Difficult Macromolecular Structures Determined Using X-ray Diffraction Techniques

Macromolecular X-ray crystallography (XRC), nuclear magnetic resonance (NMR), and cryo-electron microscopy (cryoEM) are the primary techniques for determining atomic-level, three-dimensional structures of macromolecules essential for drug discovery. With advancements in artificial intelligence (AI) and cryoEM, the Protein Data Bank (PDB) [1] is solidifying its role as a key resource for 3D macromolecular structures. This position underscores the growing need for enhanced quality metrics and robust validation standards for all experimental structures. This presentation examines recent advancements in cryoEM [2] and X-ray [3], analyzing structure quality metrics (i.a. see Fig.1: Average Q-score vs. CCMASK for cryoEM structures), resolution improvements, ligand and water molecule identification, refinement software, and the identification of duplicate submissions that are not necessary duplicates [4]. Nearly 800 protein crystal structures determined at a resolution of 2.5 Å or better are present in the current PDB release without any water molecules. At the same time, some other depositions exhibit unusually low or high solvent occupancies [5]. We will discuss the role of both X-ray and cryoEM techniques in drug discovery in light of two significant findings: the demonstration that cryoEM can resolve hydrogen atom positions and water networks [6], and the report of an atomic-resolution (1.09 Å) protein structure (8RQB) that reveals double conformations [7].

Since the 1970s and for about 40years, X-ray crystallography has been by far the most powerful approach for determining virus structures at close to atomic resolutions. Information provided by these studies has deeply and extensively enriched and shaped our vision of the virus world. In turn, the ever-increasing complexity and size of the virus structures being investigated have constituted a major driving force for methodological and conceptual developments in X-ray macromolecular crystallography (MX). Landmarks of the structure determination of viral particles, such as the ones from the first animal viruses or from the first membrane-containing viruses, have often been associated with methodological breakthroughs in X-ray crystallography.In recent years, the advent of new detectors with fast frame rate, high sensitivity, and low-noise background has changed the way MX data is collected, enabling new types of studies at X-ray free-electron laser and synchrotron facilities. In parallel, a very high degree of automation has been established at most MX synchrotron beamlines, allowing the screening of large number of crystals with very high throughputs. This has proved crucial for fragment-based drug design projects, of special relevance for the identification of new antiviral drugs.This change in the usage of X-ray crystallography is also mirrored in the recent advances in cryo-electron microscopy (cryo-EM), which can nowadays produce macromolecule structures at resolutions comparable to those obtained by MX. Since this technique is especially amenable for large protein assemblies, cryo-EM has progressively turned into the favored technique to study the structure of large viral particles at high resolution.In this chapter, we present the common ground of proteins and virus crystallography with an emphasis in the peculiarities of virus-related studies.

The histidine kinase (HK) domain belonging to the light-oxygen-voltage histidine kinase (LOV-HK) from Brucella abortus is a member of the HWE family, for which no structural information is available, and has low sequence identity (20%) to the closest HK present in the PDB. The `off-edge' S-SAD method in macromolecular X-ray crystallography was used to solve the structure of the HK domain from LOV-HK at low resolution from crystals in a low-symmetry space group (P21) and with four copies in the asymmetric unit (∼108 kDa). Data were collected both from multiple crystals (diffraction limit varying from 2.90 to 3.25 Å) and from multiple orientations of the same crystal, using the κ-geometry goniostat on SOLEIL beamline PROXIMA 1, to obtain `true redundancy'. Data from three different crystals were combined for structure determination. An optimized HK construct bearing a shorter cloning artifact yielded crystals that diffracted X-rays to 2.51 Å resolution and that were used for final refinement of the model. Moreover, a thorough a posteriori analysis using several different combinations of data sets allowed us to investigate the impact of the data-collection strategy on the success of the structure determination.

