teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering.

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.

HSP70-binding motifs function as protein quality control degrons.

Protein quality control (PQC) degrons are short protein segments that target misfolded proteins for proteasomal degradation, and thus protect cells against the accumulation of potentially toxic non-native proteins. Studies have shown that PQC degrons are hydrophobic and rarely contain negatively charged residues, features which are shared with chaperone-binding regions. Here we explore the notion that chaperone-binding regions may function as PQC degrons. When directly tested, we found that a canonical Hsp70-binding motif (the APPY peptide) functioned as a dose-dependent PQC degron both in yeast and in human cells. In yeast, Hsp70, Hsp110, Fes1, and the E3 Ubr1 target the APPY degron. Screening revealed that the sequence space within the chaperone-binding region of APPY that is compatible with degron function is vast. We find that the number of exposed Hsp70-binding sites in the yeast proteome correlates with a reduced protein abundance and half-life. Our results suggest that when protein folding fails, chaperone-binding sites may operate as PQC degrons, and that the sequence properties leading to PQC-linked degradation therefore overlap with those of chaperone binding.

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

Modular 5'-UTR hexamers for context-independent tuning of protein expression in eukaryotes.

Functional characterization of regulatory DNA elements in broad genetic contexts is a prerequisite for forward engineering of biological systems. Translation initiation site (TIS) sequences are attractive to use for regulating gene activity and metabolic pathway fluxes because the genetic changes are minimal. However, limited knowledge is available on tuning gene outputs by varying TISs in different genetic and environmental contexts. Here, we created TIS hexamer libraries in baker's yeast Saccharomyces cerevisiae directly 5' end of a reporter gene in various promoter contexts and measured gene activity distributions for each library. Next, selected TIS sequences, resulted in almost 10-fold changes in reporter outputs, were experimentally characterized in various environmental and genetic contexts in both yeast and mammalian cells. From our analyses, we observed strong linear correlations (R2 = 0.75-0.98) between all pairwise combinations of TIS order and gene activity. Finally, our analysis enabled the identification of a TIS with almost 50% stronger output than a commonly used TIS for protein expression in mammalian cells, and selected TISs were also used to tune gene activities in yeast at a metabolic branch point in order to prototype fitness and carotenoid production landscapes. Taken together, the characterized TISs support reliable context-independent forward engineering of translation initiation in eukaryotes.

Snake Venomics Display: An online toolbox for visualization of snake venomics data.

With the introduction of powerful mass spectrometry equipment into the field of snake venom proteomics, a large body of venomics data is accumulating. To allow for better comparison between venom compositions from different snake species and to provide an online database containing this data, we devised the Snake Venomics Display toolbox for visualization of snake venomics data on linear scales. This toolbox is freely available to be used online at https://tropicalpharmacology.com/tools/snake-venomics-display/ and allows researchers to visualize venomics data in a Relative Abundance (%) visualization mode and in an Absolute Abundance (mg) visualization mode, the latter taking venom yields into account. The curated venomics data for all snake species included in this database is also made available in a downloadable Excel file format. The Snake Venomics Display toolbox represents a simple way of handling snake venomics data, which is better suited for large data sets of venom compositions from multiple snake species.

Functional mining of transporters using synthetic selections.

Only 25% of bacterial membrane transporters have functional annotation owing to the difficulty of experimental study and of accurate prediction of their function. Here we report a sequence-independent method for high-throughput mining of novel transporters. The method is based on ligand-responsive biosensor systems that enable selective growth of cells only if they encode a ligand-specific importer. We developed such a synthetic selection system for thiamine pyrophosphate and mined soil and gut metagenomes for thiamine-uptake functions. We identified several members of a novel class of thiamine transporters, PnuT, which is widely distributed across multiple bacterial phyla. We demonstrate that with modular replacement of the biosensor, we could expand our method to xanthine and identify xanthine permeases from gut and soil metagenomes. Our results demonstrate how synthetic-biology approaches can effectively be deployed to functionally mine metagenomes and elucidate sequence-function relationships of small-molecule transport systems in bacteria.