of pea protein isolate as investigated by capillary rheometry. J. Food Eng. 208:66–76
    [Google Scholar]
  12. Becktel WJ, Schellman JA. 1987. Protein stability curves. Biopolymers 26:111859–77
    [Google Scholar]
  13. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN et al. 2000. The Protein Data Bank. Nucleic Acids Res 28:1235–42
    [Google Scholar]
  14. Bienert S, Waterhouse A, de Beer TAP, Tauriello G, Studer G et al. 2016. The SWISS-MODEL repository—new features and functionality. Nucleic Acids Res. 45:D1D313–19
    [Google Scholar]
  15. Brazas MD, Yamada JT, Ouellette BFF. 2010. Providing web servers and training in bioinformatics: 2010 update on the bioinformatics links directory. Nucleic Acids Res. 38:Suppl. 2W3–6
    [Google Scholar]
  16. Brown SD, Babbitt PC. 2014. New insights about enzyme evolution from large scale studies of sequence and structure relationships. J. Biol. Chem. 289:4430221–28
    [Google Scholar]
  17. Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. 2008. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res 37:Suppl. 1D233–38
    [Google Scholar]
  18. Case DA, Cheatham TE III, Darden T, Gohlke H, Luo R et al. 2005. The Amber biomolecular simulation programs. J. Comput. Chem. 26:161668–88
    [Google Scholar]
  19. Chakravorty A, Jia Z, Li L, Alexov E 2017. A new DelPhi feature for modeling electrostatic potential around proteins: role of bound ions and implications for zeta-potential. Langmuir 33:92283–95
    [Google Scholar]
  20. Chen VB, Arendall WB III, Headd JJ, Keedy DA, Immormino RM et al. 2010. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. Sect. D 66:112–21
    [Google Scholar]
  21. Christoffer C, Chen S, Bharadwaj V, Aderinwale T, Kumar V et al. 2021. LZerD webserver for pairwise and multiple protein-protein docking. Nucleic Acids Res 49:W1W359–65
    [Google Scholar]
  22. Csaba G, Birzele F, Zimmer R. 2009. Systematic comparison of SCOP and CATH: a new gold standard for protein structure analysis. BMC Struct. Biol. 9:23
    [Google Scholar]
  23. Dianda N, Rouf TB, Bonilla J, Hendrick V, Kokini J. 2019. Effect of solvent polarity on the secondary structure, surface and mechanical properties of biodegradable kafirin films. J. Cereal Sci. 90:102856
    [Google Scholar]
  24. Dickie AM, Kokini JL. 1983. An improved model for food thickness from non-Newtonian fluid mechanics in the mouth. J. Food Sci. 48:157–61
    [Google Scholar]
  25. Dolinsky TJ, Nielsen JE, McCammon JA, Baker NA. 2004. PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res 32:Suppl. 2W665–67
    [Google Scholar]
  26. Eddy SR. 1998. Profile hidden Markov models. Bioinformatics 14:9755–63
    [Google Scholar]
  27. Finn RD, Clements J, Eddy SR. 2011. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 39:Suppl. 2W29–37
    [Google Scholar]
  28. Fiser A, Šali A 2003. Modeller: generation and refinement of homology-based protein structure models. Methods in Enzymology, Vol. 374 CW Carter Jr., RM Sweet 461–91. Cambridge, MA: Academic
    [Google Scholar]
  29. Garcia-Moreno P, Gregersen S, Nedamani E, Olsen T, Maracatili P et al. 2020. Identification of emulsifier potato peptides by bioinformatics: application to omega-3 delivery emulsions and release from potato industry side streams. Sci. Rep. 10:690
    [Google Scholar]
  30. Gauthier J, Vincent AT, Charette SJ, Derome N. 2018. A brief history of bioinformatics. Brief. Bioinform. 20:61981–96
    [Google Scholar]
  31. Goodman RE, Ebisawa M, Ferreira F, Sampson HA, van Ree R et al. 2016. AllergenOnline: a peer-reviewed, curated allergen database to assess novel food proteins for potential cross-reactivity. Mol. Nutr. Food Res. 60:51183–98
    [Google Scholar]
