Experimental Structure Determination Research Articles

Phase Prediction via Crystal Structure Similarity in the Periodic Number Representation.

The periodic number (PN) representation of the chemical systems, introduced by Dmitri Mendeleev, uncovers the fundamental principle of chemical similarity in a straightforward way. In this framework, the rows correspond to the principal quantum numbers of the elements' electronic configurations when considered isolated atoms. This systematic arrangement allows for a deeper understanding of the relationships and patterns among the elements. In this study, we propose a novel strategy for structure type (prototype) prediction by utilizing the PN concept to identify possible modifications and phase stability of unexplored chemical systems. Our PN-based crystal structure prediction (PNcsp) program, which evaluates similarity through PN neighboring in the phase map, provides the most probable prototypes for unknown and unreported modifications of given phases in binary and higher order chemical systems. We applied PNcsp to 59 distinct chemical systems whose equimolar phases are indicated in the respective phase diagrams but lack accurate experimental structure determination. Our methodology identified 93 prototypes for these 59 equiatomic phases, of which 47 exhibit mechanical and dynamic stability. Notably, this approach discovered 19 entirely novel, fully stable polymorphic phases, thereby expanding the known landscape of potential materials. Furthermore, we demonstrated that this method is also effective for nonequimolar and higher order systems.

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data.

Protein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we present CSP_Rank, an integrated modeling approach to determine the structures of protein-peptide complexes. This method combines AlphaFold2 (AF2) enhanced sampling methods with a Bayesian conformational selection process based on experimental Nuclear Magnetic Resonance (NMR) Chemical Shift Perturbation (CSP) data and AF2 confidence metrics. Using a curated dataset of 108 protein-peptide complexes from the Biological Magnetic Resonance Data Bank (BMRB), we observe that while AF2 typically yields models with excellent consistency with experimental CSP data, applying enhanced sampling followed by data-guided conformational selection routinely results in ensembles of structures with improved agreement with NMR observables. For two systems, we cross-validate the CSP-selected models using independently acquired nuclear Overhauser effect (NOE) NMR data and demonstrate how CSP and NMR can be combined using our Bayesian framework for model selection. CSP_Rank is a novel method for integrative modeling of protein-peptide complexes and has broad implications for studies of protein-peptide interactions and aiding in understanding their biological functions.

Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design.

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

AlphaFold2 as a replacement for solution NMR structure determination of small proteins: Not so fast!

The determination of a protein’s structure is often a first step towards the development of a mechanistic understanding of its function. Considerable advances in computational protein structure prediction have been made in recent years, with AlphaFold2 (AF2) emerging as the primary tool used by researchers for this purpose. While AF2 generally predicts accurate structures of folded proteins, we present here a case where AF2 incorrectly predicts the structure of a small, folded and compact protein with high confidence. This protein, pro-interleukin-18 (pro-IL-18), is the precursor of the cytokine IL-18. Interestingly, the structure of pro-IL-18 predicted by AF2 matches that of the mature cytokine, and not the corresponding experimentally determined structure of the pro-form of the protein. Thus, while computational structure prediction holds immense promise for addressing problems in protein biophysics, there is still a need for experimental structure determination, even in the context of small well-folded, globular proteins.

The structural landscape and diversity of Pyricularia oryzae MAX effectors revisited.

Magnaporthe AVRs and ToxB-like (MAX) effectors constitute a family of secreted virulence proteins in the fungus Pyricularia oryzae (syn. Magnaporthe oryzae), which causes blast disease on numerous cereals and grasses. In spite of high sequence divergence, MAX effectors share a common fold characterized by a ß-sandwich core stabilized by a conserved disulfide bond. In this study, we investigated the structural landscape and diversity within the MAX effector repertoire of P. oryzae. Combining experimental protein structure determination and in silico structure modeling we validated the presence of the conserved MAX effector core domain in 77 out of 94 groups of orthologs (OG) identified in a previous population genomic study. Four novel MAX effector structures determined by NMR were in remarkably good agreement with AlphaFold2 (AF2) predictions. Based on the comparison of the AF2-generated 3D models we propose a classification of the MAX effectors superfamily in 20 structural groups that vary in the canonical MAX fold, disulfide bond patterns, and additional secondary structures in N- and C-terminal extensions. About one-third of the MAX family members remain singletons, without strong structural relationship to other MAX effectors. Analysis of the surface properties of the AF2 MAX models also highlights the high variability within the MAX family at the structural level, potentially reflecting the wide diversity of their virulence functions and host targets.

Structure-Based Protein Assembly Simulations Including Various Binding Sites and Conformations.

