Could AlphaFold revolutionize chemical therapeutics?

Interactions of Potential Endocrine-Disrupting Chemicals with Whole Human Proteome Predicted by AlphaFold2 Using an In Silico Approach

Binding with proteins is a critical molecular initiating event through which environmental pollutants exert toxic effects in humans. Previous studies have been limited by the availability of three-dimensional (3D) protein structures and have focused on only a small set of environmental contaminants. Using the highly accurate 3D protein structure predicted by AlphaFold2, this study explored over 60 million interactions obtained through molecular docking between 20,503 human proteins and 1251 potential endocrine-disrupting chemicals. A total of 66,613,773 docking results were obtained, 1.2% of which were considered to be high binding, as their docking scores were lower than -7. Monocyte to macrophage differentiation factor 2 (MMD2) was predicted to interact with the highest number of environmental pollutants (526), with polychlorinated biphenyls and polychlorinated dibenzofurans accounting for a significant proportion. Dimension reduction and clustering analysis revealed distinct protein profiles characterized by high binding affinities for perfluoroalkyl and polyfluoroalkyl substances (PFAS), phthalate-like chemicals, and other pollutants, consistent with their uniquely enriched pathways. Further structural analysis indicated that binding pockets with a high proportion of charged amino acid residues, relatively low α-helix content, and high β-sheet content were more likely to bind to PFAS than others. This study provides insights into the toxicity pathways of various pollutants impacting human health and offers novel perspectives for the establishment and expansion of adverse outcome pathway-based models.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

ABBREVIATIONS

AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like.

Type II bacterial toxin-antitoxins: hypotheses, facts, and the newfound plethora of the PezAT system

Toxin-antitoxin (TA) systems are entities found in the prokaryotic genomes, with eight reported types. Type II, the best characterized, is comprised of two genes organized as an operon. Whereas toxins impair growth, the cognate antitoxin neutralizes its activity. TAs appeared to be involved in plasmid maintenance, persistence, virulence, and defence against bacteriophages. Most Type II toxins target the bacterial translational machinery. They seem to be antecessors of Higher Eukaryotes and Prokaryotes Nucleotide-binding (HEPN) RNases, minimal nucleotidyltransferase domains, or CRISPR-Cas systems. A total of four TAs encoded by Streptococcus pneumoniae, RelBE, YefMYoeB, Phd-Doc, and HicAB, belong to HEPN-RNases. The fifth is represented by PezAT/Epsilon-Zeta. PezT/Zeta toxins phosphorylate the peptidoglycan precursors, thereby blocking cell wall synthesis. We explore the body of knowledge (facts) and hypotheses procured for Type II TAs and analyse the data accumulated on the PezAT family. Bioinformatics analyses showed that homologues of PezT/Zeta toxin are abundantly distributed among 14 bacterial phyla mostly in Proteobacteria (48%), Firmicutes (27%), and Actinobacteria (18%), showing the widespread distribution of this TA. The pezAT locus was found to be mainly chromosomally encoded whereas its homologue, the tripartite omega-epsilon-zeta locus, was found mostly on plasmids. We found several orphan pezT/zeta toxins, unaccompanied by a cognate antitoxin.

Sequence-based drug design as a concept in computational drug design

Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

Targeting the human gut microbiome with small-molecule inhibitors

The human gut microbiome is a complex microbial community that is strongly linked to both host health and disease. However, the detailed molecular mechanisms underlying the effects of these microorganisms on host biology remain largely uncharacterized. The development of non-lethal, small-molecule inhibitors that target specific gut microbial activities enables a powerful but underutilized approach to studying the gut microbiome and a promising therapeutic strategy. In this Review, we will discuss the challenges of studying this microbial community, the historic use of small-molecule inhibitors in microbial ecology, and recent applications of this strategy. We also discuss the evidence suggesting that host-targeted drugs can affect the growth and metabolism of gut microbes. Finally, we address the issues of developing and implementing microbiome-targeted small-molecule inhibitors and define important future directions for this research.

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.