High resolution information is important for accurate structure modelling. However, this level of detail is typically difficult to attain in macromolecular crystallography because the diffracted intensities rapidly fade with increasing resolution. The problem cannot be circumvented by increasing the fluence as this leads to detrimental radiation damage. Previously, we demonstrated that high quality MicroED data can be obtained at low flux conditions using electron counting with direct electron detectors. The improved sensitivity and accuracy of these detectors essentially eliminate the read-out noise, such that the measurement of faint high-resolution reflections is limited by other sources of noise. Inelastic scattering is a major contributor of such noise, increasing background counts and broadening diffraction spots. Here, we demonstrate that a substantial improvement in signal-to-noise ratio can be achieved using an energy filter to largely remove the inelastically scattered electrons. This strategy resulted in sub-atomic resolution MicroED data from proteinase K crystals, enabling accurate structure modelling and the visualization of detailed features. Interestingly, filtering out the noise revealed diffuse scattering phenomena that can hold additional structural information. Our findings suggest that combining energy filtering and electron counting can provide more accurate measurements at higher resolution, providing better insights into protein function and facilitating more precise model refinement.

After more than half a century of evolution, machine learning and artificial intelligence, in general, are entering a truly exciting era of broad application in commercial and research sectors. In X-ray crystallography, and its application to structural biology, machine learning is finding a home within expert and automated systems, is forecasting experiment and data analysis outcomes, is predicting whether crystals can be grown and even generating macromolecular structures. This review provides a historical perspective on AI and machine learning, offers an introduction and guide to its application in crystallography and concludes with topical examples of how it is currently influencing macromolecular crystallography.

Chapter 20 - An Introduction to the Crystallographic Study of Enzymes for Organic Chemists, with a Sample Analysis of Extradiol Ring-Cleavage Dioxygenases

Since it was discovered 3 decades ago the H,K-ATPase has come to be recognized as the key both to the generation and pharmacologic suppression of gastric acid secretion. Although 30 years of concerted research has answered many questions, it is perhaps not surprising that these efforts have raised many new and crucial issues that await elucidation. These can be divided into 5 broad categories that relate to structure, mechanism, regulation, trafficking, and macromolecular interactions. It is probably safe to predict that the growing sophistication of x-ray crystallographic techniques will yield a picture of the pump's molecular structure in the near future. These insights will, in turn, illuminate the details of the process through which enzymatic hydrolysis is coupled to ion translocation with unprecedented clarity. The gastric parietal cell employs an extremely complicated system of receptors, kinases, and second messengers to maintain tight control over pump function. Upon activation, this cell also performs a massive and elegant membrane trafficking transformation that plays a critical role in the regulatory process. Finally, it is becoming clear that every ion transport protein is a component in a large macromolecular complex whose constituents help to determine all of the transport system's fundamental physiologic properties. These are the major topics that will drive H,K pump research in the future, and it is likely that their resolution will create the foundations for the next generation of therapies aimed at controlling gastric acid secretion and its clinical consequences.

Structural biology is entering an exciting time where many new high-resolution structures of large complexes and membrane proteins (MPs) are determined regularly. These advances have been driven by over 15 years of technological improvements, first in macromolecular crystallography, and recently in cryo-electron microscopy. Obtaining information about MP higher order structure and interactions is also a frontier, important but challenging owing to their unique properties and the need to choose suitable detergents/lipids for their study. The development of mass spectrometry (MS), both instruments and methodology in the past 10 years, has also advanced it as a complementary method to study MP structure and interactions. In this review, we discuss advances in MS-based footprinting for MPs and highlight recent methodologies that offer new promise for MP study by chemical footprinting and mass spectrometry.

The various modules in multimodular carbohydrate-active enzymes (CAZymes) may function in catalysis, carbohydrate binding, protein-protein interactions or as linkers. Here, we describe how combining the biophysical techniques of Small Angle X-ray Scattering (SAXS) and macromolecular X-ray crystallography (XRC) provides a powerful tool for examination into questions related to overall structural organization of ultra multimodular CAZymes.

