G6P-capturing molecules in the periplasm of Escherichia coli accelerate the shikimate pathway.

Escherichia coli, the most studied prokaryote, is an excellent host for producing valuable chemicals from renewable resources as it is easy to manipulate genetically. Since the periplasmic environment can be easily controlled externally, elucidating how the localization of specific proteins or small molecules in the periplasm affects metabolism may lead to bioproduction development using E. coli. We investigated metabolic changes and its mechanisms occurring when specific proteins are localized to the E. coli periplasm. We found that the periplasmic localization of ß-glucosidase promoted the shikimate pathway involved in the synthesis of aromatic chemicals. The periplasmic localization of other proteins with an affinity for glucose-6-phosphate (G6P), such as inactivated mutants of Pgi, Zwf, and PhoA, similarly accelerated the shikimate pathway. Our results indicate that G6P is transported from the cytoplasm to the periplasm by the glucose transporter protein EIICBGlc, and then captured by ß-glucosidase.

Extensive antibody search with whole spectrum black-box optimization.

In designing functional biological sequences with machine learning, the activity predictor tends to be inaccurate due to shortage of data. Top ranked sequences are thus unlikely to contain effective ones. This paper proposes to take prediction stability into account to provide domain experts with a reasonable list of sequences to choose from. In our approach, multiple prediction models are trained by subsampling the training set and the multi-objective optimization problem, where one objective is the average activity and the other is the standard deviation, is solved. The Pareto front represents a list of sequences with the whole spectrum of activity and stability. Using this method, we designed VHH (Variable domain of Heavy chain of Heavy chain) antibodies based on the dataset obtained from deep mutational screening. To solve multi-objective optimization, we employed our sequence design software MOQA that uses quantum annealing. By applying several selection criteria to 19,778 designed sequences, five sequences were selected for wet-lab validation. One sequence, 16 mutations away from the closest training sequence, was successfully expressed and found to possess desired binding specificity. Our whole spectrum approach provides a balanced way of dealing with the prediction uncertainty, and can possibly be applied to extensive search of functional sequences.

Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning.

Despite the advances in surface-display systems for directed evolution, variants with high affinity are not always enriched due to undesirable biases that increase target-unrelated variants during biopanning. Here, our goal was to design a library containing improved variants from the information of the "weakly enriched" library where functional variants were weakly enriched. Deep sequencing for the previous biopanning result, where no functional antibody mimetics were experimentally identified, revealed that weak enrichment was partly due to undesirable biases during phage infection and amplification steps. The clustering analysis of the deep sequencing data from appropriate steps revealed no distinct sequence patterns, but a Bayesian machine learning model trained with the selected deep sequencing data supplied nine clusters with distinct sequence patterns. Phage libraries were designed on the basis of the sequence patterns identified, and four improved variants with target-specific affinity (EC50 = 80-277 nM) were identified by biopanning. The selection and use of deep sequencing data without undesirable bias enabled us to extract the information on prospective variants. In summary, the use of appropriate deep sequencing data and machine learning with the sequence data has the possibility of finding sequence space where functional variants are enriched.

Synthesis of epitope-targeting nanobody based on native protein-protein interactions for FtsZ filamentation suppressor.

Phage display and biopanning are powerful tools for generating binding molecules for a specific target. However, the selection process based only on binding affinity provides no assurance for the antibody's affinity to the target epitope. In this study, we propose a molecular-evolution approach guided by native protein-protein interactions to generate epitope-targeting antibodies. The binding-site sequence in a native protein was grafted into a complementarity-determining region (CDR) in the nanobody, and a nonrelated CDR loop (in the grafted nanobody) was randomized to create a phage display library. In this construction of nanobodies by integrating graft and evolution technology (CAnIGET method), suitable grafting of the functional sequence added functionality to the nanobody, and the molecular-evolution approach enhanced the binding function to inhibit the native protein-protein interactions. To apply for biological tool with growth screening, model nanobodies with an affinity for filamenting temperature-sensitive mutant Z (FtsZ) from Staphylococcus aureus were constructed and completely inhibited the polymerization of FtsZ as a function. Consequently, the expression of these nanobodies drastically decreased the cell division rate. We demonstrate the potential of the CAnIGET method with the use of native protein-protein interactions for steady epitope-specific evolutionary engineering.