Assessment of the Contribution of a Thermodynamic and Mechanical Destabilization of Myosin-Binding Protein C Domain C2 to the Pathomechanism of Hypertrophic Cardiomyopathy-Causing Double Mutation MYBPC3Δ25bp/D389V.

Mutations in the gene encoding cardiac myosin-binding protein-C (MyBPC), a thick filament assembly protein that stabilizes sarcomeric structure and regulates cardiac function, are a common cause for the development of hypertrophic cardiomyopathy. About 10% of carriers of the Δ25bp variant of MYBPC3, which is common in individuals from South Asia, are also carriers of the D389V variant on the same allele. Compared with noncarriers and those with MYBPC3Δ25bp alone, indicators for the development of hypertrophic cardiomyopathy occur with increased frequency in MYBPC3Δ25bp/D389V carriers. Residue D389 lies in the IgI-like C2 domain that is part of the N-terminal region of MyBPC. To probe the effects of mutation D389V on structure, thermostability, and protein-protein interactions, we produced and characterized wild-type and mutant constructs corresponding to the isolated 10 kDa C2 domain and a 52 kDa N-terminal fragment that includes subdomains C0 to C2. Our results show marked reductions in the melting temperatures of D389V mutant constructs. Interactions of construct C0-C2 D389V with the cardiac isoforms of myosin-2 and actin remain unchanged. Molecular dynamics simulations reveal changes in the stiffness and conformer dynamics of domain C2 caused by mutation D389V. Our results suggest a pathomechanism for the development of HCM based on the toxic buildup of misfolded protein in young MYBPC3Δ25bp/D389V carriers that is supplanted and enhanced by C-zone haploinsufficiency at older ages.

CORE-MD II: A fast, adaptive, and accurate enhanced sampling method.

In this paper, we present a fast and adaptive correlation guided enhanced sampling method (CORE-MD II). The CORE-MD II technique relies, in part, on partitioning of the entire pathway into short trajectories that we refer to as instances. The sampling within each instance is accelerated by adaptive path-dependent metadynamics simulations. The second part of this approach involves kinetic Monte Carlo (kMC) sampling between the different states that have been accessed during each instance. Through the combination of the partition of the total simulation into short non-equilibrium simulations and the kMC sampling, the CORE-MD II method is capable of sampling protein folding without any a priori definitions of reaction pathways and additional parameters. In the validation simulations, we applied the CORE-MD II on the dialanine peptide and the folding of two peptides: TrpCage and TrpZip2. In a comparison with long time equilibrium Molecular Dynamics (MD), 1 µs replica exchange MD (REMD), and CORE-MD I simulations, we find that the level of convergence of the CORE-MD II method is improved by a factor of 8.8, while the CORE-MD II method reaches acceleration factors of ∼120. In the CORE-MD II simulation of TrpZip2, we observe the formation of the native state in contrast to the REMD and the CORE-MD I simulations. The method is broadly applicable for MD simulations and is not restricted to simulations of protein folding or even biomolecules but also applicable to simulations of protein aggregation, protein signaling, or even materials science simulations.

The inhibitory effect of a coronavirus spike protein fragment with ACE2.

In this article, we investigate the binding processes of a fragment of the coronavirus spike protein receptor binding domain (RBD), the hexapeptide YKYRYL on the angiotensin-converting enzyme 2 (ACE2) receptor, and its inhibitory effect on the binding and activation of the coronavirus-2 spike protein CoV-2 RBD at ACE2. In agreement with an experimental study, we find a high affinity of the hexapeptide to the binding interface between CoV-2 RBD and ACE2, which we investigate using 20 independent equilibrium molecular dynamics (MD) simulations over a total of 1 µs and a 200-ns enhanced correlation guided MD simulation. We then evaluate the effect of the hexapeptide on the assembly process of the CoV-2 RBD to ACE2 in long-time enhanced correlation guided MD simulations. In that set of simulations, we find that CoV-2 RBD does not bind to ACE2 with the binding motif shown in experiments, but it rotates because of an electrostatic repulsion and forms a hydrophobic interface with ACE2. Surprisingly, we observe that the hexapeptide binds to CoV-2 RBD, which has the effect that this protein only weakly attaches to ACE2 so that the activation of CoV-2 RBD might be inhibited in this case. Our results indicate that the hexapeptide might be a possible treatment option that prevents the viral activation through the inhibition of the interaction between ACE2 and CoV-2 RBD.

