Experimental Structure Determination Research Articles

State-of-the-art web services for de novo protein structure prediction.

Residue coevolution estimations coupled to machine learning methods are revolutionizing the ability of protein structure prediction approaches to model proteins that lack clear homologous templates in the Protein Data Bank (PDB). This has been patent in the last round of the Critical Assessment of Structure Prediction (CASP), which presented several very good models for the hardest targets. Unfortunately, literature reporting on these advances often lacks digests tailored to lay end users; moreover, some of the top-ranking predictors do not provide webservers that can be used by nonexperts. How can then end users benefit from these advances and correctly interpret the predicted models? Here we review the web resources that biologists can use today to take advantage of these state-of-the-art methods in their research, including not only the best de novo modeling servers but also datasets of models precomputed by experts for structurally uncharacterized protein families. We highlight their features, advantages and pitfalls for predicting structures of proteins without clear templates. We present a broad number of applications that span from driving forward biochemical investigations that lack experimental structures to actually assisting experimental structure determination in X-ray diffraction, cryo-EM and other forms of integrative modeling. We also discuss issues that must be considered by users yet still require further developments, such as global and residue-wise model quality estimates and sources of residue coevolution other than monomeric tertiary structure.

Structures of the intrinsically disordered Aβ, tau and α-synuclein proteins in aqueous solution from computer simulations

Intrinsically disordered proteins (IDPs) play many biological roles in the human proteome ranging from vesicular transport, signal transduction to neurodegenerative diseases. The Aβ and tau proteins, and the α-synuclein protein, key players in Alzheimer’s and Parkinson’s diseases, respectively are fully disordered at the monomer level. The structural heterogeneity of the monomeric and oligomeric states and the high self-assembly propensity of these three IDPs have precluded experimental structural determination. Simulations have been used to determine the atomic structures of these IDPs. In this article, we review recent computer models to capture the equilibrium ensemble of Aβ, tau and α-synuclein proteins at different association steps in aqueous solution and present new results of the PEP-FOLD framework on α-synuclein monomer.

An Improved Topology Prediction of Alpha-Helical Transmembrane Protein Based on Deep Multi-Scale Convolutional Neural Network.

Alpha-helical proteins ( αTMPs) are essential in various biological processes. Despite their tertiary structures are crucial for revealing complex functions, experimental structure determination remains challenging and costly. In the past decades, various sequence-based topology prediction methods have been developed to bridge the gap between the sequences and structures by characterizing the structural features, but significant improvements are still required. Deep learning brings a great opportunity for its powerful representation learning capability from limited original data. In this work, we improved our αTMP topology prediction method DMCTOP using deep learning, which composed of two deep convolutional blocks to simultaneously extract local and global contextual features. Consequently, the inputs were simplified to reflect the original features of the sequence, including a protein sequence feature and an evolutionary conservation feature. DMCTOP can efficiently and accurately identify all topological types and the N-terminal orientation for an αTMP sequence. To validate the effectiveness of our method, we benchmarked DMCTOP against 13 peer methods according to the whole sequence, the transmembrane segment and the traditional criterion in testing experiments. All the results reveal that our method achieved the highest prediction accuracy and outperformed all the previous methods. The method is available at https://icdtools.nenu.edu.cn/dmctop.

Extended experimental inferential structure determination method in determining the structural ensembles of disordered protein states

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. Here we introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, which calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii (Rh), single molecule fluorescence Förster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.

Graph-Directed Approach for Downselecting Toxins for Experimental Structure Determination

Conotoxins are short, cysteine-rich peptides of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. Currently, of the approximately 6000 known conotoxin sequences, only about 3% have associated structural characterization, which leads to a bottleneck in rapid high-throughput screening (HTS) for identification of potential leads or threats. In this work, we combine a graph-based approach with homology modeling to expand the library of conotoxin structures and to identify those conotoxin sequences that are of the greatest value for experimental structural characterization. The latter would allow for the rapid expansion of the known structural space for generating high quality template-based models. Our approach generalizes to other evolutionarily-related, short, cysteine-rich venoms of interest. Overall, we present and validate an approach for venom structure modeling and experimental guidance and employ it to produce a 290%-larger library of approximate conotoxin structures for HTS. We also provide a set of ranked conotoxin sequences for experimental structure determination to further expand this library.