  32. Goormaghtigh E, Gasper R, Bénard A, Goldsztein A, Raussens V. 2009. Protein secondary structure content in solution, films and tissues: redundancy and complementarity of the information content in circular dichroism, transmission and ATR FTIR spectra. Biochim. Biophys. Acta 1794:91332–43
    [Google Scholar]
  33. Guex N, Peitsch MC. 1997. SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 18:152714–23
    [Google Scholar]
  34. Guldiken B, Stobbs J, Nickerson M. 2021. Heat induced gelation of pulse protein networks. Food Chem. 350:129158
    [Google Scholar]
  35. Gupta JK, Adams DJ, Berry NG. 2016. Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints. Chem. Sci. 7:74713–19
    [Google Scholar]
  36. Haas J, Barbato A, Behringer D, Studer G, Roth S et al. 2018. Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12. Proteins Struct. Funct. Bioinform. 86:S1387–98
    [Google Scholar]
  37. Heldt CL, Zahid A, Vijayaragavan KS, Mi X. 2017. Experimental and computational surface hydrophobicity analysis of a non-enveloped virus and proteins. Colloids Surfaces B 153:77–84
    [Google Scholar]
  38. Helmick H, Hartanto C, Bhunia A, Liceaga A, Kokini JL. 2021a. Validation of bioinformatic modeling for the zeta potential of vicilin, legumin, and commercial pea protein isolate. Food Biophys 16:474–83
    [Google Scholar]
  39. Helmick H, Hartanto C, Ettestad S, Liceaga A, Bhunia AK, Kokini JL 2023. Quantitative structure-property relationships of thermoset pea protein gels with ethanol, shear, and sub-zero temperature pretreatments. Food Hydrocoll. 135:108066
    [Google Scholar]
  40. Helmick H, Turasan H, Yildirim M, Bhunia A, Liceaga A, Kokini J 2021b. Cold denaturation of proteins: where bioinformatics meets thermodynamics to offer a mechanistic understanding: pea protein as a case study. J. Agric. Food Chem. 26:226339–50
    [Google Scholar]
  41. Hirata F, Sugita M, Yoshida M, Akasaka K. 2018. Perspective: structural fluctuation of protein and Anfinsen's thermodynamic hypothesis. J. Chem. Phys. 148:2020901
    [Google Scholar]
  42. Honig B, Nichols A. 1992. Classical electrostatics in biology and chemistry. Science 268:52141144–49
    [Google Scholar]
  43. Hou Q, Kwasigroch JM, Rooman M, Pucci F. 2019. SOLart: a structure-based method to predict protein solubility and aggregation. Bioinformatics 36:51445–52
    [Google Scholar]
  44. Hsin J, Arkhipov A, Yin Y, Stone JE, Schulten K. 2008. Using VMD: an introductory tutorial. Curr. Protoc. Bioinform. 24:15.7.1–5.7.48
    [Google Scholar]
  45. Hubbard TJP, Murzin AG, Brenner SE, Chothia C. 1997. SCOP: a Structural Classification of Proteins database. Nucleic Acids Res 25:1236–39
    [Google Scholar]
  46. Humphrey W, Dalke A, Schulten K. 1996. VMD: visual molecular dynamics. J. Mol. Graph. 14:133–38
    [Google Scholar]
  47. Jo S, Kim T, Iyer VG, Im W. 2008. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29:111859–65
    [Google Scholar]
  48. Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. 2008. NCBI BLAST: a better web interface. Nucleic Acids Res 36:Suppl. 2W5–9
    [Google Scholar]
  49. Jumper J, Evans R, Pritzel A, Green T, Figurnov M et al. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596:7873583–89
    [Google Scholar]
  50. Jurrus E, Engel D, Star K, Monson K, Brandi J et al. 2018. Improvements to the APBS biomolecular solvation software suite. Protein Sci. 27:1112–28
    [Google Scholar]
  51. Katritzky AR, Kuanar M, Slavov S, Hall CD, Karelson M et al. 2010. Quantitative correlation of physical and chemical properties with chemical structure: utility for prediction. Chem. Rev. 110:105714–89
    [Google Scholar]
