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BackgroundThe membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires the utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur orders of magnitude increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources.New methodHere, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of the graphics processing unit (GPU) to accelerate neuronal simulation.Results & comparison with existing methodsNeuroGPU can simulate most biologically detailed models 10-200 times faster than NEURON simulation running on a single core and 5 times faster than GPU simulators (CoreNEURON). NeuroGPU is designed for model parameter tuning and best performs when the GPU is fully utilized by running multiple (> 100) instances of the same model with different parameters. When using multiple GPUs, NeuroGPU can reach to a speed-up of 800 fold compared to single core simulations, especially when simulating the same model morphology with different parameters. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data.ConclusionsThus, NeuroGPU is the fastest available platform that enables rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.

• Neural network models fail to incorporate many dimensions of physiological function regulated by metabotropic receptors. • Enumeration of the dimensions of metabotropic regulation of physiological function reveals unexplored areas of model space. • Not enough models address metabotropic regulation of adaptation, persistent and rebound spiking, and presynaptic inhibition. • Underexplored properties include metabotropic regulation of nonlinear dendritic interactions and synaptic plasticity. The space of possible neural models is enormous and under-explored. Single cell computational neuroscience models account for a range of dynamical properties of membrane potential, but typically do not address network function. In contrast, most models focused on network function address the dimensions of excitatory weight matrices and firing thresholds without addressing the complexities of metabotropic receptor effects on intrinsic properties. There are many under-explored dimensions of neural parameter space, and the field needs a framework for representing what has been explored and what has not. Possible frameworks include maps of parameter spaces, or efforts to categorize the fundamental elements and molecules of neural circuit function. Here we review dimensions that are under-explored in network models that include the metabotropic modulation of synaptic plasticity and presynaptic inhibition, spike frequency adaptation due to calcium-dependent potassium currents, and afterdepolarization due to calcium-sensitive non-specific cation currents and hyperpolarization activated cation currents. Neuroscience research should more effectively explore possible functional models incorporating under-explored dimensions of neural function. [ABSTRACT FROM AUTHOR]

The inflicted chaos instigated by the SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) globally continues with the emergence of novel variants. The current global outbreak is aggravated by the manifestation of novel variants, which affect the effectiveness of the vaccine, attachment with hACE2 (human Angiotensin-converting enzyme 2) and immune evasion. Recently, a new variant named University Hospital Institute (IHU) (B.1.640.2) was reported in France in November 2021 and is spreading globally affecting public healthcare. The B.1.640.2 SARS-CoV-2 strain revealed 14 mutations and 9 deletions in spike protein. Thus, it is important to understand how these variations in the spike protein impact the communication with the host. A protein coupling approach along with molecular simulation protocols was used to interpret the variation in the binding of the wild type (WT) and B.1.640.2 variant with hACE2 and Glucose-regulating protein 78 (GRP78) receptors. The initial docking scores revealed a stronger binding of the B.1.640.2-RBD with both the hACE2 and GRP78. To further understand the crucial dynamic changes, we looked at the structural and dynamic characteristics and also explored the variations in the bonding networks between the WT and B.1.640.2-RBD (receptor-binding domain) in association with hACE2 and GRP78, respectively. Our findings revealed that the variant complex demonstrated distinct dynamic properties in contrast to the wild type due to the acquired mutations. Finally, to provide conclusive evidence on the higher binding by the B.1.640.2 variant the TBE was computed for each complex. For the WT with hACE2 the TBE was quantified to be-61.38 ± 0.96 kcal/mol and for B.1.640.2 variant the TBE was estimated to be -70.47 ± 1.00 kcal/mol. For the WT-RBD-GRP78 the TBE -was computed to be 32.32 ± 0.56 kcal/mol and for the B.1.640.2-RBD a TBE of -50.39 ± 0.88 kcal/mol was reported. This show that these mutations are the basis for higher binding and infectivity produced by B.1.640.2 variant and can be targeted for drug designing against it.Communicated by Ramaswamy H. Sarma.

Epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor important in diverse biological processes including cell proliferation and survival. Upregulation of EGFR activity due to over-expression or mutation is widely implicated in cancer. Activating somatic mutations of the EGFR kinase are postulated to affect the conformation and/or stability of the protein, shifting the EGFR inactive-active state equilibrium towards the activated state. Here, we examined a common EGFR deletion mutation, Δ746ELREA750, which is frequently observed in non-small cell lung cancer patients. By using molecular dynamics simulation, we investigated the structural effects of the mutation that lead to the experimentally reported increases in kinase activity. Simulations of the active form wild-type and ΔELREA EGFRs revealed the deletion stabilizes the αC helix of the kinase domain, which is located adjacent to the deletion site, by rigidifying the flexible β3-αC loop that accommodates the ELREA sequence. Consequently, the αC helix is stabilized in the “αC-in” active conformation that would prolong the time of the activated state. Moreover, in the mutant kinase, a salt bridge between E762 and K745, which is key for EGFR activity, was also stabilized during the simulation. Additionally, the interaction between EGFR and ATP was favored by ΔELREA EGFR over wild-type EGFR, as reflected by the number of hydrogen bonds formed and the free energy of binding. Simulation of inactive EGFR suggested the deletion would promote a shift from the inactive conformation towards active EGFR, which is supported by the inward movement of the αC helix. The MDS results also align with the effects of tyrosine kinase inhibitors on ΔELREA and wild-type EGFR lung cancer cell lines, where more pronounced inhibition was observed against ΔELREA than for wild-type EGFR by inhibitors recognizing the active kinase conformation. [ABSTRACT FROM AUTHOR]

It remains unclear what is the real safe limit of torque magnitude during Bilateral Apical Vertebral Derotation (BAVD) in thoracic curve correction. Up to author’s knowledge there is no study except this one, to reproduce in–vivo real measurements and intraoperative conditions during BAVD maneuver. The objective of this study was to evaluate the torsional strength of the instrumented thoracic spine under axial rotation moment as well as to define safety limits under BAVD corrective maneuver in scoliosis surgery. 10 fresh, full-length, young and intact human cadavers were tested. After proper assembly of the apparatus, the torque was applied through its apical part, simulating thoracic curve derotation. During each experiment the torque magnitude and angular range of derotation were evaluated. For more accurate analysis after every experiment the examined section of the spine was resected from the cadaver and evaluated morphologically and with a CT scan. The average torque to failure during BAVD simulation was 73,3 ± 5,49Nm. The average angle of BAVD to failure was 44,5 ± 8,16°. The majority of failures were in apical area. There was no significant difference between the fracture occurrence of left or right side of lateral wall of the pedicle. There was no spinal canal breach and/or medial wall failure in any specimen. The safety limits of thoracic spine and efficacy of BAVD for axial plane correction in the treatment of Adolescent Idiopathic Scoliosis (AIS) were established. It provided qualitative and quantitative information essential for the spinal derotation under safe loading limits. [ABSTRACT FROM AUTHOR]

