Likelihood-based interactive local docking into cryo-EM maps in ChimeraX.

The interpretation of cryo-EM maps often includes the docking of known or predicted structures of the components, which is particularly useful when the map resolution is worse than 4 Å. Although it can be effective to search the entire map to find the best placement of a component, the process can be slow when the maps are large. However, frequently there is a well-founded hypothesis about where particular components are located.

The Phenix-AlphaFold webservice: Enabling AlphaFold predictions for use in Phenix.

Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo-electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model-building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix-AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1.

Engineering highly stable variants of Corynactis californica green fluorescent proteins.

Fluorescent proteins (FPs) are versatile biomarkers that facilitate effective detection and tracking of macromolecules of interest in real time. Engineered FPs such as superfolder green fluorescent protein (sfGFP) and superfolder Cherry (sfCherry) have exceptional refolding capability capable of delivering fluorescent readout in harsh environments where most proteins lose their native functions. Our recent work on the development of a split FP from a species of strawberry anemone, Corynactis californica, delivered pairs of fragments with up to threefold faster complementation than split GFP.

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination.

Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps.

Overall protein structure quality assessment using hydrogen-bonding parameters.

Atomic model refinement at low resolution is often a challenging task. This is mostly because the experimental data are not sufficiently detailed to be described by atomic models. To make refinement practical and ensure that a refined atomic model is geometrically meaningful, additional information needs to be used such as restraints on Ramachandran plot distributions or residue side-chain rotameric states.

Crystal structures of multidrug efflux transporters from suggest details of transport mechanism.

BpeB and BpeF are multidrug efflux transporters from that enable multidrug resistance. Here, we report the crystal structures of BpeB and BpeF at 2.94 Å and 3.

Likelihood-based docking of models into cryo-EM maps.

Optimized docking of models into cryo-EM maps requires exploiting an understanding of the signal expected in the data to minimize the calculation time while maintaining sufficient signal. The likelihood-based rotation function used in crystallography can be employed to establish plausible orientations in a docking search. A phased likelihood translation function yields scores for the placement and rigid-body refinement of oriented models.

Likelihood-based signal and noise analysis for docking of models into cryo-EM maps.

Fast, reliable docking of models into cryo-EM maps requires understanding of the errors in the maps and the models. Likelihood-based approaches to errors have proven to be powerful and adaptable in experimental structural biology, finding applications in both crystallography and cryo-EM. Indeed, previous crystallographic work on the errors in structural models is directly applicable to likelihood targets in cryo-EM.

Accelerating crystal structure determination with iterative AlphaFold prediction.

Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle.

Label-free affinity screening, design and synthesis of inhibitors targeting the Mycobacterium tuberculosis L-alanine dehydrogenase.

The ability of Mycobacterium tuberculosis (Mtb) to persist in its host may enable an evolutionary advantage for drug resistant variants to emerge. A potential strategy to prevent persistence and gain drug efficacy is to directly target the activity of enzymes that are crucial for persistence. We present a method for expedited discovery and structure-based design of lead compounds by targeting the hypoxia-associated enzyme L-alanine dehydrogenase (AlaDH).

Improved AlphaFold modeling with implicit experimental information.

Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions.

A monomeric mycobacteriophage immunity repressor utilizes two domains to recognize an asymmetric DNA sequence.

Regulation of bacteriophage gene expression involves repressor proteins that bind and downregulate early lytic promoters. A large group of mycobacteriophages code for repressors that are unusual in also terminating transcription elongation at numerous binding sites (stoperators) distributed across the phage genome. Here we provide the X-ray crystal structure of a mycobacteriophage immunity repressor bound to DNA, which reveals the binding of a monomer to an asymmetric DNA sequence using two independent DNA binding domains.

Engineering an efficient and bright split Corynactis californica green fluorescent protein.

Split green fluorescent protein (GFP) has been used in a panoply of cellular biology applications to study protein translocation, monitor protein solubility and aggregation, detect protein-protein interactions, enhance protein crystallization, and even map neuron contacts. Recent work shows the utility of split fluorescent proteins for large scale labeling of proteins in cells using CRISPR, but sets of efficient split fluorescent proteins that do not cross-react are needed for multiplexing experiments. We present a new monomeric split green fluorescent protein (ccGFP) engineered from a tetrameric GFP found in Corynactis californica, a bright red colonial anthozoan similar to sea anemones and scleractinian stony corals.

