Structure Determination Of Complexes Research Articles

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.

A Single Interfacial Point Mutation Rescues Solution Structure Determination of the Complex of HMG-D with a DNA Bulge.

Broadening of signals from atoms at interfaces can often be a limiting factor in applying solution NMR to the structure determination of complexes. Common contributors to such problems include exchange between free and bound states and the increased molecular weight of complexes relative to the free components, but another cause that can be more difficult to deal with occurs when conformational dynamics within the interface takes place at an intermediate rate on the chemical shift timescale. In this work we show how a carefully chosen mutation in the protein HMG-D rescued such a situation, making possible high-resolution structure determination of its complex with a dA2 bulge DNA ligand designed to mimic a natural DNA bend, and thereby revealing a new spatial organization of the complex.

An image processing pipeline for electron cryo-tomography in RELION-5.

Electron tomography of frozen, hydrated samples allows structure determination of macromolecular complexes that are embedded in complex environments. Provided that the target complexes may be localised in noisy, three-dimensional tomographic reconstructions, averaging images of multiple instances of these molecules can lead to structures with sufficient resolution for de novo atomic modelling. Although many research groups have contributed image processing tools for these tasks, a lack of standardisation and interoperability represents a barrier for newcomers to the field. Here, we present an image processing pipeline for electron tomography data in RELION-5, with functionality ranging from the import of unprocessed movies to the automated building of atomic models in the final maps. Our explicit definition of metadata items that describe the steps of our pipeline has been designed for interoperability with other software tools and provides a framework for further standardisation.

Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor–protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.

Phase Identification and Discovery of an Elusive Polymorph of Drug‐Polymer Inclusion Complex Using Automated 3D Electron Diffraction

Abstract3D electron diffraction (3D ED) has shown great potential in crystal structure determination in materials, small organic molecules, and macromolecules. In this work, an automated, low‐dose and low‐bias 3D ED protocol has been implemented to identify six phases from a multiple‐phase melt‐crystallisation product of an active pharmaceutical ingredient, griseofulvin (GSF). Batch data collection under low‐dose conditions using a widely available commercial software was combined with automated data analysis to collect and process over 230 datasets in three days. Accurate unit cell parameters obtained from 3D ED data allowed direct phase identification of GSF Forms III, I and the known GSF inclusion complex (IC) with polyethylene glycol (PEG) (GSF‐PEG IC‐I), as well as three minor phases, namely GSF Forms II, V and an elusive new phase, GSF‐PEG IC‐II. Their structures were then directly determined by 3D ED. Furthermore, we reveal how the stabilities of the two GSF‐PEG IC polymorphs are closely related to their crystal structures. These results demonstrate the power of automated 3D ED for accurate phase identification and direct structure determination of complex, beam‐sensitive crystallisation products, which is significant for drug development where solid form screening is crucial for the overall efficacy of the drug product.

Phase Identification and Discovery of an Elusive Polymorph of Drug-Polymer Inclusion Complex Using Automated 3D Electron Diffraction.

3D electron diffraction (3D ED) has shown great potential in crystal structure determination in materials, small organic molecules, and macromolecules. In this work, an automated, low-dose and low-bias 3D ED protocol has been implemented to identify six phases from a multiple-phase melt-crystallisation product of an active pharmaceutical ingredient, griseofulvin (GSF). Batch data collection under low-dose conditions using a widely available commercial software was combined with automated data analysis to collect and process over 230 datasets in three days. Accurate unit cell parameters obtained from 3D ED data allowed direct phase identification of GSF Forms III, I and the known GSF inclusion complex (IC) with polyethylene glycol (PEG) (GSF-PEG IC-I), as well as three minor phases, namely GSF Forms II, V and an elusive new phase, GSF-PEG IC-II. Their structures were then directly determined by 3D ED. Furthermore, we reveal how the stabilities of the two GSF-PEG IC polymorphs are closely related to their crystal structures. These results demonstrate the power of automated 3D ED for accurate phase identification and direct structure determination of complex, beam-sensitive crystallisation products, which is significant for drug development where solid form screening is crucial for the overall efficacy of the drug product.

