Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. ...In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using
, and automated EM-map-guided complex assembly. We apply this method to a 4.6 Å resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it comprises 6557 residues, only 1897 of which are covered by homology models. In the published model built from this map, only 387 residues could be assigned to the specific subunits with confidence. By building and placing into density 42 deep-learning-guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease-related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure.
There has been considerable recent progress in designing new proteins using deep-learning methods
. Despite this progress, a general deep-learning framework for protein design that enables solution ...of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models
have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.
We use computational design coupled with experimental characterization to systematically investigate the design principles for macrocycle membrane permeability and oral bioavailability. We designed ...184 6–12 residue macrocycles with a wide range of predicted structures containing noncanonical backbone modifications and experimentally determined structures of 35; 29 are very close to the computational models. With such control, we show that membrane permeability can be systematically achieved by ensuring all amide (NH) groups are engaged in internal hydrogen bonding interactions. 84 designs over the 6–12 residue size range cross membranes with an apparent permeability greater than 1 × 10−6 cm/s. Designs with exposed NH groups can be made membrane permeable through the design of an alternative isoenergetic fully hydrogen-bonded state favored in the lipid membrane. The ability to robustly design membrane-permeable and orally bioavailable peptides with high structural accuracy should contribute to the next generation of designed macrocycle therapeutics.
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•Computational design of diverse permeable macrocycles beyond the “rule-of-five” space•X-ray and NMR structures of designed macrocycles match their computational models•Designed macrocycles are permeable in vitro and orally bioavailable in vivo•Designed chameleonic peptides show solvent-dependent conformational switching
An investigation of the design principles of macrocyclic peptide membrane permeability and oral bioavailability enables the generation of synthetic macrocycles that fold into the predicted conformation, can cross membranes, and even adopt different conformations depending on polar versus nonpolar contexts.
Malonic acid esters of
trans-1-substituted-3-trifluoromethyl-2-butene-1-ols underwent stereoselective, SET induced cyclization reaction sequence in the presence of iodine, potassium carbonate and ...quaternary ammonium salt. The allyl substituents of the starting materials influenced the diastereoisomeric composition of the new products.
We report on a new isomeric impurity of danazol. This impurity designated as isodanazol was detected by reversed-phase high-performance liquid chromatography (HPLC) and thin-layer chromatography ...(TLC). Its structure was determined after separation by preparative HPLC. Mass spectrometry revealed the isomeric nature of the impurity while the UV spectrum indicated profound difference in the isoxazole moieties. The structure of the isomeric isoxazole ring in isodanazol was determined by NMR spectroscopy using COSY, HETCOR and NOE measurements. The difference between the UV spectra of danazol and isodanazol is explained on the basis of the difference between the aromaticities of their isoxazole rings supported by quantum chemical calculations. The quantitative determination of the impurity down to the 0.05% level can be performed by HPLC, gas chromatography and TLC densitometry.
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