Polar flow of the phytohormone auxin requires plasma membrane-associated PIN proteins and underlies multiple developmental processes in plants. Here we address the importance of the polarity of subcellular PIN localization for the directionality of auxin transport in Arabidopsis thaliana. Expression of different PINs in the root epidermis revealed the importance of PIN polar positions for directional auxin flow and root gravitropic growth. Interfering with sequence-embedded polarity signals directly demonstrates that PIN polarity is a primary factor in determining the direction of auxin flow in meristematic tissues. This finding provides a crucial piece in the puzzle of how auxin flow can be redirected via rapid changes in PIN polarity.

Severe quantitative loss of protein is often observed in high-resolution two-dimensional electrophoresis of membrane proteins, while the resolution is usually not affected. To improve the solubility of proteins in this technique, we tested denaturing cocktails containing various detergents and chaotropes. Best results were obtained with a denaturing solution containing urea, thiourea, and zwitterionic detergents, synthesized for this purpose. Among the dozen detergents synthesized and tested, amidosulfobetaines with an alkyl tail containing 14-16 carbons proved most efficient, solubilizing previously undetected membrane proteins.

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.