Members of the carboxylesterase 2 (Ces2/CES2) family have been studied intensively with respect to their hydrolytic function on (pro)drugs, whereas their physiological role in lipid and energy metabolism has been realized only within the last few years. Humans have one CES2 gene which is highly expressed in liver, intestine, and kidney. Interestingly, eight homologous Ces2 (Ces2a to Ces2h) genes exist in mice and the individual roles of the corresponding proteins are incompletely understood. Mouse Ces2c (mCes2c) is suggested as potential ortholog of human CES2. Therefore, we aimed at its structural and biophysical characterization. Here, we present the first crystal structure of mCes2c to 2.12 resolution. The overall structure of mCes2c resembles that of the human CES1 (hCES1). The core domain adopts an alpha/beta hydrolase-fold with S230, E347, and H459 forming a catalytic triad. Access to the active site is restricted by the cap, the flexible lid, and the regulatory domain. The conserved gate (M417) and switch (F418) residues might have a function in product release similar as suggested for hCES1. Biophysical characterization confirms that mCes2c is a monomer in solution. Thus, this study broadens our understanding of the mammalian carboxylesterase family and assists in delineating the similarities and differences of the different family members.
DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.