Single-nucleotide polymorphism characterization of gametocyte development 1 gene in Plasmodium falciparum isolates from Baringo, Uasin Gishu, and Nandi Counties, Kenya

IntroductionPlasmodium falciparum relies on gametocytogenesis to transmit from humans to mosquitoes. Gametocyte development 1 (Pfgdv1) is an upstream activator and epigenetic controller of gametocytogenesis. The emergence of drug resistance is a major public health concern and this requires the development of new strategies that target the transmission of malaria. As a putative drug target, Pfgdv1 has not been characterized to identify its polymorphisms and alleles under selection and how such polymorphisms influence protein structure. MethodsThis study characterized single-nucleotide polymorphisms (SNPs) in primary sequences (n = 30) of Pfgdv1 gene generated from thirty blood samples collected from patients infected with P. falciparum and secondary sequences (n = 216) retrieved from PlasmoDB. ChromasPro, MUSCLE, Tajima's D statistic, SLAC, and STRUM were used in editing raw sequences, performing multiple sequence alignment (MSA), identifying signatures of selection, detecting codon sites under selection pressure, and determining the effect of SNPs, respectively. ResultsMSA of primary and secondary sequences established the existence of five SNPs, consisting of four non-synonymous substitutions (nsSNPs) (p.P217H, p.R398Q, p.H417N, and p.D497E), and a synonymous substitution (p.S514S). The analysis of amino acid changes reveals that p.P217H, p.R398Q, and p.H417N comprise non-conservative changes. Tajima's D statistic showed that these SNPs were under balancing selection, while SLAC analysis identified p.P217H to be under the strongest positive selection. . Further analysis based on thermodynamics indicated that p.P217H has a destabilizing effect, while p.R398Q and p.D497E have stabilizing effects on the protein structure. ConclusionsThe existence of four nsSNPs implies that Pfgdv1 has a minimal diversity in the encoded protein. Selection analysis demonstrates that these nsSNPs are under balancing selection in both local and global populations. However, p.P217H exhibits positive directional selection consistent with previous reports where it showed differentiatial selection of P. falciparum in low and high transmission regions. Therefore, in-silico prediction and experimental determination of protein structure are necessary to evaluate Pfgdv1 as a target candidate for drug design and development.

TopModel: Template-Based Protein Structure Prediction at Low Sequence Identity Using Top-Down Consensus and Deep Neural Networks.

Knowledge of protein structures is essential to understand proteins' functions, evolution, dynamics, stabilities, and interactions and for data-driven protein- or drug design. Yet, experimental structure determination rates are far exceeded by that of next-generation sequencing, resulting in less than 1/1000th of proteins having an experimentally known 3D structure. Computational structure prediction seeks to alleviate this problem, and the Critical Assessment of Protein Structure Prediction (CASP) has shown the value of consensus and meta-methods that utilize complementary algorithms. However, traditionally, such methods employ majority voting during template selection and model averaging during refinement, which can drive the model away from the native fold if it is underrepresented in the ensemble. Here, we present TopModel, a fully automated meta-method for protein structure prediction. In contrast to traditional consensus and meta-methods, TopModel uses top-down consensus and deep neural networks to select templates and identify and correct wrongly modeled regions. TopModel combines a broad range of state-of-the-art methods for threading, alignment, and model quality estimation and provides a versatile workflow and toolbox for template-based structure prediction. TopModel shows a superior template selection, alignment accuracy, and model quality for template-based structure prediction on the CASP10-12 datasets compared to 12 state-of-the-art stand-alone primary predictors. TopModel was validated by prospective predictions of the nisin resistance protein (NSR) protein from Streptococcus agalactiae and LipoP from Clostridium difficile, showing far better agreement with experimental data than any of its constituent primary predictors. These results, in general, demonstrate the utility of TopModel for protein structure prediction and, in particular, show how combining computational structure prediction with sparse or low-resolution experimental data can improve the final model.

