DeepMind presented remarkably accurate predictions at the recent CASP14 protein structure prediction assessment conference. We explored network architectures incorporating related ideas and obtained the best performance with a three-track network in which information at the 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-EM 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 which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.
DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate models of protein-protein complexes from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.
Type I restriction-modification enzymes are differentiated from type II and type III enzymes by their recognition of two specific dsDNA sequences separated by a given spacer and cleaving DNA randomly away from the recognition sites. They are oligomeric proteins formed by three subunits: a specificity subunit, a methylation subunit, and a restriction subunit. We solved the crystal structure of a specificity subunit from Methanococcus jannaschii at 2.4-Å resolution. Two highly conserved regions (CRs) in the middle and at the C terminus form a coiled-coil of long antiparallel ␣-helices. Two target recognition domains form globular structures with almost identical topologies and two separate DNA binding clefts with a modeled DNA helix axis positioned across the CR helices. The structure suggests that the coiled-coil CRs act as a molecular ruler for the separation between two recognized DNA sequences. Furthermore, the relative orientation of the two DNA binding clefts suggests kinking of bound dsDNA and exposing of target adenines from the recognized DNA sequences. structural genomics ͉ gi 15669898
The sesquiterpene bisabolene was recently identified as a biosynthetic precursor to bisabolane, an advanced biofuel with physicochemical properties similar to those of D2 diesel. High-titer microbial bisabolene production was achieved using Abies grandis α-bisabolene synthase (AgBIS). Here, we report the structure of AgBIS, a three-domain plant sesquiterpene synthase, crystallized in its apo form and bound to five different inhibitors. Structural and biochemical characterization of the AgBIS terpene synthase Class I active site leads us to propose a catalytic mechanism for the cyclization of farnesyl diphosphate into bisabolene via a bisabolyl cation intermediate. Further, we describe the nonfunctional AgBIS Class II active site whose high similarity to bifunctional diterpene synthases makes it an important link in understanding terpene synthase evolution. Practically, the AgBIS crystal structure is important in future protein engineering efforts to increase the microbial production of bisabolene.
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