An agnostic analysis of the human AlphaFold2 proteome using local protein conformations

Knowledge of the 3D structure of proteins is a valuable asset for understanding their precise biological mechanisms. However, the cost of production of 3D structures and experimental difficulties limit their obtaining. The proposal of 3D structural models is consequently an appealing alternative. The release of the AlphaFold Deep Learning approach has revolutionized the field. The recent near-complete human proteome proposal makes it possible to analyse large amounts of data and evaluate the results of the approach in greater depth. The 3D human proteome was thus analysed in light of the classic secondary structures, and many less-used protein local conformations (PolyProline II helices, type of γ-turns, of β-turns and of β-bulges, curvature of the helices, and a structural alphabet). Without questioning the global quality of the approach, this analysis highlights certain local conformations, which maybe poorly predicted and they could therefore be better addressed.

Sequence and structure alignments in post-AlphaFold era

Sequence alignment is fundamental for analyzing protein structure and function. For all but closely-related proteins, alignments based on structures are more accurate than alignments based purely on amino-acid sequences. However, the disparity between the large amount of sequence data and the relative paucity of experimentally-determined structures has precluded the general applicability of structure alignment. Based on the success of AlphaFold (and its likes) in producing high-quality structure predictions, we suggest that when aligning homologous proteins, lacking experimental structures, better results can be obtained by a structural alignment of predicted structures than by an alignment based only on amino-acid sequences. We present a quantitative evaluation, based on pairwise alignments of sequences and structures (both predicted and experimental) to support this hypothesis.

DPAM: A domain parser for AlphaFold models

The recent breakthroughs in structure prediction, where methods such as AlphaFold demonstrated near-atomic accuracy, herald a paradigm shift in structural biology. The 200 million high-accuracy models released in the AlphaFold Database are expected to guide protein science in the coming decades. Partitioning these AlphaFold models into domains and assigning them to an evolutionary hierarchy provide an efficient way to gain functional insights into proteins. However, classifying such a large number of predicted structures challenges the infrastructure of current structure classifications, including our Evolutionary Classification of protein Domains (ECOD). Better computational tools are urgently needed to parse and classify domains from AlphaFold models automatically. Here we present a Domain Parser for AlphaFold Models (DPAM) that can automatically recognize globular domains from these models based on inter-residue distances in 3D structures, predicted aligned errors, and ECOD domains found by sequence (HHsuite) and structural (Dali) similarity searches. Based on a benchmark of 18,759 AlphaFold models, we demonstrate that DPAM can recognize 98.8% of domains and assign correct boundaries for 87.5%, significantly outperforming structure-based domain parsers and homology-based domain assignment using ECOD domains found by HHsuite or Dali. Application of DPAM to the massive AlphaFold models will enable efficient classification of domains, providing evolutionary contexts and facilitating functional studies.

It's ok to be outnumbered - sub-stoichiometric modulation of homomeric protein complexes

An arsenal of molecular tools with increasingly diversified mechanisms of action is being developed by the scientific community to enable biological interrogation and pharmaceutical modulation of targets and pathways of ever increasing complexity. While most small molecules interact with the target of interest in a 1 : 1 relationship, a noteworthy number of recent examples were reported to bind in a sub-stoichiometric manner to a homomeric protein complex. This approach requires molecular understanding of the physiologically relevant protein assemblies and in-depth characterization of the compound's mechanism of action. The recent literature examples summarized here were selected to illustrate methods used to identify and characterize molecules with such mechanisms. The concept of one small molecule targeting a homomeric protein assembly is not new but the subject deserves renewed inspection in light of emerging technologies and increasingly diverse target biology, to ensure relevant in vitro systems are used and valuable compounds with potentially novel sub-stoichiometric mechanisms of action aren't overlooked.

Substrate Recognition Properties from an Intermediate Structural State of the UreA Transporter

