1,721,079 research outputs found
Critical Assessment of Common Force Fields for Molecular Dynamics Simulations of Potassium Channels
Full text linkFor the last two decades, the KcsA K+ channel has served as a case study to understand how potassium channels operate at the atomic scale, and molecular dynamics simulations have contributed significantly to the current knowledge of the atomic mechanisms of conduction, inactivation, and gating in this family of membrane proteins. Currently, microsecond trajectories are becoming the new standard in the field, and consequently, it is critical to assess and compare the performance of the classical force fields ordinarily used in simulations of biological systems as well as to quantitatively assess the agreement with experimental data for trajectories of this order of magnitude. To that extent, we performed classical molecular dynamics simulations with CHARMM36 and AMBER-ff14sb force fields using atomic models based on the experimental structure of the KcsA channel in the open/conductive state, at conditions of ionic concentrations and membrane potentials resembling the ones adopted in experiments. In simulations using the CHARMM force field, the experimental open/conductive structure of the channel exhibited high conformational plasticity and fast collapse toward an occluded state. In contrast, in an identical set of simulations using the AMBER force field, no major deviations from the experimental structure were recorded. Force field development is a complex process in which many approximations are typically required and adopted. The results presented here provide additional motivation to further improve the existing models and, crucially, alert practitioners about limitations
Non-atomistic Simulations of Ion Channels
No full textMathematical modeling and numerical simulations are powerful tools for the analysis of the structure–function relation in ion channels. The continuous increase in the number of experimental structures of membrane proteins at high resolution has promoted the development of methods based on full atomistic descriptions of ion channels. However, the computational cost of atomistic simulations is still prohibitively high for a systematic study of conduction in ion channels. This chapter describes simplified models of conductions based on the implicit treatment of solvent molecules. In simplified models of ion channels, only a well-reasoned set of features is explicitly described. Thus, these methods are more than a mere way to increase the computational efficiency. Identifying which features are important, and how they impact on the functional properties, might offer a more profound understanding of the simulated systems. The chapter also discusses how to combine simplified models with atomistic simulations. These multi-scale models are a promising strategy to investigate the structure–function relation in complex biological molecules such as ion channels. © The Royal Society of Chemistry 2017
A computational model of gene expression in an inducible synthetic circuit
Full text linkSynthetic biology aims to the rational design of gene circuits with predictable behaviours. Great efforts have been done so far to introduce in the field mathematical models that could facilitate the design of synthetic networks. Here we present a mathematical model of a synthetic gene-circuit with a negative feedback. The closed loop configuration allows the control of transcription by an inducer molecule (IPTG). Escherichia coli bacterial cells were transformed and expression of a fluorescent reporter (GFP) was measured for different inducer levels. Computer model simulations well reproduced the experimental induction data, using a single fitting parameter. Independent genetic components were used to assemble the synthetic circuit. The mathematical model here presented could be useful to predict how changes in these genetic components affect the behaviour of the synthetic circuit
Effects of the Protonation States of the EEEE Motif of a Bacterial Na+-Channel on Conduction and Pore Structure
No full textIdentification via numerical computation of transcriptional determinants of a cell phenotype decision making
Full text linkComplex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers
From bivariate to multivariate analysis of cytometric data: overview of computational methods and their application in vaccination studies
Full text linkFlow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented
Dynamics, energetics, and selectivity of the low-K+ KcsA channel structure.
No full textPotassium channels are a diverse family of integral membrane proteins through which K(+) can pass selectively. There is ongoing debate about the nature of conformational changes associated with the opening/closing and conductive/nonconductive states of potassium channels. The channels partly exert their function by varying their conductance through a mechanism known as C-type inactivation. Shortly after the activation of K(+) channels, their selectivity filter stops conducting ions at a rate that depends on various stimuli. The molecular mechanism of C-type inactivation has not been fully understood yet. However, the X-ray structure of the KcsA channel obtained in the presence of low K(+) concentration is thought to be representative of a K(+) channel in the C-type inactivated state. Here, extensive, fully atomistic molecular dynamics and free-energy simulations of the low-K(+) KcsA structure in an explicit lipid bilayer are performed to evaluate the stability of this structure and the selectivity of its binding sites. We find that the low-K(+) KcsA structure is stable on the timescale of the molecular dynamics simulations performed, and that ions preferably remain in S1 and S4. In the absence of ions, the selectivity filter evolves toward an asymmetric architecture, as already observed in other computations of the high-K(+) structure of KcsA and KirBac. The low-K(+) KcsA structure is not permeable by Na(+), K(+), or Rb(+), and the selectivity of its binding sites is different from that of the high-K(+) structure
Selectivity and permeation of alkali metal ions in K +-channels
No full textIon conduction in K +-channels is usually described in terms of concerted movements of K + progressing in a single file through a narrow pore. Permeation is driven by an incoming ion knocking on those ions already inside the protein. A fine-tuned balance between high-affinity binding and electrostatic repulsive forces between permeant ions is needed to achieve efficient conduction. While K +-channels are known to be highly selective for K + over Na +, some K + channels conduct Na + in the absence of K +. Other ions are known to permeate K +-channels with a more moderate preference and unusual conduction features. We describe an extensive computational study on ion conduction in K +-channels rendering free energy profiles for the translocation of three different alkali ions and some of their mixtures. The free energy maps for Rb + translocation show at atomic level why experimental Rb + conductance is slightly lower than that of K +. In contrast to K + or Rb +, external Na + block K + currents, and the sites where Na + transport is hindered are characterized. Translocation of K +/Na + mixtures is energetically unfavorable owing to the absence of equally spaced ion-binding sites for Na +, excluding Na + from a channel already loaded with K +. © 2011 Elsevier Ltd. All rights reserved
On ionic conduction in potassium channels.
No full textIn ref. 1, we presented an alternative mechanism for conduc- tion of K+ in K+ channels where site vacancies are involved, and we proposed that coexistence of several ion permeation mechanisms is energetically possible. Specifically, we found that conduction can be described as a more anarchic phe- nomenon than previously habitually characterized by the con- certed translocations of K+-water-K+. This alternative pathway entails the possible presence of vacancies, with neither K+ nor water molecules in certain sites; sometimes, ions can even be found at adjacent binding sites. Moreover, we sug- gested that this mechanism is likely to be just one example among a plethora of alternative configurations and conduction pathways that ions and water may adopt during permeation, and that it can be viewed as a perturbation to the long- standing accepted mechanism involving neat, organized ion–water fluxe
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