An unusual ΔE693 mutation in the amyloid precursor protein (APP) producing a β-amyloid (Aβ) peptide lacking glutamic acid at position 22 (Glu22) was recently discovered, and dabbed the Osaka mutant (ΔE22). Previously, several point mutations in the Aβ peptide involving Glu22 substitutions were identified and implicated in the early onset of familial Alzheimer’s disease (FAD). Despite the absence of Glu22, the Osaka mutant is also associated with FAD, showing a recessive inheritance in families affected by the disease. To see whether this aggregation-prone Aβ mutant could directly relate to the Aβ ion channel-mediated mechanism as observed for the wild type (WT) Aβ peptide in AD pathology, we modeled Osaka mutant β-barrels in a lipid bilayer. Using molecular dynamics (MD) simulations, two conformer ΔE22 barrels with the U-shaped monomer conformation derived from NMR-based WT Aβ fibrils were simulated in explicit lipid environment. Here, we show that the ΔE22 barrels obtain the lipid-relaxed β-sheet channel topology, indistinguishable from the WT Aβ1–42 barrels, as do the outer and pore dimensions of octadecameric (18-mer) ΔE22 barrels. Although the ΔE22 barrels lose the cationic binding site in the pore which is normally provided by the negatively charged Glu22 side-chains, the mutant pores gain a new cationic binding site by Glu11 at the lower bilayer leaflet, and exhibit ion fluctuations similar to the WT barrels. Of particular interest, this deletion mutant suggests that toxic WT Aβ1–42 would preferentially adopt a less C-terminal turn similar to that observed for Aβ17–42, and explains why the solid state NMR data for Aβ1–40 point to a more C-terminal turn conformation. The observed ΔE22 barrels conformational preferences also suggest an explanation for the lower neurotoxicity in rat primary neurons as compared to WT Aβ1–42.
Aβ mutant; point mutation; Alzheimer’s; toxic oligomer; β-sheet channel; molecular dynamics simulation
Adenosine-5’-triphosphate (ATP) is generally regarded as a substrate for energy currency and protein modification. Recent findings uncovered the allosteric function of ATP in cellular signal transduction but little is understood about this critical behavior of ATP. Through extensive analysis of ATP in solution and proteins, we found that the free ATP can exist in the compact and extended conformations in solution, and the two different conformational characteristics may be responsible for ATP to exert distinct biological functions: ATP molecules adopt both compact and extended conformations in the allosteric binding sites but conserve extended conformations in the substrate binding sites. Nudged elastic band simulations unveiled the distinct dynamic processes of ATP binding to the corresponding allosteric and substrate binding sites of uridine monophosphate kinase, and suggested that in solution ATP preferentially binds to the substrate binding sites of proteins. When the ATP molecules occupy the allosteric binding sites, the allosteric trigger from ATP to fuel allosteric communication between allosteric and functional sites is stemmed mainly from the triphosphate part of ATP, with a small number from the adenine part of ATP. Taken together, our results provide overall understanding of ATP allosteric functions responsible for regulation in biological systems.
The endogenous ATP can be regarded as a substrate and an allosteric modulator in cellular signal transduction. We analyzed the properties of allosteric and substrate ATP-binding sites and found that the allosteric ATP-binding sites are less conserved than the substrate ATP-binding sites. Allosteric ATP molecules adopt both compact and extended conformations in the allosteric binding sites, while substrate ATP molecules adopt extended conformations in the substrate binding sites. The two different conformational characteristics may be responsible for ATP to exert distinct biological functions in cell signaling. In addition, to our knowledge, this study illuminates the first comprehensive atomic level investigations of ATP access to the corresponding allosteric and substrate ATP-binding sites. Specially, both the adenine and triphosphate parts of ATP could be an allosteric trigger to propagate the signal from the allosteric to functional sites. The detailed mechanism presented in this study may apply to other enzymes in complex with allosteric or substrate ATP molecules, and provide important insights for the molecular basis of ATP acting as a substrate and an allosteric modulator in cell signaling.
