Amyloid beta(1-42) in aqueous environments: effects of ionic strength and E22Q (Dutch) mutation

Modulation of Aβ 16-22 aggregation by glucose

The self-assembly of amyloid-beta (Aβ) peptides into fibrillar structures in the brain is a signature of Alzheimer's disease. Recent studies have reported correlations between Alzheimer's disease and type-2 diabetes. Structurally, hyperglycemia induces covalent protein crosslinkings by advanced glycation end products (AGE), which can affect the stability of Aβ oligomers. In this work, we leverage physics-based coarse-grained molecular simulations to probe alternate thermodynamic pathways that affect peptide aggregation propensities at varying concentrations of glucose molecules. Similar to previous experimental reports, our simulations show a glucose concentration-dependent increase in Aβ aggregation rates, without changes in the overall secondary structure content. We discovered that glucose molecules prefer partitioning onto the aggregate-water interface at a specific orientation, resulting in a loss of molecular rotational entropy. This effectively hastens the aggregation rates, as peptide self-assembly can reduce the available surface area for peptide-glucose interactions. This work introduces a new thermodynamic-driven pathway, beyond chemical cross-linking, that can modulate Aβ aggregation.

The Dynamics of OXA-23 β-Lactamase from Acinetobacter baumannii

Antibiotic resistance is a pressing topic, which also affects β-lactam antibiotic molecules. Until a few years ago, it was considered no more than an interesting species from an academic point of view, Acinetobacter baumanii is today one of the most serious threats to public health, so much so that it has been declared one of the species for which the search for new antibiotics, or new ways to avoid its resistance, is an absolute priority according to WHO. Although there are several molecular mechanisms that are responsible for the extreme resistance of A. baumanii to antibiotics, a class D β-lactamase is the main cause for the clinical concern of this bacterial species. In this work, we analyzed the A. baumanii OXA-23 protein via molecular dynamics. The results obtained show that this protein is able to assume different conformations, especially in some regions around the active site. Part of the OXA-23 protein has considerable conformational motility, while the rest is less mobile. The importance of these observations for understanding the functioning mechanism of the enzyme as well as for designing new effective molecules for the treatment of A. baumanii is discussed.

AI-Aided Search for New HIV-1 Protease Ligands

The availability of drugs capable of blocking the replication of microorganisms has been one of the greatest triumphs in the history of medicine, but the emergence of an ever-increasing number of resistant strains poses a serious problem for the treatment of infectious diseases. The search for new potential ligands for proteins involved in the life cycle of pathogens is, therefore, an extremely important research field today. In this work, we have considered the HIV-1 protease, one of the main targets for AIDS therapy. Several drugs are used today in clinical practice whose mechanism of action is based on the inhibition of this enzyme, but after years of use, even these molecules are beginning to be interested by resistance phenomena. We used a simple artificial intelligence system for the initial screening of a data set of potential ligands. These results were validated by docking and molecular dynamics, leading to the identification of a potential new ligand of the enzyme which does not belong to any known class of HIV-1 protease inhibitors. The computational protocol used in this work is simple and does not require large computational power. Furthermore, the availability of a large number of structural information on viral proteins and the presence of numerous experimental data on their ligands, with which it is possible to compare the results obtained with computational methods, make this research field the ideal terrain for the application of these new computational techniques.

The Impact of the Cellular Environment and Aging on Modeling Alzheimer's Disease in 3D Cell Culture Models

