Small-molecule responsive protein switches are crucial components to control synthetic cellular activities. However, the repertoire of small-molecule protein switches is insufficient for many applications, including those in the translational spaces, where properties such as safety, immunogenicity, drug half-life, and drug side-effects are critical. Here, we present a computational protein design strategy to repurpose drug-inhibited protein-protein interactions as OFF- and ON-switches. The designed binders and drug-receptors form chemically-disruptable heterodimers (CDH) which dissociate in the presence of small molecules. To design ON-switches, we converted the CDHs into a multi-domain architecture which we refer to as activation by inhibitor release switches (AIR) that incorporate a rationally designed drug-insensitive receptor protein. CDHs and AIRs showed excellent performance as drug responsive switches to control combinations of synthetic circuits in mammalian cells. This approach effectively expands the chemical space and logic responses in living cells and provides a blueprint to develop new ON- and OFF-switches.

Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biotherapeutic and bioengineering applications. The prediction of antibody affinity changes upon mutations ($${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$ Δ Δ G binding ) is important for antibody engineering. Numerous computational methods have been proposed based on different approaches including molecular mechanics and machine learning. However, the accuracy by each individual predictor is not enough for efficient antibody development. In this study, we develop a new prediction method by combining multiple predictors based on machine learning. Our method was tested on the SiPMAB database, evaluating the Pearson’s correlation coefficient between predicted and experimental $${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$ Δ Δ G binding . Our method achieved higher accuracy (R = 0.69) than previous molecular mechanics or machine-learning based methods (R = 0.59) and the previous method using the average of multiple predictors (R = 0.64). Feature importance analysis indicated that the improved accuracy was obtained by combining predictors with different importance, which have different protocols for calculating energies and for generating mutant and unbound state structures. This study demonstrates that machine learning is a powerful framework for combining different approaches to predict antibody affinity changes.

A Q-body capable of detecting target molecules in solutions could serve as a simple molecular detection tool. The position of the fluorescent dye in a Q-body affects sensitivity and therefore must be optimized. This report describes the development of Nef Q-bodies that recognize Nef protein, one of the human immunodeficiency virus (HIV)’s gene products, in which fluorescent dye molecules were placed at various positions using an in vivo unnatural amino acid incorporation system. A maximum change in fluorescence intensity of 2-fold was observed after optimization of the dye position. During the process, some tryptophan residues of the antibody were found to quench the fluorescence. Moreover, analysis of the epitope indicated that some amino acid residues of the antigen located near the epitope affected the fluorescence intensity.

Natural products have numerous bioactivities and are expected to be a resource for potent drugs. However, their direct targets in cells often remain unclear. We found that rabdosianone I, which is a bitter diterpene from an oriental herb for longevity, Isodon japonicus Hara, markedly inhibited the growth of human colorectal cancer cells by downregulating the expression of thymidylate synthase (TS). Next, using rabdosianone I-immobilized nano-magnetic beads, we identified two mitochondrial inner membrane proteins, adenine nucleotide translocase 2 (ANT2) and prohibitin 2 (PHB2), as direct targets of rabdosianone I. Consistent with the action of rabdosianone I, the depletion of ANT2 or PHB2 reduced TS expression in a different manner. The knockdown of ANT2 or PHB2 promoted proteasomal degradation of TS protein, whereas that of not ANT2 but PHB2 reduced TS mRNA levels. Thus, our study reveals the ANT2- and PHB2-mediated pleiotropic regulation of TS expression and demonstrates the possibility of rabdosianone I as a lead compound of TS suppressor.

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 (EC 50 = 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.