Many biological functions are mediated by large complexes formed by multiple proteins and other cellular macromolecules. Recent progress in experimental structure determination, as well as in integrative modeling and protein structure prediction using deep learning approaches, has resulted in a rapid increase in the number of solved multiprotein assemblies. However, the assembly process of large complexes from their components is much less well-studied. We introduce a rapid computational structure-based (SB) model, GoCa, that allows to follow the assembly process of large multiprotein complexes based on a known native structure. Beyond existing SB Go̅-type models, it distinguishes between intra- and intersubunit interactions, allowing us to include coupled folding and binding. It accounts automatically for the permutation of identical subunits in a complex and allows the definition of multiple minima (native) structures in the case of proteins that undergo global transitions during assembly. The model is successfully tested on several multiprotein complexes. The source code of the GoCa program including a tutorial is publicly available on Github: https://github.com/ZachariasLab/GoCa. We also provide a web source that allows users to quickly generate the necessary input files for a GoCa simulation: https://goca.t38webservices.nat.tum.de.

IBio-GATS-A Semi-Automated Workflow for Structural Modelling of Insect Odorant Receptors.

Insects utilize seven transmembrane (7TM) odorant receptor (iOR) proteins, with an inverted topology compared to G-protein coupled receptors (GPCRs), to detect chemical cues in the environment. For pest biocontrol, chemical attractants are used to trap insect pests. However, with the influx of invasive insect pests, novel odorants are urgently needed, specifically designed to match 3D iOR structures. Experimental structural determination of these membrane receptors remains challenging and only four experimental iOR structures from two evolutionarily distant organisms have been solved. Template-based modelling (TBM) is a complementary approach, to generate model structures, selecting templates based on sequence identity. As the iOR family is highly divergent, a different template selection approach than sequence identity is needed. Bio-GATS template selection for GPCRs, based on hydrophobicity correspondence, has been morphed into iBio-GATS, for template selection from available experimental iOR structures. This easy-to-use semi-automated workflow has been extended to generate high-quality models from any iOR sequence from the selected template, using Python and shell scripting. This workflow was successfully validated on Apocrypta bakeri Orco and Machilis hrabei OR5 structures. iBio-GATS models generated for the fruit fly iOR, OR59b and Orco, yielded functional ligand binding results concordant with experimental mutagenesis findings, compared to AlphaFold2 models.

Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy.

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.

Pressure-Assisted Synthesis of Highly Crystalline 1T''-Lix MoS2.

Lix MoS2 is not only a lithium battery material, but is also an important precursor for the synthesis of MoS2 nanomaterials. Current syntheses of MoS2 , such as in n-butyllithium/LiBH4 or electrochemically, are not satisfying in terms of defined stoichiometry and crystallinity, so an accurate experimental crystal structure determination of this important and widely used material has been long awaited. A high-pressure/high-temperature synthesis yielded highly crystalline 1T''-Lix MoS2 (x=1, 1.333). 1T''-LiMoS2 crystallizes in the space group P with a=6.2482(3) Å, b=6.6336(3) Å, c=6.7480(3) Å, α=119.321(2)°, β=90.010(2)° and γ=90.077(2)°. The arrangement of Mo atoms within the b-c-plane confirmed a predicted Peierls distortion. A similar atom distribution pattern to that of Mo is also observed for the lithium atoms. Calculation of bond valence site energies gave an activation barrier of 1.244 eV for 2D lithium-ion migration. For x=1.333, a phase-pure synthesis was achieved.

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. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

How AlphaFold2 shaped the structural coverage of the human transmembrane proteome

AlphaFold2 (AF2) provides a 3D structure for every known or predicted protein, opening up new prospects for virtually every field in structural biology. However, working with transmembrane protein molecules pose a notorious challenge for scientists, resulting in a limited number of experimentally determined structures. Consequently, algorithms trained on this finite training set also face difficulties. To address this issue, we recently launched the TmAlphaFold database, where predicted AlphaFold2 structures are embedded into the membrane plane and a quality assessment (plausibility of the membrane-embedded structure) is provided for each prediction using geometrical evaluation. In this paper, we analyze how AF2 has improved the structural coverage of membrane proteins compared to earlier years when only experimental structures were available, and high-throughput structure prediction was greatly limited. We also evaluate how AF2 can be used to search for (distant) homologs in highly diverse protein families. By combining quality assessment and homology search, we can pinpoint protein families where AF2 accuracy is still limited, and experimental structure determination would be desirable.

Phase-Dependence of Molecular Structures Arising from Weak London Dispersion Interactions.