  52. Keller R. 2018. Identification of potential lipid binding regions in cereal proteins and peptides with the use of bioinformatics. J. Cereal Sci. 80:128–34
    [Google Scholar]
  53. Klemmer K, Stone W, Nickerson M. 2010. Complex coacervation of pea protein isolate and alginate polysaccharides. Food Chem. 130:3710–15
    [Google Scholar]
  54. Knudsen M, Wiuf C. 2010. The CATH database. Hum. Genom. 4:207–12
    [Google Scholar]
  55. Kozlowski LP. 2016. Isoelectric point for PDB proteins Dec 2015. RepOD https://doi.org/10.18150/repod.1549954
    [Google Scholar]
  56. Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. 2021. Critical assessment of methods of protein structure prediction (CASP)—round XIV. Proteins Struct. Funct. Bioinform. 89:121607–17
    [Google Scholar]
  57. Li R, Wang X, Liu J, Cui Q, Wang X et al. 2019. Relationship between molecular flexibility and emulsifying properties of soy protein isolate-glucose conjugates. J. Agric. Food Chem. 67:144089–97
    [Google Scholar]
  58. Liang H-N, Tang C-H. 2013. Emulsifying and interfacial properties of vicilins: role of conformational flexibility at quaternary and/or tertiary levels. J. Agric. Food Chem. 61:4611140–50
    [Google Scholar]
  59. Lienqueo ME, Mahn A, Asenjo JA. 2002. Mathematical correlations for predicting protein retention times in hydrophobic interaction chromatography. J. Chromatogr. A 978:171–79
    [Google Scholar]
  60. Lin D, Lu W, Kelly AL, Zhang L, Zheng B, Miao S. 2017. Interactions of vegetable proteins with other polymers: structure-function relationships and applications in the food industry. Trends Food Sci. Technol. 68:130–44
    [Google Scholar]
  61. Lomize MA, Pogozheva ID, Joo H, Mosberg HI, Lomize AL. 2011. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res 40:D1D370–76
    [Google Scholar]
  62. Malhotra A, Coupland JN. 2004. The effect of surfactants on the solubility, zeta potential, and viscosity of soy protein isolates. Food Hydrocoll. 18:1101–8
    [Google Scholar]
  63. Mirdita M, Konstantin S, Yoshitaka M, Lim H, Sergey O, Martin S 2022. ColabFold: making protein folding accessible to all. Nat. Methods 19:6679–82
    [Google Scholar]
  64. Miyazawa S, Jernigan RL. 1996. Residue–residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J. Mol. Biol. 256:3623–44
    [Google Scholar]
  65. Mooers BHM. 2020. Shortcuts for faster image creation in PyMOL. Protein Sci 29:1268–76
    [Google Scholar]
  66. Moro A, Gatti C, Delorenzi N. 2001. Hydrophobicity of whey protein concentrates measured by fluorescence quenching and its relation with surface functional properties. J. Agric. Food Chem. 49:104784–89
    [Google Scholar]
  67. Nakai S. 1983. Structure-function relationships of food proteins: with an emphasis on the importance of protein hydrophobicity. J. Agric. Food Chem. 31:4676–83
    [Google Scholar]
  68. Nnyigide OS, Hyun K 2020. The protection of bovine serum albumin against thermal denaturation and gelation by sodium dodecyl sulfate studied by rheology and molecular dynamics simulation. Food Hydrocoll. 103:105656
    [Google Scholar]
  69. Ouellette RJ, Rawn JD. 2015. Principles of Organic Chemistry Cambridge, MA: Academic
  70. Patel B, Singh V, Patel S 2019. Structural bioinformatics. Essentials of Bioinformatics, Vol. 1 NA Shaik, KR Hakeem, B Banaganapalli, R Elango 169–99. Cham: Springer
    [Google Scholar]
  71. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM et al. 2004. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25:131605–12
    [Google Scholar]
  72. Pettersen EF, Goddard TD, Huang CC, Meng EC, Couch GS et al. 2021. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci 30:170–82
    [Google Scholar]
  73. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E et al. 2005. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26:161781–802