Through a combination of comparative modeling, site-directed and classical random mutagenesis approaches, we previously identified critical residues for binding, recognition, and translocation of urea, and its inhibition by 2-thiourea and acetamide in the Aspergillus nidulans urea transporter, UreA. To deepen the structural characterization of UreA, we employed the artificial intelligence (AI) based AlphaFold2 (AF2) program. In this analysis, the resulting AF2 models lacked inward- and outward-facing cavities, suggesting a structural intermediate state of UreA. Moreover, the orientation of the W82, W84, N279, and T282 side chains showed a large variability, which in the case of W82 and W84, may operate as a gating mechanism in the ligand pathway. To test this hypothesis non-conservative and conservative substitutions of these amino acids were introduced, and binding and transport assessed for urea and its toxic analogue 2-thiourea, as well as binding of the structural analogue acetamide. As a result, residues W82, W84, N279, and T282 were implicated in substrate identification, selection, and translocation. Using molecular docking with Autodock Vina with flexible side chains, we corroborated the AF2 theoretical intermediate model, showing a remarkable correlation between docking scores and experimental affinities determined in wild-type and UreA mutants. The combination of AI-based modeling with classical docking, validated by comprehensive mutational analysis at the binding region, would suggest an unforeseen option to determine structural level details on a challenging family of proteins.

Anti-trypanosomatid structure-based drug design - lessons learned from targeting the folate pathway

INTRODUCTION

Trypanosomatidic parasitic infections of humans and animals caused by Trypanosoma brucei, Trypanosoma cruzi, and Leishmania species pose a significant health and economic burden in developing countries. There are few effective and accessible treatments for these diseases, and the existing therapies suffer from problems such as parasite resistance and side effects. Structure-based drug design (SBDD) is one of the strategies that has been applied to discover new compounds targeting trypanosomatid-borne diseases.

AREAS COVERED

We review the current literature (mostly over the last 5 years, searched in PubMed database on Nov 11^th 2021) on the application of structure-based drug design approaches to identify new anti-trypanosomatidic compounds that interfere with a validated target biochemical pathway, the trypanosomatid folate pathway.

EXPERT OPINION

The application of structure-based drug design approaches to perturb the trypanosomatid folate pathway has successfully provided many new inhibitors with good selectivity profiles, most of which are natural products or their derivatives or have scaffolds of known drugs. However, the inhibitory effect against the target protein(s) often does not translate to anti-parasitic activity. Further progress is hampered by our incomplete understanding of parasite biology and biochemistry, which is necessary to complement SBDD in a multiparameter optimization approach to discovering selective anti-parasitic drugs.

Novel insights into RB1 mutation

Since the discovery of the retinoblastoma susceptibility gene (RB1) decades ago, RB1 has been regarded as a prototype tumor suppressor gene providing a paradigm for tumor genetic research. Constant research has updated the understanding of RB1-related pathways and their impact on tumor and nontumor diseases. Mutation of RB1 gene has been observed in multiple types of malignant tumors including prostate cancer, lung cancer, breast cancer, and almost every familial and sporadic case of retinoblastoma. Even if well-known and long-investigated, the application potential of RB1 mutation has not been fully tapped. In this review, we focus on the mechanism underlying RB1 mutation during oncogenesis. Therapeutically, we have further discussed potential clinical strategies by targeting RB1-mutated cancers. The unsolved problems and prospects of RB1 mutation are also discussed.

Structural basis for receptor selectivity and inverse agonism in S1P5 receptors

The bioactive lysophospholipid sphingosine-1-phosphate (S1P) acts via five different subtypes of S1P receptors (S1PRs) - S1P1-5. S1P5 is predominantly expressed in nervous and immune systems, regulating the egress of natural killer cells from lymph nodes and playing a role in immune and neurodegenerative disorders, as well as carcinogenesis. Several S1PR therapeutic drugs have been developed to treat these diseases; however, they lack receptor subtype selectivity, which leads to side effects. In this article, we describe a 2.2 Å resolution room temperature crystal structure of the human S1P5 receptor in complex with a selective inverse agonist determined by serial femtosecond crystallography (SFX) at the Pohang Accelerator Laboratory X-Ray Free Electron Laser (PAL-XFEL) and analyze its structure-activity relationship data. The structure demonstrates a unique ligand-binding mode, involving an allosteric sub-pocket, which clarifies the receptor subtype selectivity and provides a template for structure-based drug design. Together with previously published S1PR structures in complex with antagonists and agonists, our structure with S1P5-inverse agonist sheds light on the activation mechanism and reveals structural determinants of the inverse agonism in the S1PR family.