Scoring Functions for Protein-RNA Complex Structure Prediction: Advances, Applications, and Future Directions.

Protein-RNA interaction is among the most essential of biological events in living cells, being involved in protein synthesizing, RNA processing and transport, DNA transcription, and regulation of gene expression, and many other critical bio-molecular activities. A thorough understanding of this interaction is of paramount importance in fundamental study of a variety of vital cellular processes and therapeutic application for remedy of a broad range of diseases. Experimental high-resolution 3D structure determination is the primary source of knowledge for protein-RNA complexes. However, due to technical limitations, the existing techniques for experimental structure determination couldn't match the demand from fast growing interest in academia and industry. This problem necessitates the alternative high-throughput computational method for protein-RNA complex structure prediction. Similar to the in silico methods used for protein-protein and protein-DNA interactions, a reliable prediction of protein-RNA complex structure requires a scoring function with commensurate discriminatory power. Derived from determined structures and purposed to predict the to-be-determined structures, the scoring function is not only a predictive tool but also a gauge of our knowledge of protein-RNA interaction. In this review, we present an overview of the status of existing scoring functions and the scientific principle behind their constructions as well as their strengths and limitations. Finally, we will discuss about future directions of the scoring function development for protein-RNA structure prediction.

High-accuracy protein structures by combining machine-learning with physics-based refinement.

Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from coevolutionary analysis via machine learning to have significantly expanded the ability to predict structures for sequences without templates. One such method, AlphaFold, also performs well on sequences where templates are available but without using such information directly. Here we show that combining machine-learning based models from AlphaFold with state-of-the-art physics-based refinement via molecular dynamics simulations further improves predictions to outperform any other prediction method tested during the latest round of CASP. The resulting models have highly accurate global and local structures, including high accuracy at functionally important interface residues, and they are highly suitable as initial models for crystal structure determination via molecular replacement.

Computational methods for the ab initio identification of novel microRNA in plants: a systematic review.

BackgroundMicroRNAs (miRNAs) play a vital role as post-transcriptional regulators in gene expression. Experimental determination of miRNA sequence and structure is both expensive and time consuming. The next-generation sequencing revolution, which facilitated the rapid accumulation of biological data has brought biology into the “big data” domain. As such, developing computational methods to predict miRNAs has become an active area of inter-disciplinary research.ObjectiveThe objective of this systematic review is to focus on the developments of ab initio plant miRNA identification methods over the last decade.Data sourcesFive databases were searched for relevant articles, according to a well-defined review protocol.Study selectionThe search results were further filtered using the selection criteria that only included studies on novel plant miRNA identification using machine learning.Data extractionRelevant data from each study were extracted in order to carry out an analysis on their methodologies and findings.ResultsResults depict that in the last decade, there were 20 articles published on novel miRNA identification methods in plants of which only 11 of them were primarily focused on plant microRNA identification. Our findings suggest a need for more stringent plant-focused miRNA identification studies.ConclusionOverall, the study accuracies are of a satisfactory level, although they may generate a considerable number of false negatives. In future, attention must be paid to the biological plausibility of computationally identified miRNAs to prevent further propagation of biologically questionable miRNA sequences.

Determination of band alignment at the CdTe/SnTe heterojunction interface for CdTe thin-film solar cells

The Ohmic back contact for CdTe is a key issue to realize high-efficiency CdTe thin film solar cells because of the high work function of CdTe. CdTe/SnTe heterojunctions (HJs) have been implemented to address this issue which shows promising potential, but the band alignment at the HJs is unknown. The valence band offsets in MBE-grown cadmium telluride (CdTe)/tin telluride (SnTe) (111) heterostructures were measured with X-ray photoelectron spectroscopy (XPS). The XPS results show that the heterostructure has an ideal type-I band structure for CdTe solar cells applications, with a valence band offset of and a conduction band offset of , which expedites hole transport from the CdTe absorber to the hole electrode and improves the Ohmic contact for CdTe. Experimental determination of the band structure of CdTe/SnTe HJs can help improve the photovoltaic performance of CdTe thin film solar cells and facilitate the design and fabrication of CdTe/SnTe related devices. Furthermore, we inserted a SnTe back contact buffer layer into the CdTe thin film solar cells, and it was compared with the cell structure without the SnTe buffer layer. The feasibility of using SnTe as a solar cell back contact is confirmed.