Spontaneous sub-cellular calcium release events (SCRE) are conjectured to promote rapid arrhythmias associated with conditions such as heart failure and atrial fibrillation: they can underlie the emergence of spontaneous action potentials in single cells which can lead to arrhythmogenic triggers in tissue. The multi-scale mechanisms of the development of SCRE into arrhythmia triggers, and their dynamic interaction with the tissue substrate, remain elusive; rigorous and simultaneous study of dynamics from the nanometre to the centimetre scale is a major challenge. The aim of this study was to develop a computational approach to overcome this challenge and study potential bi-directional coupling between sub-cellular and tissue-scale arrhythmia phenomena. A framework comprising a hierarchy of computational models was developed, which includes detailed single-cell models describing spatio-temporal calcium dynamics in 3D, efficient non-spatial cell models, and both idealised and realistic tissue models. A phenomenological approach was implemented to reproduce SCRE morphology and variability in the efficient cell models, comprising the definition of analytical Spontaneous Release Functions (SRF) whose parameters may be randomly sampled from appropriate distributions in order to match either the 3D cell models or experimental data. Pro-arrhythmogenic pacing protocols were applied to initiate re-entry and promote calcium overload, leading to the emergence of SCRE. The SRF accurately reproduced the dynamics of SCRE and its dependence on environment variables under multiple different conditions. Sustained re-entrant excitation promoted calcium overload, and led to the emergence of focal excitations after termination. A purely functional mechanism of re-entry and focal activity localisation was demonstrated, related to the unexcited spiral wave core. In conclusion, a novel approach has been developed to dynamically model SCRE at the tissue scale, which facilitates novel, detailed multi-scale mechanistic analysis. It was revealed that complex re-entrant excitation patterns and SCRE may be bi-directionally coupled, promoting novel mechanisms of arrhythmia perpetuation. [ABSTRACT FROM AUTHOR]

Larval dispersal is a critically important yet enigmatic process in marine ecology, evolution, and conservation. Determining the distance and direction that tiny larvae travel in the open ocean continues to be a challenge. Our current understanding of larval dispersal patterns at management-relevant scales is principally and separately informed by genetic parentage data and biological-oceanographic (biophysical) models. Parentage datasets provide clear evidence of individual larval dispersal events, but their findings are spatially and temporally limited. Biophysical models offer a more complete picture of dispersal patterns at regional scales but are of uncertain accuracy. Here, we develop statistical techniques that integrate these two important sources of information on larval dispersal. We then apply these methods to an extensive genetic parentage dataset to successfully validate a high-resolution biophysical model for the economically important reef fish species Plectropomus maculatus in the southern Great Barrier Reef. Our results demonstrate that biophysical models can provide accurate descriptions of larval dispersal at spatial and temporal scales that are relevant to management. They also show that genetic parentage datasets provide enough statistical power to exclude poor biophysical models. Biophysical models that included species-specific larval behaviour provided markedly better fits to the parentage data than assuming passive behaviour, but incorrect behavioural assumptions led to worse predictions than ignoring behaviour altogether. Our approach capitalises on the complementary strengths of genetic parentage datasets and high-resolution biophysical models to produce an accurate picture of larval dispersal patterns at regional scales. The results provide essential empirical support for the use of accurately parameterised biophysical larval dispersal models in marine spatial planning and management. [ABSTRACT FROM AUTHOR]

Carrier proteins are four-helix bundles that covalently hold metabolites and secondary metabolites, such as fatty acids, polyketides and non-ribosomal peptides. These proteins mediate the production of many pharmaceutically important compounds including antibiotics and anticancer agents. Acyl carrier proteins (ACPs) can be found as part of a multi-domain polypeptide (Type I ACPs), or as part of a multiprotein complex (Type II). Here, the main focus is on ACP2 and ACP3, domains from the type I trans-AT polyketide synthase MmpA, which is a core component of the biosynthetic pathway of the antibiotic mupirocin. During molecular dynamics simulations of their apo, holo and acyl forms ACP2 and ACP3 both form a substrate-binding surface-groove. The substrates bound to this surface-groove have polar groups on their acyl chain exposed and forming hydrogen bonds with the solvent. Bulky hydrophobic residues in the GXDS motif common to all ACPs, and similar residues on helix III, appear to prohibit the formation of a deep tunnel in type I ACPs and type II ACPs from polyketide synthases. In contrast, the equivalent positions in ACPs from type II fatty acid synthases, which do form a deep solvent-excluded substrate-binding tunnel, have the small residue alanine. During simulation, ACP3 with mutations I61A L36A W44L forms a deep tunnel that can fully bury a saturated substrate in the core of the ACP, in contrast to the surface groove of the wild type ACP3. Similarly, in the ACP from E. coli fatty acid synthase, a type II ACP, mutations can change ligand binding from being inside a deep tunnel to being in a surface groove, thus demonstrating how changing a few residues can modify the possibilities for ligand binding. [ABSTRACT FROM AUTHOR]

The actin family of cytoskeletal proteins is essential to the physiology of virtually all archaea, bacteria, and eukaryotes. While X-ray crystallography and electron microscopy have revealed structural homologies among actin-family proteins, these techniques cannot probe molecular-scale conformational dynamics. Here, we use all-atom molecular dynamic simulations to reveal conserved dynamical behaviors in four prokaryotic actin homologs: MreB, FtsA, ParM, and crenactin. We demonstrate that the majority of the conformational dynamics of prokaryotic actins can be explained by treating the four subdomains as rigid bodies. MreB, ParM, and FtsA monomers exhibited nucleotide-dependent dihedral and opening angles, while crenactin monomer dynamics were nucleotide-independent. We further show that the opening angle of ParM is sensitive to a specific interaction between subdomains. Steered molecular dynamics simulations of MreB, FtsA, and crenactin dimers revealed that changes in subunit dihedral angle lead to intersubunit bending or twist, suggesting a conserved mechanism for regulating filament structure. Taken together, our results provide molecular-scale insights into the nucleotide and polymerization dependencies of the structure of prokaryotic actins, suggesting mechanisms for how these structural features are linked to their diverse functions. [ABSTRACT FROM AUTHOR]

The fundamental aim of structural analyses in biophysics is to reveal a mutual relation between a molecule's dynamic structure and its physiological function. Small-angle X-ray scattering (SAXS) is an experimental technique for structural characterization of macromolecules in solution and enables time-resolved analysis of conformational changes under physiological conditions. As such experiments measure spatially averaged low-resolution scattering intensities only, the sparse information obtained is not sufficient to uniquely reconstruct a three-dimensional atomistic model. Here, we integrate the information from SAXS into molecular dynamics simulations using computationally efficient native structure-based models. Dynamically fitting an initial structure towards a scattering intensity, such simulations produce atomistic models in agreement with the target data. In this way, SAXS data can be rapidly interpreted while retaining physico-chemical knowledge and sampling power of the underlying force field. We demonstrate our method's performance using the example of three protein systems. Simulations are faster than full molecular dynamics approaches by more than two orders of magnitude and consistently achieve comparable accuracy. Computational demands are reduced sufficiently to run the simulations on commodity desktop computers instead of high-performance computing systems. These results underline that scattering-guided structure-based simulations provide a suitable framework for rapid early-stage refinement of structures towards SAXS data with particular focus on minimal computational resources and time. [ABSTRACT FROM AUTHOR]