Selective chemical probes can untangle the complexity of the plant cell endomembrane system

The endomembrane system is critical for plant growth and development and understanding its function and regulation is of great interest for plant biology research. Small-molecule targeting distinctive endomembrane components have proven powerful tools to dissect membrane trafficking in plant cells. However, unambiguous elucidation of the complex and dynamic trafficking processes requires chemical probes with enhanced precision. Determination of the mechanism of action of a compound, which is facilitated by various chemoproteomic approaches, opens new avenues for the improvement of its specificity. Moreover, rational molecule design and reverse chemical genetics with the aid of virtual screening and artificial intelligence will enable us to discover highly precise chemical probes more efficiently. The next decade will witness the emergence of more such accurate tools, which together with advanced live quantitative imaging techniques of subcellular phenotypes, will deepen our insights into the plant endomembrane system.

Assessment of AlphaFold2 for Human Proteins via Residue Solvent Exposure

As only 35% of human proteins feature (often partial) PDB structures, the protein structure prediction tool AlphaFold2 (AF2) could have massive impact on human biology and medicine fields, making independent benchmarks of interest. We studied AF2's ability to describe the backbone solvent exposure as a functionally important and easily interpretable "natural coordinate" of protein conformation, using human proteins as test case. After screening for appropriate comparative sets, we matched 1818 human proteins predicted by AF2 against 7585 unique experimental PDBs, and after curation for sequence overlap, we assessed 1264 comparative pairs comprising 115 unique AF2 structures and 652 unique experimental structures. AF2 performed markedly worse for multimers, whereas ligands, cofactors, and experimental resolution were interestingly not very important for performance. AF2 performed excellently for monomer proteins. Challenges relating to specific groups of residues and multimers were analyzed. We identified larger deviations for lower-confidence scores (pLDDT), and exposed residues and polar residues (e.g., Asp, Glu, Asn) being less accurately described than hydrophobic residues. Proline conformations were the hardest to predict, probably due to a common location in dynamic solvent-accessible parts. In summary, using solvent exposure as a metric, we quantified the performance of AF2 for human proteins and provided estimates of the expected agreement as a function of ligand presence, multimer/monomer status, local residue solvent exposure, pLDDT, and amino acid type. Overall performance was found to be excellent.

AlphaFold2 models indicate that protein sequence determines both structure and dynamics

AlphaFold 2 (AF2) has placed Molecular Biology in a new era where we can visualize, analyze and interpret the structures and functions of all proteins solely from their primary sequences. We performed AF2 structure predictions for various protein systems, including globular proteins, a multi-domain protein, an intrinsically disordered protein (IDP), a randomized protein, two larger proteins (> 1000 AA), a heterodimer and a homodimer protein complex. Our results show that along with the three dimensional (3D) structures, AF2 also decodes protein sequences into residue flexibilities via both the predicted local distance difference test (pLDDT) scores of the models, and the predicted aligned error (PAE) maps. We show that PAE maps from AF2 are correlated with the distance variation (DV) matrices from molecular dynamics (MD) simulations, which reveals that the PAE maps can predict the dynamical nature of protein residues. Here, we introduce the AF2-scores, which are simply derived from pLDDT scores and are in the range of [0, 1]. We found that for most protein models, including large proteins and protein complexes, the AF2-scores are highly correlated with the root mean square fluctuations (RMSF) calculated from MD simulations. However, for an IDP and a randomized protein, the AF2-scores do not correlate with the RMSF from MD, especially for the IDP. Our results indicate that the protein structures predicted by AF2 also convey information of the residue flexibility, i.e., protein dynamics.

Thyroid endocrine disruption and hepatotoxicity induced by bisphenol AF: Integrated zebrafish embryotoxicity test and deep learning

Bisphenol AF (BPAF) is an emerging contaminant prevalent in the environment as one of main substitutes of bisphenol A (BPA). It was found that BPAF exhibited estrogenic effects in zebrafish larvae in our previous study, while little is known about its effects on the thyroid and liver. A 7 d zebrafish embryotoxicity test was conducted to study the potential thyroid disruption and hepatotoxicity of BPAF. BPAF decreased levels of thyroid hormones and deiodinases but increased expressions of transthyretin at 12.5 and 125 μg/L after 7 d exposure, indicating that both the metabolism and transport of thyroid hormones were perturbed. The thyroid hormone receptor (TR) levels decreased significantly upon exposure to ≥12.5 μg/L BPAF, implying that BPAF acts as a TR antagonist, which coincided well with the prediction from the Direct Message Passing Neural Network. The liver impairment (mainly cell necrosis of hepatocytes) and apoptosis were triggered by 125 μg/L and ≥12.5 μg/L BPAF respectively, accompanied by the increased activities of caspase 3 and caspase 9. Thus BPAF might not be a safe alternative to BPA given the thyroid and liver toxicity. DMPNN appears useful to screen for thyroid disrupting activity from molecular structures.