Cancer treatment decisions rely on genetics, large data screens and clinical pharmacology. Here we point out that genetic analysis and treatment decisions may overlook critical elements in cancer development, progression and drug resistance. Two critical structural elements are missing in genetics-based decision-making: the mechanisms of oncogenic mutations and the cellular network which is rewired in cancer. These lay the foundation for the structural basis for cancer treatment decisions, which is rooted in the physical principles of the molecular conformational behavior of single molecules and their interactions. Improved tumor mutational analysis platforms and knowledge of the redundant pathways which can take over in cancer, may not only supplement known actionable findings, but forecast possible cancer progression and resistance. Such forward-looking can be powerful, endowing the oncologist with mechanistic insight and cancer prognosis, and consequently more informed treatment options. Examples include redundant pathways taking over after inhibition of EGFR constitutive activation, mutations in PIK3CA p110α and p85, and the non-hotspot AKT1 mutants conferring constitutive membrane localization.
Cancer treatment decisions; driver mutations; driver genes; redundant pathways; parallel pathways; computational biology; protein structure; cellular network; oncogenic mutations
p53 protein has about thirty phosphorylation sites located at the N- and C-termini and in the core domain. The phosphorylation sites are relatively less mutated than other residues in p53. To understand why and how p53 phosphorylation sites are rarely mutated in human cancer, using a bioinformatics approaches, we examined the phosphorylation site and its nearby flanking residues, focusing on the consensus phosphorylation motif pattern, amino-acid correlations within the phosphorylation motifs, the propensity of structural disorder of the phosphorylation motifs, and cancer mutations observed within the phosphorylation motifs. Many p53 phosphorylation sites are targets for several kinases. The phosphorylation sites match 17 consensus sequence motifs out of the 29 classified. In addition to proline, which is common in kinase specificity-determining sites, we found high propensity of acidic residues to be adjacent to phosphorylation sites. Analysis of human cancer mutations in the phosphorylation motifs revealed that motifs with adjacent acidic residues generally have fewer mutations, in contrast to phosphorylation sites near proline residues. p53 phosphorylation motifs are mostly disordered. However, human cancer mutations within phosphorylation motifs tend to decrease the disorder propensity. Our results suggest that combination of acidic residues Asp and Glu with phosphorylation sites provide charge redundancy which may safe guard against loss-of-function mutations, and that the natively disordered nature of p53 phosphorylation motifs may help reduce mutational damage. Our results further suggest that engineering acidic amino acids adjacent to potential phosphorylation sites could be a p53 gene therapy strategy.
phosphorylation; p53 protein; p63; p73; protein binding site; cancer; intrinsically disordered proteins
The current paradigm in the amyloid hypothesis brands small β-amyloid (Aβ) oligomers as the toxic species in Alzheimer’s disease (AD). These oligomers are fibril-like; contain β-sheet structure, and present exposed hydrophobic surface. Oligomers with this motif are capable of penetrating the cell membrane, gathering to form toxic ion channels. Current agents suppressing precursor Aβ cleavage have only met partial success; and to date, those targeting the peptides and their assemblies in the aqueous environment of the extracellular space largely fail in clinical trials. One possible reason is failure to reach membrane-embedded targets of disease-‘infected’ cells. Here we provide an overview, point to the need to account for the lipid environment when aiming to prevent the formation of toxic channels, and propose a combination therapy to target the species spectrum.
Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than ‘blind’ docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested ‘difficult’ cases in a docking-benchmark dataset, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from ~26% to 66%, and 57% of the interactions were successfully predicted in an ‘unbiased’ scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome-scale. We constructed the structural network of ERK interacting proteins as a case study.
protein-protein interaction prediction; PRISM; structural network; knowledge-based method; conformations; docking
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
Cancer; Diabetes; Drug target; Network; Side-effects; Toxicity
Inflammation has significant roles in all phases of tumor development, including initiation, progression and metastasis. Interleukin-10 (IL-10) is a well-known immuno-modulatory cytokine with an anti-inflammatory activity. Lack of IL-10 allows induction of pro-inflammatory cytokines and hinders anti-tumor immunity, thereby favoring tumor growth. The IL-10 network is among the most important paths linking cancer and inflammation. The simple node-and-edge network representation is useful, but limited, hampering the understanding of the mechanistic details of signaling pathways. Structural networks complete the missing parts, and provide details. The IL-10 structural network may shed light on the mechanisms through which disease-related mutations work and the pathogenesis of malignancies.