Creating a cellular model of Alzheimer's disease (AD) that accurately recapitulates disease pathology has been a longstanding challenge. Recent studies showed that human AD neural cells, integrated into three-dimensional (3D) hydrogel matrix, display key features of AD neuropathology. Like in the human brain, the extracellular matrix (ECM) plays a critical role in determining the rate of neuropathogenesis in hydrogel-based 3D cellular models. Aging, the greatest risk factor for AD, significantly alters brain ECM properties. Therefore, it is important to understand how age-associated changes in ECM affect accumulation of pathogenic molecules, neuroinflammation, and neurodegeneration in AD patients and in vitro models. In this review, mechanistic hypotheses is presented to address the impact of the ECM properties and their changes with aging on AD and AD-related dementias. Altered ECM characteristics in aged brains, including matrix stiffness, pore size, and composition, will contribute to disease pathogenesis by modulating the accumulation, propagation, and spreading of pathogenic molecules of AD. Emerging hydrogel-based disease models with differing ECM properties provide an exciting opportunity to study the impact of brain ECM aging on AD pathogenesis, providing novel mechanistic insights. Understanding the role of ECM aging in AD pathogenesis should also improve modeling AD in 3D hydrogel systems.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

High-resolution probing of early events in amyloid-β aggregation related to Alzheimer's disease

In Alzheimer's disease (AD), soluble oligomers of amyloid-β (Aβ) are emerging as a crucial entity in driving disease progression as compared to insoluble amyloid deposits. The lacuna in establishing the structure to function relationship for Aβ oligomers prevents the development of an effective treatment for AD. While the transient and heterogeneous properties of Aβ oligomers impose many challenges for structural investigation, an effective use of a combination of NMR techniques has successfully identified and characterized them at atomic-resolution. Here, we review the successful utilization of solution and solid-state NMR techniques to probe the aggregation and structures of small and large oligomers of Aβ. Biophysical studies utilizing the commonly used solution and 19F based NMR experiments to identify the formation of small size early intermediates and to obtain their structures, and dock-lock mechanism of fiber growth at atomic-resolution are discussed. In addition, the use of proton-detected magic angle spinning (MAS) solid-state NMR experiments to obtain high-resolution insights into the aggregation pathways and structures of large oligomers and other aggregates is also presented. We expect these NMR based studies to be valuable for real-time monitoring of the depletion of monomers and the formation of toxic oligomers and high-order aggregates under a variety of conditions, and to solve the high-resolution structures of small and large size oligomers for most amyloid proteins, and therefore to develop inhibitors and drugs.

A New Look at the Structures of Old Sepsis Actors by Exploratory Data Analysis Tools

Sepsis is a life-threatening condition that accounts for numerous deaths worldwide, usually complications of common community infections (i.e., pneumonia, etc), or infections acquired during the hospital stay. Sepsis and septic shock, its most severe evolution, involve the whole organism, recruiting and producing a lot of molecules, mostly proteins. Proteins are dynamic entities, and a large number of techniques and studies have been devoted to elucidating the relationship between the conformations adopted by proteins and what is their function. Although molecular dynamics has a key role in understanding these relationships, the number of protein structures available in the databases is so high that it is currently possible to build data sets obtained from experimentally determined structures. Techniques for dimensionality reduction and clustering can be applied in exploratory data analysis in order to obtain information on the function of these molecules, and this may be very useful in immunology to better understand the structure-activity relationship of the numerous proteins involved in host defense, moreover in septic patients. The large number of degrees of freedom that characterize the biomolecules requires special techniques which are able to analyze this kind of data sets (with a small number of entries respect to the number of degrees of freedom). In this work we analyzed the ability of two different types of algorithms to provide information on the structures present in three data sets built using the experimental structures of allosteric proteins involved in sepsis. The results obtained by means of a principal component analysis algorithm and those obtained by a random projection algorithm are largely comparable, proving the effectiveness of random projection methods in structural bioinformatics. The usefulness of random projection in exploratory data analysis is discussed, including validation of the obtained clusters. We have chosen these proteins because of their involvement in sepsis and septic shock, aimed to highlight the potentiality of bioinformatics to point out new diagnostic and prognostic tools for the patients.