Visualization of protein sequence space with force-directed graphs, and their application to the choice of target-template pairs for homology modelling

The protein sequence-structure gap results from the contrast between rapid, low-cost deep sequencing, and slow, expensive experimental structure determination techniques. Comparative homology modelling may have the potential to close this gap by predicting protein structure in target sequences using existing experimentally solved structures as templates. This paper presents the first use of force-directed graphs for the visualization of sequence space in two dimensions, and applies them to the choice of suitable RNA-dependent RNA polymerase (RdRP) target-template pairs within human-infective RNA virus genera. Measures of centrality in protein sequence space for each genus were also derived and used to identify centroid nearest-neighbour sequences (CNNs) potentially useful for production of homology models most representative of their genera. Homology modelling was then carried out for target-template pairs in different species, different genera and different families, and model quality assessed using several metrics. Reconstructed ancestral RdRP sequences for individual genera were also used as templates for the production of ancestral RdRP homology models. High quality ancestral RdRP models were consistently produced, as were good quality models for target-template pairs in the same genus. Homology modelling between genera in the same family produced mixed results and inter-family modelling was unreliable. We present a protocol for the production of optimal RdRP homology models for use in further experiments, e.g. docking to discover novel anti-viral compounds. (219 words)

Synthesis, X-Ray Structure Determination and Related Physical Properties of Thiazolidinone Derivative by DFT Quantum Chemical Method

In this paper we report the synthesis and characterization of the (Z)-3-N-(ethyl)-2-N'-((3-methoxyphenyl)imino)thiazolidine- 4-one by means of FT-IR, 1H and 13C NMR and by single crystal X-ray diffraction. The experimental determination of the crystal structure of the compound has been achieved using X-ray diffraction data. The important characteristic of the structure is the existence of a dihedral angle formed by the benzene and thiazolidinone rings being equal to 86.0° indicating an absence of α-α stacking as well as that the structure is non planar. In the crystal, the molecules are linked by C-H···O and C-H···N hydrogen bonds, these bonds being responsible for the three-dimensional molecular structure packing. In order to compare the experimental results with those of the theoretical calculation, quantum chemical DFT calculations were carried out using B3LYP/6-311G(d,p) basis set. In this context, the molecular electrostatic potential around the molecule and HOMO-LUMO energy levels were also computed. The dipole moment orientations were determined in order to understand the nature of inter- and intramolecular charge transfer. Finally, the stability of the title compound was confirmed throughout the calculation of the chemical reactivity descriptors.

Observing crystal nucleation in four dimensions using atomic electron tomography.

Nucleation plays a critical role in many physical and biological phenomena that range from crystallization, melting and evaporation to the formation of clouds and the initiation of neurodegenerative diseases1-3. However, nucleation is a challenging process to study experimentally, especially in its early stages, when several atoms or molecules start to form a new phase from a parent phase. A number of experimental and computational methods have been used to investigate nucleation processes4-17, but experimental determination of the three-dimensional atomic structure and the dynamics of early-stage nuclei has been unachievable. Here we use atomic electron tomography to study early-stage nucleation in four dimensions (that is, including time) at atomic resolution. Using FePt nanoparticles as a model system, we find that early-stage nuclei are irregularly shaped, each has a core of one to a few atoms with the maximum order parameter, and the order parameter gradient points from the core to the boundary of the nucleus. We capture the structure and dynamics of the same nuclei undergoing growth, fluctuation, dissolution, merging and/or division, which are regulated by the order parameter distribution and its gradient. These experimental observations are corroborated by molecular dynamics simulations of heterogeneous and homogeneous nucleation in liquid-solid phase transitions of Pt. Our experimental and molecular dynamics results indicate that a theory beyond classical nucleation theory1,2,18 is needed to describe early-stage nucleation at the atomic scale. We anticipate that the reported approach will open the door to the study of many fundamental problems in materials science, nanoscience, condensed matter physics and chemistry, such as phase transition, atomic diffusion, grain boundary dynamics, interface motion, defect dynamics and surface reconstruction with four-dimensional atomic resolution.