Using PRISM (a PRotein Interactions by Structural Matching tool), we constructed the structural network of IL-10, which includes its first and second degree protein neighbor interactions. We predicted the structures of complexes involved in these interactions, thereby enriching the available structural data. In order to reveal the significance of the interactions, we exploited mutations identified in cancer patients, mapping them onto key proteins of this network. We analyzed the effect of these mutations on the interactions, and demonstrated a relation between these and inflammation and cancer. Our results suggest that mutations that disrupt the interactions of IL-10 with its receptors (IL-10RA and IL-10RB) and α2-macroglobulin (A2M) may enhance inflammation and modulate anti-tumor immunity. Likewise, mutations that weaken the A2M-APP (amyloid precursor protein) association may increase the proliferative effect of APP through preventing β-amyloid degradation by the A2M receptor, and mutations that abolish the A2M-Kallikrein-13 (KLK13) interaction may lead to cell proliferation and metastasis through the destructive effect of KLK13 on the extracellular matrix.
Prediction of protein-protein interactions through structural matching can enrich the available cellular pathways. In addition, the structural data of protein complexes suggest how oncogenic mutations influence the interactions and explain their potential impact on IL-10 signaling in cancer and inflammation.
The PRISM web server enables fast and accurate prediction of protein–protein interactions (PPIs). The prediction algorithm is knowledge-based. It combines structural similarity and accounts for evolutionary conservation in the template interfaces. The predicted models are stored in its repository. Given two protein structures, PRISM will provide a structural model of their complex if a matching template interface is available. Users can download the complex structure, retrieve the interface residues and visualize the complex model. The PRISM web server is user friendly, free and open to all users at http://cosbi.ku.edu.tr/prism.
We computationally and experimentally showed that tau protein fibrils can be formed at high temperature. When cooled, the fibrils dissociate back to monomers. Heparin promotes tau fibril formation and prevents its reversion. Our results revealed the physicochemical mechanism of reversible formation of tau fibrils.
Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can ‘attack’ nodes or edges of a protein-protein interaction network. In this work, we propose a new network strategy, “The Interface Attack”, based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model which we call “Protein Interface and Interaction Network (P2IN)”, which is the integration of protein-protein interface structures and protein interaction networks. This interface-based network organization clarifies which protein pairs have structurally similar interfaces, and which proteins may compete to bind the same surface region. We built the P2IN of p53 signaling network and performed network robustness analysis. We show that (1) ‘hitting’ frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes); (2) frequent interfaces are not always topologically critical elements in the network; and (3) interface attack may reveal functional changes in the system better than attack of single proteins. In the off-target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D.
We constructed and simulated a ‘minimal proteome’ model using Langevin dynamics. It contains 206 essential protein types which were compiled from the literature. For comparison, we generated six proteomes with randomized concentrations. We found that the net charges and molecular weights of the proteins in the minimal genome are not random. The net charge of a protein decreases linearly with molecular weight, with small proteins being mostly positively charged and large proteins negatively charged. The protein copy numbers in the minimal genome have the tendency to maximize the number of protein-protein interactions in the network. Negatively charged proteins which tend to have larger sizes can provide large collision cross-section allowing them to interact with other proteins; on the other hand, the smaller positively charged proteins could have higher diffusion speed and are more likely to collide with other proteins. Proteomes with random charge/mass populations form less stable clusters than those with experimental protein copy numbers. Our study suggests that ‘proper’ populations of negatively and positively charged proteins are important for maintaining a protein-protein interaction network in a proteome. It is interesting to note that the minimal genome model based on the charge and mass of E. Coli may have a larger protein-protein interaction network than that based on the lower organism M. pneumoniae.
