Abstract: This disclosure provides a technology for developing alternative protein sources for use in industrial food production. The technology evaluates naturally occurring proteins by a process that is done partly in silico and partly by empirical evaluation. A database is created in which each individual protein is characterized by vector representations of structural and functional features. Clusters of individual proteins are formed by pairwise comparison of each protein's vector representation, adjusting the degree of similarity used to define clusters until a desired number of clusters are obtained. A protein representative is selected from each cluster for evaluation by high-throughput expression and laboratory testing for a particular food function. High scoring representatives identify clusters that can be mined for additional protein candidates. Multiple cycles of the machine learning, database mining, expression and testing yield ingredients suitable for assessment as part of a commercial food product.

Abstract: The present disclosure describes the engineering of microbial cells for fermentative production of 4-APEA and related products and provides novel engineered microbial cells and cultures, as well as related 4-APEA production methods.

Abstract: The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

Abstract: Systems, methods, and non-transitory computer-readable media are described for identifying candidate biological sequences for screening to determine whether the candidate biological sequences enable a biological function. Identification may be based upon (a) degrees of similarity between test sequences and reference sequences that are known to enable the function, and (b) comparisons of regions in the test sequences to reference regions that are known to bind to a molecule. Identification may also be based upon determining sequence similarities between reference sequences, grouping the reference sequences into clusters that each correspond to a molecule that is indicated as capable of being bound by a reference sequence in the cluster, and identifying matching test sequences based upon a comparison of the test sequences with reference sequences in the clusters.

Abstract: The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bioinformatic tools and techniques.

Abstract: The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

Abstract: The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

Abstract: The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.