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The space-filling fibrin network is a major part of clots and thrombi formed in blood. Fibrin polymerization starts when fibrinogen, a plasma protein, is proteolytically converted to fibrin, which self-assembles to form double-stranded protofibrils. When reaching a critical length, these intermediate species aggregate laterally to transform into fibers arranged into branched fibrin network. We combined multiscale modeling in silico with atomic force microscopy (AFM) imaging to reconstruct complete atomic models of double-stranded fibrin protofibrils with γ-γ crosslinking, A:a and B:b knob-hole bonds, and αC regions-all important structural determinants not resolved crystallographically. Structures of fibrin oligomers and protofibrils containing up to 19 monomers were successfully validated by quantitative comparison with high-resolution AFM images. We characterized the protofibril twisting, bending, kinking, and reversibility of A:a knob-hole bonds, and calculated hydrodynamic parameters of fibrin oligomers. Atomic structures of protofibrils provide a basis to understand mechanisms of early stages of fibrin polymerization.
Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.
Protein-DNA interactions play a central role in transcriptional regulation and other biological processes. Investigating the mechanism of binding affinity and specificity in protein-DNA complexes is thus an important goal. Here we develop a simple physical energy function, which uses electrostatics, solvation, hydrogen bonds and atom-packing terms to model direct readout and sequence-specific DNA conformational energy to model indirect readout of DNA sequence by the bound protein. The predictive capability of the model is tested against another model based only on the knowledge of the consensus sequence and the number of contacts between amino acids and DNA bases. Both models are used to carry out predictions of protein-DNA binding affinities which are then compared with experimental measurements. The nearly additive nature of protein-DNA interaction energies in our model allows us to construct position-specific weight matrices by computing base pair probabilities independently for each position in the binding site. Our approach is less data intensive than knowledge-based models of protein-DNA interactions, and is not limited to any specific family of transcription factors. However, native structures of protein-DNA complexes or their close homologs are required as input to the model. Use of homology modeling can significantly increase the extent of our approach, making it a useful tool for studying regulatory pathways in many organisms and cell types.
Ultra-violet (UV) irradiation has a significant impact on the structure and function of proteins that is supposed to be in relationship with the tryptophan-mediated photolysis of disulfide bonds. To investigate the correlation between the photoexcitation of Trp residues in polypeptides and the associated reduction of disulfide bridges, a series of small, cyclic oligopeptide models were analyzed in this work. Average distances between the aromatic side chains and the disulfide bridge were determined following molecular mechanics (MM) geometry optimizations. In this way, the possibility of cation⁻π interactions was also investigated. Molecular mechanics calculations revealed that the shortest distance between the side chain of the Trp residues and the disulfide bridge is approximately 5 Å in the cyclic pentapeptide models. Based on this, three tryptophan-containing cyclopeptide models were synthesized and analyzed by nuclear magnetic resonance (NMR) spectroscopy. Experimental data and detailed molecular dynamics (MD) simulations were in good agreement with MM geometry calculations. Selected model peptides were subjected to photolytic degradation to study the correlation of structural features and the photolytic cleavage of disulfide bonds in solution. Formation of free sulfhydryl groups upon illumination with near UV light was monitored by fluorescence spectroscopy after chemical derivatization with 7-diethylamino-3-(4-maleimidophenyl)-4-methylcoumarin (CPM) and mass spectrometry. Liquid cromatography-mass spectrometry (LC-MS) measurements indicated the presence of multiple photooxidation products (e.g., dimers, multimers and other oxidated products), suggesting that besides the photolysis of disulfide bonds secondary photolytic processes take place.
Experimental conditions or the presence of interacting components can lead to variations in the structural models of macromolecules. However, the role of these factors in conformational selection is often omitted by in silico methods to extract dynamic information from protein structural models. Structures of small peptides, considered building blocks for larger macromolecular structural models, can substantially differ in the context of a larger protein. This limitation is more evident in the case of modeling large multi-subunit macromolecular complexes using structures of the individual protein components. Here we report an analysis of variations in structural models of proteins with high sequence similarity. These models were analyzed for sequence features of the protein, the role of scaffolding segments including interacting proteins or affinity tags and the chemical components in the experimental conditions. Conformational features in these structural models could be rationalized by conformational selection events, perhaps induced by experimental conditions. This analysis was performed on a non-redundant dataset of protein structures from different SCOP classes. The sequence-conformation correlations that we note here suggest additional features that could be incorporated by in silico methods to extract dynamic information from protein structural models.