Virtual cytoplasm; preorganization; diffusion-collision; cellular response times; protein-protein interaction network; Langevin dynamics; positively charged proteins
Cytokines are messengers between tissues and the immune system. They play essential roles in cancer initiation, promotion, metastasis, and immunotherapy. Structural pathways of cytokine signaling which contain their interactions can help understand their action in the tumor microenvironment. Here, our aim is to provide an overview of the role of cytokines in tumor development from a structural perspective. Atomic details of protein-protein interactions can help in understanding how an upstream signal is transduced; how higher-order oligomerization modes of proteins can influence their function; how mutations, inhibitors or antagonists can change cellular consequences; why the same protein can lead to distinct outcomes, and which alternative parallel pathways can take over. They also help to design drugs/inhibitors against proteins de novo or by mimicking natural antagonists as in the case of interferon-γ. Since the structural database (PDB) is limited, structural pathways are largely built from a series of predicted binary protein-protein interactions. Below, to illustrate how protein-protein interactions can help illuminate roles played by cytokines, we model some cytokine interaction complexes exploiting a powerful algorithm (PRotein Interactions by Structural Matching—PRISM).
structural pathways; cytokines; structures of cytokines; structural pathways of cytokines; protein interactions; protein interaction prediction; cancer immunotherapy
The Ubiquitin-Proteasome System is involved in many cellular processes including protein degradation. Degradation of a protein via this system involves two successive steps: ubiquitination and degradation. Ubiquitination tags the target protein with ubiquitin-like proteins, such as ubiquitin, SUMO and NEDD8, via a cascade involving three enzymes: activating enzyme E1, conjugating enzyme E2, and E3 ubiquitin ligases. The proteasomes recognize the ubiquitin-like protein tagged substrate proteins and degrade them. Accumulating evidence indicates that allostery is a central player in the regulation of ubiquitination, as well as deubiquitination and degradation. Here, we provide an overview of the key mechanistic roles played by allostery in all steps of these processes, and highlight allosteric drugs targeting them. Throughout the review, we emphasize the crucial mechanistic role played by linkers in allosterically controlling the Ubiquitin-Proteasome System action by biasing the sampling of the conformational space, which facilitate the catalytic reactions of the ubiquitination and degradation. Finally, we propose that allostery may similarly play key roles in the regulation of molecular machines in the cell, and as such allosteric drugs can be expected to be increasingly exploited in therapeutic regimes.
Interleukin-1 (IL-1) is a large cytokine family closely related to innate immunity and inflammation. IL-1 proteins are key players in signaling pathways such as apoptosis, TLR, MAPK, NLR and NF-κB. The IL-1 pathway is also associated with cancer, and chronic inflammation increases the risk of tumor development via oncogenic mutations. Here we illustrate that the structures of interfaces between proteins in this pathway bearing the mutations may reveal how. Proteins are frequently regulated via their interactions, which can turn them ON or OFF. We show that oncogenic mutations are significantly at or adjoining interface regions, and can abolish (or enhance) the protein-protein interaction, making the protein constitutively active (or inactive, if it is a repressor). We combine known structures of protein-protein complexes and those that we have predicted for the IL-1 pathway, and integrate them with literature information. In the reconstructed pathway there are 104 interactions between proteins whose three dimensional structures are experimentally identified; only 15 have experimentally-determined structures of the interacting complexes. By predicting the protein-protein complexes throughout the pathway via the PRISM algorithm, the structural coverage increases from 15% to 71%. In silico mutagenesis and comparison of the predicted binding energies reveal the mechanisms of how oncogenic and single nucleotide polymorphism (SNP) mutations can abrogate the interactions or increase the binding affinity of the mutant to the native partner. Computational mapping of mutations on the interface of the predicted complexes may constitute a powerful strategy to explain the mechanisms of activation/inhibition. It can also help explain how an oncogenic mutation or SNP works.