VHH Structural Modelling Approaches: A Critical Review

VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.

Where to From Here?

The biological-biochemical community has been shocked and delighted by the remarkable progress that has recently been made on a problem that has consumed the attention, energy, and resources of many, if not most of the scientists in the field for the past 50 years. The problem has been to predict the tertiary structure of a protein merely from its amino acid sequence. Nature does it easily enough, but it has been an incredibly difficult problem, often considered intractable, for humankind. The breakthrough has come in the form of two computer-based approaches, AlphaFold2 and RoseTTAFold in conjunction with factors such as the use of vast computing power, the field of artificial intelligence, and the existence of huge protein sequence databases. The advancement of these tools depended upon and was stimulated by the last 50 years of development of smaller and smaller and more and more powerful electronics components, mainly processors and memory. Along with the problem of protein folding, determining the function or mechanism of action of proteins has similarly limped along as did protein folding until the recent breakthroughs. Perhaps AlphaFold2 and RoseTTAFold can substantially aid in protein mechanistic studies. Now it is not completely insane to consider what might be the next grand challenge in biochemistry-biology. We offer several possibilities.

Deconvolution of the MBP-Bri2 Interaction by a Yeast Two Hybrid System and Synergy of the AlphaFold2 and High Ambiguity Driven Protein-Protein Docking

Myelin basic protein (MBP) is one of the key proteins in the development of multiple sclerosis (MS). However, very few intracellular MBP partners have been identified up to now. In order to find proteins interacting with MBP in the brain, an expression library from the human brain was screened using a yeast two-hybrid system. Here we showed that MBP interacts with the C-terminal 24-residue peptide of Integral transmembrane protein II associated with familial British and Danish dementia (ITM2B/Bri2 or Bri2). This peptide (Bri23R) was one residue longer than the known Bri23 peptide, which is cleaved from the C-terminus of Bri2 during its maturation in the Golgi and has physiological activity as a modulator of amyloid precursor protein processing. Since the spatial structures for both MBP and Bri2 were not known, we used computational methods of structural biology including an artificial intelligence system AlphaFold2 and high ambiguity driven protein-protein docking (HADDOCK 2.1) to gain a mechanistic explanation of the found protein-protein interaction and elucidate a possible structure of the complex of MBP with Bri23R peptide. As expected, MBP was mostly unstructured, although it has well-defined α-helical regions, while Bri23R forms a stable β-hairpin. Simulation of the interaction between MBP and Bri23R in two different environments, as parts of the two-hybrid system fusion proteins and in the form of single polypeptides, showed that MBP twists around Bri23R. The observed interaction results in the adjustment of the size of the internal space between MBP α-helices to the size of the β-hairpin of Bri23R. Since Bri23 is known to inhibit aggregation of amyloid oligomers, and the association of MBP to the inner leaflet of the membrane bilayer shares features with amyloid fibril formation, Bri23 may serve as a peptide chaperon for MBP, thus participating in myelin membrane assembly.

High Molecular Weight Kininogen: A Review of the Structural Literature

Kininogens are multidomain glycoproteins found in the blood of most vertebrates. High molecular weight kininogen demonstrate both carrier and co-factor activity as part of the intrinsic pathway of coagulation, leading to thrombin generation. Kininogens are the source of the vasoactive nonapeptide bradykinin. To date, attempts to crystallize kininogen have failed, and very little is known about the shape of kininogen at an atomic level. New advancements in the field of cryo-electron microscopy (cryoEM) have enabled researchers to crack the structure of proteins that has been refractory to traditional crystallography techniques. High molecular weight kininogen is a good candidate for structural investigation by cryoEM. The goal of this review is to summarize the findings of kininogen structural studies.