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      Combining Plant Proteins to Achieve Amino Acid Profiles Adapted to Various Nutritional Objectives—An Exploratory Analysis Using Linear Programming

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          Abstract

          Although plant proteins are often considered to have less nutritional quality because of their suboptimal amino acid (AA) content, the wide variety of their sources, both conventional and emerging, suggests potential opportunities from complementarity between food sources. This study therefore aimed to explore whether, and to what extent, combinations of protein ingredients could reproduce an AA profile set as a nutritional objective, and to identify theoretical solutions and limitations. We collected compositional data on protein ingredients and raw plant foods ( n = 151), and then ran several series of linear optimization to identify protein ingredient mixes that maximized the content in indispensable AA and reproduced various objective profiles: a “balanced profile,” based on AA requirements for adults; “animal profiles” corresponding to conventional animal protein compositions, and a “cardioprotective profile,” which has been associated with a lower cardiovascular risk. We assumed a very good digestibility of plant protein isolates. As expected, obtaining a balanced profile was obvious, but we also identified numerous plant protein mixtures that met demanding AA profiles. Only for particularly demanding profiles, such as mimicking a particular animal protein, did solutions require the use of protein fractions from more specific sources such as pea or canola. Optimal plant blends could mimic animal proteins such as egg white, cow milk, chicken, whey or casein with a similarity reaching 94.2, 98.8, 86.4, 92.4, and 98.0%, respectively. The limiting constraints were mainly isoleucine, lysine, and histidine target contents. These different solutions offer potential for the formulation of mixtures adapted to specific populations or the design of plant-based substitutes. Some ingredients are not commercially available but they could be developed.

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          Converting nitrogen into protein--beyond 6.25 and Jones' factors.

          The protein content in foodstuffs is estimated by multiplying the determined nitrogen content by a nitrogen-to-protein conversion factor. Jones' factors for a series of foodstuffs, including 6.25 as the standard, default conversion factor, have now been used for 75 years. This review provides a brief history of these factors and their underlying paradigm, with an insight into what is meant by "protein." We also review other compelling data on specific conversion factors which may have been overlooked. On the one hand, when 6.25 is used irrespective of the foodstuff, "protein" is simply nitrogen expressed using a different unit and says little about protein (s.s.). On the other hand, conversion factors specific to foodstuffs, such as those provided by Jones, are scientifically flawed. However, the nitrogen:protein ratio does vary according to the foodstuff considered. Therefore, from a scientific point of view, it would be reasonable not to apply current specific factors any longer, but they have continued to be used because scientists fear opening the Pandora's box. But because conversion factors are critical to enabling the simple conversion of determined nitrogen values into protein values and thus accurately evaluating the quantity and the quality of protein in foodstuffs, we propose a set of specific conversion factors for different foodstuffs, together with a default conversion factor (5.6). This would be far more accurate and scientifically sound, and preferable when specifically expressing nitrogen as protein. These factors are of particular importance when "protein" basically means "amino acids," this being the principal nutritional viewpoint.
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            Protein content and amino acid composition of commercially available plant-based protein isolates

            The postprandial rise in essential amino acid (EAA) concentrations modulates the increase in muscle protein synthesis rates after protein ingestion. The EAA content and AA composition of the dietary protein source contribute to the differential muscle protein synthetic response to the ingestion of different proteins. Lower EAA contents and specific lack of sufficient leucine, lysine, and/or methionine may be responsible for the lower anabolic capacity of plant-based compared with animal-based proteins. We compared EAA contents and AA composition of a large selection of plant-based protein sources with animal-based proteins and human skeletal muscle protein. AA composition of oat, lupin, wheat, hemp, microalgae, soy, brown rice, pea, corn, potato, milk, whey, caseinate, casein, egg, and human skeletal muscle protein were assessed using UPLC–MS/MS. EAA contents of plant-based protein isolates such as oat (21%), lupin (21%), and wheat (22%) were lower than animal-based proteins (whey 43%, milk 39%, casein 34%, and egg 32%) and muscle protein (38%). AA profiles largely differed among plant-based proteins with leucine contents ranging from 5.1% for hemp to 13.5% for corn protein, compared to 9.0% for milk, 7.0% for egg, and 7.6% for muscle protein. Methionine and lysine were typically lower in plant-based proteins (1.0 ± 0.3 and 3.6 ± 0.6%) compared with animal-based proteins (2.5 ± 0.1 and 7.0 ± 0.6%) and muscle protein (2.0 and 7.8%, respectively). In conclusion, there are large differences in EAA contents and AA composition between various plant-based protein isolates. Combinations of various plant-based protein isolates or blends of animal and plant-based proteins can provide protein characteristics that closely reflect the typical characteristics of animal-based proteins.
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              Pulse proteins: Processing, characterization, functional properties and applications in food and feed

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                Author and article information

                Contributors
                Journal
                Front Nutr
                Front Nutr
                Front. Nutr.
                Frontiers in Nutrition
                Frontiers Media S.A.
                2296-861X
                03 February 2022
                2021
                : 8
                : 809685
                Affiliations
                [1] 1Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA , Paris, France
                [2] 2Université Clermont Auvergne, INRAE, UMR UNH , Clermont-Ferrand, France
                Author notes

                Edited by: Akinbode A. Adedeji, University of Kentucky, United States

                Reviewed by: Paul Moughan, Massey University, New Zealand; Amelie Deglaire, Agrocampus Ouest, France

                *Correspondence: François Mariotti francois.mariotti@ 123456agroparistech.fr

                This article was submitted to Nutrition and Food Science Technology, a section of the journal Frontiers in Nutrition

                Article
                10.3389/fnut.2021.809685
                8850771
                35187024
                dbe31433-ba32-4009-938f-0e57d7da2908
                Copyright © 2022 Dimina, Rémond, Huneau and Mariotti.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 05 November 2021
                : 29 December 2021
                Page count
                Figures: 4, Tables: 1, Equations: 3, References: 53, Pages: 11, Words: 7703
                Funding
                Funded by: Agence Nationale de la Recherche, doi 10.13039/501100001665;
                Categories
                Nutrition
                Original Research

                indispensable amino acids,linear optimization,plant-based protein isolate,protein blend,amino acid profile,cardiometabolic health

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