When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification. In this manuscript, the use of convolutional neural networks (CNNs) for segmentation of the initial experimental phasing electron-density maps is proposed. The results reported demonstrate that a CNN with U-net architecture, trained on several thousands of electron-density maps generated mainly using X-ray data from the Protein Data Bank in a supervised learning, can improve current density-modification methods.

To determine a substructure from single-wavelength anomalous diffraction (SAD) data using Patterson or direct methods, the substructure-factor amplitude (|Fa|) is first estimated. Currently, the absolute value of the Bijvoet difference is widely used as an estimate of |Fa| values for SAD data. Here, an equation is derived from multivariate statistics and tested that takes into account the correlation between the observed positive (F+) and negative (F-) Friedel pairs and Fa along with measurement errors in the observed data. The multivariate estimation of |Fa| has been implemented in a new program, Afro. Results on over 180 test cases show that Afro provides a higher correlation to the final substructure-factor amplitudes (calculated from the refined, final substructures) than the Bijvoet differences and improves the robustness of direct-methods substructure detection.

We have developed a generally applicable methodology for cysteine mutagenesis of nanobody (Nb) framework region serine residues. This strategy allows for subsequent labeling with thiol-reactive compounds without disrupting Nb antigen binding. We provide a protocol for production, labeling, and affinity determination of cysteine-engineered Nbs (cys-Nbs) with Alexa Fluor 488-maleimide and the mercury compound para-chloromercuribenzoic acid (PCMB). Alexa Fluor 488- and PCMB-labeled cys-Nbs can be used for immunofluorescence microscopy and experimental phasing in crystallography, respectively.

Pivotal to the regulation of key cellular processes such as the transcription, replication and repair of DNA, DNA-binding proteins play vital roles in all aspects of genetic activity. The determination of high-quality structures of DNA-binding proteins, particularly those in complexes with DNA, provides crucial insights into the understanding of these processes. The presence in such complexes of phosphate-rich oligonucleotides offers the choice of a rapid method for the routine solution of DNA-binding proteins through the use of long-wavelength beamlines such as I23 at Diamond Light Source. This article reports the use of native intrinsic phosphorus and sulfur single-wavelength anomalous dispersion methods to solve the complex of the DNA-binding domain (DBD) of interferon regulatory factor 4 (IRF4) bound to its interferon-stimulated response element (ISRE). The structure unexpectedly shows three molecules of the IRF4 DBD bound to one ISRE. The sole reliance on native intrinsic anomalous scattering elements that belong to DNA-protein complexes renders the method of general applicability to a large number of such protein complexes that cannot be solved by molecular replacement or by other phasing methods.

The phase problem is a persistent bottleneck that impedes the structure-determination pipeline and must be solved to obtain atomic resolution crystal structures of macromolecules. Although molecular replacement has become the predominant method of solving the phase problem, many scenarios still exist in which experimental phasing is needed. Here, a proof-of-concept study is presented that shows the efficacy of using tetrabromoterephthalic acid (B4C) as an experimental phasing compound. Incorporating B4C into the crystal lattice using co-crystallization, the crystal structure of hen egg-white lysozyme was solved using MAD phasing. The strong anomalous signal generated by its four Br atoms coupled with its compatibility with commonly used crystallization reagents render B4C an effective experimental phasing compound that can be used to overcome the phase problem.

The structure determination of soluble and membrane proteins can be hindered by the crystallographic phase problem, especially in the absence of a suitable homologous structure. Experimental phasing is the method of choice for novel structures; however, it often requires heavy-atom derivatization, which can be difficult and time-consuming. Here, a novel and rapid method to obtain experimental phases for protein structure determination by vanadium phasing is reported. Vanadate is a transition-state mimic of phosphoryl-transfer reactions and it has the advantage of binding specifically to the active site of numerous enzymes catalyzing this reaction. The applicability of vanadium phasing has been validated by determining the structures of three different protein-vanadium complexes, two of which are integral membrane proteins: the rabbit sarcoplasmic reticulum Ca2+-ATPase, the antibacterial peptide ATP-binding cassette transporter McjD from Escherichia coli and the soluble enzyme RNAse A from Bos taurus. Vanadium phasing was successful even at low resolution and despite severe anisotropy in the data. This method is principally applicable to a large number of proteins, representing six of the seven Enzyme Commission classes. It relies exclusively on the specific chemistry of the protein and it does not require any modifications, making it a very powerful addition to the phasing toolkit. In addition to the phasing power of this technique, the protein-vanadium complexes also provide detailed insights into the reaction mechanisms of the studied proteins.

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In this article, a new approach to experimental phasing for macromolecular crystallography (MX) at synchrotrons is introduced and described for the first time. It makes use of automated robotics applied to a multi-crystal framework in which human intervention is reduced to a minimum. Hundreds of samples are automatically soaked in heavy-atom solutions, using a Labcyte Inc. Echo 550 Liquid Handler, in a highly controlled and optimized fashion in order to generate derivatized and isomorphous crystals. Partial data sets obtained on MX beamlines using an in situ setup for data collection are processed with the aim of producing good-quality anomalous signal leading to successful experimental phasing.

This study describes a method to estimate the likelihood of success in determining a macromolecular structure by X-ray crystallography and experimental single-wavelength anomalous dispersion (SAD) or multiple-wavelength anomalous dispersion (MAD) phasing based on initial data-processing statistics and sample crystal properties. Such a predictive tool can rapidly assess the usefulness of data and guide the collection of an optimal data set. The increase in data rates from modern macromolecular crystallography beamlines, together with a demand from users for real-time feedback, has led to pressure on computational resources and a need for smarter data handling. Statistical and machine-learning methods have been applied to construct a classifier that displays 95% accuracy for training and testing data sets compiled from 440 solved structures. Applying this classifier to new data achieved 79% accuracy. These scores already provide clear guidance as to the effective use of computing resources and offer a starting point for a personalized data-collection assistant.

Single-wavelength anomalous diffraction (SAD) is the most common method for de novo elucidation of macromolecular structures by X-ray crystallography. It requires an anomalous scatterer in a crystal to calculate phases. A recent study by Panneerselvam et al. emphasized the utility of cadmium ions for SAD phasing at the standard synchrotron wavelength of 1 Å. Here we show that cadmium is also useful for phasing of crystals collected in-house with CuKα radiation. Using a crystal of single-domain antibody as an experimental model, we demonstrate how cadmium SAD can be conveniently employed to solve a CuKα dataset. We then discuss the factors which make this method generally applicable.