Mammalian mitochondrial DNA (mtDNA) encodes 13 core proteins of oxidative phosphorylation, 12S and 16S ribosomal RNAs, and 22 transfer RNAs. Mutations and deletions of mtDNA and/or nuclear genes encoding mitochondrial proteins have been implicated in a wide range of diseases. Thus, cell survival and health of the organism require some steady-state level of the mitochondrial genome and its expression. In mammalian systems, the mitochondrial transcription factor B2 (mtTFB2 or TFB2M) is indispensable for transcription initiation. TFB2M along with two other proteins, mitochondrial RNA polymerase (mtRNAP or POLRMT) and mitochondrial transcription factor A (mtTFA or TFAM), are key components of the core mitochondrial transcription apparatus. Structural information for POLRMT and TFAM from humans is available; however, there is no available structure for TFB2M. In the present study, three-dimensional structure of TFB2M from humans was modeled using a combination of homology modeling and small-angle X-ray scattering (SAXS). The TFB2M structural model adds substantively to our understanding of TFB2M function. An explanation for the low or absent RNA methyltransferase activity is provided. A putative nucleic acid-binding site is revealed. The amino and carboxy termini, while likely lacking defined secondary structure, appear to adopt compact, globular conformations, thus "capping" the ends of the protein. Finally, sites of interaction of TFB2M with other factors, protein and/or nucleic acid, are suggested by the identification of species-specific clusters on the surface of the protein.
The Hv1 proton channel is evidently unique among voltage sensor domain proteins in mediating an intrinsic 'aqueous' H+ conductance (GAQ). Mutation of a highly conserved 'gating charge' residue in the S4 helix (R1H) confers a resting-state H+ 'shuttle' conductance (GSH) in VGCs and Ci VSP, and we now report that R1H is sufficient to reconstitute GSH in Hv1 without abrogating GAQ. Second-site mutations in S3 (D185A/H) and S4 (N4R) experimentally separate GSH and GAQ gating, which report thermodynamically distinct initial and final steps, respectively, in the Hv1 activation pathway. The effects of Hv1 mutations on GSH and GAQ are used to constrain the positions of key side chains in resting- and activated-state VS model structures, providing new insights into the structural basis of VS activation and H+ transfer mechanisms in Hv1.
Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data.
This paper presents a simple method to increase the sensitivity of protein family comparisons by incorporating secondary structure (SS) information. We build upon the effective information theory approach towards profile-profile comparison described in [Yona & Levitt 2002]. Our method augments profile columns using PSIPRED secondary structure predictions and assesses statistical similarity using information theoretical principles.
Allergic contact dermatitis is increasingly of interest for the hazard characterization of chemicals. in vivo animal testing is usually adopted but in silico approaches are becoming the new frontier due to their swiftness and economic efficiency. Indeed, in silico models can rationalise the experimental outcomes besides having predictive ability. The aim of the present work was to explore the electrophilic chemical behaviour responsible for allergic contact dermatitis using quantitative QSAR regression models. Eight models were proposed, using an experimental LLNA dataset of 366 chemicals. Each model is unique to encode a type of electrophilic reactivity domain. The models were obtained using autocorrelation, electro-topological and atom centered fragment based on two-dimensional descriptors, which incorporated the electronic and stereochemical features of substances interacting with skin proteins to induce skin cell proliferation. Finally, simple steps were proposed to integrate the eight models for the application on the test chemicals.
Structural plasticity in motoneurons may be influenced by activation history and motoneuron-muscle fiber interactions. The goal of this study was to examine the morphological adaptations of phrenic motoneurons following imposed motoneuron inactivity while controlling for diaphragm muscle inactivity. Well-characterized rat models were used including unilateral C2 spinal hemisection (SH; ipsilateral phrenic motoneurons and diaphragm muscle are inactive) and tetrodotoxin phrenic nerve blockade (TTX; ipsilateral diaphragm muscle is paralyzed while phrenic motoneuron activity is preserved). We hypothesized that inactivity of phrenic motoneurons would result in a decrease in motoneuron size, consistent with a homeostatic increase in excitability. Phrenic motoneurons were retrogradely labeled by ipsilateral diaphragm muscle injection of fluorescent dextrans or cholera toxin subunit B. Following 2 weeks of diaphragm muscle paralysis, morphological parameters of labeled ipsilateral phrenic motoneurons were assessed quantitatively using fluorescence confocal microscopy. Compared to controls, phrenic motoneuron somal volumes and surface areas decreased with SH, but increased with TTX. Total phrenic motoneuron surface area was unchanged by SH, but increased with TTX. Dendritic surface area was estimated from primary dendrite diameter using a power equation obtained from three-dimensional reconstructed phrenic motoneurons. Estimated dendritic surface area was not significantly different between control and SH, but increased with TTX. Similarly, TTX significantly increased total phrenic motoneuron surface area. These results suggest that ipsilateral phrenic motoneuron morphological adaptations are consistent with a normalization of motoneuron excitability following prolonged alterations in motoneuron activity. Phrenic motoneuron structural plasticity is likely more dependent on motoneuron activity (or descending input) than muscle fiber activity.