C-Terminal Plays as the Possible Nucleation of the Self-Aggregation of the S-Shape Aβ11-42 Tetramer in Solution: Intensive MD Study

Amyloid beta (Aβ) peptides are characterized as the major factors associated with neuron death in Alzheimer's disease, which is listed as the most common form of neurodegeneration. Disordered Aβ peptides are released from proteolysis of the amyloid precursor protein. The Aβ self-assembly process roughly takes place via five steps: disordered forms → oligomers → photofibrils → mature fibrils → plaques. Although Aβ fibrils are often observed in patient brains, oligomers were recently indicated to be major neurotoxic elements. In this work, the neurotoxic compound S-shape Aβ11-42 tetramer (S4Aβ11-42) was investigated over 10 μs of unbiased MD simulations. In particular, the S4Aβ11-42 oligomer adopted a high dynamics structure, resulting in unsuccessful determination of their structures in experiments. The C-terminal was suggested as the possible nucleation of the Aβ42 aggregation. The sequences 27-35 and 39-40 formed rich β-content, whereas other residues mostly adopted coil structures. The mean value of the β-content over the equilibrium interval is ∼42 ± 3%. Furthermore, the dissociation free energy of the S4Aβ11-42 peptide was predicted using a biased sampling method. The obtained free energy is ΔG US = -58.44 kcal/mol which is roughly the same level as the corresponding value of the U-shape Aβ17-42 peptide. We anticipate that the obtained S4Aβ11-42 structures could be used as targets for AD inhibitor screening over the in silico study.

Translational control mechanisms in cutaneous malignant melanoma: the role of eIF2α

BACKGROUND

Melanoma cells develop adaptive responses in order to cope with particular conditions of tumor microenvironment, characterized by stress conditions and deregulated proliferation. Recently, the interplay between the stress response and the gene expression programs leading to metastatic spread has been reported.

METHODS

We evaluated levels and localization of eIF2α/peIF2α in V600BRAF and wtBRAF metastatic melanoma cell lines by means of western blot and confocal microscopy analyses. Furthermore, we performed a sequence analyses and structure and dynamics studies of eIF2α protein to reveal the role of eIF2α and its correlations in different pathways involved in the invasive phase of melanoma.

RESULTS

We found peIF2α both in cytoplasm and nucleus. Nuclear localization was more represented in V600BRAF melanoma cell lines. Our studies on eIF2α protein sequence indicated the presence of a predicted bipartite NLS as well as a nuclear export signal NES and an S1 domain, typical of RNA interacting proteins. Furthermore, we found high levels of transcription factor EB (TFEB), a component of the MiT/TFE family, and low β-catenin levels in V600BRAF cells.

CONCLUSIONS

Based on our results, we suggest that peIF2α nuclear localization can be crucial in ER stress response and in driving the metastatic spread of melanoma, through lysosomal signaling and Wnt/β-catenin pathway. In conclusion, this is the first evidence of nuclear localization of peIF2α, representing a possible target for future therapeutic approaches for metastatic melanoma.

Cytochrome c oxidase structures suggest a four-state stochastic pump mechanism

A simple stochastic model for a cytochrome c oxidase proton pump.

The influences of E22Q mutant on solvated 3Aβ11-40 peptide: A REMD study

The residue E22 plays a critical role in the aggregation process of Amyloid beta (Aβ) peptides. The effect of E22Q mutant on the shapes of the solvated Aβ_11-40 trimer is clarified using a replica exchange molecular dynamics (REMD) simulation employing ∼20.6 μs of MD simulations with 48 disparate replicas. The increase of intramolecular polar contacts and salt bridge between the residue D23 to residues (24-29) was observed. The residual secondary structure of the mutated trimer is shifted in a similar way to the picture observed in previous investigations of F19W mutant. The free energy surface (FES) of the mutated E22Q system has a fewer number of minima in comparison with the wild-type trimer. The optimized shapes of the mutated E22Q form a significant increase in beta structure (47%) and serious decrease in coil content (46%) compared with the wild-type (of 36 and 56%, respectively). The binding affinity of constituting chains to the rest is of -43.7 ± 6.5 kcal/mol, implying that the representative structure of E22Q is more stable than the wild-type one. Furthermore, the E22Q mutant increases the size of stable structures due to larger collision cross section (CCS) and solvent accessible area (SASA). The observed results may enhance the Aβ inhibition throughout the contribution to the knowledge of the Aβ oligomerization/aggregation.

