Biology subjects

Pande, V. S.

Publications and source records attributed to Pande, V. S..

12 recordsLinked to original sources

bioRxiv2018

Modelling Intrinsically Disordered Protein Dynamics as Networks of Transient Secondary Structure

Describing the dynamics and conformational landscapes of Intrinsically Disordered Proteins (IDPs) is of paramount importance to understanding their functions. Markov State Models (MSMs) are often used to characterize the dynamics of more structured proteins, but models of IDPs built using conventional MSM modelling protocols can be difficult to interpret due to the inherent nature of IDPs, which exhibit fast transitions between disordered microstates. We propose a new method of determining MSM states from all-atom molecular dynamics simulation data of IDPs by using per-residue secondary structure assignments as input features in a MSM model. Because such secondary structure algorithms use a select set of features for assignment (dihedral angles, contact distances, etc.), they represent a knowledge-based refinement of feature sets used for model-building. This method adds interpretability to IDP conformational landscapes, which are increasingly viewed as composed of transient secondary structure, and allows us to readily use MSM analysis tools in this paradigm. We demonstrate the use of our method with the transcription factor p53 c-terminal domain (p53-CTD), a commonly-studied IDP. We are able to characterize the full secondary structure phase space observed for p53-CTD, and describe characteristics of p53-CTD as a network of transient helical and beta-hairpin structures with different network behaviors in different domains of secondary structure. This analysis provides a novel example of how IDPs can be studied and how researchers might better understand a disordered protein conformational landscape.

biophysics↗

bioRxiv2018

Bayesian analysis of isothermal titration calorimetry for binding thermodynamics

Isothermal titration calorimetry (ITC) is the only technique able to determine both the enthalpy and entropy of noncovalent association in a single experiment. The standard data analysis method based on nonlinear regression, however, provides unrealistically small uncertainty estimates due to its neglect of dominant sources of error. Here, we present a Bayesian framework for sampling from the posterior distribution of all thermodynamic parameters and other quantities of interest from one or more ITC experiments, allowing uncertainties and correlations to be quantitatively assessed. For a series of ITC measurements on metal:chelator and protein:ligand systems, the Bayesian approach yields uncertainties which represent the variability from experiment to experiment more accurately than the standard data analysis. In some datasets, the median enthalpy of binding is shifted by as much as 1.5 kcal/mol. A Python implementation suitable for analysis of data generated by MicroCal instruments (and adaptable to other calorimeters) is freely available online.

biophysics↗

bioRxiv2017

Dynamical model of the CLC-2 ion channel exhibits a two-step gating mechanism

This work reports a dynamical Markov state model of CLC-2 "fast" (pore) gating, based on 600 microseconds of molecular dynamics (MD) simulation. In the starting conformation of our CLC-2 model, both outer and inner channel gates are closed. The first conformational change in our dataset involves rotation of the inner-gate backbone along residues S168-G169-I170. This change is strikingly similar to that observed in the cryo-EM structure of the bovine CLC-K channel, though the volume of the intracellular (inner) region of the ion conduction pathway is further expanded in our model. From this state (inner gate open and outer gate closed), two additional states are observed, each involving a unique rotameric flip of the outer-gate residue GLUex. Both additional states involve conformational changes that orient GLUex away from the extracellular (outer) region of the ion conduction pathway. In the first additional state, the rotameric flip of GLUex results in an open, or near-open, channel pore. The equilibrium population of this state is low ([~]1%), consistent with the low open probability of CLC-2 observed experimentally in the absence of a membrane potential stimulus (0 mV). In the second additional state, GLUex rotates to occlude the channel pore. This state, which has a low equilibrium population ([~]1%), is only accessible when GLUex is protonated. Together, these pathways model the opening of both an inner and outer gate within the CLC-2 selectivity filter, as a function of GLUex protonation. Collectively, our findings are consistent with published experimental analyses of CLC-2 gating and provide a high-resolution structural model to guide future investigations. Author summaryIn the brain, the roles and mechanisms of sodium-, potassium-, and calcium-selective ion channels are well established. In contrast, chloride-selective channels have been studied much less and are not sufficiently understood, despite known associations of chloride-channel defects with brain disorders. The most broadly expressed voltage-activated chloride channel in the brain is CLC-2 (one of 9 human CLC homologs). In this work, we use simulations to model the conformational dynamics of the CLC-2 chloride ion channel selectivity filter (SF), which is the part of the protein that controls whether the channel is in an ion-conducting or non-conducting state. Our analysis identifies four primary conformational states and a specific progression through these states. Our results are consistent with structural and functional data in the literature and provide a high-resolution model for guiding further studies of CLC-2. These results will inform our understanding of how CLC-2 governs electrical activity and ion homeostasis in the brain.

