Structural Investigation of MscL Gating Using Experimental Data and Coarse Grained MD Simulations

AbstractUbiquitylation is an eminent posttranslational modification referring to the covalent attachment of single ubiquitin molecules or polyubiquitin chains to a target protein dictating the fate of such labeled polypeptide chains. Here, we have biochemically produced artificially Lys11-, and Lys27-, and Lys63-linked ubiquitin dimers based on click-chemistry generating milligram quantities in high purity. We show that the artificial linkage used for the conjugation of two ubiquitin moieties represents a fully reliable surrogate of the natural isopeptide bond by acquiring highly resolved nuclear magnetic resonance (NMR) spectroscopic data including ligand binding studies. Extensive coarse grained and atomistic molecular dynamics (MD) simulations allow to extract structures representing the ensemble of domain-domain conformations used to verify the experimental data. Advantageously, this methodology does not require individual isotopic labeling of both ubiquitin moieties as NMR data have been acquired on the isotopically labeled proximal moiety and complementary MD simulations have been used to fully interpret the experimental data in terms of domain-domain conformation. This combined approach intertwining NMR spectroscopy with MD simulations makes it possible to describe the conformational space non-canonically Lys11-, and Lys27-linked ubiquitin dimers occupy in a solution averaged ensemble by taking atomically resolved information representing all residues in ubiquitin dimers into account.

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Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

Nature Communications ◽

10.1038/s41467-021-27241-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Stephan Thaler ◽

Julija Zavadlav

Keyword(s):

Neural Network ◽

Experimental Data ◽

Coarse Graining ◽

Md Simulations ◽

Coarse Grained ◽

Thermodynamic Perturbation Theory ◽

Top Down ◽

Bottom Up ◽

Coarse Grained Model ◽

Gradient Computation

AbstractIn molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent observables. Leveraging thermodynamic perturbation theory, we avoid exploding gradients and achieve around 2 orders of magnitude speed-up in gradient computation for top-down learning. We show effectiveness of DiffTRe in learning NN potentials for an atomistic model of diamond and a coarse-grained model of water based on diverse experimental observables including thermodynamic, structural and mechanical properties. Importantly, DiffTRe also generalizes bottom-up structural coarse-graining methods such as iterative Boltzmann inversion to arbitrary potentials. The presented method constitutes an important milestone towards enriching NN potentials with experimental data, particularly when accurate bottom-up data is unavailable.

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Molecular dynamics simulations and CD spectroscopy reveal hydration-induced unfolding of the intrinsically disordered LEA proteins COR15A and COR15B from Arabidopsis thaliana

Physical Chemistry Chemical Physics ◽

10.1039/c6cp02272c ◽

2016 ◽

Vol 18 (37) ◽

pp. 25806-25816 ◽

Cited By ~ 13

Author(s):

Carlos Navarro-Retamal ◽

Anne Bremer ◽

Jans Alzate-Morales ◽

Julio Caballero ◽

Dirk K. Hincha ◽

...

Keyword(s):

Experimental Data ◽

Molecular Dynamics ◽

Arabidopsis Thaliana ◽

Molecular Dynamics Simulations ◽

Md Simulations ◽

Cd Spectroscopy ◽

Lea Proteins ◽

Intrinsically Disordered ◽

Dynamics Simulations ◽

Crowded Environment

Unfolding of intrinsically unstructured full-length LEA proteins in a differentially crowded environment can be modeled by 30 ns MD simulations in accordance with experimental data.

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Molecular Dynamics Scoring of Protein–Peptide Models Derived from Coarse-Grained Docking

Molecules ◽

10.3390/molecules26113293 ◽

2021 ◽

Vol 26 (11) ◽

pp. 3293

Author(s):

Mateusz Zalewski ◽

Sebastian Kmiecik ◽

Michał Koliński

Keyword(s):

Molecular Dynamics ◽

Geometry Optimization ◽

Computational Prediction ◽

Md Simulations ◽

Coarse Grained ◽

Ligand Interaction ◽

Vast Number ◽

Docking Simulations ◽

Peptide Models ◽

Peptide Complexes

One of the major challenges in the computational prediction of protein–peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein–peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor–ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein–peptide complexes show that the proposed MD-based scoring approach can be used to identify protein–peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.

