Pathological mutations promote proteolysis of mitochondrial tRNA-specific 2-thiouridylase 1 (MTU1) via mitochondrial caseinolytic peptidase (CLPP).

MTU1 controls intramitochondrial protein synthesis by catalyzing the 2-thiouridine modification of mitochondrial transfer RNAs (mt-tRNAs). Missense mutations in the MTU1 gene are associated with life-threatening reversible infantile hepatic failure. However, the molecular pathogenesis is not well understood. Here, we investigated 17 mutations associated with this disease, and our results showed that most disease-related mutations are partial loss-of-function mutations, with three mutations being particularly severe. Mutant MTU1 is rapidly degraded by mitochondrial caseinolytic peptidase (CLPP) through a direct interaction with its chaperone protein CLPX. Notably, knockdown of CLPP significantly increased mutant MTU1 protein expression and mt-tRNA 2-thiolation, suggesting that accelerated proteolysis of mutant MTU1 plays a role in disease pathogenesis. In addition, molecular dynamics simulations demonstrated that disease-associated mutations may lead to abnormal intermolecular interactions, thereby impairing MTU1 enzyme activity. Finally, clinical data analysis underscores a significant correlation between patient prognosis and residual 2-thiolation levels, which is partially consistent with the AlphaMissense predictions. These findings provide a comprehensive understanding of MTU1-related diseases, offering prospects for modification-based diagnostics and novel therapeutic strategies centered on targeting CLPP.

Machine learning-based QSAR and LB-PaCS-MD guided design of SARS-CoV-2 main protease inhibitors.

The global outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 virus had led to profound respiratory health implications. This study focused on designing organoselenium-based inhibitors targeting the SARS-CoV-2 main protease (Mpro). The ligand-binding pathway sampling method based on parallel cascade selection molecular dynamics (LB-PaCS-MD) simulations was employed to elucidate plausible paths and conformations of ebselen, a synthetic organoselenium drug, within the Mpro catalytic site. Ebselen effectively engaged the active site, adopting proximity to H41 and interacting through the benzoisoselenazole ring in a π-π T-shaped arrangement, with an additional π-sulfur interaction with C145. In addition, the ligand-based drug design using the QSAR with GFA-MLR, RF, and ANN models were employed for biological activity prediction. The QSAR-ANN model showed robust statistical performance, with an r2training exceeding 0.98 and an RMSEtest of 0.21, indicating its suitability for predicting biological activities. Integration the ANN model with the LB-PaCS-MD insights enabled the rational design of novel compounds anchored in the ebselen core structure, identifying promising candidates with favorable predicted IC50 values. The designed compounds exhibited suitable drug-like characteristics and adopted an active conformation similar to ebselen, inhibiting Mpro function. These findings represent a synergistic approach merging ligand and structure-based drug design; with the potential to guide experimental synthesis and enzyme assay testing.

Free-Energy Profiles for Membrane Permeation of Compounds Calculated Using Rare-Event Sampling Methods.

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 free-energy 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 (R2) = 0.8689), indicating that the hybrid conformational search successfully calculated the free-energy profiles and membrane permeability coefficients of the seven compounds.

Efficient screening of protein-ligand complexes in lipid bilayers using LoCoMock score.

Membrane proteins are attractive targets for drug discovery due to their crucial roles in various biological processes. Studying the binding poses of amphipathic molecules to membrane proteins is essential for understanding the functions of membrane proteins and docking simulations can facilitate the screening of protein-ligand complexes at low computational costs. However, identifying docking poses for a ligand in non-aqueous environments such as lipid bilayers can be challenging. To address this issue, we propose a new docking score called logP-corrected membrane docking (LoCoMock) score. To screen putative protein-ligand complexes embedded in a membrane, the LoCoMock score considers the affinity between a target ligand and the membrane. It combines the docking score of the protein-ligand complex with the logP of the target ligand. In demonstrations using several model ligands, the LoCoMock score screened more putative complexes than the conventional docking score. As extended docking, the LoCoMock score makes it possible to screen membrane proteins more effectively as drug targets than the conventional docking.