biophysics↗

bioRxiv2017

Computer Simulations Predict High Structural Heterogeneity of Functional State of NMDA Receptors

It is unclear how the known atomic structures of neuronal NMDA receptors (NMDARs) relate to the functional states of NMDARs inferred from electrophysiological recordings. We address this problem by all-atom computer simulations, a method successfully applied in the past to much smaller biomolecules. Our simulations predict that four non-active cryoEM structures of NMDARs rapidly interconvert on submicrosecond timescales, and therefore, correspond to the same functional state of the receptor.

neuroscience↗

bioRxiv2017

Kinetic Machine Learning Unravels Ligand-Directed Conformational Change of μ Opioid Receptor

The Opioid Receptor (OR) is a G-Protein Coupled Receptor (GPCR) that mediates pain and is a key target for clinically administered analgesics. The current generation of prescribed opiates - drugs that bind to OR - engender dangerous side effects such as respiratory depression and addiction in part by stabilizing off-target conformations of the receptor. To determine both the key conformations of OR to atomic resolution as well as the transitions between them, long timescale molecular dynamics (MD) simulations were conducted and analyzed. These simulations predict new and potentially druggable metastable states that have not been observed by crystallography. We applied cutting edge algorithms (e.g., tICA and Transfer Entropy) to guide our analysis and distill the key events and conformations from simulation, presenting a transferrable and systematic analysis scheme. Our approach provides a complete, predictive model of the dynamics, structure of states, and structure-ligand relationships of OR with broad applicability to GPCR biophysics and medicinal chemistry.

biophysics↗

bioRxiv2017

Modeling the Mechanism of CLN025Beta-Hairpin Formation

Beta-hairpins are a substructure found in proteins that can lend insight into more complex systems. Furthermore, the folding of beta-hairpins is a valuable test case for benchmarking experimental and theoretical methods. Here, we simulate the folding of CLN025, a miniprotein with a beta-hairpin structure, at its experimental melting temperature using a range of state-of-the-art protein force fields. We construct Markov state models in order to examine the thermodynamics, kinetics, mechanism, and rate-determining step of folding. Mechanistically, we find the folding process is rate-limited by the formation of the turn region hydrogen bonds, which occurs following the downhill hydrophobic collapse of the extended denatured protein. These results are presented in the context of established and contradictory theories of the beta-hairpin folding process. Furthermore, our analysis suggests that the AMBER-FB15 force field, at this temperature, best describes the characteristics of the full experimental CLN025 conformational ensemble, while the AMBER ff99SB-ILDN and CHARMM22* force fields display a tendency to overstabilize the native state.

biophysics↗

bioRxiv2017

Millisecond Dynamics Of BTK Reveal Kinome-Wide Conformational Plasticity Within The Apo Kinase Domain

