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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.

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.

A contractile ring (CR) is involved in cytokinesis in animal and yeast cells. Although several types of actin-bundling proteins associate with F-actin in the CR, their individual roles in the CR have not yet been elucidated in detail. Ain1 is the sole α-actinin homologue in the fission yeast Schizosaccharomyces pombe and specifically localizes to the CR with a high turnover rate. S. pombe cells lacking the ain1+ gene show defects in cytokinesis under stress conditions. We herein investigated the biochemical activity and cellular localization mechanisms of Ain1. Ain1 showed weaker affinity to F-actin in vitro than other actin-bundling proteins in S. pombe. We identified a mutation that presumably loosened the interaction between two calponin-homology domains constituting the single actin-binding domain (ABD) of Ain1, which strengthened the actin-binding activity of Ain1. This mutant protein induced a deformation in the ring shape of the CR. Neither a truncated protein consisting only of an N-terminal ABD nor a truncated protein lacking a C-terminal region containing an EF-hand motif localized to the CR, whereas the latter was involved in the bundling of F-actin in vitro. We herein propose detailed mechanisms for how each part of the molecule is involved in the proper cellular localization and function of Ain1.

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.

The free-energy profile of a compound is an essential measurement in evaluating the membrane permeation process by means of theoretical methods. Computationally, molecular dynamics (MD) simulation allows the free-energy profile calculation. However, MD simulations frequently fail to sample membrane permeation because they are rare events induced in longer timescales than the accessible timescale of MD, leading to an insufficient conformational search to calculate an incorrect free-energy profile. To achieve a sufficient conformational search, several enhanced sampling methods have been developed and elucidated the membrane permeation process. In addition to these enhanced sampling methods, we proposed a simple yet powerful freeenergy calculation of a compound for the membrane permeation process based on originally rare-event sampling methods developed by us. Our methods have a weak dependency on external biases and their optimizations to promote the membrane permeation process. Based on distributed computing, our methods only require the selection of initial structures and their conformational resampling, whereas the enhanced sampling methods may be required to adjust external biases. Furthermore, our methods efficiently search membrane permeation processes with simple scripts without modifying any MD program. As demonstrations, we calculated the free-energy profiles of seven linear compounds for their membrane permeation based on a hybrid conformational search using two rare-event sampling methods, that is, (1) parallel cascade selection MD (PaCS-MD) and (2) outlier flooding method (OFLOOD), combined with a Markov state model (MSM) construction. In the first step, PaCS-MD generated initial membrane permeation paths of a compound. In the second step, OFLOOD expanded the unsearched conformational area around the initial paths, allowing for a broad conformational search. Finally, the trajectories were employed to construct reliable MSMs, enabling correct free-energy profile calculations. Furthermore, we estimated the membrane permeability coefficients of all compounds by constructing the reliable MSMs for their membrane permeation. In conclusion, the calculated coefficients were qualitatively correlated with the experimental measurements (correlation coefficient (R 2 ) = 0.8689), indicating that the hybrid conformational search successfully calculated the free-energy profiles and membrane permeability coefficients of the seven compounds.

In the fission yeast Schizosaccharomyces pombe, α-actinin Ain1 bundles Factin into the contractile ring (CR) in the middle of the cell. Previous studies have proposed that a conformational change of the actin-binding domain (ABD) of Ain1 enhances the actin-binding activity. However, the molecular mechanism of the conformational change remains to be unveiled at an atomic resolution due to the difficulties of experimental techniques to observe them. In the present study, we performed a set of microsecond-order molecular dynamics (MD) simulations for ABD of Ain1. Our MD simulations for a pathogenic point mutation (R216E) in ABD did not result in large domain motions as previously expected. However, local motions of the loop regions were detected. Besides the three conventional actin-binding sites, we found characteristic electrostatic interactions with the N-terminal of actin. The mutagenesis experiment in fission yeast showed that collapses of the electrostatic interactions at the binding site abolished the proper localization of Ain1 to the CR. Furthermore, the MD simulation of F-actin with the Ain1 ABD R216E indicated that the stronger affinity is caused by a direct interaction of the point mutation. Our findings might be applicable to other highly conserved ABP family proteins to explain their binding affinities.