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Dynamical Behavior and Conformational Selection Mechanism of the Intrinsically Disordered Sic1 Kinase-Inhibitor Domain

Life ◽

10.3390/life10070110 ◽

2020 ◽

Vol 10 (7) ◽

pp. 110 ◽

Cited By ~ 1

Author(s):

Davide Sala ◽

Ugo Cosentino ◽

Anna Ranaudo ◽

Claudio Greco ◽

Giorgio Moro

Keyword(s):

Kinase Inhibitor ◽

Dynamical Behavior ◽

Md Simulations ◽

Molecular Shape ◽

Conformational Space ◽

Coarse Grained ◽

Conformational Ensemble ◽

Selection Mechanism ◽

Conformational Selection ◽

Intrinsically Disordered

Intrinsically Disordered Peptides and Proteins (IDPs) in solution can span a broad range of conformations that often are hard to characterize by both experimental and computational methods. However, obtaining a significant representation of the conformational space is important to understand mechanisms underlying protein functions such as partner recognition. In this work, we investigated the behavior of the Sic1 Kinase-Inhibitor Domain (KID) in solution by Molecular Dynamics (MD) simulations. Our results point out that application of common descriptors of molecular shape such as Solvent Accessible Surface (SAS) area can lead to misleading outcomes. Instead, more appropriate molecular descriptors can be used to define 3D structures. In particular, we exploited Weighted Holistic Invariant Molecular (WHIM) descriptors to get a coarse-grained but accurate definition of the variegated Sic1 KID conformational ensemble. We found that Sic1 is able to form a variable amount of folded structures even in absence of partners. Among them, there were some conformations very close to the structure that Sic1 is supposed to assume in the binding with its physiological complexes. Therefore, our results support the hypothesis that this protein relies on the conformational selection mechanism to recognize the correct molecular partners.

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Biophysical characterization of the SARS-CoV-2 E protein

10.1101/2021.05.28.446179 ◽

2021 ◽

Author(s):

Aujan Mehregan ◽

Sergio Perez-Conesa ◽

Yuxuan Zhuang ◽

Ahmad Elbahnsi ◽

Diletta Pasini ◽

...

Keyword(s):

Recombinant Expression ◽

Structural Data ◽

Md Simulations ◽

Full Length ◽

Coarse Grained ◽

Biophysical Characterization ◽

Viral Budding ◽

Nmr Structures ◽

E Protein

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic which continues to wreak havoc across the world, over a year and a half after its effects were first reported in the general media. Current fundamental research efforts largely focus on the SARS-CoV-2 Spike protein. Since successful antiviral therapies are likely to target multiple viral components, there is considerable interest in understanding the biophysical role of its other proteins, in particular structural membrane proteins. Here, we have focused our efforts on the characterization of the full-length E protein from SARS-CoV-2, combining experimental and computational approaches. Recombinant expression of the full-length E protein from SARS-CoV-2 reveals that this membrane protein is capable of independent multimerization, possibly as a tetrameric or smaller species. Fluorescence microscopy shows that the protein localizes intracellularly, and coarse-grained MD simulations indicate it causes bending of the surrounding lipid bilayer, corroborating a potential role for the E protein in viral budding. Although we did not find robust electrophysiological evidence of ion-channel activity, cells transfected with the E protein exhibited reduced intracellular Ca2+, which may further promote viral replication. However, our atomistic MD simulations revealed that previous NMR structures are relatively unstable, and result in models incapable of ion conduction. Our study highlights the importance of using high-resolution structural data obtained from a full-length protein to gain detailed molecular insights, and eventually permitting virtual drug screening.