Structural pathways are important because they provide insight into signaling mechanisms; help understand the mechanism of disease-related mutations; and help in drug discovery. While extremely useful, common pathway diagrams lacking structural data are unable to provide mechanistic insight to explain oncogenic mutations or SNPs. Here we focus on the construction of the IL-1 structural pathway and map oncogenic mutations and SNPs to complexes in this pathway. Our results indicate that computational modeling of protein-protein interactions on a large scale can provide accurate, structural atom-level detail of signaling pathways in the human cell and help delineate the mechanism through which a mutation leads to disease. We show that the mutations either thwart the interactions, activating the proteins even in their absence or stabilize them, leading to the same uncontrolled outcome. Computational mapping of mutations on the interface of the predicted complexes may constitute an effective strategy to explain the mechanisms of mutations- constitutive activation or deactivation.
The question of how allostery works was posed almost 50 years ago. Since then it has been the focus of much effort. This is for two reasons: first, the intellectual curiosity of basic science and the desire to understand fundamental phenomena, and second, its vast practical importance. Allostery is at play in all processes in the living cell, and increasingly in drug discovery. Many models have been successfully formulated, and are able to describe allostery even in the absence of a detailed structural mechanism. However, conceptual schemes designed to qualitatively explain allosteric mechanisms usually lack a quantitative mathematical model, and are unable to link its thermodynamic and structural foundations. This hampers insight into oncogenic mutations in cancer progression and biased agonists' actions. Here, we describe how allostery works from three different standpoints: thermodynamics, free energy landscape of population shift, and structure; all with exactly the same allosteric descriptors. This results in a unified view which not only clarifies the elusive allosteric mechanism but also provides structural grasp of agonist-mediated signaling pathways, and guides allosteric drug discovery. Of note, the unified view reasons that allosteric coupling (or communication) does not determine the allosteric efficacy; however, a communication channel is what makes potential binding sites allosteric.
Improvements in experimental techniques increasingly provide structural data relating to protein-protein interactions. Classification of structural details of protein-protein interactions can provide valuable insights for modeling and abstracting design principles. Here, we aim to cluster protein-protein interactions by their interface structures, and to exploit these clusters to obtain and study shared and distinct protein binding sites. We find that there are 22604 unique interface structures in the PDB. These unique interfaces, which provide a rich resource of structural data of protein-protein interactions, can be used for template-based docking. We test the specificity of these non-redundant unique interface structures by finding protein pairs which have multiple binding sites. We suggest that residues with more than 40% relative accessible surface area should be considered as surface residues in template-based docking studies. This comprehensive study of protein interface structures can serve as a resource for the community. The dataset can be accessed at http://prism.ccbb.ku.edu.tr/piface.
Amyloid-β (Aβ) oligomers destabilize cellular ionic homeostasis, mediating Alzheimer's disease (AD). It is still unclear whether the mechanism (i) is mediated by cell surface receptors; (ii) is direct, with Aβ oligomers interacting with membrane lipids; or (iii) both mechanisms take place. Recent studies indicate that Aβ oligomers may act by either of the last two. Little is known about the oligomers’ structures and how they spontaneously insert into the membrane. Using explicit solvent molecular dynamics (MD) simulations, we show that fibril-like Aβ17-42 (p3) oligomer is capable of penetrating the membrane. Insertion is similar to that observed for protegrin-1 (PG-1), a cytolytic β-sheet-rich antimicrobial peptide (AMP). Both Aβ and PG-1 favor the amphipathic interface of the lipid bilayer in the early stage of interaction with the membrane. U-shaped Aβ oligomers are observed in solution and in the membrane, suggesting that the preformed seeds can be shared by amyloid fibrils in the growth phase and membrane toxicity. Here we provide sequential events in possible Aβ oligomer membrane-insertion pathways. We speculate that for the U-shaped motif, a trimer is the minimal oligomer size to insert effectively. We propose that monomers and dimers may insert in (apparently on-pathway) aggregation-intermediate β-hairpin state, and may (or may not) convert to a U-shape in the bilayer. Together with earlier observations, our results point to a non-specific, broadly heterogeneous landscape of membrane-inserting oligomer conformations, pathways, and membrane-mediated toxicity of β-rich oligomers.