Structural consequences in differently substituted haloarylcarboxylates and non-covalent interactions on crystal packing and biological efficacy of copper(II) complexes with N,N,N′,N′-tetramethylethylenediamine N-donor ligand

In this work, we reported four new copper(II) halo-benzoate complexes (2-chloro-4-nitrobenzoate (2-Cl-4-NO2-BZ), 3,5-DICBZ, 2-chloro-5-nitrobenzoate (2-Cl-5-NO2-BZ), 3,5-difluoro benzoate (3,5-DIFBZ), and 3,5-dichlorobenzene (3,5-DICBZ) respectively in complexes 1–4) with N,N,N’,N’- tetramethylethylene diamine (temed) N-donor ligand in order to study the interesting coordination features and biological evaluation of such complexes under ambient reaction conditions. All the complexes 1–4 were characterized by elemental analyses, and spectroscopic techniques, including FT-IR, UV–vis, and EPR etc. Single crystal X-ray structure determination (SCXRD) of complexes revealed the distorted octahedral geometry in all complexes. Notably, the halobenzoate ligands exhibited a bidentate chelation mode in complexes 1 and 2, while they showed a monodentate mode in complex 3. In complex 4, the carboxylate ligand exhibited both monodentate as well as bidentate coordination. Furthermore, detailed packing analyses of complexes 1–4 highlighted the significance of halogen bonding in lattice stabilization besides hydrogen bonding and π∙∙∙π interactions as evidenced by Hirshfeld surface analyses and crystal analysis. Moreover, packing analyses displayed that complex 1 exhibited a layered or wave-like arrangement, while complex 2 featured a helical arrangement supported by a supramolecular halogen-bonded network guided by Cl∙∙∙O interactions. Complex 3 displayed a zig-zag layered arrangement with a stacking topology, and Complex 4 showcased a ribbon-like arrangement stabilized by F∙∙∙F and Cl∙∙∙Cl halogen bonding interactions. Furthermore, sigma hole on halogen atoms has also been examined (which play an important role in halogen bonding) by electrostatic-potential isosurfaces. In order to exploit the biological efficacy of complexes 1–4 owing to the inherent biological activity of copper metal, molecular docking analyses against gram +ve bacteria i.e. S. epidermidis (PDB ID; 8DO6), B. subtilis (1QD9), as well as two gram -ve bacteria, namely, P. aeruginosa (5WZE) and S. dysenteriae (1DM0), have also been carried out. Interestingly, values of docking scores and inhibition constant of all complexes 1–4 (with a maximum binding score (inhibition constant) of -9.8 kcal/mol (0.063 µMol) in 1 against 8DO6 and 1DM0) revealed higher biological efficacy than that of standard drugs.

First Gallium and Indium Crystal Structures of Curcuminoid Homoleptic Complexes: All-Different Ligand Stereochemistry and Cytotoxic Potential.

The crystal structure determination of metal complexes of curcuminoids is a relevant topic to assess their unequivocal molecular structure. We report herein the first two X-ray crystal structures of homoleptic metal complexes of a curcuminoid, namely Dimethoxycurcumin (DiMeOC), with gallium and indium. Such successful achievement can be attributed to the suppression of interactions from the phenolic groups, which favor an appropriate molecular setup, rendering Dimethoxycurcumin gallium ((DiMeOC)2-Ga) and Dimethoxycurcumin indium ((DiMeOC)3-In) crystals. Surprisingly, the conformation of ligands in the crystal structures shows differences in each metal complex. Thus, the ligands in the (DiMeOC)2-Ga complex show two different conformers in the two molecules of the asymmetric unit. However, the ligands in the (DiMeOC)3-In complex exhibit three different conformations within the same molecule of the asymmetric unit, constituting the first such case described for an ML3 complex. The cytotoxic activity of the (DiMeOC)2-Ga complex is 4-fold higher than cisplatin against the K562 cell line and has comparable activity towards U251 and PC-3 cell lines. Interestingly, this complex exhibit three times lesser toxicity than cisplatin and even slightly lesser cytotoxicity than curcumin itself.

`Cryo-EM': electron cryomicroscopy, cryo electron microscopy or something else?

Structural biology continues to benefit from an expanding toolkit, which is helping to gain unprecedented insight into the assembly and organization of multi-protein machineries, enzyme mechanisms and ligand/inhibitor binding. During the last ten years, cryoEM has become widely available and has provided a major boost to structure determination of membrane proteins and large multi-protein complexes. Many of the structures have now been made available at resolutions around 2 Å, where fundamental questions regarding enzyme mechanisms can be addressed. Over the years, the abbreviation cryoEM has been understood to stand for different things. We wish the wider community to engage and clarify the definition of cryoEM so that the expanding literature involving cryoEM is unified.