The role of ATP in solubilizing RNA-binding protein fused in sarcoma.

Intrinsically disordered protein (IDP) plays an important role in liquid-liquid phase separation (LLPS). RNA-binding protein fused in sarcoma (FUS) is a well-studied IDP that induces LLPS since its low-complexity core region (FUS-LC-core) is essential for droplet formation through contacts between FUS-LC-cores. Several experimental studies have reported that adenosine triphosphate (ATP) concentrations modulate LLPS-driven droplet formation through the dissolution of FUS. To elucidate the role of ATP in this dissolution, microsecond-order all-atom molecular dynamics (MD) simulations were performed for a crowded system of FUS-LC-cores in the presence of multiple ATP molecules. Our analysis revealed that the adenine group of ATP frequently contacted the FUS-LC-core, and the phosphoric acid group of ATP was exposed to the external solvent, which promoted both hydration and solubilization of FUS.

Phosphorylation in the accessory domain of yeast histone chaperone protein 1 exposes the nuclear export signal sequence.

Histone chaperone proteins assist in the formation of the histone octamers, the scaffold proteins that facilitate the packing of DNA into nucleosomes in the cell nucleus. One such histone chaperone protein is yeast nucleosome assembly protein 1 (yNap1), the crystal structure of which has been determined and found to have a nuclear export signal (NES) sequence within its long α-helix. Experimental evidence obtained from mutagenesis studies of the budding yeast suggests that the NES is necessary for the transport of yNap1 from the cell nucleus to the cytosol. However, the NES sequence is masked by an accessory domain, the exact role of which has not yet been elucidated, especially in nucleocytoplasmic transport. To clarify the role of the accessory domain, we focused on its phosphorylation, because proteomic experiments have identified multiple phosphorylation sites on yNap1. To study this phenomenon computationally, all-atom molecular dynamics simulations of the non-phosphorylated yNap1 (Nap1-nonP) and phosphorylated yNap1 (Nap1-P) systems were performed. Specifically, we addressed how the NES sequence is exposed to the protein surface by measuring its solvent-accessible surface area (SASA). It was found that the median of the SASA distribution of Nap1-P was greater than that of Nap1-nonP, indicating that phosphorylation in the accessory domain exposes the NES, resulting in its increased accessibility. In conclusion, yNap1 might modulate the accessibility of the NES by dislocating the accessory domain through its phosphorylation.

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.

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.

Comprehensive predictions of secondary structures for comparative analysis in different species.

Protein structures are directly linked to biological functions. However, there is a gap of knowledge between the decoded genome and the structure. To bridge the gap, we focused on the secondary structure (SS). From a comprehensive analysis of predicted SS of proteins in different types of organisms, we have arrived at the following findings: The proportions of SS in genomes were different among phylogenic domains. The distributions of strand lengths indicated structural limitations in all of the species. Different from bacteria and archaea, eukaryotes have an abundance of α-helical and random coil proteins. Interestingly, there was a relationship between SS and post-translational modifications. By calculating hydrophobicity moments of helices and strands, highly amphipathic fragments of SS were found, which might be related to the biological functions. In conclusion, comprehensive predictions of SS will provide valuable perspectives to understand the entire protein structures in genomes and will help one to discover or design functional proteins.

In silico mutational analyses reveal different ligand-binding abilities of double pockets of medaka fish taste receptor type 1 essential for efficient taste recognition.