    [Google Scholar]
  74. Pripp AH, Isaksson T, Stepaniak L, Sørhaug T, Ardö Y. 2005. Quantitative structure activity relationship modelling of peptides and proteins as a tool in food science. Trends Food Sci. Technol. 16:11484–94
    [Google Scholar]
  75. Pucci F, Kwasigroch JM, Rooman M. 2017. SCooP: an accurate and fast predictor of protein stability curves as a function of temperature. Bioinformatics 33:213415–22
    [Google Scholar]
  76. Qin Z, Buehler MJ. 2010. Molecular dynamics simulation of the α-helix to β-sheet transition in coiled protein filaments: evidence for a critical filament length scale. Phys. Rev. Lett. 104:19198304
    [Google Scholar]
  77. Rahman M, Browne JJ, Van Crugten J, Hasan MdF, Liu L, Barkla BJ. 2020. In silico, molecular docking and in vitro antimicrobial activity of the major rapeseed seed storage proteins. Front. Pharmacol. 11:1340
    [Google Scholar]
  78. Rasheed F, Markgren J, Hedenqvist M, Johansson E. 2020. Modeling to understand plant protein structure-function relationships—implications for seed storage proteins. Molecules 25:4873
    [Google Scholar]
  79. Remmert M, Biegert A, Hauser A, Söding J. 2012. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods 9:2173–75
    [Google Scholar]
  80. Reznikov G, Baars A, Delgado A. 2011. The initial stage of high-pressure induced β-lactoglobulin aggregation: the long-run simulation. Int. J. Food Sci. Technol. 46:122603–10
    [Google Scholar]
  81. Robertson AD, Murphy KP. 1997. Protein structure and the energetics of protein stability. Chem. Rev. 97:51251–68
    [Google Scholar]
  82. Rose PW, Prlić A, Altunkaya A, Bi C, Bradley AR et al. 2017. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res 45:D1D271–81
    [Google Scholar]
  83. Salgado JC, Rapaport I, Asenjo JA. 2005. Is it possible to predict the average surface hydrophobicity of a protein using only its amino acid composition?. J. Chromatogr. A 1075:1133–43
    [Google Scholar]
  84. Sang S, Zhang H, Xu L, Chen Y, Xu X et al. 2018. Functionality of ovalbumin during Chinese steamed bread-making processing. Food Chem 253:203–10
    [Google Scholar]
  85. Schwede T, Kopp J, Guex N, Peitsch MC. 2003. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31:133381–85
    [Google Scholar]
  86. Seeliger D, de Groot BL. 2010. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comput.-Aided Mol. Des. 24:5417–22
    [Google Scholar]
  87. Shen M, Sali A. 2006. Statistical potential for assessment and prediction of protein structures. Protein Sci 15:112507–24
    [Google Scholar]
  88. Studer G, Rempfer C, Waterhouse A, Gumienny R, Haas J, Schwede T. 2020. QMEANDisCo—distance constraints applied on model quality estimation. Bioinformatics 36:61765–71
    [Google Scholar]
  89. Sun X, Arntfield S 2010. Dynamic oscillatory rheological measurement and thermal properties of pea protein extracted by salt method: effect of pH and NaCl. J. Food Eng. 105:3577–82
    [Google Scholar]
  90. Tandang-Silvas MR, Cabanos CS, Peña LDC, De la Rosa APB, Osuna-Castro JA et al. 2012. Crystal structure of a major seed storage protein, 11S proglobulin, from Amaranthus hypochondriacus: insight into its physico-chemical properties. Food Chem 135:2819–26
    [Google Scholar]
  91. Tandang-Silvas MRG, Fukuda T, Fukuda C, Prak K, Cabanos C et al. 2010. Conservation and divergence on plant seed 11S globulins based on crystal structures. Biochim. Biophys. Acta 1804:71432–42
    [Google Scholar]
  92. Tandang-Silvas MRG, Tecson-Mendoza EM, Mikami B, Utsumi S, Maruyama N. 2011. Molecular design of seed storage proteins for enhanced food physicochemical properties. Annu. Rev. Food Sci. Technol. 2:59–73
    [Google Scholar]