NDH-1 is a gigantic redox-driven proton pump linked with respiration and cyclic electron flow in cyanobacterial cells. Based on experimentally resolved X-ray and cryo-EM structures of the respiratory complex I, we derive here molecular models of two isoforms of the cyanobacterial NDH-1 complex involved in redox-driven proton pumping (NDH-1L) and CO2-fixation (NDH-1MS). Our models show distinct structural and dynamic similarities to the core architecture of the bacterial and mammalian respiratory complex I. We identify putative plastoquinone-binding sites that are coupled by an electrostatic wire to the proton pumping elements in the membrane domain of the enzyme. Molecular simulations suggest that the NDH-1L isoform undergoes large-scale hydration changes that support proton-pumping within antiporter-like subunits, whereas the terminal subunit of the NDH-1MS isoform lacks such structural motifs. Our work provides a putative molecular blueprint for the complex I-analogue in the photosynthetic energy transduction machinery and demonstrates that general mechanistic features of the long-range proton-pumping machinery are evolutionary conserved in the complex I-superfamily.
The free fatty acid receptors (FFAs), including FFA1 (orphan name: GPR40), FFA2 (GPR43) and FFA3 (GPR41) are G protein-coupled receptors (GPCRs) involved in energy and metabolic homeostasis. Understanding the structural basis of ligand binding at FFAs is an essential step toward designing potent and selective small molecule modulators.
Photoreceptor degeneration is a cause of irreversible vision loss in incurable blinding retinal diseases including retinitis pigmentosa (RP) and atrophic age-related macular degeneration. We found in two separate mouse models of photoreceptor degeneration that tamoxifen, a selective estrogen receptor modulator and a drug previously linked with retinal toxicity, paradoxically provided potent neuroprotective effects. In a light-induced degeneration model, tamoxifen prevented onset of photoreceptor apoptosis and atrophy and maintained near-normal levels of electroretinographic responses. Rescue effects were correlated with decreased microglial activation and inflammatory cytokine production in the retina in vivo and a reduction of microglia-mediated toxicity to photoreceptors in vitro, indicating a microglia-mediated mechanism of rescue. Tamoxifen also rescued degeneration in a genetic (Pde6brd10) model of RP, significantly improving retinal structure, electrophysiological responses, and visual behavior. These prominent neuroprotective effects warrant the consideration of tamoxifen as a drug suitable for being repurposed to treat photoreceptor degenerative disease.SIGNIFICANCE STATEMENT Photoreceptor degeneration is a cause of irreversible blindness in a number of retinal diseases such as retinitis pigmentosa (RP) and atrophic age-related macular degeneration. Tamoxifen, a selective estrogen receptor modulator approved for the treatment of breast cancer and previously linked to a low incidence of retinal toxicity, was unexpectedly found to exert marked protective effects against photoreceptor degeneration. Structural and functional protective effects were found for an acute model of light-induced photoreceptor injury and for a genetic model for RP. The mechanism of protection involved the modulation of microglial activation and the production of inflammatory cytokines, highlighting the role of inflammatory mechanisms in photoreceptor degeneration. Tamoxifen may be suitable for clinical study as a potential treatment for diseases involving photoreceptor degeneration.