Toxic amyloid oligomer; β-sheet conformation; cytolytic activity; membrane insertion; molecular dynamics simulation
Interactions of human islet amyloid polypeptide (hIAPP or amylin) with the cell membrane are correlated with the dysfunction and death of pancreatic islet β-cells in type II diabetes. Formation of receptor-independent channels by hIAPP in membrane is regarded as one of the membrane-damaging mechanisms that induce ion homeostasis and toxicity in islet β-cells. Here, we investigate the dynamic structure, ion conductivity, and membrane interactions of hIAPP channels in the DOPC bilayer using molecular modeling and molecular dynamics simulations. We use the NMR-derived β-strand-turn-β-strand motif as a building block to computationally construct a series of annular-like hIAPP structures with different sizes and topologies. In the simulated lipid environments, the channels lose their initial continuous β-sheet network and break into oligomeric subunits, which are still loosely associated to form heterogeneous channel conformations. The channels’ shapes, morphologies and dimensions are compatible with the doughnut-like images obtained by atomic force microscopy, and with those of modeled channels for Aβ, the β2-microglobulin-derived K3 peptides, and the β-hairpin-based channels of antimicrobial peptide PG-1. Further, all channels induce directional permeability of multiple ions across the bilayers from the lower to the upper leaflet. This similarity suggests that loosely-associated β-structure motifs can be a general feature of toxic, unregulated channels. In the absence of experimental high-resolution atomic structures of hIAPP channels in the membrane, this study represents a first attempt to delineate some of the main structural features of the hIAPP channels, for a better understanding of the origin of amyloid toxicity and the development of pharmaceutical agents.
hIAPP; ion channel; directional permeability; molecular dynamics
Apoptosis is a matter of life and death for cells and both inhibited and enhanced apoptosis may be involved in the pathogenesis of human diseases. The structures of protein-protein complexes in the apoptosis signaling pathway are important as the structural pathway helps in understanding the mechanism of the regulation and information transfer, and in identifying targets for drug design. Here, we aim to predict the structures toward a more informative pathway than currently available. Based on the 3D structures of complexes in the target pathway and a protein-protein interaction modeling tool which allows accurate and proteome-scale applications, we modeled the structures of 29 interactions, 21 of which were previously unknown. Next, 27 interactions which were not listed in the KEGG apoptosis pathway were predicted and subsequently validated by the experimental data in the literature. Additional interactions are also predicted. The multi-partner hub proteins are analyzed and interactions that can and cannot co-exist are identified. Overall, our results enrich the understanding of the pathway with interactions and provide structural details for the human apoptosis pathway. They also illustrate that computational modeling of protein-protein interactions on a large scale can help validate experimental data and provide accurate, structural atom-level detail of signaling pathways in the human cell.
apoptosis; protein-protein interaction; protein-protein complex; signaling network; PRISM; 3D structure
More than two dozen clinical syndromes known as amyloid diseases are characterized by the buildup of extended insoluble fibrillar deposits in tissues. These amorphous Congo red staining deposits known as amyloids exhibit a characteristic green birefringence and cross-β structure. Substantial evidence implicates oligomeric intermediates of amyloids as toxic species in the pathogenesis of these chronic disease states. A growing body of data has suggested that these toxic species form ion channels in cellular membranes causing disruption of calcium homeostasis, membrane depolarization, energy drainage, and in some cases apoptosis. Amyloid peptide channels exhibit a number of common biological properties including the universal U-shape β-strand-turn-β-strand structure, irreversible and spontaneous insertion into membranes, production of large heterogeneous single-channel conductances, relatively poor ion selectivity, inhibition by Congo red, and channel blockade by zinc. Recent evidence has suggested that increased amounts of amyloids are not only toxic to its host target cells but also possess antimicrobial activity. Furthermore, at least one human antimicrobial peptide, protegrin-1, which kills microbes by a channel-forming mechanism, has been shown to possess the ability to form extended amyloid fibrils very similar to those of classic disease-forming amyloids. In this paper, we will review the reported antimicrobial properties of amyloids and the implications of these discoveries for our understanding of amyloid structure and function.
Amyloid ion channels; β-strand-turn-β-strand motif; cytotoxicity; antimicrobial activity