Early bacterial surface colonization is not a random process wherein cells arbitrarily attach to surfaces and grow; but rather, attachment events, movement and cellular interactions induce non-random spatial organization. We have only begun to understand how the apparent self-organization affects the fitness of the population. A key factor contributing to fitness is the tradeoff between solitary-planktonic and aggregated surface-attached biofilm lifestyles. Though planktonic cells typically grow faster, bacteria in aggregates are more resistant to stress such as desiccation, antibiotics and predation. Here we ask if and to what extent informed surface-attachments improve fitness during early surface colonization under periodic stress conditions. We use an individual-based modeling approach to simulate foraging planktonic cells colonizing a surface under alternating wet-dry cycles. Such cycles are common in the largest terrestrial microbial habitats–soil, roots, and leaf surfaces–that are not constantly saturated with water and experience daily periods of desiccation stress. We compared different surface-attachment strategies, and analyzed the emerging spatio-temporal dynamics of surface colonization and population yield as a measure of fitness. We demonstrate that a simple strategy of preferential attachment (PA), biased to dense sites, carries a large fitness advantage over any random attachment across a broad range of environmental conditions–particularly under periodic stress. [ABSTRACT FROM AUTHOR]

Interacting-particle reaction dynamics (iPRD) combines the simulation of dynamical trajectories of interacting particles as in molecular dynamics (MD) simulations with reaction kinetics, in which particles appear, disappear, or change their type and interactions based on a set of reaction rules. This combination facilitates the simulation of reaction kinetics in crowded environments, involving complex molecular geometries such as polymers, and employing complex reaction mechanisms such as breaking and fusion of polymers. iPRD simulations are ideal to simulate the detailed spatiotemporal reaction mechanism in complex and dense environments, such as in signalling processes at cellular membranes, or in nano- to microscale chemical reactors. Here we introduce the iPRD software ReaDDy 2, which provides a Python interface in which the simulation environment, particle interactions and reaction rules can be conveniently defined and the simulation can be run, stored and analyzed. A C++ interface is available to enable deeper and more flexible interactions with the framework. The main computational work of ReaDDy 2 is done in hardware-specific simulation kernels. While the version introduced here provides single- and multi-threading CPU kernels, the architecture is ready to implement GPU and multi-node kernels. We demonstrate the efficiency and validity of ReaDDy 2 using several benchmark examples. ReaDDy 2 is available at the website. [ABSTRACT FROM AUTHOR]

An editorial is presented on the creation of open access and reproducible molecular simulations of biological systems. It expresses several rules to molecular dynamics that involves input files simulation protocol should be provided; all topology and parameters should be publicly accessible; and full information about all software used needs to be provided.

Nucleotide-binding and oligomerization domain (NOD)-like receptors (NLRs), the first line of defense, are the cytosolic pattern recognition receptors (PRRs) that regulate the inflammatory activity in response to invading pathogens. NLRs are the members of AAA+ ATPase superfamily that comprises of N-terminal EBD(s), a centrally positioned NOD/NACHT and varying range of LRRs towards the C-terminal end. Due to the lack of structural data, the functional aspects of NLRP-signaling mechanism, which includes pathogen recognition, nucleotide-binding, and sensor-adaptor-effector interactions, are not fully understood. In this study, we implemented structural bioinformatics approaches including protein modeling, docking, and molecular dynamics simulations to explore the structural-dynamic features of ADP-/ATP-Mg2+ binding in NLRPNACHT models. Our results indicate a similar mode of ATP-Mg2+ binding in all NLRPNACHT models and the interacting residues are found consistent with reported mutagenesis data. Accompanied by the key amino acids (proposed to be crucial for ATP-Mg2+ coordination), we further have noticed that some additional conserved residues (including ‘Trp’ of the PhhC motif, and ‘Phe’ and ‘Tyr’ of the GxxxxRxxF motif) are potentially interacting with ATP during dynamics; which require further experimentation for legitimacy. Overall, this study will help in understanding the ADP-/ATP-Mg2+ binding mechanisms in NLRPs in a broader perspective and the proposed ATP-binding pocket will aid in designing novel inhibitors for the regulation of inflammasome activity. [ABSTRACT FROM AUTHOR]

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations. [ABSTRACT FROM AUTHOR]

Filamentous actin (F-actin) and non-muscle myosin II motors drive cell motility and cell shape changes that guide large scale tissue movements during embryonic morphogenesis. To gain a better understanding of the role of actomyosin in vivo, we have developed a two-dimensional (2D) computational model to study emergent phenomena of dynamic unbranched actomyosin arrays in the cell cortex. These phenomena include actomyosin punctuated contractions, or "actin asters" that form within quiescent F-actin networks. Punctuated contractions involve both formation of high intensity aster-like structures and disassembly of those same structures. Our 2D model allows us to explore the kinematics of filament polarity sorting, segregation of motors, and morphology of F-actin arrays that emerge as the model structure and biophysical properties are varied. Our model demonstrates the complex, emergent feedback between filament reorganization and motor transport that generate as well as disassemble actin asters. Since intracellular actomyosin dynamics are thought to be controlled by localization of scaffold proteins that bind F-actin or their myosin motors we also apply our 2D model to recapitulate in vitro studies that have revealed complex patterns of actomyosin that assemble from patterning filaments and motor complexes with microcontact printing. Although we use a minimal representation of filament, motor, and cross-linker biophysics, our model establishes a framework for investigating the role of other actin binding proteins, how they might alter actomyosin dynamics, and makes predictions that can be tested experimentally within live cells as well as within in vitro models. [ABSTRACT FROM AUTHOR]

Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally resolved homologous template structures. In order to generate more accurate models, extensive physics based molecular dynamics (MD) refinement simulations are performed to sample many different conformations to find improved conformational states. In this study, we propose a deep recurrent network model, called DeepTrajectory, that is able to identify these improved conformational states, with high precision, from a variety of different MD based sampling protocols. The proposed model learns the temporal patterns of features computed from MD trajectory data in order to classify whether each recorded simulation snapshot is an improved quality conformational state, decreased quality conformational state or whether there is no perceivable change in state with respect to the starting conformation. The model was trained and tested on 904 trajectories from 42 different protein systems with a cumulative number of more than 1.7 million snapshots. We show that our model outperforms other state of the art machine-learning algorithms that do not consider temporal dependencies. To our knowledge, DeepTrajectory is the first implementation of a time-dependent deep-learning protocol that is re-trainable and able to adapt to any new MD based sampling procedure, thereby demonstrating how a neural network can be used to learn the latter part of the protein folding funnel. [ABSTRACT FROM AUTHOR]