Bruton tyrosine kinase (BTK) is a key enzyme in B-cell development whose improper regulation causes severe immunodeficiency diseases. Design of selective BTK therapeutics would benefit from improved, in-silico structural modeling of the kinases solution ensemble. However, this remains challenging due to the immense computational cost of sampling events on biological timescales. In this work, we combine multi-millisecond molecular dynamics (MD) simulations with Markov state models (MSMs) to report on the thermodynamics, kinetics, and accessible states of BTKs kinase domain. Our conformational landscape links the active state to several inactive states, connected via a structurally diverse intermediate. Our calculations predict a kinome-wide conformational plasticity, and indicate the presence of several new potentially druggable BTK states. We further find that the population of these states and the kinetics of their inter-conversion are modulated by protonation of an aspartate residue, establishing the power of MD & MSMs in predicting effects of chemical perturbations.

biophysics↗

bioRxiv2017

Landmark Kernel tICA For Conformational Dynamics

Molecular dynamics simulations of biomolecules produce a very high dimensional time-series dataset. Performing analysis necessarily involves projection onto a lower dimensional space. A priori selection of projection coordinates requires (perhaps unavailable) prior information or intuition about the system. At best, such a projection can only confirm the intuition. At worst, a poor projection can obscure new features of the system absent from the intuition. Previous statistical methods such a time-structure based independent component analysis (tICA) and Markov state modeling (MSMs) have offered relatively unbiased means of projecting conformations onto coordinates or state labels, respectively. These analyses are underpinned by the propagator formalism and the assumption that slow dynamics are biologically interesting. Although arising from the same mathematics, tICA and MSMs have different strengths and weaknesses. We introduce a unifying method which we term \"landmark kernel tICA\" (lktICA) which uses a variant of the Nystrom kernel approximation to permit approximate non-linear solutions to the tICA problem. We show that lktICA is equivalent to MSMs with \"soft\" states. We demonstrate the advantages of this united method by finding improved projections of (a) a 1D potential surface (b) a peptide folding trajectory and (c) an ion channel conformational change.

biophysics↗

bioRxiv2016

Markov modeling reveals novel intracellular modulation of the human TREK-2 selectivity filter

Two-pore domain potassium (K2P) channel ion conductance is regulated by diverse stimuli that directly or indirectly gate the channel selectivity filter (SF). Recent crystal structures for the TREK-2 member of the K2P family reveal distinct \"up\" and \"down\" states assumed during activation via mechanical stretch. We performed 195 ps of all-atom, unbiased molecular dynamics simulations of the TREK-2 channel to probe how membrane stretch regulates the SF gate. Markov modeling reveals a novel \"pinched\" SF configuration that stretch activation rapidly destabilizes. Free-energy barrier heights calculated for critical steps in the conduction pathway indicate that this pinched state impairs ion conduction. Our simulations predict that this low-conductance state is accessed exclusively in the compressed, \"down\" conformation in which the intracellular helix arrangement allosterically pinches the SF. By explicitly relating structure to function, we contribute a critical piece of understanding to the evolving K2P puzzle.

biophysics↗

bioRxiv2016

OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics

OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.

biophysics↗

bioRxiv2016

MSMBuilder: Statistical Models for Biomolecular Dynamics

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov State Models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models (HMMs) and time-structure based independent component analysis (tICA). MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python API (application programming interface). MSMBuilder is developed with careful consideration for compatibility with the broader machine-learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics but is just as applicable to other computational or experimental time-series measurements. http://msmbuilder.org

biophysics↗

bioRxiv2016

Energy Conservation as a Measure of Simulation Accuracy

Energy conservation is widely used as a measure of accuracy for molecular simulations. When reporting rates of energy drift, researchers usually assume it is linear in the simulation length, temperature, and system size. We study these assumptions and find that all three are incorrect. Energy drift is too complicated to characterize with a single number, and a more sophisticated analysis is needed to identify the effects of systematic versus random drift, and of integration error versus numerical error. We further argue that energy conservation is not a reliable measure of accuracy. Having small overall drift on long time scales is not a sufficient condition, and in some cases not a necessary condition, for a simulation to produce meaningful results.

biophysics↗