PointDE: Protein Docking Evaluation Using 3D Point Cloud Neural Network.

Protein-protein interactions (PPIs) play essential roles in many vital movements and the determination of protein complex structure is helpful to discover the mechanism of PPI. Protein-protein docking is being developed to model the structure of the protein. However, there is still a challenge to selecting the near-native decoys generated by protein-protein docking. Here, we propose a docking evaluation method using 3D point cloud neural network named PointDE. PointDE transforms protein structure to the point cloud. Using the state-of-the-art point cloud network architecture and a novel grouping mechanism, PointDE can capture the geometries of the point cloud and learn the interaction information from the protein interface. On public datasets, PointDE surpasses the state-of-the-art method using deep learning. To further explore the ability of our method in different types of protein structures, we developed a new dataset generated by high-quality antibody-antigen complexes. The result in this antibody-antigen dataset shows the strong performance of PointDE, which will be helpful for the understanding of PPI mechanisms.

Cryo-EM structure of the Mycobacterium abscessus F1-ATPase

The cases of lung disease caused by non-tuberculous mycobacterium Mycobacterium abscessus (Mab) are increasing and not reliably curable. Repurposing of anti-tuberculosis inhibitors brought the oxidative phosphorylation pathway with its final product ATP, formed by the essential F1FO-ATP synthase (subunits α3:β3:γ:δ:ε:a:b:b′:c9), into focus as an attractive inhibitor target against Mab. Because of the pharmacological attractiveness of this enzyme, we generated and purified a recombinant and enzymatically active Mab F1-ATPase complex, including subunits α3:β3:γ:δ:ε (MabF1-αβγδε) to achieve mechanistic, regulatory, and structural insights. The high purity of the complex enabled the first cryo-electron microscopy structure determination of the Mab F1-ATPase complex to 7.3 Å resolution. The enzyme showed low ATP hydrolysis activity, which was stimulated by trypsin treatment. No effect was observed in the presence of the detergent lauryldimethylamine oxide.

Improvement of cryo-EM maps by simultaneous local and non-local deep learning

Cryo-EM has emerged as the most important technique for structure determination of macromolecular complexes. However, raw cryo-EM maps often exhibit loss of contrast at high resolution and heterogeneity over the entire map. As such, various post-processing methods have been proposed to improve cryo-EM maps. Nevertheless, it is still challenging to improve both the quality and interpretability of EM maps. Addressing the challenge, we present a three-dimensional Swin-Conv-UNet-based deep learning framework to improve cryo-EM maps, named EMReady, by not only implementing both local and non-local modeling modules in a multiscale UNet architecture but also simultaneously minimizing the local smooth L1 distance and maximizing the non-local structural similarity between processed experimental and simulated target maps in the loss function. EMReady was extensively evaluated on diverse test sets of 110 primary cryo-EM maps and 25 pairs of half-maps at 3.0–6.0 Å resolutions, and compared with five state-of-the-art map post-processing methods. It is shown that EMReady can not only robustly enhance the quality of cryo-EM maps in terms of map-model correlations, but also improve the interpretability of the maps in automatic de novo model building.

Discovery, structural optimization, and anti-tumor bioactivity evaluations of betulinic acid derivatives as a new type of RORγ antagonists

Betulinic acid (BA) is a natural pentacyclic triterpenoid that has a wide range of biological and pharmacological effects. Here, computational methods such as pharmacophore screening and reverse docking were used to predict the potential target for BA. Retinoic acid receptor-related orphan receptor gamma (RORγ) was confirmed as its target by several molecular assays as well as crystal complex structure determination. RORγ has been the focus of metabolic regulation, but its potential role in cancer treatment has only recently come to the fore. In this study, rationale optimization of BA was performed and several new derivatives were generated. Among them, the compound 22 showed stronger binding affinity with RORγ (KD = 180 nM), good anti-proliferative activity against cancer cell lines, and potent anti-tumor efficacy with a TGI value of 71.6% (at a dose of 15 mg/kg) in the HPAF-II pancreatic cancer xenograft model. Further RNA-seq analysis and cellular validation experiments supported that RORγ antagonism was closely related to the antitumor activity of BA and 22, resulting in suppression of the RAS/MAPK and AKT/mTORC1 pathway and inducing caspase-dependent apoptosis in pancreatic cancer cells. RORγ was highly expressed in cancer cells and tissues and positively correlated with the poor prognosis of cancer patients. These results suggest that BA derivatives are potential RORγ antagonists worthy of further exploration.