Protein identification from electron cryomicroscopy maps by automated model building and side-chain matching.

Using single-particle electron cryo-microscopy (cryo-EM), it is possible to obtain multiple reconstructions showing the 3D structures of proteins imaged as a mixture. Here, it is shown that automatic map interpretation based on such reconstructions can be used to create atomic models of proteins as well as to match the proteins to the correct sequences and thereby to identify them. This procedure was tested using two proteins previously identified from a mixture at resolutions of 3.

Publisher Correction: A genome-wide structure-based survey of nucleotide binding proteins in M. tuberculosis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Density modification of cryo-EM maps.

Density modification uses expectations about features of a map such as a flat solvent and expected distributions of density in the region of the macromolecule to improve individual Fourier terms representing the map. This process transfers information from one part of a map to another and can improve the accuracy of a map. Here, the assumptions behind density modification for maps from electron cryomicroscopy are examined and a procedure is presented that allows the incorporation of model-based information.

Bottom-up structural proteomics: cryoEM of protein complexes enriched from the cellular milieu.

X-ray crystallography often requires non-native constructs involving mutations or truncations, and is challenged by membrane proteins and large multicomponent complexes. We present here a bottom-up endogenous structural proteomics approach whereby near-atomic-resolution cryo electron microscopy (cryoEM) maps are reconstructed ab initio from unidentified protein complexes enriched directly from the endogenous cellular milieu, followed by identification and atomic modeling of the proteins. The proteins in each complex are identified using cryoID, a program we developed to identify proteins in ab initio cryoEM maps.

Cryo-EM map interpretation and protein model-building using iterative map segmentation.

A procedure for building protein chains into maps produced by single-particle electron cryo-microscopy (cryo-EM) is described. The procedure is similar to the way an experienced structural biologist might analyze a map, focusing first on secondary structure elements such as helices and sheets, then varying the contour level to identify connections between these elements. Since the high density in a map typically follows the main-chain of the protein, the main-chain connection between secondary structure elements can often be identified as the unbranched path between them with the highest minimum value along the path.

Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix.

Diffraction (X-ray, neutron and electron) and electron cryo-microscopy are powerful methods to determine three-dimensional macromolecular structures, which are required to understand biological processes and to develop new therapeutics against diseases. The overall structure-solution workflow is similar for these techniques, but nuances exist because the properties of the reduced experimental data are different. Software tools for structure determination should therefore be tailored for each method.

BpeB, a major resistance-nodulation-cell division transporter from Burkholderia cenocepacia: construct design, crystallization and preliminary structural analysis.

Burkholderia cenocepacia is an opportunistic pathogen that infects cystic fibrosis patients, causing pneumonia and septicemia. B. cenocepacia has intrinsic antibiotic resistance against monobactams, aminoglycosides, chloramphenicol and fluoroquinolones that is contributed by a homologue of BpeB, which is a member of the resistance-nodulation-cell division (RND)-type multidrug-efflux transporters.

A fully automatic method yielding initial models from high-resolution cryo-electron microscopy maps.

We report a fully automated procedure for the optimization and interpretation of reconstructions from cryo-electron microscopy (cryo-EM) data, available in Phenix as phenix.map_to_model. We applied our approach to 476 datasets with resolution of 4.

New tools for the analysis and validation of cryo-EM maps and atomic models.

Recent advances in the field of electron cryomicroscopy (cryo-EM) have resulted in a rapidly increasing number of atomic models of biomacromolecules that have been solved using this technique and deposited in the Protein Data Bank and the Electron Microscopy Data Bank. Similar to macromolecular crystallography, validation tools for these models and maps are required. While some of these validation tools may be borrowed from crystallography, new methods specifically designed for cryo-EM validation are required.

Map segmentation, automated model-building and their application to the Cryo-EM Model Challenge.

A recently-developed method for identifying a compact, contiguous region representing the unique part of a density map was applied to 218 Cryo-EM maps with resolutions of 4.5 Å or better. The key elements of the segmentation procedure are (1) identification of all regions of density above a threshold and (2) choice of a unique set of these regions, taking symmetry into consideration, that maximize connectivity and compactness.