There is a significant interest in the neuroscience community in the development of large-scale network models that would integrate diverse sets of experimental data to help elucidate mechanisms underlying neuronal activity and computations. Although powerful numerical simulators (e.g., NEURON, NEST) exist, data-driven large-scale modeling remains challenging due to difficulties involved in setting up and running network simulations. We developed a high-level application programming interface (API) in Python that facilitates building large-scale biophysically detailed networks and simulating them with NEURON on parallel computer architecture. This tool, termed “BioNet”, is designed to support a modular workflow whereby the description of a constructed model is saved as files that could be subsequently loaded for further refinement and/or simulation. The API supports both NEURON’s built-in as well as user-defined models of cells and synapses. It is capable of simulating a variety of observables directly supported by NEURON (e.g., spikes, membrane voltage, intracellular [Ca++]), as well as plugging in modules for computing additional observables (e.g. extracellular potential). The high-level API platform obviates the time-consuming development of custom code for implementing individual models, and enables easy model sharing via standardized files. This tool will help refocus neuroscientists on addressing outstanding scientific questions rather than developing narrow-purpose modeling code. [ABSTRACT FROM AUTHOR]

Among α-transglucosylases from Glycoside-Hydrolase family 70, the ΔN123-GB-CD2 enzyme derived from the bifunctional DSR-E from L. citreum NRRL B-1299 is particularly interesting as it was the first described engineered Branching Sucrase, not able to elongate glucan polymers from sucrose substrate. The previously reported overall structural organization of this multi-domain enzyme is an intricate U-shape fold conserved among GH70 enzymes which showed a certain conformational variability of the so-called domain V, assumed to play a role in the control of product structures, in available X-ray structures. Understanding the role of functional dynamics on enzyme reaction and substrate recognition is of utmost interest although it remains a challenge for biophysical methods. By combining long molecular dynamics simulation (1μs) and multiple analyses (NMA, PCA, Morelet Continuous Wavelet Transform and Cross Correlations Dynamics), we investigated here the dynamics of ΔN123-GB-CD2 alone and in interaction with sucrose substrate. Overall, our results provide the detailed picture at atomic level of the hierarchy of motions occurring along different timescales and how they are correlated, in agreement with experimental structural data. In particular, detailed analysis of the different structural domains revealed cooperative dynamic behaviors such as twisting, bending and wobbling through anti- and correlated motions, and also two structural hinge regions, of which one was unreported. Several highly flexible loops surrounding the catalytic pocket were also highlighted, suggesting a potential role in the acceptor promiscuity of ΔN123-GBD-CD2. Normal modes and essential dynamics underlined an interesting two-fold dynamic of the catalytic domain A, pivoting about an axis splitting the catalytic gorge in two parts. The comparison of the conformational free energy landscapes using principal component analysis of the enzyme in absence or in presence of sucrose, also revealed a more harmonic basin when sucrose is bound with a shift population of the bending mode, consistent with the substrate binding event. [ABSTRACT FROM AUTHOR]

The non-homologous end joining of a DNA double strand break is initiated by the MRE11-NBS1-RAD50 complex whose subunits are the first three proteins to arrive to the breakage site thereby making the recruitment time of MRE11, NBS1 and RAD50 essential for cell survival. In the present investigation, the nature of MRE11 and NBS1 transportation from the cytoplasm to the nucleus, hosting the damaged DNA strand, is hypothesized to be a passive diffusive process. The feasibility of such a mechanism is addressed through theoretical and computational approaches which permit establishing the characteristic recruitment time of MRE11 and NBS1 by the nucleus. A computational model of a cell is constructed from a set of biological parameters and the kinetic Monte Carlo algorithm is used to simulate the diffusing MRE11 and NBS1 particles as a random walk process. To accurately describe the experimented data, it is discovered that MRE11 and NBS1 should start diffusion from significantly different starting positions which suggests that diffusion might not be the only transport mechanism of repair protein recruitment to the DNA break. [ABSTRACT FROM AUTHOR]

Knowledge on successful interventions against livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) within pig herds is sparse. In situations like this, a mechanistic simulation model can be a valuable tool for assessing the effect of potential intervention strategies, and prioritising which should be tested in the field. We have simulated on-farm interventions in a farrow-to-finish pig herd, with a previously published LA-MRSA spread model, within four different areas: 1) Reduced antimicrobial consumption, 2) Reduced number of pigs within each section, 3) Reduced mixing of pigs, and 4) Improved internal biosecurity. To model a decrease in the selective pressure, the transmission rates were reduced after LA-MRSA had become fully established within a herd, which resulted in a marked decrease in the prevalence within all stable units. However, LA-MRSA rarely disappeared completely from the herd; this was only observed in scenarios where the transmission rates were reduced to ≤ 30% of the original level. While changes in antimicrobial consumption patterns might be a very important step towards reducing the spread of LA-MRSA, the simulation results indicate that it may need to be paired with other preventive or intervention measures. Reducing the number of pigs within each section, reducing mixing of pigs, or improving internal biosecurity after LA-MRSA had become established within the herd only resulted in marginal changes in the median prevalence within the herd. However, these factors might be important in relation to being able to achieve or maintain a low level of antimicrobial consumption, and thus still indirectly influence the LA-MRSA prevalence within the herd. The results of a sensitivity analysis indicated the assumptions regarding the existence of pigs persistently shedding MRSA have a noticeable influence on the model results. The assumptions regarding transmission from sow to offspring at the day of birth also had a considerable influence on the MRSA prevalence within the farrowing unit but did not cause any marked changes in the simulated effect of interventions. Effects might differ between different farm types contaminated in different levels and this simulation study highlights a strong need for more knowledge from on-farm trials. [ABSTRACT FROM AUTHOR]

Chemical and mechanical pattern formation is fundamental during embryogenesis and tissue development. Yet, the underlying molecular and cellular mechanisms are still elusive in many cases. Most current theories assume that tissue development is driven by chemical processes: either as a sequence of chemical patterns each depending on the previous one, or by patterns spontaneously arising from specific chemical interactions (such as “Turing-patterns”). Within both theories, mechanical patterns are usually regarded as passive by-products of chemical pre-patters. However, several experiments question these theories, and an increasing number of studies shows that tissue mechanics can actively influence chemical patterns during development. In this study, we thus focus on the interplay between chemical and mechanical processes during tissue development. On one hand, based on recent experimental data, we develop new mechanochemical simulation models of evolving tissues, in which the full 3D representation of the tissue appears to be critical for obtaining a realistic mechanochemical behaviour. The presented modelling approach is flexible and numerically studied using state of the art finite element methods. Thus, it may serve as a basis to combine simulations with new experimental methods in tissue development. On the other hand, we apply the developed approach and demonstrate that even simple interactions between tissue mechanics and chemistry spontaneously lead to robust and complex mechanochemical patterns. Especially, we demonstrate that the main contradictions arising in the framework of purely chemical theories are naturally and automatically resolved using the mechanochemical patterning theory. [ABSTRACT FROM AUTHOR]