CORE-MD, a path correlated molecular dynamics simulation method.

We present an enhanced Molecular Dynamics (MD) simulation method, which is free from the requirement of a priori structural information of the system. The technique is capable of folding proteins with very low computational effort and requires only an energy parameter. The path correlated MD (CORE-MD) method uses the autocorrelation of the path integral over the reduced action and propagates the system along the history dependent path correlation. We validate the new technique in simulations of the conformational landscapes of dialanine and the TrpCage mini-peptide. We find that the novel method accelerates the sampling by three orders of magnitude and observe convergence of the conformational sampling in both cases. We conclude that the new method is broadly applicable for the enhanced sampling in MD simulations. The CORE-MD algorithm reaches a high accuracy compared with long time equilibrium MD simulations.

Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set.

RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated data set of about 70 RNA structures of high resolution and compare different nucleotide-nucleotide contact prediction methods available in the literature. We observe only minor differences between the performances of the different methods. Moreover, we discuss how robust these predictions are for different contact definitions and how strongly they depend on procedures used to curate and align the families of homologous RNA sequences.

pydca v1.0: a comprehensive software for direct coupling analysis of RNA and protein sequences.

MOTIVATION: The ongoing advances in sequencing technologies have provided a massive increase in the availability of sequence data. This made it possible to study the patterns of correlated substitution between residues in families of homologous proteins or RNAs and to retrieve structural and stability information. Direct coupling analysis (DCA) infers coevolutionary couplings between pairs of residues indicating their spatial proximity, making such information a valuable input for subsequent structure prediction. RESULTS: Here, we present pydca, a standalone Python-based software package for the DCA of protein- and RNA-homologous families. It is based on two popular inverse statistical approaches, namely, the mean-field and the pseudo-likelihood maximization and is equipped with a series of functionalities that range from multiple sequence alignment trimming to contact map visualization. Thanks to its efficient implementation, features and user-friendly command line interface, pydca is a modular and easy-to-use tool that can be used by researchers with a wide range of backgrounds. AVAILABILITY AND IMPLEMENTATION: pydca can be obtained from https://github.com/KIT-MBS/pydca or from the Python Package Index under the MIT License. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Hybrid Hamiltonian for the Accelerated Sampling along Experimental Restraints.

In this article, we present an enhanced sampling method based on a hybrid Hamiltonian which combines experimental distance restraints with a bias dependent from multiple path-dependent variables. This simulation method determines the bias-coordinates on the fly and does not require a priori knowledge about reaction coordinates. The hybrid Hamiltonian accelerates the sampling of proteins, and, combined with experimental distance information, the technique considers the restraints adaptively and in dependency of the system's intrinsic dynamics. We validate the methodology on the dipole relaxation of two water models and the conformational landscape of dialanine. Using experimental NMR-restraint data, we explore the folding landscape of the TrpCage mini-protein and in a second example apply distance restraints from chemical crosslinking/mass spectrometry experiments for the sampling of the conformation space of the Killer Cell Lectin-like Receptor Subfamily B Member 1A (NKR-P1A). The new methodology has the potential to adaptively introduce experimental restraints without affecting the conformational space of the system along an ergodic trajectory. Since only a limited number of input- and no-order parameters are required for the setup of the simulation, the method is broadly applicable and has the potential to be combined with coarse-graining methods.

Enriched Conformational Sampling of DNA and Proteins with a Hybrid Hamiltonian Derived from the Protein Data Bank.