Dengue is a vector-borne disease recognized as the major arbovirose with four immunologically distant dengue serotypes coexisting in many endemic areas. Several mathematical models have been developed to understand the transmission dynamics of dengue, including the role of cross-reactive antibodies for the four different dengue serotypes. We aimed to review deterministic models of dengue transmission, in order to summarize the evolution of insights for, and provided by, such models, and to identify important characteristics for future model development. We identified relevant publications using PubMed and ISI Web of Knowledge, focusing on mathematical deterministic models of dengue transmission. Model assumptions were systematically extracted from each reviewed model structure, and were linked with their underlying epidemiological concepts. After defining common terms in vector-borne disease modelling, we generally categorised fourty-two published models of interest into single serotype and multiserotype models. The multi-serotype models assumed either vector-host or direct host-to-host transmission (ignoring the vector component). For each approach, we discussed the underlying structural and parameter assumptions, threshold behaviour and the projected impact of interventions. In view of the expected availability of dengue vaccines, modelling approaches will increasingly focus on the effectiveness and cost-effectiveness of vaccination options. For this purpose, the level of representation of the vector and host populations seems pivotal. Since vector-host transmission models would be required for projections of combined vaccination and vector control interventions, we advocate their use as most relevant to advice health policy in the future. The limited understanding of the factors which influence dengue transmission as well as limited data availability remain important concerns when applying dengue models to real-world decision problems.
The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights - as well as the possibility of controlling the system - may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.
BrainK is a set of automated procedures for characterizing the tissues of the human head from MRI, CT, and photogrammetry images. The tissue segmentation and cortical surface extraction support the primary goal of modeling the propagation of electrical currents through head tissues with a finite difference model (FDM) or finite element model (FEM) created from the BrainK geometries. The electrical head model is necessary for accurate source localization of dense array electroencephalographic (dEEG) measures from head surface electrodes. It is also necessary for accurate targeting of cerebral structures with transcranial current injection from those surface electrodes. BrainK must achieve five major tasks: image segmentation, registration of the MRI, CT, and sensor photogrammetry images, cortical surface reconstruction, dipole tessellation of the cortical surface, and Talairach transformation. We describe the approach to each task, and we compare the accuracies for the key tasks of tissue segmentation and cortical surface extraction in relation to existing research tools (FreeSurfer, FSL, SPM, and BrainVisa). BrainK achieves good accuracy with minimal or no user intervention, it deals well with poor quality MR images and tissue abnormalities, and it provides improved computational efficiency over existing research packages.
SARS-CoV-2 is the novel coronavirus responsible for the outbreak of COVID-19, a disease that has spread to over 100 countries and, as of the 26th July 2020, has infected over 16 million people. Despite the urgent need to find effective therapeutics, research on SARS-CoV-2 has been affected by a lack of suitable animal models. To facilitate the development of medical approaches and novel treatments, we compared the ACE2 receptor, and TMPRSS2 and Furin proteases usage of the SARS-CoV-2 Spike glycoprotein in human and in a panel of animal models, i.e. guinea pig, dog, cat, rat, rabbit, ferret, mouse, hamster and macaque. Here we showed that ACE2, but not TMPRSS2 or Furin, has a higher level of sequence variability in the Spike protein interaction surface, which greatly influences Spike protein binding mode. Using molecular docking simulations we compared the SARS-CoV and SARS-CoV-2 Spike proteins in complex with the ACE2 receptor and showed that the SARS-CoV-2 Spike glycoprotein is compatible to bind the human ACE2 with high specificity. In contrast, TMPRSS2 and Furin are sufficiently similar in the considered hosts not to drive susceptibility differences. Computational analysis of binding modes and protein contacts indicates that macaque, ferrets and hamster are the most suitable models for the study of inhibitory antibodies and small molecules targeting the SARS-CoV-2 Spike protein interaction with ACE2. Since TMPRSS2 and Furin are similar across species, our data also suggest that transgenic animal models expressing human ACE2, such as the hACE2 transgenic mouse, are also likely to be useful models for studies investigating viral entry.
Transitions between different conformational states are ubiquitous in proteins, being involved in signaling, catalysis, and other fundamental activities in cells. However, modeling those processes is extremely difficult, due to the need of efficiently exploring a vast conformational space in order to seek for the actual transition path for systems whose complexity is already high in the stable states. Here we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to Calmodulin, based on an empirical force field with a partial structural bias, to explore the transition paths between the apo-closed state and the Ca-bound open state of the protein. We then select representative structures along the trajectory based on a structural clustering algorithm and build a cleaned-up trajectory with them. We finally compare this trajectory with that produced by the online tool MinActionPath, by minimizing the action integral using a harmonic network model, and with that obtained by the PROMPT morphing method, based on an optimal mass transportation-type approach including physical constraints. The comparison is performed both on the structural and energetic level, using the coarse-grained and the atomistic force fields upon reconstruction. Our analysis indicates that this method returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multi-stable proteins transition pathways.
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