Prospects and Limitations of High-Resolution Single-Particle Cryo-Electron Microscopy

Single particle cryo-electron microscopy (cryo-EM) has matured into a robust method for the determination of biological macromolecule structures in the past decade, complementing X-ray crystallography and nuclear magnetic resonance. Constant methodological improvements in both cryo-EM hardware and image processing software continue to contribute to an exponential growth in the number of structures solved annually. In this review, we provide a historical view of the many steps that were required to make cryo-EM a successful method for the determination of high-resolution protein complex structures. We further discuss aspects of cryo-EM methodology that are the greatest pitfalls challenging successful structure determination to date. Lastly, we highlight and propose potential future developments that would improve the method even further in the near future.

Structural analysis of cancer-relevant TCR-CD3 and peptide-MHC complexes by cryoEM

The recognition of antigenic peptide-MHC (pMHC) molecules by T-cell receptors (TCR) initiates the T-cell mediated immune response. Structural characterization is key for understanding the specificity of TCR-pMHC interactions and informing the development of therapeutics. Despite the rapid rise of single particle cryoelectron microscopy (cryoEM), x-ray crystallography has remained the preferred method for structure determination of TCR-pMHC complexes. Here, we report cryoEM structures of two distinct full-length α/β TCR-CD3 complexes bound to their pMHC ligand, the cancer-testis antigen HLA-A2/MAGEA4 (230–239). We also determined cryoEM structures of pMHCs containing MAGEA4 (230–239) peptide and the closely related MAGEA8 (232–241) peptide in the absence of TCR, which provided a structural explanation for the MAGEA4 preference displayed by the TCRs. These findings provide insights into the TCR recognition of a clinically relevant cancer antigen and demonstrate the utility of cryoEM for high-resolution structural analysis of TCR-pMHC interactions.

Structure Determination of Challenging Protein-Peptide Complexes Combining NMR Chemical Shift Data and Molecular Dynamics Simulations.

Intrinsically disordered regions of proteins often mediate important protein-protein interactions. However, the folding-upon-binding nature of many polypeptide-protein interactions limits the ability of modeling tools to predict the three-dimensional structures of such complexes. To address this problem, we have taken a tandem approach combining NMR chemical shift data and molecular simulations to determine the structures of peptide-protein complexes. Here, we use the MELD (Modeling Employing Limited Data) technique applied to polypeptide complexes formed with the extraterminal domain (ET) of bromo and extraterminal domain (BET) proteins, which exhibit a high degree of binding plasticity. This system is particularly challenging as the binding process includes allosteric changes across the ET receptor upon binding, and the polypeptide binding partners can adopt different conformations (e.g., helices and hairpins) in the complex. In a blind study, the new approach successfully modeled bound-state conformations and binding poses, using only protein receptor backbone chemical shift data, in excellent agreement with experimentally determined structures for moderately tight (Kd ∼100 nM) binders. The hybrid MELD + NMR approach required additional peptide ligand chemical shift data for weaker (Kd ∼250 μM) peptide binding partners. AlphaFold also successfully predicts the structures of some of these peptide-protein complexes. However, whereas AlphaFold can provide qualitative peptide rankings, MELD can directly estimate relative binding affinities. The hybrid MELD + NMR approach offers a powerful new tool for structural analysis of protein-polypeptide complexes involving disorder-to-order transitions upon complex formation, which are not successfully modeled with most other complex prediction methods, providing both the 3D structures of peptide-protein complexes and their relative binding affinities.

Low information NMR data guided protein-peptide complex structure determination with MELDXMD.