Structure and disorder in ice VII on the approach to hydrogen-bond symmetrization

We have collected neutron-diffraction data of sufficient quality for structural refinement from deuterated ice VII to over 60 GPa. At these pressures, we are approaching the onset of molecular dissociation where the water molecule should be significantly distorted. We fitted our data with both the conventional structural model for ice VII and a recently proposed ``interstitial'' model. We found these to be statistically indistinguishable and, therefore, adopted the conventional model in this work. Our refinements yield the first direct experimental determination of the molecular structure in this pressure regime. They show a clear elongation of the covalent bond, the rate of which rapidly increases with pressure, indicating we have entered a distinct regime on the approach to ice X. In addition, we have directly measured the pressure dependence of the separation of the two equivalent deuteron sites along the hydrogen bonds, which provides an upper bound on the hydrogen-bond centering pressure.

Compressed glassy carbon maintaining graphite-like structure with linkage formation between graphene layers

Amorphous diamond, formed by high-pressure compression of glassy carbon, is of interests for new carbon materials with unique properties such as high compressive strength. Previous studies attributed the ultrahigh strength of the compressed glassy carbon to structural transformation from graphite-like sp2-bonded structure to diamond-like sp3-bonded structure. However, there is no direct experimental determination of the bond structure of the compressed glassy carbon, because of experimental challenges. Here we succeeded to experimentally determine pair distribution functions of a glassy carbon at ultrahigh pressures up to 49.0 GPa by utilizing our recently developed double-stage large volume cell. Our results show that the C-C-C bond angle in the glassy carbon remains close to 120°, which is the ideal angle for the sp2-bonded honey-comb structure, up to 49.0 GPa. Our data clearly indicate that the glassy carbon maintains graphite-like structure up to 49.0 GPa. In contrast, graphene interlayer distance decreases sharply with increasing pressure, approaching values of the second neighbor C-C distance above 31.4 GPa. Linkages between the graphene layers may be formed with such a short distance, but not in the form of tetrahedral sp3 bond. The unique structure of the compressed glassy carbon may be the key to the ultrahigh strength.

(Invited) X-Ray Reflectivity Studies of Interfaces in Lithium-Ion Batteries

In lithium-ion batteries (LIBs), the arrangement of electrolyte molecules directly at the electrode interface, and the corresponding electric double layer (EDL) are expected to govern the interfacial ion transport during charge/discharge and the electrochemical stability, i.e. the origin and properties of the solid electrolyte interphase (SEI). The SEI is formed on anode surfaces due to electrolyte decomposition at low potentials outside the electrolyte's electrochemical stability window, and dictates cell chemistry, cycle life, and electrochemical reversibility. Towards this end, the first topic of this talk covers an Ångstrom-resolution combined experimental and theoretical structural determination of solid–liquid interfaces relevant to LIBs, specifically a baseline organic electrolyte containing various concentrations of lithium hexafluorophosphate (LiPF6) salt at a metal oxide interface. We find good agreement between our x-ray reflectivity (XRR) and molecular dynamics (MD) simulations. These show well defined interfacial layering of electrolyte molecules and that solvent molecules in the first interfacial layer are aligned surface-parallel. Furthermore, we observe reorientation of solvent molecules in the presence of ions [1,2]. The second portion of this talk describes a multi-property characterization study of the physical and chemical properties as well as the nucleation and growth of the SEI on silicon anodes relevant to LIBs. We combined in situ XRR, linear sweep voltammetry (LSV), and ex situ x-ray photoelectron spectroscopy (XPS) to develop a holistic understanding of the SEI formation mechanism and properties on native oxide terminated single crystalline Si electrodes. Our self-consistent findings show the formation of two distinct inorganic SEI layers, and reveal the critical role of the surface oxide in the surface electrochemistry of silicon anodes. These novel insights will be discussed with regard to the performance of LIB in general [3,4,5]. Finally, we will present recent results from combining in situ XRR and precision electrochemical measurements to understand origins of coulombic efficiencies, capacity fading, and charging rate capabilities in thin film silicon batteries. ­­­­References H.-G. Steinrück, C. Cao, Y. Tsao, C. J. Takacs, O. Konovalov, J. Vatamanu, O. Borodin, M. F. Toney, Energy Environ. Sci., 11, 594-602 (2018)Horowitz, H.-G. Steinrück, H.-L. Han, C. Cao, I. I. Abate, Y. Tsao, M. F. Toney, G. A. Somorjai, Nano Lett. 18, 2105-2111 (2018)Cao, H.-G. Steinrück, B. Shyam, K. H. Stone, M. F. Toney, Nano Lett.16, 7394-7401 (2016)Cao, H.-G. Steinrück, B. Shyam, M. F. Toney, Adv. Mater. Interfaces 4, 1700771 (2017)C. Cao, I. I. Abate, E. Sivonxay, B. Shyam, C. Jia, B. Moritz, T. P. Devereaux, K. A. Persson, H.-G. Steinrück, M. F. Toney, Joule (accepted 2018)