A random version of principal component analysis in data clustering

Principal component analysis (PCA) is a widespread technique for data analysis that relies on the covariance/correlation matrix of the analyzed data. However, to properly work with high-dimensional data sets, PCA poses severe mathematical constraints on the minimum number of different replicates, or samples, that must be included in the analysis. Generally, improper sampling is due to a small number of data respect to the number of the degrees of freedom that characterize the ensemble. In the field of life sciences it is often important to have an algorithm that can accept poorly dimensioned data sets, including degenerated ones. Here a new random projection algorithm is proposed, in which a random symmetric matrix surrogates the covariance/correlation matrix of PCA, while maintaining the data clustering capacity. We demonstrate that what is important for clustering efficiency of PCA is not the exact form of the covariance/correlation matrix, but simply its symmetry.

Analysis of the conformations of the HIV-1 protease from a large crystallographic data set

The HIV-1 protease performs essential roles in viral maturation by processing specific cleavage sites in the Gag and Gag-Pol precursor polyproteins to release their mature forms. Here the analysis of a large HIV-1 protease data set (containing 552 dimer structures) are reported. These data are related to article entitled “Conformations of the HIV-1 protease: a crystal structure data set analysis” (Palese, 2017) [1].

Conformations of the HIV-1 protease: A crystal structure data set analysis

The HIV protease is an important drug target for HIV/AIDS therapy, and its structure and function have been extensively investigated. This enzyme performs an essential role in viral maturation by processing specific cleavage sites in the Gag and Gag-Pol precursor polyproteins so as to release their mature forms. This 99 amino acid aspartic protease works as a homodimer, with the active site localized in a central cavity capped by two flexible flap regions. The dimer presents closed or open conformations, which are involved in the substrate binding and release. Here the results of the analysis of a HIV-1 protease data set containing 552 dimer structures are reported. Different dimensionality reduction methods have been used in order to get information from this multidimensional database. Most of the structures in the data set belong to two conformational clusters. An interesting observation that comes from the analysis of these data is that some protease sequences are localized preferentially in specific areas of the conformational landscape of this protein.

Computational approach for the assessment of inhibitory potency against beta-amyloid aggregation

Beta-amyloid (Aβ) plaques are one of the hallmarks of Alzheimer's disease. Their presence in the brain leads to neurodegeneration and memory decline. Therefore, search for new drugs able to decrease formation of such deposits is of great interest. Our previously developed multifunctional compounds inhibited transformation of monomers into fibrils. Herein, we describe the computational approach for the assessment of inhibitory activity against Aβ aggregation. The influence of novel inhibitors on amyloid Aβ_17-42 was studied by employing of molecular docking and all-atom molecular dynamics simulations. We found that the number of intermolecular backbone hydrogen bonds at the end of 100ns MD simulation was correlated with the level of anti-aggregation potency of studied compounds. Such data may be successfully applied to in silico design of novel inhibitors of Aβ aggregation.

Protein States as Symmetry Transitions in the Correlation Matrices

Over the last few years, there has been significant progress in the knowledge on protein folding. However, some aspects of protein folding still need further attention. One of these is the exact relationship between the folded and unfolded states and the differences between them. Whereas the folded state is well known, at least from a structural point of view (just think of the thousands of structures in online databases), the unfolded state is more elusive. Also, these are dynamic states of matter, and this aspect cannot be overlooked. Molecular dynamics-derived correlation matrices are an invaluable source of information on the protein dynamics. Here, bulk eigenvalue spectra of the correlation matrices obtained from the Trp-cage dynamics in the folded and unfolded states have been analyzed. The associated modes represent localized vibrations and are significantly affected by the fine details of the structure and interactions. Therefore, these bulk modes can be used as probes of the protein local dynamics in different states. The results of these analyses show that the correlation matrices describing the folded and unfolded dynamics belong to different symmetry classes. This finding provides new support to the phase-transition models of protein folding.

