What's happened over the last five years with high-throughput protein crystallization screening?

AI-driven pan-proteome analyses reveal insights into the biohydrometallurgical properties of Acidithiobacillia

Microorganism-mediated biohydrometallurgy, a sustainable approach for metal recovery from ores, relies on the metabolic activity of acidophilic bacteria. Acidithiobacillia with sulfur/iron-oxidizing capacities are extensively studied and applied in biohydrometallurgy-related processes. However, only 14 distinct proteins from Acidithiobacillia have experimentally determined structures currently available. This significantly hampers in-depth investigations of Acidithiobacillia's structure-based biological mechanisms pertaining to its relevant biohydrometallurgical processes. To address this issue, we employed a state-of-the-art artificial intelligence (AI)-driven approach, with a median model confidence of 0.80, to perform high-quality full-chain structure predictions on the pan-proteome (10,458 proteins) of the type strain Acidithiobacillia. Additionally, we conducted various case studies on de novo protein structural prediction, including sulfate transporter and iron oxidase, to demonstrate how accurate structure predictions and gene co-occurrence networks can contribute to the development of mechanistic insights and hypotheses regarding sulfur and iron utilization proteins. Furthermore, for the unannotated proteins that constitute 35.8% of the Acidithiobacillia proteome, we employed the deep-learning algorithm DeepFRI to make structure-based functional predictions. As a result, we successfully obtained gene ontology (GO) terms for 93.6% of these previously unknown proteins. This study has a significant impact on improving protein structure and function predictions, as well as developing state-of-the-art techniques for high-throughput analysis of large proteomic data.

In Situ Kinetic Observations on Crystal Nucleation and Growth

Nucleation and growth are critical steps in crystallization, which plays an important role in determining crystal structure, size, morphology, and purity. Therefore, understanding the mechanisms of nucleation and growth is crucial to realize the controllable fabrication of crystalline products with desired and reproducible properties. Based on classical models, the initial crystal nucleus is formed by the spontaneous aggregation of ions, atoms, or molecules, and crystal growth is dependent on the monomer's diffusion and the surface reaction. Recently, numerous in situ investigations on crystallization dynamics have uncovered the existence of nonclassical mechanisms. This review provides a summary and highlights the in situ studies of crystal nucleation and growth, with a particular emphasis on the state-of-the-art research progress since the year 2016, and includes technological advances, atomic-scale observations, substrate- and temperature-dependent nucleation and growth, and the progress achieved in the various materials: metals, alloys, metallic compounds, colloids, and proteins. Finally, the forthcoming opportunities and challenges in this fascinating field are discussed.

Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models

Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD<4Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.

Combining X-rays, neutrons and electrons, and NMR, for precision and accuracy in structure-function studies

The distinctive features of the physics-based probes used in understanding the structure of matter focusing on biological sciences, but not exclusively, are described in the modern context. This is set in a wider scope of holistic biology and the scepticism about `reductionism', what is called the `molecular level', and how to respond constructively. These topics will be set alongside the principles of accuracy and precision, and their boundaries. The combination of probes and their application together is the usual way of realizing accuracy. The distinction between precision and accuracy can be blurred by the predictive force of a precise structure, thereby lending confidence in its potential accuracy. These descriptions will be applied to the comparison of cryo and room-temperature protein crystal structures as well as the solid state of a crystal and the same molecules studied by small-angle X-ray scattering in solution and by electron microscopy on a sample grid. Examples will include: time-resolved X-ray Laue crystallography of an enzyme Michaelis complex formed directly in a crystal equivalent to in vivo; a new iodoplatin for radiation therapy predicted from studies of platin crystal structures; and the field of colouration of carotenoids, as an effective assay of function, i.e. their colouration, when unbound and bound to a protein. The complementarity of probes, as well as their combinatory use, is then at the foundation of real (biologically relevant), probe-artefacts-free, structure-function studies. The foundations of our methodologies are being transformed by colossal improvements in technologies of X-ray and neutron sources and their beamline instruments, as well as improved electron microscopes and NMR spectrometers. The success of protein structure prediction from gene sequence recently reported by CASP14 also opens new doors to change and extend the foundations of the structural sciences.

Public Baseline and shared response structures support the theory of antibody repertoire functional commonality

The naïve antibody/B-cell receptor (BCR) repertoires of different individuals ought to exhibit significant functional commonality, given that most pathogens trigger an effective antibody response to immunodominant epitopes. Sequence-based repertoire analysis has so far offered little evidence for this phenomenon. For example, a recent study estimated the number of shared ('public') antibody clonotypes in circulating baseline repertoires to be around 0.02% across ten unrelated individuals. However, to engage the same epitope, antibodies only require a similar binding site structure and the presence of key paratope interactions, which can occur even when their sequences are dissimilar. Here, we search for evidence of geometric similarity/convergence across human antibody repertoires. We first structurally profile naïve ('baseline') antibody diversity using snapshots from 41 unrelated individuals, predicting all modellable distinct structures within each repertoire. This analysis uncovers a high (much greater than random) degree of structural commonality. For instance, around 3% of distinct structures are common to the ten most diverse individual samples ('Public Baseline' structures). Our approach is the first computational method to find levels of BCR commonality commensurate with epitope immunodominance and could therefore be harnessed to find more genetically distant antibodies with same-epitope complementarity. We then apply the same structural profiling approach to repertoire snapshots from three individuals before and after flu vaccination, detecting a convergent structural drift indicative of recognising similar epitopes ('Public Response' structures). We show that Antibody Model Libraries derived from Public Baseline and Public Response structures represent a powerful geometric basis set of low-immunogenicity candidates exploitable for general or target-focused therapeutic antibody screening.