Increasing sophistication in molecular-replacement (MR) software and the rapid expansion of the PDB in recent years have allowed the technique to become the dominant method for determining the phases of a target structure in macromolecular X-ray crystallography. In addition, improvements in bioinformatic techniques for finding suitable homologous structures for use as MR search models, combined with developments in refinement and model-building techniques, have pushed the applicability of MR to lower sequence identities and made weak MR solutions more amenable to refinement and improvement. MrBUMP is a CCP4 pipeline which automates all stages of the MR procedure. Its scope covers everything from the sourcing and preparation of suitable search models right through to rebuilding of the positioned search model. Recent improvements to the pipeline include the adoption of more sensitive bioinformatic tools for sourcing search models, enhanced model-preparation techniques including better ensembling of homologues, and the use of phase improvement and model building on the resulting solution. The pipeline has also been deployed as an online service through CCP4 online, which allows its users to exploit large bioinformatic databases and coarse-grained parallelism to speed up the determination of a possible solution. Finally, the molecular-graphics application CCP4mg has been combined with MrBUMP to provide an interactive visual aid to the user during the process of selecting and manipulating search models for use in MR. Here, these developments in MrBUMP are described with a case study to explore how some of the enhancements to the pipeline and to CCP4mg can help to solve a difficult case.

Both intensity and phase information are needed for structure determination by macromolecular X-ray crystallography. The diffraction experiment provides intensities. Phases must be accessed indirectly by molecular replacement, or by experimental phasing. A popular method for crystallising membrane proteins employs a lipid cubic mesophase (the in meso method). Monoolein is the most popular lipid for in meso crystallisation. Invariably, the lipid co-crystallises with the protein recapitulating the biomembrane from whence it came. We reasoned that such a lipid bearing a heavy atom could be used for experimental phasing. In this study, we replaced half the monoolein in the mesophase with a seleno-labelled analogue (Se-MAG), which has a selenium atom in the fatty acyl chain of the lipid. The lipid mixture formed the cubic mesophase and grew crystals by the in meso method of the alginate transporter, AlgE, and the lipoprotein N-acyltransferase, Lnt. Se-MAGs co-crystallised with both proteins and were used to obtain phases for high-resolution structure determination by the selenium single-wavelength anomalous diffraction method. The use of such a mixed lipid system may prove to be a general strategy for the experimental phasing part of crystallographic structure determination of membrane proteins that crystallise via the in meso method.

5 - Experimental Techniques and Data Banks

Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein-ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode. Here, a new approach, based on the estimation of the major energetic terms of protein-ligand interaction, is introduced for the automated identification of crystallographic ligands in the indicated binding site with ARP/wARP. The applicability of the method to the validation of protein-ligand models from the Protein Data Bank is demonstrated by the detection of models that are `questionable' and the pinpointing of unfavourable interatomic contacts.

The geometry of phosphate-Lewis acid interactions has been examined by using the Cambridge Structural Database. Interactions involving metal ions display preferentially syn, unidentate, and out-of-plane coordination stereochemistry with regard to the phosphinyl (-PO 2 − -) group. In contrast, hydrogen bond donors display a very slight preference for anti stereochemistry with the phosphinyl group, and these interactions do not tend toward in-plane interaction with the phosphinyl group. In general, preferential phosphate-Lewis acid geometry contrasts markedly with carboxylate-Lewis acid geometry, and our results are useful in the interpretation of nucleic acid structures and protein-nuclei acid interactions observed by macromolecular X-ray crystallography

In macromolecular X-ray crystallography, typical data sets have substantial multiplicity. This can be used to calculate the consistency of repeated measurements and thereby assess data quality. Recently, the properties of a correlation coefficient, CC1/2, that can be used for this purpose were characterized and it was shown that CC1/2 has superior properties compared with `merging' R values. A derived quantity, CC*, links data and model quality. Using experimental data sets, the behaviour of CC1/2 and the more conventional indicators were compared in two situations of practical importance: merging data sets from different crystals and selectively rejecting weak observations or (merged) unique reflections from a data set. In these situations controlled `paired-refinement' tests show that even though discarding the weaker data leads to improvements in the merging R values, the refined models based on these data are of lower quality. These results show the folly of such data-filtering practices aimed at improving the merging R values. Interestingly, in all of these tests CC1/2 is the one data-quality indicator for which the behaviour accurately reflects which of the alternative data-handling strategies results in the best-quality refined model. Its properties in the presence of systematic error are documented and discussed.