Taste receptors are important sensors for the detection of nutrient concentrations in animals. Tastes are recognized by interactions between chemical substances and taste receptors. Recently, the high-resolution X-ray crystal structure of the extracellular ligand-binding domains (LBDs) of medaka fish (Oryzias latipes) taste receptor type 1 (T1r) complexed with ligands (amino acids) was determined. Medaka fish T1r is a heterodimer composed of two different LBDs, T1r2aLBD and T1r3LBD. In this study, we performed all-atom molecular dynamics (MD) simulations on this heterodimer (T1r2aLBD-T1r3LBD) to address mutational effects on key residues near each ligand-binding pocket in recognizing one of the ligands (L-Gln). For T1r2aLBD, Ser165 is important in ligand recognition due to its direct hydrogen bonding with the ligand. After mutating Ser165 to Ile or Ala, the direct hydrogen bonds between the ligand and the binding pocket were weakened, which destabilized the ligand-binding form of T1r2aLBD. For T1r3LBD, Ser300 is important in ligand recognition. The water-mediated hydrogen bond with the side-chain hydroxyl group of Ser300 is a single interaction that maintains the ligand-binding form of T1r3LBD. After mutating Ser300 to Glu or Ala, both mutant systems almost maintained their ligand-binding form. As a mechanism for maintaining the binding form of T1r3LBD, alternative hydrogen bonds were formed as direct interactions instead of the indirect water-mediated interactions found in the wild-type system, which stabilized the binding form of T1r3LBD. Judging from our in silico mutational analyses, T1r2aLBD was structurally destabilized by the amino acid mutations. Therefore, it might be required that the ligand-binding pocket of T1r2aLBD is composed of a set of specific residues to maintain its ligand-binding form. On the contrary, T1r3LBD was robust enough to withstand the amino acid mutations. These different ligand-binding abilities of both LBDs provide multiple binding modes, which might be helpful for discriminating various taste substances or detecting concentrations of nutrients efficiently.

Residue Folding Degree-Relationship to Secondary Structure Categories and Use as Collective Variable.

Recently, we have shown that the residue folding degree, a network-based measure of folded content in proteins, is able to capture backbone conformational transitions related to the formation of secondary structures in molecular dynamics (MD) simulations. In this work, we focus primarily on developing a collective variable (CV) for MD based on this residue-bound parameter to be able to trace the evolution of secondary structure in segments of the protein. We show that this CV can do just that and that the related energy profiles (potentials of mean force, PMF) and transition barriers are comparable to those found by others for particular events in the folding process of the model mini protein Trp-cage. Hence, we conclude that the relative segment folding degree (the newly proposed CV) is a computationally viable option to gain insight into the formation of secondary structures in protein dynamics. We also show that this CV can be directly used as a measure of the amount of α-helical content in a selected segment.

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.

Protein Dynamics and the Folding Degree.

The analysis of folding trajectories for proteins is an open challenge. One of the problems is how to describe the amount of folded secondary structure in a protein. We extend the use of Estradas' folding degree (Bioinformatics 2002, 18, 697) for the analysis of the evolution of the folding stage during molecular dynamics (MD) simulation. It is shown that residue contribution to the total folding degree is a predominantly local property, well-defined by the backbone dihedral angles at the given residue, without significant contribution from the backbone conformation of other residues. Moreover, the magnitude of this residue contribution can be quite easily associated with characteristic motifs of secondary protein structures such as the α-helix, ß-sheet (hairpin), and so on by means of a Ramachandran-like plot as a function of backbone dihedral angles φ,ψ. Additionally, the understanding of the free energy profile associated with the folding process becomes much simpler. Often a 1D profile is sufficient to locate global minima and the corresponding structure for short peptides.

Coulomb and CH-π interactions in (6-4) photolyase-DNA complex dominate DNA binding and repair abilities.

