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Soybean is a rich source of vegetal protein for both animal and human consumption. Despite the high levels of protein in soybean seeds, industrial processing to obtain soybean bran significantly decreases the final protein content of the byproducts. To overcome this problem, cultivars with higher protein contents must be developed. However, selecting the target proteins is difficult because of the lack of information on the proteome profile of soybean bran. Therefore, this study obtained the comparative proteomic profiles of both natural coatless seeds and defatted bran from an elite tropical-soybean cultivar. Thus, their extracts were characterized using LC–MS/MS and a total of 550 proteins were identified. Among these, 526 proteins were detected in coatless seeds and 319 proteins in defatted bran. Moreover, a total of 139 proteins were identified as presenting different levels of content in coatless seeds and defatted bran. Among them, only 46 were retained after the seed processing. These proteins were clustered in several important metabolic pathways, such as amino-acid biosynthesis, sugar biosynthesis, and antioxidant activity, meaning that they could act as targets for bioactive products or genome editing to improve protein quality and quantity in soybean grains. These findings can enhance our understanding regarding protein robustness for both soybean crops and the commercial bran improvement because target proteins must remain intact after processing and must be bioactive when overexpressed. Overall, the soybean bran proteomic profile was explored for the first time, providing a valuable catalogue of target proteins that can tolerate the industrial process.

Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.