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

Abstract

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 βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases and enzymes involved in the polyamine synthesis 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 βαβ 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 features of the βαβ motif. The first utilizes contextualized sequence embeddings, whereas the second relies on structures represented as graphs. A benchmark on two test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 cases of the experimentally confirmed redesign of the cofactor specificity from NAD to NADP and vice versa, revealed nearly-perfect performance (~95% accuracy) of the two methods. Finally, by combining the two approaches, we built a pipeline for the design of cofactor-switching mutations. Both prediction methods 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.