(6-4) Photolyases ((6-4)PLs) are flavoenzymes that repair the carcinogenic UV-induced DNA damage, pyrimidine(6-4)pyrimidone photoproducts ((6-4)PPs), in a light-dependent manner. Although the reaction mechanism of DNA photorepair by (6-4)PLs has been intensively investigated, the molecular mechanism of the lesion recognition remains obscure. We show that a well-conserved arginine residue in Xenopus laevis (6-4)PL (Xl64) participates in DNA binding, through Coulomb and CH-π interactions. Fragment molecular orbital calculations estimated attractive interaction energies of -80-100 kcal mol-1 for the Coulomb interaction and -6 kcal mol-1 for the CH-π interaction, and the loss of either of them significantly reduced the affinity for (6-4)PP-containing oligonucleotides, as well as the quantum yield of DNA photorepair. From experimental and theoretical observations, we formulated a DNA binding model of (6-4)PLs. Based on the binding model, we mutated this Arg in Xl64 to His, which is well conserved among the animal cryptochromes (CRYs), and found that the CRY-type mutant exhibited reduced affinity for the (6-4)PP-containing oligonucleotides, implying the possible molecular origin of the functional diversity of the photolyase/cryptochrome superfamily.

Temperature-pressure shuffling outlier flooding method enhances the conformational sampling of proteins.

Outlier flooding method (OFLOOD) is an efficient conformational sampling method developed by the authors. In the present study, to further enhance the conformational sampling efficiency, a set of parameters (temperatures and pressures) specified as inputs in the original OFLOOD were shuffled before restarting the short-time molecular dynamics (MD) simulations. Because of the diversity of these parameters, it was confirmed that the extended OFLOOD becomes superior to the original one in finding the folding pathways of Trp-cage. © 2019 Wiley Periodicals, Inc.

Selection Rules for Outliers in Outlier Flooding Method Regulate Its Conformational Sampling Efficiency.

The outlier flooding method (OFLOOD) has been proposed as an enhanced conformational sampling method of proteins. In OFLOOD, rarely occurring states of proteins are detected as sparse conformational distributions (outliers) with a clustering algorithm. The detected outliers are intensively resampled with short-time molecular dynamics (MD) simulations. As a set of cycles, OFLOOD repeats selections of outliers and their conformational resampling. Herein, as an essential issue to be tackled to perform OFLOOD efficiently, a selection rule for outliers should be carefully specified. Generally, many outliers are detected from distributions on conformational subspaces with the clustering. Judging from its computational costs, it is unreasonable to select all the detected outliers upon the conformational resampling. Therefore, it is important to consider which outliers should be selected from the sparse distributions when restarting their short-time MD simulations with limited computational costs. In this sense, we investigated the conformational sampling efficiency of OFLOOD by changing the selection rules for outliers. To address the conformational sampling efficiency of OFLOOD depending on its selection rules, outliers to be resampled were selected by focusing their probability occurrences (populations of outliers). As a comparison, a random selection rule for outliers was also considered. Through the present assessment, the random selection of outliers showed the most efficient conformational sampling efficiency compared to the other OFLOOD trials using the biased selection rules, indicating that a variety of outliers should be selected and resampled during the OFLOOD cycles. In conclusion, the random outlier selection rule is the best strategy to perform OFLOOD efficiently.

Nontargeted Parallel Cascade Selection Molecular Dynamics Based on a Nonredundant Selection Rule for Initial Structures Enhances Conformational Sampling of Proteins.

Nontargeted parallel cascade selection molecular dynamics (nt-PaCS-MD) is a method for enhanced conformational sampling of proteins. To search a broad conformational subspace, nt-PaCS-MD repeats cycles of conformational resampling from relevant initial structures. Generally, the conformational sampling efficiency of nt-PaCS-MD depends on a selection rule for the initial structures. In the original nt-PaCS-MD, the initial structures were selected by referring to structural distributions of protein configurations generated by conformational resampling (multiple short-time MD simulations). However, their structural redundancy among the initial structures was neglected for the cycles of conformational resampling, indicating that similar protein configurations might be frequently specified and resampled in every cycle in the original nt-PaCS-MD. To reduce the possibility of resampling from redundant initial structures, we propose an alternative selection rule that accounts for structural similarity among the initial structures. Specifically, a pairwise root-mean-square deviation (RMSD) is defined for all of the initial structures selected for all of the past cycles. Then a set of protein configurations with a larger pairwise RMSD is sequentially specified and resampled in the next cycle, which is regarded to as a history-dependent selection of initial structures by considering a profile of the past specified initial structures. The present scheme, termed extended nt-PaCS-MD, prevents us from resampling a set of redundant protein configurations. To check the conformational sampling efficiency of the extended nt-PaCS-MD, we used a middle-sized protein, T4 lysozyme, in explicit water. Through the assessment, this extended nt-PaCS-MD identified the open-closed transitions of T4 lysozyme more efficiently than the original nt-PaCS-MD.

