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We describe a general approach to designing two-dimensional (2D) protein arrays mediated by noncovalent protein-protein interfaces. Protein homo-oligomers are placed into one of the seventeen 2D layer groups, the degrees of freedom of the lattice are sampled to identify configurations with shape-complementary interacting surfaces, and the interaction energy is minimized using sequence design calculations. We used the method to design proteins that self-assemble into layer groups P 3 2 1, P 4 21 2, and P 6. Projection maps of micrometer-scale arrays, assembled both in vitro and in vivo, are consistent with the design models and display the target layer group symmetry. Such programmable 2D protein lattices should enable new approaches to structure determination, sensing, and nanomaterial engineering.

Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency. Recently, a pipeline for the design of protein-binding proteins using only the structure of the target protein was reported. Here, the authors report that the incorporation of deep learning methods into the original pipeline increases experimental success rate by ten-fold.

Alzheimer's disease is a major cause of death in the elderly. Disease progression is associated with the accumulation of neurofibrillary tangles composed of tau, a protein important for neuronal development and function. Tangle formation is preceded by phosphorylation events that cause tau to dissociate from its native binding partner, microtubules. Microtubule-tau interactions have been mysterious. Kellogg et al. used cryo–electron microscopy and molecular modeling to show how tau interacts with the outer surface of the microtubule, stapling together tubulin subunits and thus stabilizing the polymer. A key tau amino acid within the tightly bound segment between tubulin subunits corresponds to a clinically relevant site of tau phosphorylation, explaining the competition between microtubule interaction and tau aggregation. Science , this issue p. 1242 A near-atomic model of microtubule-bound tau provides an explanation for disease-associated phosphorylation data. Tau is a developmentally regulated axonal protein that stabilizes and bundles microtubules (MTs). Its hyperphosphorylation is thought to cause detachment from MTs and subsequent aggregation into fibrils implicated in Alzheimer’s disease. It is unclear which tau residues are crucial for tau-MT interactions, where tau binds on MTs, and how it stabilizes them. We used cryo–electron microscopy to visualize different tau constructs on MTs and computational approaches to generate atomic models of tau-tubulin interactions. The conserved tubulin-binding repeats within tau adopt similar extended structures along the crest of the protofilament, stabilizing the interface between tubulin dimers. Our structures explain the effect of phosphorylation on MT affinity and lead to a model of tau repeats binding in tandem along protofilaments, tethering together tubulin dimers and stabilizing polymerization interfaces.

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel Na V 1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to Na V 1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery. The authors in this work introduce RosettaVS, an AI-accelerated open-source drug discovery platform. They apply this tool to multi-billion compound libraries, where it was able to identify compounds that bind important targets KLHDC2 and Na V 1.7.

The fit of atomic models of protein structures to high-resolution cryo-electron microscopy maps can be assessed with a validation tool, EMRinger. Advances in high-resolution cryo-electron microscopy (cryo-EM) require the development of validation metrics to independently assess map quality and model geometry. We report EMRinger, a tool that assesses the precise fitting of an atomic model into the map during refinement and shows how radiation damage alters scattering from negatively charged amino acids. EMRinger ( https://github.com/fraser-lab/EMRinger ) will be useful for monitoring progress in resolving and modeling high-resolution features in cryo-EM.

Advances in cryo-electron microscopy (cryoEM) and deep-learning guided protein structure prediction have expedited structural studies of protein complexes. However, methods for accurately determining ligand conformations are lacking. In this manuscript, we develop EMERALD, a tool for automatically determining ligand structures guided by medium-resolution cryoEM density. We show this method is robust at predicting ligands along with surrounding side chains in maps as low as 4.5 Å local resolution. Combining this with a measure of placement confidence and running on all protein/ligand structures in the EMDB, we show that 57% of ligands replicate the deposited model, 16% confidently find alternate conformations, 22% have ambiguous density where multiple conformations might be present, and 5% are incorrectly placed. For five cases where our approach finds an alternate conformation with high confidence, high-resolution crystal structures validate our placement. EMERALD and the resulting analysis should prove critical in using cryoEM to solve protein-ligand complexes. As cryo-EM becomes commonplace in drug discovery, tools for automating small molecule structure determination are needed. Here, authors show a map-guided ligand modeling approach to building ligand structures at resolutions common in cryo-EM.