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system for model authoring, simulation, and teaching that facilitates building reproducible dynamical models and reusing models by 1) providing a notebook environment which allows models, Python code, and narrative to be intermixed, 2) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 3) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python–based Jupyter–like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in–line, human–readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards–compliant models and simulations, run the simulations in–line, and re–export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse. [ABSTRACT FROM AUTHOR]

Many types of large cells have multiple nuclei. In skeletal muscle fibers, the nuclei are distributed along the cell to maximize their internuclear distances. This myonuclear positioning is crucial for cell function. Although microtubules, microtubule associated proteins, and motors have been implicated, mechanisms responsible for myonuclear positioning remain unclear. We used a combination of rough interacting particle and detailed agent-based modeling to examine computationally the hypothesis that a force balance generated by microtubules positions the muscle nuclei. Rather than assuming the nature and identity of the forces, we simulated various types of forces between the pairs of nuclei and between the nuclei and cell boundary to position the myonuclei according to the laws of mechanics. We started with a large number of potential interacting particle models and computationally screened these models for their ability to fit biological data on nuclear positions in hundreds of Drosophila larval muscle cells. This reverse engineering approach resulted in a small number of feasible models, the one with the best fit suggests that the nuclei repel each other and the cell boundary with forces that decrease with distance. The model makes nontrivial predictions about the increased nuclear density near the cell poles, the zigzag patterns of the nuclear positions in wider cells, and about correlations between the cell width and elongated nuclear shapes, all of which we confirm by image analysis of the biological data. We support the predictions of the interacting particle model with simulations of an agent-based mechanical model. Taken together, our data suggest that microtubules growing from nuclear envelopes push on the neighboring nuclei and the cell boundaries, which is sufficient to establish the nearly-uniform nuclear spreading observed in muscle fibers. [ABSTRACT FROM AUTHOR]

Molecular networks are at the core of most cellular decisions, but are often difficult to comprehend. Reverse engineering of network architecture from their functions has proved fruitful to classify and predict the structure and function of molecular networks, suggesting new experimental tests and biological predictions. We present φ-evo, an open-source program to evolve in silico phenotypic networks performing a given biological function. We include implementations for evolution of biochemical adaptation, adaptive sorting for immune recognition, metazoan development (somitogenesis, hox patterning), as well as Pareto evolution. We detail the program architecture based on , , and a interface for project configuration and network analysis. We illustrate the predictive power of φ-evo by first recovering the asymmetrical structure of the lac operon regulation from an objective function with symmetrical constraints. Second, we use the problem of hox-like embryonic patterning to show how a single effective fitness can emerge from multi-objective (Pareto) evolution. φ-evo provides an efficient approach and user-friendly interface for the phenotypic prediction of networks and the numerical study of evolution itself. [ABSTRACT FROM AUTHOR]

Ligand binding sites in proteins are often localized to deeply buried cavities, inaccessible to bulk solvent. Yet, in many cases binding of cognate ligands occurs rapidly. An intriguing system is presented by the L99A cavity mutant of T4 Lysozyme (T4L L99A) that rapidly binds benzene (~106 M-1s-1). Although the protein has long served as a model system for protein thermodynamics and crystal structures of both free and benzene-bound T4L L99A are available, the kinetic pathways by which benzene reaches its solvent-inaccessible binding cavity remain elusive. The current work, using extensive molecular dynamics simulation, achieves this by capturing the complete process of spontaneous recognition of benzene by T4L L99A at atomistic resolution. A series of multi-microsecond unbiased molecular dynamics simulation trajectories unequivocally reveal how benzene, starting in bulk solvent, diffuses to the protein and spontaneously reaches the solvent inaccessible cavity of T4L L99A. The simulated and high-resolution X-ray derived bound structures are in excellent agreement. A robust four-state Markov model, developed using cumulative 60 μs trajectories, identifies and quantifies multiple ligand binding pathways with low activation barriers. Interestingly, none of these identified binding pathways required large conformational changes for ligand access to the buried cavity. Rather, these involve transient but crucial opening of a channel to the cavity via subtle displacements in the positions of key helices (helix4/helix6, helix7/helix9) leading to rapid binding. Free energy simulations further elucidate that these channel-opening events would have been unfavorable in wild type T4L. Taken together and via integrating with results from experiments, these simulations provide unprecedented mechanistic insights into the complete ligand recognition process in a buried cavity. By illustrating the power of subtle helix movements in opening up multiple pathways for ligand access, this work offers an alternate view of ligand recognition in a solvent-inaccessible cavity, contrary to the common perception of a single dominant pathway for ligand binding. [ABSTRACT FROM AUTHOR]

Mechanical stability is a key feature in the regulation of structural scaffolding proteins and their functions. Despite the abundance of α-helical structures among the human proteome and their undisputed importance in health and disease, the fundamental principles of their behavior under mechanical load are poorly understood. Talin and α-catenin are two key molecules in focal adhesions and adherens junctions, respectively. In this study, we used a combination of atomistic steered molecular dynamics (SMD) simulations, polyprotein engineering, and single-molecule atomic force microscopy (smAFM) to investigate unfolding of these proteins. SMD simulations revealed that talin rod α-helix bundles as well as α-catenin α-helix domains unfold through stable 3-helix intermediates. While the 5-helix bundles were found to be mechanically stable, a second stable conformation corresponding to the 3-helix state was revealed. Mechanically weaker 4-helix bundles easily unfolded into a stable 3-helix conformation. The results of smAFM experiments were in agreement with the findings of the computational simulations. The disulfide clamp mutants, (resistant to mechanical unfolding) designed to protect the stable state, support the 3-helix intermediate model in both experimental and computational setups. As a result, multiple discrete unfolding intermediate states in the talin and α-catenin unfolding pathway were discovered. Better understanding of the mechanical unfolding mechanism of α-helix proteins is a key step towards comprehensive models describing the mechanoregulation of proteins. [ABSTRACT FROM AUTHOR]

The complex morphologies observed in many biofilms play a critical role in the survival of these microbial communities. Recently, the formation of wrinkles has been the focus of many studies aimed at finding fundamental information on morphogenesis during development. While the underlying genetic mechanisms of wrinkling are not well-understood, recent discoveries have led to the counterintuitive idea that wrinkle formation is triggered by localized cell death. This work examines the hypothesis that the material properties of a biofilm both power and control wrinkle formation within biofilms in response to localized cell death. Using an agent-based model and a high-performance platform (Biocellion), we built a model that qualitatively reproduced wrinkle formation in biofilms due to cell death. Through the use of computational simulations, we determined important relationships between cellular level mechanical interactions and changes in colony morphology. These simulations were also used to identify significant cellular interactions that are required for wrinkle formation. These results are a first step towards more comprehensive models that, in combination with experimental observations, will improve our understanding of the morphological development of bacterial biofilms. [ABSTRACT FROM AUTHOR]

