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@ARTICLE{BarrosodaSilva:884079,
      author       = {Barroso da Silva, Fernando Luís and Carloni, Paolo and
                      Cheung, David and Cottone, Grazia and Donnini, Serena and
                      Foegeding, E. Allen and Gulzar, Muhammad and Jacquier, Jean
                      Christophe and Lobaskin, Vladimir and MacKernan, Donal and
                      Mohammad Hosseini Naveh, Zeynab and Radhakrishnan, Ravi and
                      Santiso, Erik E.},
      title        = {{U}nderstanding and {C}ontrolling {F}ood {P}rotein
                      {S}tructure and {F}unction in {F}oods: {P}erspectives from
                      {E}xperiments and {C}omputer {S}imulations},
      journal      = {Annual review of food science and technology},
      volume       = {11},
      number       = {1},
      issn         = {1941-1413},
      address      = {Palo Alto, Calif.},
      publisher    = {Annual Reviews64535},
      reportid     = {FZJ-2020-03080},
      pages        = {365-387},
      year         = {2020},
      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.},
      cin          = {IAS-5 / INM-9},
      ddc          = {630},
      cid          = {I:(DE-Juel1)IAS-5-20120330 / I:(DE-Juel1)INM-9-20140121},
      pnm          = {899 - ohne Topic (POF3-899)},
      pid          = {G:(DE-HGF)POF3-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31951485},
      UT           = {WOS:000523954500016},
      doi          = {10.1146/annurev-food-032519-051640},
      url          = {https://juser.fz-juelich.de/record/884079},
}