Cryo-EM and mass spectrometry analyses of the spike glycoprotein trimer from coronavirus HcoV-NL63 reveal an extensive glycan shield that covers the protein surface, including an epitope targeted by neutralizing antibodies against several coronaviruses. The threat of a major coronavirus pandemic urges the development of strategies to combat these pathogens. Human coronavirus NL63 (HCoV-NL63) is an α-coronavirus that can cause severe lower-respiratory-tract infections requiring hospitalization. We report here the 3.4-Å-resolution cryo-EM reconstruction of the HCoV-NL63 coronavirus spike glycoprotein trimer, which mediates entry into host cells and is the main target of neutralizing antibodies during infection. The map resolves the extensive glycan shield obstructing the protein surface and, in combination with mass spectrometry, provides a structural framework to understand the accessibility to antibodies. The structure reveals the complete architecture of the fusion machinery including the triggering loop and the C-terminal domains, which contribute to anchoring the trimer to the viral membrane. Our data further suggest that HCoV-NL63 and other coronaviruses use molecular trickery, based on epitope masking with glycans and activating conformational changes, to evade the immune system of infected hosts.

Highly coordinated water molecules are frequently an integral part of protein-protein and protein-ligand interfaces. We introduce an updated energy model that efficiently captures the energetic effects of these ordered water molecules on the surfaces of proteins. A two-stage method is developed in which polar groups arranged in geometries suitable for water placement are first identified, then a modified Monte Carlo simulation allows highly coordinated waters to be placed on the surface of a protein while simultaneously sampling amino acid side chain orientations. This \"semi-explicit\" water model is implemented in Rosetta and is suitable for both structure prediction and protein design. We show that our new approach and energy model yield significant improvements in native structure recovery of protein-protein and protein-ligand docking discrimination tests.

Cryo-electron microscopy analysis of yeast Rad26 bound to RNA polymerase II provides insight into the initiation of the transcription-coupled DNA repair mechanism in eukaryotes. Transcription-coupled repair complex Transcription-coupled DNA repair removes DNA lesions from the template strand that present obstacles to the translocation of RNA polymerase II (Pol II). The process is initiated by the recruitment of the Cockayne syndrome group B (CSB) protein in humans—or the equivalent Rad26 in the yeast ( Saccharomyces cerevisiae )—to the arrested polymerase complex. Here, Andres Leschziner, Dong Wang and colleagues have used cryo-electron microscopy to solve the structure of a complex of S. cerevisiae Rad26 bound to Pol II. Rad26 binds to DNA upstream of Pol II and causes marked bending of the DNA, and the Swi2/Snf2-family ATPase domain of Rad26 is proposed to promote forward movement of Pol II. The authors suggest a mechanistic model whereby Rad26 ensures transcription-coupled recognition of DNA lesions while also functioning as a transcription elongation factor. Eukaryotic transcription-coupled repair (TCR) is an important and well-conserved sub-pathway of nucleotide excision repair that preferentially removes DNA lesions from the template strand that block translocation of RNA polymerase II (Pol II) 1 , 2 . Cockayne syndrome group B (CSB, also known as ERCC6) protein in humans (or its yeast orthologues, Rad26 in Saccharomyces cerevisiae and Rhp26 in Schizosaccharomyces pombe ) is among the first proteins to be recruited to the lesion-arrested Pol II during the initiation of eukaryotic TCR 1 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 . Mutations in CSB are associated with the autosomal-recessive neurological disorder Cockayne syndrome, which is characterized by progeriod features, growth failure and photosensitivity 1 . The molecular mechanism of eukaryotic TCR initiation remains unclear, with several long-standing unanswered questions. How cells distinguish DNA lesion-arrested Pol II from other forms of arrested Pol II, the role of CSB in TCR initiation, and how CSB interacts with the arrested Pol II complex are all unknown. The lack of structures of CSB or the Pol II–CSB complex has hindered our ability to address these questions. Here we report the structure of the S. cerevisiae Pol II–Rad26 complex solved by cryo-electron microscopy. The structure reveals that Rad26 binds to the DNA upstream of Pol II, where it markedly alters its path. Our structural and functional data suggest that the conserved Swi2/Snf2-family core ATPase domain promotes the forward movement of Pol II, and elucidate key roles for Rad26 in both TCR and transcription elongation.

Cryo-EM has revealed the structures of many challenging yet exciting macromolecular assemblies at near-atomic resolution (3–4.5Å), providing biological phenomena with molecular descriptions. However, at these resolutions, accurately positioning individual atoms remains challenging and error-prone. Manually refining thousands of amino acids – typical in a macromolecular assembly – is tedious and time-consuming. We present an automated method that can improve the atomic details in models that are manually built in near-atomic-resolution cryo-EM maps. Applying the method to three systems recently solved by cryo-EM, we are able to improve model geometry while maintaining the fit-to-density. Backbone placement errors are automatically detected and corrected, and the refinement shows a large radius of convergence. The results demonstrate that the method is amenable to structures with symmetry, of very large size, and containing RNA as well as covalently bound ligands. The method should streamline the cryo-EM structure determination process, providing accurate and unbiased atomic structure interpretation of such maps.