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Residue-Level Contact Reveals Modular Domain Interactions of PICK1 Are Driven by Both Electrostatic and Hydrophobic Forces

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.616135 ◽

2021 ◽

Vol 7 ◽

Author(s):

Amy O. Stevens ◽

Yi He

Keyword(s):

Hydrophobic Interactions ◽

Pdz Domain ◽

Md Simulations ◽

Intramolecular Interactions ◽

Coarse Grained ◽

Scaffolding Protein ◽

Concave Surface ◽

Stable Complex ◽

Bar Domain ◽

C Terminus

PICK1 is a multi-domain scaffolding protein that is uniquely comprised of both a PDZ domain and a BAR domain. While previous experiments have shown that the PDZ domain and the linker positively regulate the BAR domain and the C-terminus negatively regulates the BAR domain, the details of internal regulation mechanisms are unknown. Molecular dynamics (MD) simulations have been proven to be a useful tool in revealing the intramolecular interactions at atomic-level resolution. PICK1 performs its biological functions in a dimeric form which is extremely computationally demanding to simulate with an all-atom force field. Here, we use coarse-grained MD simulations to expose the key residues and driving forces in the internal regulations of PICK1. While the PDZ and BAR domains do not form a stable complex, our simulations show the PDZ domain preferentially interacting with the concave surface of the BAR domain over other BAR domain regions. Furthermore, our simulations show that the short helix in the linker region can form interactions with the PDZ domain. Our results reveal that the surface of the βB-βC loop, βC strand, and αA-βD loop of the PDZ domain can form a group of hydrophobic interactions surrounding the linker helix. These interactions are driven by hydrophobic forces. In contrast, our simulations reveal a very dynamic C-terminus that most often resides on the convex surface of the BAR domain rather than the previously suspected concave surface. These interactions are driven by a combination of electrostatic and hydrophobic interactions.

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Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

10.1101/344226 ◽

2018 ◽

Author(s):

Jukka Intosalmi ◽

Adrian C. Scott ◽

Michelle Hays ◽

Nicholas Flann ◽

Olli Yli-Harja ◽

...

Keyword(s):

Experimental Data ◽

Time Course ◽

Model Development ◽

Growth Dynamics ◽

Colony Growth ◽

Data Driven ◽

Coarse Grained ◽

Intercellular Signaling ◽

Growth Data ◽

Metabolic Coupling

AbstractMotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell–cell and metabolic coupling produce functionally optimized structures is still limited.ResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.AvailabilityExperimental data and a computational implementation to reproduce the results are available athttp://research.cs.aalto.fi/csb/software/multiscale/[email protected],[email protected]

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A hierarchical Bayesian framework for force field selection in molecular dynamics simulations

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2015.0032 ◽

2016 ◽

Vol 374 (2060) ◽

pp. 20150032 ◽

Cited By ~ 20

Author(s):

S. Wu ◽

P. Angelikopoulos ◽

C. Papadimitriou ◽

R. Moser ◽

P. Koumoutsakos

Keyword(s):

Experimental Data ◽

Molecular Dynamics ◽

Force Field ◽

Computational Cost ◽

Md Simulations ◽

Bayesian Framework ◽

Underlying Structure ◽

Hierarchical Bayesian ◽

Wide Range ◽

Selection Of

We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different environmental conditions with the corresponding variability in experimental data. The high computational cost associated with the hierarchical Bayesian framework is reduced by orders of magnitude through a parallelized Transitional Markov Chain Monte Carlo method combined with the Laplace Asymptotic Approximation. The suitability of the hierarchical approach is demonstrated by performing MD simulations with prescribed parameters to obtain data for transport coefficients under different conditions, which are then used to infer and evaluate the parameters of the MD model. We demonstrate the selection of MD models based on experimental data and verify that the hierarchical model can accurately quantify the uncertainty across experiments; improve the posterior probability density function estimation of the parameters, thus, improve predictions on future experiments; identify the most plausible force field to describe the underlying structure of a given dataset. The framework and associated software are applicable to a wide range of nanoscale simulations associated with experimental data with a hierarchical structure.

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