ConspectusThe structures of molecules can be different in different phases. Intermolecular forces, even those of weak noncovalent interactions (WNCIs), can lead to a preference for quite different conformations in the solid, the gas, and the liquid phases. WNCIs can cause variations in bond lengths, angles, and torsional angles. Since structure is a fundamental concept in chemistry, the knowledge of structural changes with phase is important to understand the source and effects of distorting contributions from WNCIs but also as a predictive tool for the design and stabilization of new bonding situations.X-ray crystallography is ubiquitous and now mostly straightforward to perform, but facilities for the determination of accurate gas-phase structure determination are rare, and gas-phase work is laborious and time-consuming. There are currently about 1.25 million crystal structures and more than 12 500 experimental gas-phase structures, but the intersection of the two data sets that can tell us about the structural differences of the same molecule in different phases is surprisingly small.In this Account, we describe several cases of WNCI-dominated systems for which accurate experimental structure determinations exist for both the gas phase and the solid state and, in one case, also for solution. The examples include aryl-aryl, aryl-alkyl, and alkyl-alkyl interactions; systems with chalcogen and halogen bonding; and fluorine-based interactions in arylboranes. We work out the role of WNCIs in stabilizing large, strained, or sterically overloaded molecules. We will show how flexible molecules will fold under the action of WNCIs when isolated in the gas and how they fold or unfold when they are embedded in an environment of neighbors in crystals. We will show how they can vary in strength when the substitution patterns in aryl groups are changed by different halogens and how intramolecular WNCIs, such as those forming rings, change when such systems experience additional intermolecular WNCIs.Overall, we hope that this Account will give the reader an idea of the type and magnitude of structural changes that can be expected from a free molecule in the gas phase or a single molecule calculated by quantum chemistry compared with one embedded in a crystal. This should define the limits of comparability and provide some predictive concepts of the distortions and variations to be expected.

Tertiary structure assessment at CASP15.

The results of tertiary structure assessment at CASP15 are reported. For the first time, recognizing the outstanding performance of AlphaFold 2 (AF2) at CASP14, all single-chain predictions were assessed together, irrespective of whether a template was available. At CASP15, there was no single stand-out group, with most of the best-scoring groups-led by PEZYFoldings, UM-TBM, and Yang Server-employing AF2 in one way or another. Many top groups paid special attention to generating deep Multiple Sequence Alignments (MSAs) and testing variant MSAs, thereby allowing them to successfully address some of the hardest targets. Such difficult targets, as well as lacking templates, were typically proteins with few homologues. Local divergence between prediction and target correlated with localization at crystal lattice or chain interfaces, and with regions exhibiting high B-factor factors in crystal structure targets, and should not necessarily be considered as representing error in the prediction. However, analysis of exposed and buried side chain accuracy showed room for improvement even in the latter. Nevertheless, a majority of groups produced high-quality predictions for most targets, which are valuable for experimental structure determination, functional analysis, and many other tasks across biology. These include those applying methods similar to those used to generate major resources such as the AlphaFold Protein Structure Database and the ESM Metagenomic atlas: the confidence estimates of the former were also notably accurate.

Starting at Go: Protein structure prediction succumbs to machine learning

This year’s Albert Lasker Basic Medical Research Award recognizes the invention of AlphaFold, a revolutionary advance in the history of protein research which for the first time offers the practical ability to accurately predict the three-dimensional arrangement of amino acids in the vast majority of proteins on a genomic scale on the basis of sequence alone [J. Jumper et al., Nature 596, 583–589 (2021) and K. Tunyasuvunakool et al., Nature 596, 590–596 (2021)]. This extraordinary achievement by Demis Hassabis and John Jumper and their coworkers at Google’s DeepMind and other collaborators was built on decades of experimental protein structure determination (structural biology) as well as the gradual development of multiple strategies incorporating biologically inspired statistical approaches. But when Jumper and Hassabis added a brew of innovative neural network-based machine learning approaches to the mix, the results were explosive. Realizing the half-century-old dream of predicting protein structure has already accelerated the pace and creativity of many areas of Chemistry, Biology, and Medicine.

Se-MAG Is a Convenient Additive for Experimental Phasing and Structure Determination of Membrane Proteins Crystallised by the Lipid Cubic Phase (In Meso) Method

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.

Rapid Determination of the Topology of Oligomeric α-Helical Membrane Proteins by Water- and Lipid-Edited Methyl NMR.