Fluctuating Finite Element Analysis (FFEA) is a software package designed to perform continuum mechanics simulations of proteins and other globular macromolecules. It combines conventional finite element methods with stochastic thermal noise, and is appropriate for simulations of large proteins and protein complexes at the mesoscale (length-scales in the range of 5 nm to 1 μm), where there is currently a paucity of modelling tools. It requires 3D volumetric information as input, which can be low resolution structural information such as cryo-electron tomography (cryo-ET) maps or much higher resolution atomistic co-ordinates from which volumetric information can be extracted. In this article we introduce our open source software package for performing FFEA simulations which we have released under a GPLv3 license. The software package includes a C ++ implementation of FFEA, together with tools to assist the user to set up the system from Electron Microscopy Data Bank (EMDB) or Protein Data Bank (PDB) data files. We also provide a PyMOL plugin to perform basic visualisation and additional Python tools for the analysis of FFEA simulation trajectories. This manuscript provides a basic background to the FFEA method, describing the implementation of the core mechanical model and how intermolecular interactions and the solvent environment are included within this framework. We provide prospective FFEA users with a practical overview of how to set up an FFEA simulation with reference to our publicly available online tutorials and manuals that accompany this first release of the package. [ABSTRACT FROM AUTHOR]

Motivated by in vitro time–lapse images of ovarian cancer spheroids inducing mesothelial cell clearance, the traditional agent–based model of cell migration, based on simple volume exclusion, was extended to include the possibility that a cell seeking to move into an occupied location may push the resident cell, and any cells neighbouring it, out of the way to occupy that location. In traditional discrete models of motile cells with volume exclusion such a move would be aborted. We introduce a new shoving mechanism which allows cells to choose the direction to shove cells that expends the least amount of shoving effort (to account for the likely resistance of cells to being pushed). We call this motility rule ‘smart shoving’. We examine whether agent–based simulations of different shoving mechanisms can be distinguished on the basis of single realisations and averages over many realisations. We emphasise the difficulty in distinguishing cell mechanisms from cellular automata simulations based on snap–shots of cell distributions, site–occupancy averages and the evolution of the number of cells of each species averaged over many realisations. This difficulty suggests the need for higher resolution cell tracking. [ABSTRACT FROM AUTHOR]

We report microsecond timescale molecular dynamics simulation of the complex formed between Pt(II)-phenanthroline and the 16 N-terminal residues of the Aβ peptide that is implicated in the onset of Alzheimer’s disease, along with equivalent simulations of the metal-free peptide. Simulations from a variety of starting points reach equilibrium within 100 ns, as judged by root mean square deviation and radius of gyration. Platinum-bound peptides deviate rather more from starting points, and adopt structures with larger radius of gyration, than their metal-free counterparts. Residues bound directly to Pt show smaller fluctuation, but others actually move more in the Pt-bound peptide. Hydrogen bonding within the peptide is disrupted by binding of Pt, whereas the presence of salt-bridges are enhanced. [ABSTRACT FROM AUTHOR]

M. tuberculosis oxidation sense Regulator (MosR) is a transcriptional regulator from Mycobacterium tuberculosis. It senses the environment oxidation and regulates the expression of a secreted oxidoreductase, thus defending the bacilli against oxidative stress from the phagosome. While most of the members of the Multiple antibiotics resistance Regulator (MarR) family are ligand-responsive, MosR may dissociate from its DNA site upon formation of an intrachain disulphide bond. However, the structure of MosR in its oxidized state is not known, and it is not clear how the formation of this disulphide bond would lead to the conformational changes required for dissociation of the DNA. Nonetheless, MosR presents two crystallographically resolved conformations in its reduced state: bound and unbound to DNA. We managed to simulate MosR unbound to the DNA, both in the presence and in the absence of the disulphide bond. Our results indicate that this disulphide bond precludes the N-terminal residues from adopting a conformation that stands in-between the helix α1 and the DNA binding domain (DBD) from the other chain. Once this conformation is achieved in the reduced state, this DBD detaches from the dimerization domain and becomes more flexible, being able to perform motions with higher amplitude and higher degree of collectivity. Only then, MosR may achieve a conformation where its recognition helices fit into the major grooves of its DNA site. The analysis of the collective motions performed by MosR, during the different situations sampled by the molecular dynamics (MDs), was only possible by the method of filtering harmonic modes with specific frequencies. The frequency of the collective motions performed by the DBD of MosR in the reduced state to achieve a DNA-binding conformation is in the range of 20 to 50 MHz, but it may be associated to more sporadic events since it requires the combination of a suitable conformation of the N-terminal residues. [ABSTRACT FROM AUTHOR]

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal “virtual laboratory” for such multicellular systems simulates both the biochemical microenvironment (the “stage”) and many mechanically and biochemically interacting cells (the “players” upon the stage). PhysiCell—physics-based multicellular simulator—is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility “out of the box.” The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a “cellular cargo delivery” system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at and . [ABSTRACT FROM AUTHOR]

A significant portion of bone fractures fail to heal properly, increasing healthcare costs. Advances in fracture management have slowed because translation barriers have limited generation of mechanism-based explanations for the healing process. When uncertainties are numerous, analogical modeling can be an effective strategy for developing plausible explanations of complex phenomena. We demonstrate the feasibility of engineering analogical models in software to facilitate discovery of biomimetic explanations for how fracture healing may progress. Concrete analogical models—Callus Analogs—were created using the MASON simulation toolkit. We designated a Target Region initial state within a characteristic tissue section of mouse tibia fracture at day-7 and posited a corresponding day-10 Target Region final state. The goal was to discover a coarse-grain analog mechanism that would enable the discretized initial state to transform itself into the corresponding Target Region final state, thereby providing an alternative way to study the healing process. One of nine quasi-autonomous Tissue Unit types is assigned to each grid space, which maps to an 80×80 μm region of the tissue section. All Tissue Units have an opportunity each time step to act based on individualized logic, probabilities, and information about adjacent neighbors. Action causes transition from one Tissue Unit type to another, and simulation through several thousand time steps generates a coarse-grain analog—a theory—of the healing process. We prespecified a minimum measure of success: simulated and actual Target Region states achieve ≥ 70% Similarity. We used an iterative refinement protocol to explore many combinations of Tissue Unit logic and action constraints. Workflows progressed through four stages of analog mechanisms. Similarities of 73–90% were achieved for Mechanisms 2–4. The range of Upper-Level similarities increased to 83–94% when we allowed for uncertainty about two Tissue Unit designations. We have demonstrated how Callus Analog experiments provide domain experts with a fresh medium and tools for thinking about and understanding the fracture healing process. [ABSTRACT FROM AUTHOR]

