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Jasinski, M.

Publications and source records attributed to Jasinski, M..

2 recordsLinked to original sources

Identification of an isoflavonoid transporter required for the nodule establishment of the Rhizobium-Fabaceae symbiotic interaction

Nitrogen (N) as well as Phosphorus (P) are key nutrients determining crop productivity. Legumes have developed strategies to overcome nutrient limitation by e.g., forming a symbiotic relationship with N-fixing rhizobia and the release of P-mobilizing exudates and are thus able to grow without supply of N or P fertilizers. The legume-rhizobial symbiosis starts with root release of isoflavonoids, that act as signaling molecules perceived by compatible bacteria. Subsequently, bacteria release nod factors, which induce signaling cascades allowing the formation of functional N-fixing nodules. We report here the identification and functional characterization of a plasma membrane-localized MATE-type transporter (LaMATE2) involved in the release of genistein from white lupin roots. The LaMATE2 expression in the root is upregulated under N deficiency as well as low phosphate availability, two nutritional deficiencies that induce the release of this isoflavonoid. LaMATE2 silencing reduced genistein efflux and even more the formation of symbiotic nodules, supporting the crucial role of LaMATE2 in isoflavonoid release and nodulation. Furthermore, silencing of LaMATE2 limited the P-solubilization activity of lupin root exudates. Transport assays in yeast vesicles demonstrated that LaMATE2 acts as a proton-driven isoflavonoid transporter.

plant biology↗

Graph neural networks and sequence embeddings enable the prediction and design of the cofactor specificity of Rossmann fold proteins

The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved {beta}{beta} motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-Adenosylmethionine (SAM), the oxidoreductases, depending on the family, bind nicotinamide (NAD, NADP) or flavin-based (FAD) cofactors. In this study, we show that despite its short length, the {beta}{beta} motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the {beta}{beta} motif. A benchmark on two independent test sets, one containing {beta}{beta} motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox. Key pointsO_LIThe Rossmann fold encompasses a multitude of diverse enzymes involved in most of the essential cellular pathways C_LIO_LIProteins belonging to the Rossmann fold co-evolved with their nucleoside-based cofactors and require them for the functioning C_LIO_LIManipulating the cofactor specificity is an important step in the process of enzyme engineering C_LIO_LIWe developed an end-to-end pipeline for the prediction and design of the cofactor specificity of the Rossmann fold proteins C_LIO_LIOwing to the utilization of deep learning approaches the pipeline achieved nearly perfect accuracy C_LI

bioinformatics↗