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Molecular dynamics simulations of the villin headpiece protein (fast folding NLE-NLE mutant).

It is unclear how the known atomic structures of 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. The files 'structures_and_computations.zip' and 'structures_and_computations.tar.gz' (the same contents, different archiving formats) contain scripts and high-level data mentioned in the paper (computation of time-independent based components, specification of Markov state models, etc.). The file 'trajectories.tar' can be assembled from the provided 280 files named 'trajectories.tar.part_*' by "cat trajectories.tar.part_* > trajectories.tar". The file 'trajectories.tar' contains the molecular dynamics trajectories of NMDA receptors used in this paper. Note that 'trajectories.tar' is 2.8 Tb in size, as well as the folder generated by untarring it.

The United States is in the midst of a scientific slowdown, with private funds for research and development set to outpace federal investment for the first time since WWII. Federal funding for basic research has been stagnant for the past twenty years, while accompanying legislation has incentivized academics to leave the university and commercialize their work in industry. This coincides with a trend in corporate spending, marked by a focus on development rather than research. As a result, many fear that a persistent drought in US-based innovation is inevitable. In addition, studies show that as science becomes more specialized, research projects become more costly, and in turn scientific and technological progress become more difficult to attain. This comes during a period in which corporate profits and company valuations have reached all-time highs, and yet there is tremendous income inequality, and the public welfare is suffering in various ways. An analysis of the historically successful case of AT&T and their industrial research operation, the Bell Laboratories, illuminates the factors that make consistent innovation, corporate success, and public service possible in the private sector. Moreover, the example of Bell Laboratories displays what political, regulatory and economic conditions enable this combination of private and public interests to prosper. By placing our current challenges in their historical framework, this thesis hopes to shed light on an exit path from our current innovation crisis, and urge people to realize that many aspects of ‘how things are done’ are in fact entirely new and a departure from historical precedents.

Two types of approaches to modeling molecular systems have demonstrated high practical efficiency. Density functional theory (DFT), the most widely used quantum chemical method, is a physical approach predicting energies and electron densities of molecules. Recently, numerous papers on machine learning (ML) of molecular properties have also been published. ML models greatly outperform DFT in terms of computational costs, and may even reach comparable accuracy, but they are missing physicality - a direct link to Quantum Physics - which limits their applicability. Here, we propose an approach that combines the strong sides of DFT and ML, namely, physicality and low computational cost. We derive general equations for exact electron densities and energies that can naturally guide applications of ML in Quantum Chemistry. Based on these equations, we build a deep neural network that can compute electron densities and energies of a wide range of organic molecules not only much faster, but also closer to exact physical values than current versions of DFT. In particular, we reached a mean absolute error in energies of molecules with up to eight non-hydrogen atoms as low as 0.9 kcal/mol relative to CCSD(T) values, noticeably lower than those of DFT (approaching ~2 kcal/mol) and ML (~1.5 kcal/mol) methods. A simultaneous improvement in the accuracy of predictions of electron densities and energies suggests that the proposed approach describes the physics of molecules better than DFT functionals developed by "human learning" earlier. Thus, physics-based ML offers exciting opportunities for modeling, with high-theory-level quantum chemical accuracy, of much larger molecular systems than currently possible.

Predicting biological structure has remained challenging for systems such as disordered proteins that take on myriad conformations. Hybrid simulation/experiment strategies have been undermined by difficulties in evaluating errors from computational model inaccuracies and data uncertainties. Building on recent proposals from maximum entropy theory and nonequilibrium thermodynamics, we address these issues through a Bayesian energy landscape tilting (BELT) scheme for computing Bayesian hyperensembles over conformational ensembles. BELT uses Markov chain Monte Carlo to directly sample maximum-entropy conformational ensembles consistent with a set of input experimental observables. To test this framework, we apply BELT to model trialanine, starting from disagreeing simulations with the force fields ff96, ff99, ff99sbnmr-ildn, CHARMM27, and OPLS-AA. BELT incorporation of limited chemical shift and 3J measurements gives convergent values of the peptide’s α, β, and PPII conformational populations in all cases. As a test of predictive power, all five BELT hyperensembles recover set-aside measurements not used in the fitting and report accurate errors, even when starting from highly inaccurate simulations. BELT’s principled framework thus enables practical predictions for complex biomolecular systems from discordant simulations and sparse data.