  93. Tang C-H. 2017. Emulsifying properties of soy proteins: a critical review with emphasis on the role of conformational flexibility. Crit. Rev. Food Sci. Nutr. 57:122636–79
    [Google Scholar]
  94. Tao X, Huang Y, Wang C, Chen F, Yang L et al. 2020. Recent developments in molecular docking technology applied in food science: a review. Int. J. Food Sci. Technol. 55:133–45
    [Google Scholar]
  95. Tong X, Cao J, Sun M, Liao P, Dai S et al. 2021. Physical and oxidative stability of oil-in-water (O/W) emulsions in the presence of protein (peptide): characteristics analysis and bioinformatics prediction. LWT 149:111782
    [Google Scholar]
  96. Turasan H, Barber E, Malm M, Kokini J. 2017. Mechanical and spectroscopic characterization of crosslinked zein films cast from solutions of acetic acid leading to a new mechanism for the crosslinking of oleic acid plasticized zein films. Food Res. Int. 108:357–67
    [Google Scholar]
  97. Turasan H, Kokini J. 2016. Advances in understanding the molecular structures and functionalities of biodegradable zein-based materials using spectroscopic techniques: a review. Biomacromolecules 18:331–54
    [Google Scholar]
  98. Uberto R, Moomaw EW. 2013. Protein similarity networks reveal relationships among sequence, structure, and function within the cupin superfamily. PLOS ONE 8:9e74477
    [Google Scholar]
  99. UniProt Consort 2008. The Universal Protein Resource (UniProt). Nucleic Acids Res 36:Suppl. 1D190–95
    [Google Scholar]
  100. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. 2005. GROMACS: fast, flexible, and free. J. Comput. Chem. 26:161701–18
    [Google Scholar]
  101. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S et al. 2010. CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31:4671–90
    [Google Scholar]
  102. Varadi M, Anyango S, Deshpande M, Nair S, Natassia C et al. 2022. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50:D1D439–44
    [Google Scholar]
  103. Vidal-Limon A, Aguilar-Toalá JE, Liceaga AM. 2022. Integration of molecular docking analysis and molecular dynamics simulations for studying food proteins and bioactive peptides. J. Agric. Food Chem. 70:4934–43
    [Google Scholar]
  104. Wallner B, Elofsson A. 2007. Prediction of global and local model quality in CASP7 using Pcons and ProQ. Proteins Struct. Funct. Bioinform. 69:S8184–93
    [Google Scholar]
  105. Walsh I, Pollastri G, Tosatto SCE. 2015. Correct machine learning on protein sequences: a peer-reviewing perspective. Brief. Bioinform. 17:5831–40
    [Google Scholar]
  106. Wang G, Dunbrack J, Roland L 2003. PISCES: a protein sequence culling server. Bioinformatics 19:121589–91
    [Google Scholar]
  107. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G et al. 2018. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46:W1,2W296–303
    [Google Scholar]
  108. Weininger D. 1988. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inform. Comput. Sci. 28:131–36
    [Google Scholar]
  109. Williams CJ, Headd JJ, Moriarty NW, Prisant MG, Videau LL et al. 2018. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci 27:1293–315
    [Google Scholar]
  110. Wu C, Wang T, Ren C, Ma W, Wu D et al. 2021. Advancement of food-derived mixed protein systems: interactions, aggregations, and functional properties. Compr. Rev. Food Sci. Food Saf. 20:1627–51
    [Google Scholar]
  111. Zare D, Allison JR, McGrath KM. 2016. Molecular dynamics simulation of β-lactoglobulin at different oil/water interfaces. Biomacromolecules 17:51572–81
    [Google Scholar]
/content/journals/10.1146/annurev-food-060721-022222
Loading
/content/journals/10.1146/annurev-food-060721-022222
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