In this article, we present a method for the enhanced molecular dynamics simulation of protein and DNA systems called potential of mean force (PMF)-enriched sampling. The method uses partitions derived from the potentials of mean force, which we determined from DNA and protein structures in the Protein Data Bank (PDB). We define a partition function from a set of PDB-derived PMFs, which efficiently compensates for the error introduced by the assumption of a homogeneous partition function from the PDB datasets. The bias based on the PDB-derived partitions is added in the form of a hybrid Hamiltonian using a renormalization method, which adds the PMF-enriched gradient to the system depending on a linear weighting factor and the underlying force field. We validated the method using simulations of dialanine, the folding of TrpCage, and the conformational sampling of the Dickerson⁻Drew DNA dodecamer. Our results show the potential for the PMF-enriched simulation technique to enrich the conformational space of biomolecules along their order parameters, while we also observe a considerable speed increase in the sampling by factors ranging from 13.1 to 82. The novel method can effectively be combined with enhanced sampling or coarse-graining methods to enrich conformational sampling with a partition derived from the PDB.

Adaptive enhanced sampling with a path-variable for the simulation of protein folding and aggregation.

In this article, we present a novel adaptive enhanced sampling molecular dynamics (MD) method for the accelerated simulation of protein folding and aggregation. We introduce a path-variable L based on the un-biased momenta p and displacements dq for the definition of the bias s applied to the system and derive 3 algorithms: general adaptive bias MD, adaptive path-sampling, and a hybrid method which combines the first 2 methodologies. Through the analysis of the correlations between the bias and the un-biased gradient in the system, we find that the hybrid methodology leads to an improved force correlation and acceleration in the sampling of the phase space. We apply our method on SPC/E water, where we find a conservation of the average water structure. We then use our method to sample dialanine and the folding of TrpCage, where we find a good agreement with simulation data reported in the literature. Finally, we apply our methodologies on the initial stages of aggregation of a hexamer of Alzheimer's amyloid ß fragment 25-35 (Aß 25-35) and find that transitions within the hexameric aggregate are dominated by entropic barriers, while we speculate that especially the conformation entropy plays a major role in the formation of the fibril as a rate limiting factor.

An adaptive bias - hybrid MD/kMC algorithm for protein folding and aggregation.

In this paper, we present a novel hybrid Molecular Dynamics/kinetic Monte Carlo (MD/kMC) algorithm and apply it to protein folding and aggregation in explicit solvent. The new algorithm uses a dynamical definition of biases throughout the MD component of the simulation, normalized in relation to the unbiased forces. The algorithm guarantees sampling of the underlying ensemble in dependency of one average linear coupling factor 〈α〉τ. We test the validity of the kinetics in simulations of dialanine and compare dihedral transition kinetics with long-time MD-simulations. We find that for low 〈α〉τ values, kinetics are in good quantitative agreement. In folding simulations of TrpCage and TrpZip4 in explicit solvent, we also find good quantitative agreement with experimental results and prior MD/kMC simulations. Finally, we apply our algorithm to study growth of the Alzheimer Amyloid Aß 16-22 fibril by monomer addition. We observe two possible binding modes, one at the extremity of the fibril (elongation) and one on the surface of the fibril (lateral growth), on timescales ranging from ns to 8 µs.

A canonical replica exchange molecular dynamics implementation with normal pressure in each replica.

In this paper, we present a new canonical replica exchange molecular dynamics (REMD) simulation method with normal pressure for all replicas (REMD-NV(p) T). This method is suitable for systems for which conventional constant NPT-setups are difficult to implement. In this implementation, each replica has an individual volume, with normal pressure maintained for each replica in the simulation. We derive a novel exchange term and validate this method on the structural properties of SPC/E water and dialanine (Ala2) in the bulk and in the presence of a graphene layer. Compared to conventional constant NPT-REMD and NVT-REMD simulations, we find that the structural properties of our new method are in good agreement with simulations in the NPT-ensemble at all temperatures. The structural properties of the systems considered are affected by high pressures at elevated temperatures in the constant NVT-ensemble, an effect that our method corrects for. Unprojected distributions reveal that essential motions of the peptide are affected by the presence of the barostat in the NPT implementation but that the dynamical eigenmodes of the NV(p)T method are in close quantitative agreement with the NVT-ensemble.

Coarse kMC-based replica exchange algorithms for the accelerated simulation of protein folding in explicit solvent.

In this paper, we present a coarse replica exchange molecular dynamics (REMD) approach, based on kinetic Monte Carlo (kMC). The new development significantly can reduce the amount of replicas and the computational cost needed to enhance sampling in protein simulations. We introduce 2 different methods which primarily differ in the exchange scheme between the parallel ensembles. We apply this approach on folding of 2 different ß-stranded peptides: the C-terminal ß-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2). Our results agree well with data reported in the literature. In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance. The new techniques can reduce the computational cost of REMD significantly and can be used in enhanced sampling simulations of biomolecules.

A polarizable coarse-grained protein model for dissipative particle dynamics.

We present a new coarse-grained polarizable protein model for dissipative particle dynamics (DPD) method. This method allows large timesteps in particle-based systems and speeds up sampling by many orders of magnitude. Our new model is based on the electrostatic polarization of the protein backbone and a detailed representation of the sidechains in combination with a polarizable water model. We define our model parameters using the experimental structures of two proteins, TrpZip2 and TrpCage. Backmapping and subsequent short replica-exchange molecular dynamics runs verify our approach and show convergence to the experimental structures on the atomistic level. We validate our model on five different proteins: GB1, the WW-domain, the B-domain of Protein A, the peripheral binding subunit and villin headpiece.

A kMC-MD method with generalized move-sets for the simulation of folding of α-helical and ß-stranded peptides.

In Monte-Carlo simulations of protein folding, pathways and folding times depend on the appropriate choice of the Monte-Carlo move or process path. We developed a generalized set of process paths for a hybrid kinetic Monte Carlo-Molecular dynamics algorithm, which makes use of a novel constant time-update and allows formation of α-helical and ß-stranded secondary structures. We apply our new algorithm to the folding of 3 different proteins: TrpCage, GB1, and TrpZip4. All three systems are seen to fold within the range of the experimental folding times. For the ß-hairpins, we observe that loop formation is the rate-determining process followed by collapse and formation of the native core. Cluster analysis of both peptides reveals that GB1 folds with equal likelihood along a zipper or a hydrophobic collapse mechanism, while TrpZip4 follows primarily a zipper pathway. The difference observed in the folding behavior of the two proteins can be attributed to the different arrangements of their hydrophobic core, strongly packed, and dry in case of TrpZip4, and partially hydrated in the case of GB1.

How water layers on graphene affect folding and adsorption of TrpZip2.

We present a computational study of the folding of the Trp-rich ß-hairpin TrpZip2 near graphene, a surface of interest as a platform for biosensors. The protein adsorbs to the surface, populating a new bound, folded state, coexisting with extended, adsorbed conformations. Adsorption and folding are modulated by direct interactions between the indole rings of TrpZip2 and the rings on the graphene surface, as well as by indirect water-mediated interactions. In particular, we observe strong layering of water near graphene, ice-like water configurations, and the formation of short lived hydrogen-bonds between water and protein. In order to study the effect of this layering in more detail, we modified the interactions between graphene and water to obtain two extreme cases: (1) enhanced layering of water that prevents the peptide from penetrating the water layer thereby enabling it to fold to a bulk-like structure, and (2) disruption of the water layer leading to adsorption and unfolding of the protein on the surface. These studies illuminate the roles of direct and solvent mediated interactions in modulating adsorption and folding of proteins on surfaces.

A polarizable coarse-grained water model for dissipative particle dynamics.

We present a polarizable water model for the Dissipative Particle Dynamics (DPD) method. Employing long-range electrostatics and Drude oscillators, we calibrate the model using the compressibility and the dielectric constant of water. We validate the model by sampling the dielectric properties of solutions of sodium chloride at various concentrations. Additionally, we apply our model in equilibrium and electroporation simulations of a pure dipalmitoylphosphatidylcholine (DPPC) bilayer, a pure cholesterol domain and a mixed DPPC-cholesterol membrane in polarizable water. Finally, we simulate the transport of a short DNA segment through a DPPC bilayer driven by an external electric field. The new water model is suitable for the DPD simulations of systems where polarization effects play an essential role.