Cryo-EM density map fitting driven in-silico structure of human soluble guanylate cyclase (hsGC) reveals functional aspects of inter-domain cross talk upon NO binding

The human soluble Guanylate Cyclase (hsGC) is a heterodimeric heme-containing enzyme which regulates many important physiological processes. In eukaryotes, hsGC is the only known receptor for nitric oxide (NO) signaling. Improper NO signaling results in various disease conditions such as neurodegeneration, hypertension, stroke and erectile dysfunction. To understand the mechanisms of these diseases, structure determination of the hsGC dimer complex is crucial. However, so far all the attempts for the experimental structure determination of the protein were unsuccessful. The current study explores the possibility to model the quaternary structure of hsGC using a hybrid approach that combines state-of-the-art protein structure prediction tools with cryo-EM experimental data. The resultant 3D model shows close consistency with structural and functional insights extracted from biochemistry experiment data. Overall, the atomic-level complex structure determination of hsGC helps to unveil the inter-domain communication upon NO binding, which should be of important usefulness for elucidating the biological function of this important enzyme and for developing new treatments against the hsGC associated human diseases.

Structures of Cu n+ ( n = 3-10) Clusters Obtained by Infrared Action Spectroscopy.

Coinage metal clusters are of great importance for a wide range of scientific fields, ranging from microscopy to catalysis. Despite their clear fundamental and technological importance, the experimental structural determination of copper clusters has attracted little attention. We fill this gap by elucidating the structure of cationic copper clusters through infrared (IR) photodissociation spectroscopy of Cun+–Arm complexes. Structures of Cun+ (n = 3–10) are unambiguously assigned based on the comparison of experimental IR spectra in the 70–280 cm–1 spectral range with spectra calculated using density functional theory. Whereas Cu3+ and Cu4+ are planar, starting from n = 5, Cun+ clusters adopt 3D structures. Each successive cluster size is composed of its predecessor with a single atom adsorbed onto the face, giving evidence of a stepwise growth.

Tetrameric Aβ40 and Aβ42 β-Barrel Structures by Extensive Atomistic Simulations. I. In a Bilayer Mimicking a Neuronal Membrane.

The amyloid-β (Aβ) 42 oligomers are much more toxic than Aβ40 oligomers in Alzheimer's disease. Numerous experiments indicate that toxicity could involve the formation of pores in membranes, but experimental high-resolution structure determination of these pore-forming Aβ oligomers has been impeded by aggregate heterogeneity. Using extensive atomistic simulations, low-resolution data obtained in lipid bilayers, and other theoretical factors, we proposed atomic structures of Aβ40 and Aβ42 β-barrels in a bilayer mimicking a neuronal membrane. The 3D model, which consists of tetramer subunits, two distinct β-hairpin motifs and an asymmetric arrangement of eight antiparallel β-strands, is drastically destabilized for Aβ40 compared to its Aβ42 counterpart. Our computational modeling has several implications in Alzheimer's disease, sheds light on the amyloid pore hypothesis, and explains the higher deleterious property of Aβ42.