What is the structural chemistry of the living organism at its temperature and pressure?

The three probes of the structure of matter (X-rays, neutrons and electrons) in biology have complementary properties and strengths. The balance between these three probes within their strengths and weaknesses is perceived to change, even dramatically so at times. For the study of combined states of order and disorder, NMR crystallography is also applicable. Of course, to understand biological systems the required perspectives are surely physiologically relevant temperatures and relevant chemical conditions, as well as a minimal perturbation owing to the needs of the probe itself. These remain very tough challenges because, for example, cryoEM by its very nature will never be performed at room temperature, crystallization often requires nonphysiological chemical conditions, and X-rays and electrons cause beam damage. However, integrated structural biology techniques and functional assays provide a package towards physiological relevance of any given study. Reporting of protein crystal structures, and their associated database entries, could usefully indicate how close to the biological situation they are, as discussed in detail in this feature article.

High-Throughput Crystallization Pipeline at the Crystallography Core Facility of the Institut Pasteur

The availability of whole-genome sequence data, made possible by significant advances in DNA sequencing technology, led to the emergence of structural genomics projects in the late 1990s. These projects not only significantly increased the number of 3D structures deposited in the Protein Data Bank in the last two decades, but also influenced present crystallographic strategies by introducing automation and high-throughput approaches in the structure-determination pipeline. Today, dedicated crystallization facilities, many of which are open to the general user community, routinely set up and track thousands of crystallization screening trials per day. Here, we review the current methods for high-throughput crystallization and procedures to obtain crystals suitable for X-ray diffraction studies, and we describe the crystallization pipeline implemented in the medium-scale crystallography platform at the Institut Pasteur (Paris) as an example.

The upcoming subatomic resolution revolution

Subatomic resolution macromolecular crystallography has been revealing the most fascinating details of macromolecular structures for many years. This most extreme form of macromolecular crystallography is going through rapid changes. A new generation of superbrilliant X-ray sources and detectors is facilitating the rapid acquisition of high-quality datasets. Equally important, a new breed of methods and highly integrated advanced computational tools for structure refinement and analysis is poised to change the way we use subatomic resolution data and reposition high-resolution macromolecular crystallography in medicinal chemistry studies. Subatomic resolution macromolecular crystallography may soon be a routine source of detailed molecular information besides precise geometries, including binding energies and other chemical descriptors, opening new possibilities of application.

Various salts employed as precipitant in combination with polyethylene glycol in protein/detergent particle association

Salt/polyethylene glycol (PEG) mixtures are employed as precipitants for biological macromolecules. The dependence of precipitation curves (PCs) on salt species was investigated for integral membrane protein/detergent particles. By relating this dependence to properties of ions dissociated from added salts, the following roles and effects of various ions were clarified. In the presence of ions whose interaction with water is stronger than water-water interaction, the coordination of solvent molecules is rearranged so as to strengthen short-range steric repulsion and hydrophobic attraction. Ions whose interaction with water is weaker than water-water interaction can be a hindrance to hydrophobic-hydrophobic contact. Moreover, strong electric fields of divalent cations can cause an attractive effect between electronegative or polar groups of neighboring particles. The variations of particle-particle and particle-PEG interactions depending on the state of particles and surrounding solvents were correlative. Due to this, the relationship between the horizontal positions of PC and the species of salts added could be formulated as a binary linear function of cationic and anionic species composing the salts.

A Simple Technique to Improve Microcrystals Using Gel Exclusion of Nucleation Inducing Elements

A technique is described for generating large well diffracting crystals from conditions that yield microcrystals. Crystallization using this technique is both rapid (crystals appear in <1 h) and robust (48 out of 48 co-crystallized with a fragment library, compared with 26 out of 48 using conventional hanging drop). Agarose gel is used to exclude nucleation inducing elements from the remaining crystallization cocktail. The chemicals in the crystallization cocktail are partitioned into high concentration components (presumed to induce aggregation by reducing water activity) and low concentration nucleation agents (presumed to induce nucleation through direct interaction). The nucleation agents are then combined with 2% agarose gel and deposited on the crystallization shelf of a conventional vapor diffusion plate. The remaining components are mixed with the protein and placed in contact with the agarose drop. This technique yielded well diffracting crystals of lysozyme, cubic insulin, proteinase k, and ferritin (ferritin crystals diffracted to 1.43 Å). The crystals grew rapidly, reaching large size in less than one hour (maximum size was achieved in 1–12 h). This technique is not suitable for poorly expressing proteins because small protein volumes diffuse out of the agarose gel too quickly. However, it is a useful technique for situations where crystals must grow rapidly (such as educational applications and preparation of beamline test specimens) and in situations where crystals must grow robustly (such as co-crystallization with a fragment library).