Interestingly, vProtein finds no statistically significant bias toward grain/legume pairings for protein complementation. These analyses suggest that pairings of plant-based foods should be based on the individual foods themselves instead of based on broader food group-food group pairings. Overall, the most efficient pairings include sweet corn/tomatoes, apple/coconut, and sweet corn/cherry. The top pairings also highlight the utility of less common protein sources such as the seaweeds laver and spirulina, pumpkin leaves, and lambsquarters. From a public health perspective, many of the food pairings represent novel, low cost food sources to combat malnutrition. Full analysis results are available online at http://www.foodwiki.com/vprotein .

For single foods, vProtein identifies foods with particularly balanced IAA patterns such as wheat germ, quinoa, and cauliflower. vProtein also identifies foods with particularly unbalanced IAA patterns such as macadamia nuts, degermed corn products, and wakame seaweed. Although less useful alone, some unbalanced foods provide unusually good complements, such as Brazil nuts to legumes.

vProtein uses 1251 plant-based foods listed in the United States Department of Agriculture standard release 22 database to determine the quantity of each food or pair of foods required to satisfy human IAA needs as determined by the 2005 daily recommended intake. The quantity of food in a pair is found using a linear programming approach that minimizes total calories, total excess IAAs, or the total weight of the combination.

Indispensible amino acids (IAAs) are used by the body in different proportions. Most animal-based foods provide these IAAs in roughly the needed proportions, but many plant-based foods provide different proportions of IAAs. To explore how these plant-based foods can be better used in human nutrition, we have created the computational tool vProtein to identify optimal food complements to satisfy human protein needs.

Copyright: © 2011 Woolf et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

The human body requires a small set of indispensible amino acids (IAAs) in a defined proportion. These IAAs are provided in roughly the same proportion in most animal-based foods, but are often found in different proportions in plant-based foods [1]. Humans have overcome imbalances in plant-based foods by consuming foods with complementary IAA patterns. Historic examples of these complements include beans and corn in the Americas [2], or rice and soy in Asia [3], [4]. However, given changes in food availability and an increase in data about food, what other plant-based food pairings could serve our needs as well or better than these historical complements? In this work we have developed a quantitative tool called vProtein to explore this question.

Broadly, complementation involves consuming two or more foods together to yield an amino acid pattern that is better than the sum of the two foods alone. A simplified example of complementation with three hypothetical amino acids is shown in Figure 1. In this example, the number of units that contain a complete set of amino acids determines the biological value (BV) of the complement. Once one or more amino acids are depleted, protein synthesis cannot proceed. For a single food (Fig. 1A), there is no complementation, so doubling the food intake will yield double the BV. In contrast, pairing of two foods that are optimal complements (Fig. 1B) produces a synergistic effect where the two components alone yield 2 units BV, but together they yield 4 units BV. In the case shown in Figure 1B, a 1∶1 complement is optimal in the sense that there are no excess amino acids–thus all components of the food can be used with full 100% efficiency. If the pairing is suboptimal but still complementary (Fig. 1C,D), consuming the two foods together yields more biological value than each food alone, but leaves a varying quantity of amino acids in excess, resulting in less efficient combinations. As shown in Fig. 1D, pairing food A to food B in a 1∶2 ratio yields a more efficient pairing than the 1∶1 pairing in Fig. 1C. Thus, for any set of foods there is a particular ratio that will minimize the excess amino acids to produce the most efficient combination. An example of an optimized rice-soy complement is shown in Figure S1.

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larger image TIFF original image Download: Figure 1. A simplifed example of complementation with three hypothetical amino acids (vertical axis). Note that the optimial pairing (B) is balanced in that all of the amino acids contribute to the biological value, while the suboptimal pairings (A,C, and D) are unbalanced in that there are excess amino acids (6, 2, and 1 units respectively) that do not contribute to the biological value. https://doi.org/10.1371/journal.pone.0018836.g001

The relative proportion, or pattern, of IAAs required for human health has been the topic of considerable research. IAA patterns commonly discussed include the MIT pattern [5], Millward Pattern [6], the 1985 FAO/WHO/UNU pattern [7], and the 2005 dietary reference intake (DRI) pattern published by the Food and Nutrition Board [8]. These patterns are broadly similar to each other, and are similar to the IAA patterns observed in common animal-based protein sources such as chicken breast, egg, and milk as is shown in Figure S1.

A common use for IAA patterns is for calculating the protein digestibility corrected amino acid score (PDCAAS) for particular foods [9]. PDCAAS values range from 1.0 to 0, with 1.0 representing protein sources with high BV such as egg and milk. Although PDCAAS values are widely accepted, the method has two practical limitations as noted elsewhere [10], [11]. First, PDCAAS values do not indicate possible complements. Thus combining two foods with low PDCAAS values (low BV) may or may not yield a superior IAA pattern. As a result, using PDCAAS alone would overlook potentially important food complements that may be of high BV. Second, to calculate a PDCAAS value requires knowing the true fecal digestibility of the food. Unfortunately, the fecal digestibility has been measured for only a small set of foods, and it depends on how the food is processed. However in the foods measured the range of fecal digestibility is relatively small. For example, according to a 1990 FAO/WHO study, fecal digestibility ranged from 0.98 for egg to 0.91 for wheat, where 1.0 represents full digestibility [9].

As an alternative to using PDCAAS, we have developed an analytical tool called vProtein that uses only IAA patterns to evaluate combinations of foods. Using IAA patterns provided by the United States Department of Agriculture standard release 22 (USDA sr22) database, we use vProtein to identify single foods and pairs of foods that yield an IAA pattern most similar to the 2005 dietary reference intake (DRI) pattern [8]. vProtein identifies the optimal weighting of each food using a linear programming approach. A similar approach has been successfully used in earlier work to estimate the ingredient fraction in processed foods [12]. The results identify both traditional and unexpected couplings and, in so doing, provide a data-driven resource to help inform dietary decisions.