Recent studies on relatively young and fit individuals with limb loss suggest that maintaining muscle strength after limb loss may mitigate the high metabolic cost of walking typically seen in the larger general limb loss population. However, these data are cross-sectional and the muscle strength prior to limb loss is unknown, and it is therefore difficult to draw causal inferences on changes in strength and gait energetics. Here we used musculoskeletal modeling and optimal control simulations to perform a longitudinal study (25 virtual “subjects”) of the metabolic cost of walking pre- and post-limb loss (unilateral transtibial). Simulations of walking were first performed pre-limb loss on a model with two intact biological legs, then post-limb loss on a model with a unilateral transtibial prosthesis, with a cost function that minimized the weighted sum of gait deviations plus metabolic cost. Metabolic costs were compared pre- vs. post-limb loss, with systematic modifications to the muscle strength and prosthesis type (passive, powered) in the post-limb loss model. The metabolic cost prior to limb loss was 3.44±0.13 J/m/kg. After limb loss, with a passive prosthesis the metabolic cost did not increase above the pre-limb loss cost if pre-limb loss muscle strength was maintained (mean -0.6%, p = 0.17, d = 0.17). With 10% strength loss the metabolic cost with the passive prosthesis increased (mean +5.9%, p < 0.001, d = 1.61). With a powered prosthesis, the metabolic cost was at or below the pre-limb loss cost for all subjects with strength losses of 10% and 20%, but increased for all subjects with strength loss of 30% (mean +5.9%, p < 0.001, d = 1.59). The results suggest that maintaining muscle strength may prevent an increase in the metabolic cost of walking following unilateral transtibial limb loss, and that a gait with minimal deviations can be achieved when muscle strength is sufficiently high, even when using a passive prosthesis. [ABSTRACT FROM AUTHOR]

Monoamine transporters (MATs) carry out neurotransmitter reuptake from the synaptic cleft, a key step in neurotransmission, which is targeted in the treatment of neurological disorders. Cholesterol (CHOL), a major component of the synaptic plasma membrane, has been shown to exhibit a modulatory effect on MATs. Recent crystal structures of the dopamine transporter (DAT) revealed the presence of two conserved CHOL-like molecules, suggesting a functional protein-CHOL direct interaction. Here, we present extensive atomistic molecular dynamics (MD) simulations of DAT in an outward-facing conformation. In the absence of bound CHOL, DAT undergoes structural changes reflecting early events of dopamine transport: transition to an inward-facing conformation. In contrast, in the presence of bound CHOL, these conformational changes are inhibited, seemingly by an immobilization of the intracellular interface of TM1a and TM5 by CHOL. We also provide evidence, from coarse grain MD simulations that the CHOL sites observed in the DAT crystal structures are preserved in all human transporters (dopamine, serotonin and norepinephrine), suggesting that our findings might extend to the entire family. [ABSTRACT FROM AUTHOR]

The process of desertification in the semi-arid climatic zone is considered by many as a catastrophic regime shift, since the positive feedback of vegetation density on growth rates yields a system that admits alternative steady states. Some support to this idea comes from the analysis of static patterns, where peaks of the vegetation density histogram were associated with these alternative states. Here we present a large-scale empirical study of vegetation dynamics, aimed at identifying and quantifying directly the effects of positive feedback. To do that, we have analyzed vegetation density across 2.5 × 106 km2 of the African Sahel region, with spatial resolution of 30 × 30 meters, using three consecutive snapshots. The results are mixed. The local vegetation density (measured at a single pixel) moves towards the average of the corresponding rainfall line, indicating a purely negative feedback. On the other hand, the chance of spatial clusters (of many “green” pixels) to expand in the next census is growing with their size, suggesting some positive feedback. We show that these apparently contradicting results emerge naturally in a model with positive feedback and strong demographic stochasticity, a model that allows for a catastrophic shift only in a certain range of parameters. Static patterns, like the double peak in the histogram of vegetation density, are shown to vary between censuses, with no apparent correlation with the actual dynamical features. Our work emphasizes the importance of dynamic response patterns as indicators of the state of the system, while the usefulness of static modality features appears to be quite limited. [ABSTRACT FROM AUTHOR]

Human butyrylcholinesterase (BChE) is a glycoprotein capable of bioscavenging toxic compounds such as organophosphorus (OP) nerve agents. For commercial production of BChE, it is practical to synthesize BChE in non–human expression systems, such as plants or animals. However, the glycosylation profile in these systems is significantly different from the human glycosylation profile, which could result in changes in BChE’s structure and function. From our investigation, we found that the glycan attached to ASN241 is both structurally and functionally important due to its close proximity to the BChE tetramerization domain and the active site gorge. To investigate the effects of populating glycosylation site ASN241, monomeric human BChE glycoforms were simulated with and without site ASN241 glycosylated. Our simulations indicate that the structure and function of human BChE are significantly affected by the absence of glycan 241. [ABSTRACT FROM AUTHOR]

Human butyrylcholinesterase (BChE) is a glycoprotein capable of bioscavenging toxic compounds such as organophosphorus (OP) nerve agents. For commercial production of BChE, it is practical to synthesize BChE in non–human expression systems, such as plants or animals. However, the glycosylation profile in these systems is significantly different from the human glycosylation profile, which could result in changes in BChE’s structure and function. From our investigation, we found that the glycan attached to ASN241 is both structurally and functionally important due to its close proximity to the BChE tetramerization domain and the active site gorge. To investigate the effects of populating glycosylation site ASN241, monomeric human BChE glycoforms were simulated with and without site ASN241 glycosylated. Our simulations indicate that the structure and function of human BChE are significantly affected by the absence of glycan 241. [ABSTRACT FROM AUTHOR]

The composition of ecological assemblages depends on a variety of factors including environmental filtering, biotic interactions and dispersal limitation. By evaluating the phylogenetic pattern of assemblages, we gain insight into the relative contribution of these mechanisms to generating observed assemblages. We address some limitations in the field of community phylogenetics by using simulations, biologically relevant null models, and cost distance analysis to evaluate simultaneous mechanisms leading to observed patterns of co-occurrence. Building from past studies of phylogenetic community structure, we applied our approach to hummingbird assemblages in the Northern Andes. We compared the relationship between relatedness and co-occurrence among predicted assemblages, based on estimates of suitable habitat and dispersal limitation, and observed assemblages. Hummingbird co-occurrence peaked at intermediate relatedness and decreased when a closely-related species was present. This result was most similar to simulations that included simultaneous effects of phylogenetic conservatism and repulsion. In addition, we found older sister taxa were only weakly more separated by geographic barriers, suggesting that time since dispersal is unlikely to be the sole factor influencing co-occurrence of closely related species. Our analysis highlights the role of multiple mechanisms acting simultaneously, and provides a hypothesis for the potential importance of competition at regional scales. [ABSTRACT FROM AUTHOR]

The interactions between membrane receptors and extracellular ligands control cell-cell and cell-substrate adhesion, and environmental responsiveness by representing the initial steps of cell signaling pathways. These interactions can be spatial-temporally regulated when different extracellular ligands are tethered. The detailed mechanisms of this spatial-temporal regulation, including the competition between distinct ligands with overlapping binding sites and the conformational flexibility in multi-specific ligand assemblies have not been quantitatively evaluated. We present a new coarse-grained model to realistically simulate the binding process between multi-specific ligands and membrane receptors on cell surfaces. The model simplifies each receptor and each binding site in a multi-specific ligand as a rigid body. Different numbers or types of ligands are spatially organized together in the simulation. These designs were used to test the relation between the overall binding of a multi-specific ligand and the affinity of its cognate binding site. When a variety of ligands are exposed to cells expressing different densities of surface receptors, we demonstrated that ligands with reduced affinities have higher specificity to distinguish cells based on the relative concentrations of their receptors. Finally, modification of intramolecular flexibility was shown to play a role in optimizing the binding between receptors and ligands. In summary, our studies bring new insights to the general principles of ligand-receptor interactions. Future applications of our method will pave the way for new strategies to generate next-generation biologics. [ABSTRACT FROM AUTHOR]