Calmodulin is a ubiquitous Ca2+ sensor and a crucial signaling hub in many pathways aberrantly activated in disease. However, the mechanistic basis of its ability to bind diverse signaling molecules including GPCRs, ion channels, and kinases remains poorly understood. Here we harness the high resolution of molecular dynamics simulations and the analytical power of Markov state models to dissect the molecular underpinnings of calmodulin binding diversity. Our computational model indicates that in the absence of Ca2+, substates in the folded ensemble of calmodulin’s C-terminal domain present chemically and sterically distinct topologies that may facilitate conformational selection. Furthermore, we find that local unfolding is off-pathway for the exchange process relevant for peptide binding, in contrast to prior hypotheses that unfolding might account for binding diversity. Finally, our model predicts a novel binding interface that is well-populated in the Ca2+-bound regime and thus a candidate for pharmacological intervention.

Protein folding is a classic grand challenge that is relevant to numerous human diseases, such as protein misfolding diseases like Alzheimer’s disease. Solving the folding problem will ultimately require a combination of theory, simulation, and experiment, with theory and simulation providing an atomically detailed picture of both the thermodynamics and kinetics of folding and experimental tests grounding these models in reality. However, theory and simulation generally fall orders of magnitude short of biologically relevant time scales. Here we report significant progress toward closing this gap: an atomistic model of the folding of an 80-residue fragment of the λ repressor protein with explicit solvent that captures dynamics on a 10 milliseconds time scale. In addition, we provide a number of predictions that warrant further experimental investigation. For example, our model’s native state is a kinetic hub, and biexponential kinetics arises from the presence of many free-energy basins separated by barriers of different heights rather than a single low barrier along one reaction coordinate (the previously proposed incipient downhill folding scenario).

Protein folding is a fundamental process in biology, key to understanding many human diseases. Experimentally, proteins often appear to fold via simple two- or three-state mechanisms involving mainly native-state interactions, yet recent network models built from atomistic simulations of small proteins suggest the existence of many possible metastable states and folding pathways. We reconcile these two pictures in a combined experimental and simulation study of acyl-coenzyme A binding protein (ACBP), a two-state folder (folding time ~10 ms) exhibiting residual unfolded-state structure, and a putative early folding intermediate. Using single-molecule FRET in conjunction with side-chain mutagenesis, we first demonstrate that the denatured state of ACBP at near-zero denaturant is unusually compact and enriched in long-range structure that can be perturbed by discrete hydrophobic core mutations. We then employ ultrafast laminar-flow mixing experiments to study the folding kinetics of ACBP on the microsecond time scale. These studies, along with Trp-Cys quenching measurements of unfolded-state dynamics, suggest that unfolded-state structure forms on a surprisingly slow (~100 μs) time scale, and that sequence mutations strikingly perturb both time-resolved and equilibrium smFRET measurements in a similar way. A Markov state model (MSM) of the ACBP folding reaction, constructed from over 30 ms of molecular dynamics trajectory data, predicts a complex network of metastable stables, residual unfolded-state structure, and kinetics consistent with experiment but no well-defined intermediate preceding the main folding barrier. Taken together, these experimental and simulation results suggest that the previously characterized fast kinetic phase is not due to formation of a barrier-limited intermediate but rather to a more heterogeneous and slow acquisition of unfolded-state structure.

The lab use computer simulation, statistical mechanics, and Bayesian statistics to tackle challenging problems in chemical biology, biophysics, and biomedicine.

The μ Opioid Receptor (μOR) 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 μOR -- 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 μOR 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 μOR with broad applicability to GPCR biophysics and medicinal chemistry.

This dataset contains following items: 1) MD Simulation trajectories (~23000 Trajectories for a total simulation of ~450 microseconds with frames stores at 100 ps) containing only the protein atoms in lh5 format. The lh5 format files could be converted into any other MD trajectory format using MDTraj program (http://mdtraj.org). 2) 2000 State MSM model of c-src tyrosine kinase built from the MD simulation data. 3) 100 microseconds long trajectory with 20000 frames at an interval of 5 ns generated using the MSM.