About 40% of the protein-protein interactions are mediated through small peptide epitope. Knowledge of how a peptide or an intrinsically disordered protein fragment interact with a protein would widen of the understanding of gene regulation, transcription and finally drug discovery. However, the folding upon binding nature of majority of the peptide interacting partner, limits the ability of modeling tools to predict structures of such complexes. To address this problem, we show an integrative approach combining cost-effective NMR chemical shift data and physics based molecular simulations to determine structures of protein-peptide complexes. Here in this presentation, we demonstrate our approach for polypeptide complexes formed with the extraterminal (ET) domain of bromo and extraterminal domain (BET) proteins, which exhibit a high degree of binding plasticity. This system is particularly challenging as the binding process includes allosteric changes across the ET receptor upon binding, and the polypeptide binding partners can form different conformations (e.g., helices and hairpins) in the complex. In a blind study, the new approach successfully predicts bound-state structure, using only backbone chemical shift data, in excellent agreement with experimental structure. We show, this approach is successful on not only strongly interacting complexes, but also in the case of weak binder, this method has been successful. MELD+NMR also predicts relative binding affinities of different peptides, consistent with the experimental values. The hybrid MELD+NMR approach provides a powerful new tool for structural analysis of protein-polypeptide complexes involving disorder to order transitions upon complex formation which are not successfully modeled with most other complex predication methods including state of the art docking methods, providing 3D atomistic structures of complexes as well as their relative binding affinities.

Perspective: Structure determination of protein-ligand complexes at room temperature using X-ray diffraction approaches.

The interaction between macromolecular proteins and small molecule ligands is an essential component of cellular function. Such ligands may include enzyme substrates, molecules involved in cellular signalling or pharmaceutical drugs. Together with biophysical techniques used to assess the thermodynamic and kinetic properties of ligand binding to proteins, methodology to determine high-resolution structures that enable atomic level interactions between protein and ligand(s) to be directly visualised is required. Whilst such structural approaches are well established with high throughput X-ray crystallography routinely used in the pharmaceutical sector, they provide only a static view of the complex. Recent advances in X-ray structural biology methods offer several new possibilities that can examine protein-ligand complexes at ambient temperature rather than under cryogenic conditions, enable transient binding sites and interactions to be characterised using time-resolved approaches and combine spectroscopic measurements from the same crystal that the structures themselves are determined. This Perspective reviews several recent developments in these areas and discusses new possibilities for applications of these advanced methodologies to transform our understanding of protein-ligand interactions.

Blockade of dual immune checkpoint inhibitory signals with a CD47/PD-L1 bispecific antibody for cancer treatment.

Background: Even though PD-1/PD-L1 is an identified key "don't find me" signal to active adaptive immune system for cancer treatment, the overall response rate (ORR) for all cancer patients is still limited. Other effective therapeutic modalities to bridge the innate and adaptive immunity to improve ORR are urgently needed. Recently, CD47/SIRPα interaction is confirmed as a critical "don't eat me" signal to active innate immunity. However, the red blood cell (RBC) toxicity is the big concern for the development of CD47-based anti-cancer therapeutics. Methods: Here, we report the development of a CD47/PD-L1 bispecific antibody 6MW3211 to block both PD-1/PD-L1 and CD47/SIRPα signals, and studied the effects of 6MW3211 on anti-tumor immune functions in vitro and in vivo. The pharmacokinetic and toxicity profiles of 6MW3211 were evaluated in GLP non-human primate (NHP) studies. Results: The dual immune checkpoint inhibitory signaling blocker 6MW3211 shows high binding affinity to PD-L1 and low binding affinity to CD47. This inequivalent binding affinity design makes 6MW3211 preferentially bound to PD-L1 on tumor cells followed by disrupting the interaction of CD47/SIRPα. Complex structure determination and flow cytometry assay demonstrated that 6MW3211 has no binding to either human or rhesus monkey RBCs. 6MW3211 effectively blocked both PD-1/DP-L1 and CD47/SIRPα signaling and promoted macrophage phagocytosis of tumor cells. Potent therapeutic efficacies of 6MW3211 in three different mouse models were further observed. Moreover, 6MW3211 was demonstrated to have a fairly good safety profile in a GLP NHP study. In addition, multiplex fluorescent immunohistochemistry (mIHC) staining shows that PD-L1 and CD47 co-express on several different types of human tumor tissues. Conclusions: These results support the development of 6MW3211 for the treatment of PD-L1 and CD47 double positive cancers.

Cryo-EM for Structure Determination of Mitochondrial Ribosome Samples.

Single-particle cryoelectron microscopy (cryo-EM) allows structure determination of large macromolecular complexes from conformationally and compositionally heterogeneous mixtures of particles. This technique has been used to reveal the architecture of the mitochondrial ribosome and to visualize transient states that occur during the translation cycle or during mitoribosome biogenesis. Here, we outline an exemplary workflow for the analysis of single-particle cryo-EM data of human mitoribosome samples. In addition, we provide an example dataset which can be used for training purposes alongside the protocol.