Although several computational modelling studies have investigated the conformational behaviour of inherently disordered protein (IDP) amylin, discrepancies in identifying its preferred solution conformations still exist between various forcefields and sampling methods used. Human islet amyloid polypeptide has long been a subject of research, both experimentally and theoretically, as the aggregation of this protein is believed to be the lead cause of type-II diabetes. In this work, we present a systematic forcefield assessment using one of the most advanced non-biased sampling techniques, Replica Exchange with Solute Tempering (REST2), by comparing the secondary structure preferences of monomeric amylin in solution. This study also aims to determine the ability of common forcefields to sample a transition of the protein from a helical membrane bound conformation into the disordered solution state of amylin. Our results demonstrated that the CHARMM22* forcefield showed the best ability to sample multiple conformational states inherent for amylin. It is revealed that REST2 yielded results qualitatively consistent with experiments and in quantitative agreement with other sampling methods, however far more computationally efficiently and without any bias. Therefore, combining an unbiased sampling technique such as REST2 with a vigorous forcefield testing could be suggested as an important step in developing an efficient and robust strategy for simulating IDPs. [ABSTRACT FROM AUTHOR]

The biological function of proteins is closely related to its structural motion. For instance, structurally misfolded proteins do not function properly. Although we are able to experimentally obtain structural information on proteins, it is still challenging to capture their dynamics, such as transition processes. Therefore, we need a simulation method to predict the transition pathways of a protein in order to understand and study large functional deformations. Here, we present a new simulation method called normal mode-guided elastic network interpolation (NGENI) that performs normal modes analysis iteratively to predict transition pathways of proteins. To be more specific, NGENI obtains displacement vectors that determine intermediate structures by interpolating the distance between two end-point conformations, similar to a morphing method called elastic network interpolation. However, the displacement vector is regarded as a linear combination of the normal mode vectors of each intermediate structure, in order to enhance the physical sense of the proposed pathways. As a result, we can generate more reasonable transition pathways geometrically and thermodynamically. By using not only all normal modes, but also in part using only the lowest normal modes, NGENI can still generate reasonable pathways for large deformations in proteins. This study shows that global protein transitions are dominated by collective motion, which means that a few lowest normal modes play an important role in this process. NGENI has considerable merit in terms of computational cost because it is possible to generate transition pathways by partial degrees of freedom, while conventional methods are not capable of this. [ABSTRACT FROM AUTHOR]

The article covers topics related to neural oscillations. Topics discussed include the importance of entrainment of ongoing neural oscillations as a key mechanism used by the brain to create temporal predictions and support active perception of the sensory cortices, the measures of neural phase coherence or phase concentration, and the use of non-rhythmic information like the passage of time to form temporal predictions.

CcmK proteins are major constituents of icosahedral shells of β-carboxysomes, a bacterial microcompartment that plays a key role for CO2 fixation in nature. Supported by the characterization of bidimensional (2D) layers of packed CcmK hexamers in crystal and electron microscopy structures, CcmK are assumed to be the major components of icosahedral flat facets. Here, we reassessed the validity of this model by studying CcmK isoforms from Synechocystis sp. PCC6803. Native mass spectrometry studies confirmed that CcmK are hexamers in solution. Interestingly, potential pre-assembled intermediates were also detected with CcmK2. Atomic-force microscopy (AFM) imaging under quasi-physiological conditions confirmed the formation of canonical flat sheets with CcmK4. Conversely, CcmK2 formed both canonical and striped-patterned patches, while CcmK1 assembled into remarkable supra-hexameric curved honeycomb-like mosaics. Mutational studies ascribed the propensity of CcmK1 to form round assemblies to a combination of two features shared by at least one CcmK isoform in most β-cyanobacteria: a displacement of an α helical portion towards the hexamer edge, where a potential phosphate binding funnel forms between packed hexamers, and the presence of a short C-terminal extension in CcmK1. All-atom molecular dynamics supported a contribution of phosphate molecules sandwiched between hexamers to bend CcmK1 assemblies. Formation of supra-hexameric curved structures could be reproduced in coarse-grained simulations, provided that adhesion forces to the support were weak. Apart from uncovering unprecedented CcmK self-assembly features, our data suggest the possibility that transitions between curved and flat assemblies, following cargo maturation, could be important for the biogenesis of β-carboxysomes, possibly also of other BMC. [ABSTRACT FROM AUTHOR]

We present a systematic procedure to improve the qualities of triangular molecular surface meshes and at the same time preserve the manifoldness. The procedure utilizes an algorithm to remove redundant points having three or four valences and another algorithm to smooth the mesh using a modified version of Laplacian method without causing intersecting triangles. This approach can be effectively applied to any manifold surface meshes with arbitrary complex geometry. In this paper, the tested meshes are biomolecular surface meshes exhibiting typically highly irregular geometry. The results show that the qualities of the surface meshes are greatly improved and the manifoldness of the surface meshes are preserved. Compared with the original meshes, these improved molecular surface meshes can be directly applied to boundary element simulations and generation of body-fitted volume meshes using Tetgen. The procedure has been incorporated into our triangular molecular surface mesh generator, TMSmesh 2.0. It can be also used as a standalone program and works together with any other surface triangular mesh generator to obtain qualified manifold mesh. The package is downloadable at and can be run online at . [ABSTRACT FROM AUTHOR]

The article deals with BB-SPICE (SPICE for Biochemical and Biological Systems), an extension of the famous Simulation Program with Integrated Circuit Emphasis (SPICE). BB-SPICE environment is composed of three modules: a new textual and compact description formalism for biological systems, a converter that handles this description and generates the SPICE netlist of the equivalent electronic circuit and NGSPICE which is an open-source SPICE simulator. In addition, the environment provides back and forth interfaces with SBML (System Biology Markup Language), a very common description language used in systems biology. BB-SPICE has been developed in order to bridge the gap between the simulation of biological systems on the one hand and electronics circuits on the other hand. Thus, it is suitable for applications at the interface between both domains, such as development of design tools for synthetic biology and for the virtual prototyping of biosensors and lab-on-chip. Simulation results obtained with BB-SPICE and COPASI (an open-source software used for the simulation of biochemical systems) have been compared on a benchmark of models commonly used in systems biology. Results are in accordance from a quantitative viewpoint but BB-SPICE outclasses COPASI by 1 to 3 orders of magnitude regarding the computation time. Moreover, as our software is based on NGSPICE, it could take profit of incoming updates such as the GPU implementation, of the coupling with powerful analysis and verification tools or of the integration in design automation tools (synthetic biology). [ABSTRACT FROM AUTHOR]