Single-span oligomeric α-helical transmembrane proteins are common in virus ion channels, which are targets of antiviral drugs. Knowledge about the high-resolution structures of these oligomeric α-helical bundles is so far scarce. Structure determination of these membrane proteins by solid-state NMR traditionally requires resolving and assigning protein chemical shifts and measuring many interhelical distances, which are time-consuming. To accelerate experimental structure determination, here we introduce a simple solid-state NMR approach that uses magnetization transfer from water and lipid protons to the protein. By detecting the water- and lipid-transferred intensities of the high-sensitivity methyl 13C signals of Leu, Val, and Ile residues, which are highly enriched in these membrane proteins, we can derive models of the topology of these homo-oligomeric helical bundles. The topology is specified by the positions of amino acid residues in heptad repeats and the orientations of residues relative to the channel pore, lipids, and the helical interface. We demonstrate this water- and lipid-edited methyl NMR approach on the envelope (E) protein of SARS-CoV-2, the causative agent of the COVID-19 pandemic. We show that water-edited and lipid-edited 2D 13C-13C correlation spectra can be measured with sufficient sensitivity. Even without resolving multiple residues of the same type in the NMR spectra, we can obtain the helical bundle topology. We apply these experiments to the structurally unknown E proteins of the MERS coronavirus and the human coronavirus NL63. The resulting structural topologies show interesting differences in the positions of the aromatic residues in these three E proteins, suggesting that these viroporins may have different mechanisms of activation and ion conduction.

Identification of host receptors for viral entry and beyond: a perspective from the spike of SARS-CoV-2.

Identification of the interaction between the host membrane receptor and viral receptor-binding domain (RBD) represents a crucial step for understanding viral pathophysiology and for developing drugs against pathogenic viruses. While all membrane receptors and carbohydrate chains could potentially be used as receptors for viruses, prioritized searches focus typically on membrane receptors that are known to have been used by the relatives of the pathogenic virus, e.g., ACE2 used as a receptor for SARS-CoV is a prioritized candidate receptor for SARS-CoV-2. An ideal receptor protein from a viral perspective is one that is highly expressed in epithelial cell surface of mammalian respiratory or digestive tracts, strongly conserved in evolution so many mammalian species can serve as potential hosts, and functionally important so that its expression cannot be readily downregulated by the host in response to the infection. Experimental confirmation of host receptors includes (1) infection studies with cell cultures/tissues/organs with or without candidate receptor expression, (2) experimental determination of protein structure of the complex between the putative viral RDB and the candidate host receptor, and (3) experiments with mutant candidate receptor or homologues of the candidate receptor in other species. Successful identification of the host receptor opens the door for mechanism-based development of candidate drugs and vaccines and facilitates the inference of what other animal species are vulnerable to the viral pathogen. I illustrate these approaches with research on identification of the receptor and co-factors for SARS-CoV-2.

Integrated NMR/Molecular Dynamics Determination of the Ensemble Conformation of a Thermodynamically Stable CUUG RNA Tetraloop.

Both experimental and theoretical structure determinations of RNAs have remained challenging due to the intrinsic dynamics of RNAs. We report here an integrated nuclear magnetic resonance/molecular dynamics (NMR/MD) structure determination approach to describe the dynamic structure of the CUUG tetraloop. We show that the tetraloop undergoes substantial dynamics, leading to averaging of the experimental data. These dynamics are particularly linked to the temperature-dependent presence of a hydrogen bond within the tetraloop. Interpreting the NMR data by a single structure represents the low-temperature structure well but fails to capture all conformational states occurring at a higher temperature. We integrate MD simulations, starting from structures of CUUG tetraloops within the Protein Data Bank, with an extensive set of NMR data, and provide a structural ensemble that describes the dynamic nature of the tetraloop and the experimental NMR data well. We thus show that one of the most stable and frequently found RNA tetraloops displays substantial dynamics, warranting such an integrated structural approach.

Assembly of JAZ–JAZ and JAZ–NINJA complexes in jasmonate signaling

Jasmonates (JAs) are plant hormones with crucial roles in development and stress resilience. They activate MYC transcription factors by mediating the proteolysis of MYC inhibitors called JAZ proteins. In the absence of JA, JAZ proteins bind and inhibit MYC through the assembly of MYC–JAZ–Novel Interactor of JAZ (NINJA)–TPL repressor complexes. However, JAZ and NINJA are predicted to be largely intrinsically unstructured, which has precluded their experimental structure determination. Through a combination of biochemical, mutational, and biophysical analyses and AlphaFold-derived ColabFold modeling, we characterized JAZ–JAZ and JAZ–NINJA interactions and generated models with detailed, high-confidence domain interfaces. We demonstrate that JAZ, NINJA, and MYC interface domains are dynamic in isolation and become stabilized in a stepwise order upon complex assembly. By contrast, most JAZ and NINJA regions outside of the interfaces remain highly dynamic and cannot be modeled in a single conformation. Our data indicate that the small JAZ Zinc finger expressed in Inflorescence Meristem (ZIM) motif mediates JAZ–JAZ and JAZ–NINJA interactions through separate surfaces, and our data further suggest that NINJA modulates JAZ dimerization. This study advances our understanding of JA signaling by providing insights into the dynamics, interactions, and structure of the JAZ–NINJA core of the JA repressor complex.

Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates.

Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Cα atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO.