Protein Structure Validation Derives a Smart Conformational Search in a Physically Relevant Configurational Subspace.

Since proteins perform biological functions through their dynamic properties, molecular dynamics (MD) simulation is a sophisticated strategy for investigating their functions. Analyses of trajectories provide statistical information about a specific protein as a free-energy landscape (FEL). However, the timescale of normal MD is shorter than that of biological functions, resulting in statistically insufficient conformational sampling, finally leading to unreliable FEL calculation. To search for a broad configurational subspace, an external bias is imposed on a target protein as biased sampling. However, its regulation is challenging because the optimal strength of the perturbation is unknown. Furthermore, a physically irrelevant configurational subspace was searched when imposing an inappropriate external bias. To address this issue, we newly proposed an external biased regulation scheme known as the G-factor external bias limiter (GERBIL). In GERBIL, protein configurations generated by external bias are structurally validated by an indicator (G-factor), enabling the search for a physically relevant subspace. In addition to biased sampling, nonbiased sampling might search for a physically irrelevant configurational subspace because repeating multiple MD simulations from several initial structures tends to search for an overly broad configurational subspace. For this issue, the structural qualities of configurations generated by nonbiased sampling have not been investigated. Therefore, we confirmed whether the G-factor screened the collapsed (low-quality) configurations generated by nonbiased sampling. To address this issue, the outlier flooding method (OFLOOD) was adopted in GERBIL as a nonbiased sampling method, which is referred to as OFLOOD-GERBIL. OFLOOD rapidly expands a configurational subspace by resampling the rarely occurring states of a given protein and tends to search an overly broad subspace. Thus, we considered that GERBIL might improve the excessive conformational search of OFLOOD for a physically irrelevant configurational subspace. As a demonstration, OFLOOD and OFLOOD-GERBIL were applied to a globular protein (T4 lysozyme) and their conformational search qualities were assessed. Based on our assessment, normal OFLOOD without the outlier validation frequently sampled low-quality configurations, whereas OFLOOD-GERBIL with the outlier validation intensively sampled high-quality configurations. In conclusion, OFLOOD-GERBIL derives a smart conformational search in a physically relevant configurational subspace, indicating that protein structure validation works in both nonbiased and biased sampling methods.

Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins.

Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by their G-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a "Brake" of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open-closed transitions and successfully identify their domain motions.

Autism-associated mutation in Hevin/Sparcl1 induces endoplasmic reticulum stress through structural instability.

Hevin is a secreted extracellular matrix protein that is encoded by the SPARCL1 gene. Recent studies have shown that Hevin plays an important role in regulating synaptogenesis and synaptic plasticity. Mutations in the SPARCL1 gene increase the risk of autism spectrum disorder (ASD). However, the molecular basis of how mutations in SPARCL1 increase the risk of ASD is not been fully understood. In this study, we show that one of the SPARCL1 mutations associated with ASD impairs normal Hevin secretion. We identified Hevin mutants lacking the EF-hand motif through analyzing ASD-related mice with vulnerable spliceosome functions. Hevin deletion mutants accumulate in the endoplasmic reticulum (ER), leading to the activation of unfolded protein responses. We also found that a single amino acid substitution of Trp647 with Arg in the EF-hand motif associated with a familial case of ASD causes a similar phenotype in the EF-hand deletion mutant. Importantly, molecular dynamics (MD) simulation revealed that this single amino acid substitution triggers exposure of a hydrophobic amino acid to the surface, increasing the binding of Hevin with molecular chaperons, BIP. Taken together, these data suggest that the integrity of the EF-hand motif in Hevin is crucial for proper folding and that ASD-related mutations impair the export of Hevin from the ER. Our data provide a novel mechanism linking a point mutation in the SPARCL1 gene to the molecular and cellular characteristics involved in ASD.

Histone H3 Inhibits Ubiquitin-Ubiquitin Intermolecular Interactions to Enhance Binding to DNA Methyl Transferase 1.

DNA methyltransferase 1 (Dnmt1) is crucial for cell maintenance and preferentially methylates hemimethylated DNA. Recently, a study revealed that Dnmt1 is timely and site-specifically activated by several types of two-mono-ubiquitinated histone H3 tails (H3Ts). However, the molecular mechanism of Dnmt1 activation has not yet been determined, in addition to the role of H3T. Based on experimental data, two-mono-ubiquitinated H3Ts activate Dnmt1 by binding, with different binding affinities. In contrast, ubiquitin molecules unlinked with H3T do not bind to Dnmt1. Despite the existence of experimental data, it is unclear why the binding affinities for Dnmt1 are different. To obtain new insights into the activation mechanism of Dnmt1, we performed all-atom molecular dynamics (MD) simulations on three systems: (1) K14/K18, (2) K14/K23 mono-ubiquitinated H3Ts, and (3) two ubiquitin molecules unlinked with H3T. As an analysis of our MD trajectories, these ubiquitylation patterns modulated ubiquitin-ubiquitin intermolecular interactions. More specifically, the intermolecular contacts between a pair of ubiquitin molecules linked with H3T became weak in the presence of H3T, indicating that H3T makes a cleft between them to inhibit their intermolecular interactions. For these three systems, the intermolecular interactions between the ubiquitin molecules calculated by our MD simulations are in good agreement with the binding affinities for Dnmt1 experimentally measured in a previous study. Therefore, we conclude that H3T acts as a spacer to inhibit ubiquitin-ubiquitin intermolecular interactions, enhancing binding to Dnmt1.

Independent Nontargeted Parallel Cascade Selection Molecular Dynamics (Ino-PaCS-MD) to Enhance the Conformational Sampling of Proteins.

Biological functions are related to long-time protein dynamics (rare events) that are induced over microseconds. Such protein dynamics can be investigated using molecular dynamics (MD) simulations. However, the detection of rare events remains challenging using conventional MD (cMD) since the accessible timescales of cMD are shorter than those of the biological functions. Recently, the parallel cascade selection MD (PaCS-MD) has been proposed to detect such rare events, wherein transition paths are generated between a given reactant and product. As an extension, the nontargeted PaCS-MD (nt-PaCS-MD) has been proposed to predict the transition paths without requiring reference to any product. Thus, as a further extension, we herein propose independent nt-PaCS-MD, namely, Ino-PaCS-MD, wherein multiple walkers are launched from a set of different starting configurations. Each walker repeats a cycle of restarting short-time MD simulations from configurations with high potentials for making transitions to neighboring metastable states. To further enhance the sampling ability, Ino-PaCS-MD temporarily stops the conformational search and periodically resets the starting configurations so that they are uniformly distributed in a conformational subspace, thereby preventing a given protein from being trapped in one of the metastable states. As a demonstration, Ino-PaCS-MD successfully detects rare events of a maltose-binding protein as open-close transitions with a nanosecond-order simulation time, although a microsecond-order cMD simulation failed to detect these rare events, showing the high sampling efficiency of Ino-PaCS-MD.

Efficient Conformational Sampling of Collective Motions of Proteins with Principal Component Analysis-Based Parallel Cascade Selection Molecular Dynamics.

Molecular dynamics (MD) simulation has become a powerful tool because it provides a time series of protein dynamics at high temporal-spatial resolution. However, the accessible timescales of MD simulation are shorter than those of the biologically rare events. Generally, long-time MD simulations over microseconds are required to detect the rare events. Therefore, it is desirable to develop rare-event-sampling methods. For a rare-event-sampling method, we have developed parallel cascade selection MD (PaCS-MD). PaCS-MD generates transition pathways from a given source structure to a target structure by repeating short-time MD simulations. The key point in PaCS-MD is how to select reasonable candidates (protein configurations) with high potentials to make transitions toward the target structure. In the present study, based on principal component analysis (PCA), we propose PCA-based PaCS-MD to detect rare events of collective motions of a given protein. Here, the PCA-based PaCS-MD is composed of the following two steps. At first, as a preliminary run, PCA is performed using an MD trajectory from the target structure to define a principal coordinate (PC) subspace for describing the collective motions of interest. PCA provides principal modes as eigenvectors to project a protein configuration onto the PC subspace. Then, as a production run, all the snapshots of short-time MD simulations are ranked by inner products (IPs), where an IP is defined between a snapshot and the target structure. Then, snapshots with higher values of the IP are selected as reasonable candidates, and short-time MD simulations are independently restarted from them. By referring to the values of the IP, the PCA-based PaCS-MD repeats the short-time MD simulations from the reasonable candidates that are highly correlated with the target structure. As a demonstration, we applied the PCA-based PaCS-MD to adenylate kinase and detected its large-amplitude (open-closed) transition with a nanosecond-order computational cost.