Molecular modeling of antibodies for the treatment of TNFα-related immunological diseases

Therapeutic monoclonal antibodies (mAbs) have high efficacy in treating TNF α-related immunological diseases. Other than neutralizing TNF α, these IgG1 antibodies exert Fc receptor-mediated effector functions such as the complement-dependent cytotoxicity (CDC) and antibody-dependent cell cytotoxicity (ADCC). The crystallizable fragment (Fc) of these IgG1 contains a single glycosylation site at Asn 297/300 that is essential for the CDC and ADCC. Glycosylated antibodies lacking core fucosylation showed an improved ADCC. However, no structural data are available concerning the ligand-binding interaction of these mAbs used in TNF α-related diseases and the role of the fucosylation. We therefore used comparative modeling for generating complete 3D mAb models that include the antigen-binding fragment (Fab) portions of infliximab, complexed with TNF α (4G3Y.pdb), the Fc region of the human IGHG1 fucosylated (3SGJ) and afucosylated (3SGK) complexed with the Fc receptor subtype Fcγ RIIIA, and the Fc region of a murine immunoglobulin (1IGT). After few thousand steps of energy minimization on the resulting 3D mAb models, minimized final models were used to quantify interactions occurring between Fcγ RIIIA and the fucosylated/afucosylated Fc fragments. While fucosylation does not affect Fab-TNF α interactions, we found that in the absence of fucosylation the Fc-mAb domain and Fcγ RIIIA are closer and new strong interactions are established between G129 of the receptor and S301 of the Chimera 2 Fc mAb; new polar interactions are also established between the Chimera 2 Fc residues Y299, N300, and S301 and the Fcγ RIIIA residues K128, G129, R130, and R155. These data help to explain the reduced ADCC observed in the fucosylated mAbs suggesting the specific AA residues involved in binding interactions.

Random Matrix Theory in molecular dynamics analysis

It is well known that, in some situations, principal component analysis (PCA) carried out on molecular dynamics data results in the appearance of cosine-shaped low index projections. Because this is reminiscent of the results obtained by performing PCA on a multidimensional Brownian dynamics, it has been suggested that short-time protein dynamics is essentially nothing more than a noisy signal. Here we use Random Matrix Theory to analyze a series of short-time molecular dynamics experiments which are specifically designed to be simulations with high cosine content. We use as a model system the protein apoCox17, a mitochondrial copper chaperone. Spectral analysis on correlation matrices allows to easily differentiate random correlations, simply deriving from the finite length of the process, from non-random signals reflecting the intrinsic system properties. Our results clearly show that protein dynamics is not really Brownian also in presence of the cosine-shaped low index projections on principal axes.

The respiratory chains of four strains of the alkaliphilic Bacillus clausii

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It is important to understand how alkaliphilic prokaryotes thrive at high pH.

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An interesting issue is their ability to cope with bioenergetics at high pH.

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We show that four genetically similar strains adopt different biochemical behaviors.

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Two of the strains show a functional redundancy of the terminal part of the respiratory chain.

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Biochemical data correlate with the expression of cytochrome c oxidase and quinol oxidase genes (heme-copper types).

A comparative analysis of terminal respiratory enzymes has been performed on four strains of Bacillus clausii used for preparation of a European probiotic. These four strains originated most probably from a common ancestor through early selection of stable clones for industrial propagation. They exhibit a low level of intra-specific diversity and a high degree of genomic conservation, making them an attractive model to study the different bioenergetics behaviors of alkaliphilic bacilli. The analysis of the different bioenergetics responses has been carried out revealing striking differences among the strains. Two out of the four strains have shown a functional redundancy of the terminal part of the respiratory chain. The biochemical data correlate with the expression level of the mRNA of cytochrome c oxidase and quinol oxidase genes (heme-copper type). The consequences of these different bioenergetics behaviors are also discussed.