Early neurosyphilis presenting with multiple cranial nerve palsies: A case report of management by combined penicillin-corticosteroid treatment.

Early neurosyphilis commonly appears in basilar meninges, and its meningeal inflammation can spread to neighboring cranial nerves, resulting in some cranial nerve palsies. Herein, we report a case of a 51-year-old man who presented with right peripheral facial nerve palsy. His symptoms completely disappeared with prednisolone monotherapy without antibiotics use and were not exacerbated during clinical treatment. However, 2 months after remission of seventh cranial neuropathy, fifth and eighth cranial neuropathies appeared on the right side. Serologic tests for syphilis were revealed to be abnormal. Finally, the patient was diagnosed with early neurosyphilis with multiple cranial palsies. His neurological symptoms were markedly improved by combined penicillin-corticosteroid treatment. Systemic corticosteroids could be effective as adjunctive therapy to ameliorate neurological sequelae in early neurosyphilis.

Temperature-Shuffled Structural Dissimilarity Sampling Based on a Root-Mean-Square Deviation.

Structural dissimilarity sampling (SDS) has been proposed as an enhanced conformational sampling method for finding neighboring metastable states of a given reactant or generating transition pathways starting from the reactant. SDS repeats a cycle of two steps: (1) selections of initial structures based on structural dissimilarities by referring to a measure and (2) conformational resampling by restarting short-time molecular dynamics (MD) simulations from the initial structures. In the present study, the measure was defined as the root-mean-square deviation (RMSD) among the resampled snapshots to characterize their structural dissimilarities. Additionally, the temperatures in restarting the short-time MD simulations were randomly shuffled at the beginning of each cycle to further promote the conformational transitions. We call this approach temperature-shuffled SDS (TSF-SDS). As a demonstration, TSF-SDS was applied to promote the open-closed transition of T4 lysozyme (T4L) in explicit water. TSF-SDS successfully reproduced the relevant domain motion with nanosecond-order simulation time, whereas conventional SDS without shuffling of the temperatures failed to promote the transition of T4L, indicating the high conformational sampling efficiency of TSF-SDS for promoting essential conformational transitions of proteins. Furthermore, as a wide-range application, TSF-SDS efficiently identified the native state of trp-cage and a dissociation process of ubiquitin dimer in explicit water.

How low-resolution structural data predict the conformational changes of a protein: a study on data-driven molecular dynamics simulations.

Parallel cascade selection molecular dynamics (PaCS-MD) is a conformational sampling method for generating transition pathways between a given reactant and a product. PaCS-MD repeats the following two steps: (1) selections of initial structures relevant to transitions and (2) their conformational resampling. When selecting the initial structures, several measures are utilized to identify their potential to undergo transitions. In the present study, low-resolution structural data obtained from small angle scattering (SAXS) and cryo-electron microscopy (EM) are adopted as the measures in PaCS-MD to promote the conformational transitions of proteins, which is defined as SAXS-/EM-driven targeted PaCS-MD. By selecting the essential structures that have high correlations with the low-resolution structural data, the SAXS-/EM-driven targeted PaCS-MD identifies a set of transition pathways between the reactant and the product. As a demonstration, the present method successfully predicted the open-closed transition pathway of the lysine-, arginine-, ornithine-binding protein with a ns-order simulation time, indicating that the data-driven PaCS-MD simulation might work to promote the conformational transitions of proteins efficiently.