Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation.

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.

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.

Alpha and gamma mangostins inhibit wild-type B SARS-CoV-2 more effectively than the SARS-CoV-2 variants and the major target is unlikely the 3C-like protease.

Background: Anti-SARS-CoV-2 and immunomodulatory drugs are important for treating clinically severe patients with respiratory distress symptoms. Alpha- and gamma-mangostins (AM and GM) were previously reported as potential 3C-like protease (3CLpro) and Angiotensin-converting enzyme receptor 2 (ACE2)-binding inhibitors in silico. Objective: We aimed to evaluate two active compounds, AM and GM, from Garcinia mangostana for their antivirals against SARS-CoV-2 in live virus culture systems and their cytotoxicities using standard methods. Also, we aimed to prove whether 3CLpro and ACE2 neutralization were major targets and explored whether any additional targets existed. Methods: We tested the translation and replication efficiencies of SARS-CoV-2 in the presence of AM and GM. Initial and subgenomic translations were evaluated by immunofluorescence of SARS-CoV-2 3CLpro and N expressions at 16 h after infection. The viral genome was quantified and compared with the untreated group. We also evaluated the efficacies and cytotoxicities of AM and GM against four strains of SARS-CoV-2 (wild-type B, B.1.167.2, B.1.36.16, and B.1.1.529) in Vero E6 cells. The potential targets were evaluated using cell-based anti-attachment, time-of-drug addition, in vitro 3CLpro activities, and ACE2-binding using a surrogated viral neutralization test (sVNT). Moreover, additional targets were explored using combinatorial network-based interactions and Chemical Similarity Ensemble Approach (SEA). Results: AM and GM reduced SARS-CoV-2 3CLpro and N expressions, suggesting that initial and subgenomic translations were globally inhibited. AM and GM inhibited all strains of SARS-CoV-2 at EC50 of 0.70-3.05 µM, in which wild-type B was the most susceptible strain (EC50 0.70-0.79 µM). AM was slightly more efficient in the variants (EC50 0.88-2.41 µM), resulting in higher selectivity indices (SI 3.65-10.05), compared to the GM (EC50 0.94-3.05 µM, SI 1.66-5.40). GM appeared to be more toxic than AM in both Vero E6 and Calu-3 cells. Cell-based anti-attachment and time-of-addition suggested that the potential molecular target could be at the post-infection. 3CLpro activity and ACE2 binding were interfered with in a dose-dependent manner but were insufficient to be a major target. Combinatorial network-based interaction and chemical similarity ensemble approach (SEA) suggested that fatty acid synthase (FASN), which was critical for SARS-CoV-2 replication, could be a target of AM and GM. Conclusion: AM and GM inhibited SARS-CoV-2 with the highest potency at the wild-type B and the lowest at the B.1.1.529. Multiple targets were expected to integratively inhibit viral replication in cell-based system.

Evaluating solubility, stability, and inclusion complexation of oxyresveratrol with various ß-cyclodextrin derivatives using advanced computational techniques and experimental validation.

Oxyresveratrol (OXY), a natural stilbenoid in mulberry fruits, is known for its diverse pharmacological properties. However, its clinical use is hindered by low water solubility and limited bioavailability. In the present study, the inclusion complexes of OXY with ß-cyclodextrin (ßCD) and its three analogs, dimethyl-ß-cyclodextrin (DMßCD), hydroxypropyl-ß-cyclodextrin (HPßCD) and sulfobutylether-ß-cyclodextrin (SBEßCD), were investigated using in silico and in vitro studies. Molecular docking revealed two binding orientations of OXY, namely, 4',6'-dihydroxyphenyl (A-form) and 5,7-benzenediol ring (B-form). Molecular Dynamics simulations suggested the formation of inclusion complexes with ßCDs through two distinct orientations, with OXY/SBEßCD exhibiting maximum atom contacts and the lowest solvent-exposed area in the hydrophobic cavity. These results corresponded well with the highest binding affinity observed in OXY/SBEßCD when assessed using the MM/GBSA method. Beyond traditional simulation methods, Ligand-binding Parallel Cascade Selection Molecular Dynamics method was employed to investigate how the drug enters and accommodates within the hydrophobic cavity. The in silico results aligned with stability constants: SBEßCD (2060 M-1), HPßCD (1860 M-1), DMßCD (1700 M-1), and ßCD (1420 M-1). All complexes exhibited a 1:1 binding mode (AL type), with SBEßCD enhancing OXY solubility (25-fold). SEM micrographs, DSC thermograms, FT-IR and 1H NMR spectra confirm the inclusion complex formation, revealing novel surface morphologies, distinctive thermal behaviors, and new peaks. Notably, the inhibitory impact on the proliferation of breast cancer cell lines, MCF-7, exhibited by inclusion complexes particularly OXY/DMßCD, OXY/HPßCD, and OXY/SBEßCD were markedly superior compared to that of OXY alone.

Preferential Door for Ligand Binding and Unbinding Pathways in Inhibited Human Acetylcholinesterase.

Rising global population and increased food demands have resulted in the increased use of organophosphate pesticides (OPs), leading to toxin accumulation and transmission to humans. Pralidoxime (2-PAM), an FDA-approved drug, serves as an antidote for OP therapy. However, the atomic-level detoxification mechanisms regarding the design of novel antidotes remain unclear. This is the first study to examine the binding and unbinding pathways of 2-PAM to human acetylcholinesterase (HuAChE) through three identified doors using an enhanced sampling method called ligand-binding parallel cascade selection molecular dynamics (LB-PaCS-MD). Remarkably, LB-PaCS-MD could identify a predominant in-line binding mechanism through the acyl door at 63.79% ± 6.83%, also implicating it in a potential unbinding route (90.14% ± 4.22%). Interestingly, crucial conformational shifts in key residues, W86, Y341, and Y449, and the Ω loop significantly affect door dynamics and ligand binding modes. The LB-PaCS-MD technique can study ligand-binding pathways, thereby contributing to the design of antidotes and covalent drugs.

[Development of Membrane Permeability Coefficient by Means of Novel Molecular Dynamics Methods].

The membrane permeability, and its evaluation, is crucial factor in the process of uptake of compounds from outside to inside the cell and in the inhibition of the activity of disease-causing target proteins. Although molecular dynamics (MD) simulations have been shown to be able to reproduce the conformational changes of compounds occurring during membrane permeation, it is still challenging to extract the membrane permeability at an affordable computational workload solely by conventional MD. Indeed, the time scale accessible by MD is far below the one characterizing the actual permeation process. Phenomena occurring in living organisms escaping the reach of standard MD are generally referred to as biological rare events, and the membrane permeation process is one of them. To overcome this time-scale problem, several enhanced sampling methods have been proposed over the years to improve conformational sampling. In this review, a hybrid sampling method that combines the parallel cascade selection MD (PaCS-MD) and the outlier flooding method (OFLOOD), introduced and developed by our group, is proposed as a tool to study the membrane permeation from structural sampling (rare-event sampling). The obtained trajectories are used to estimate the free energy profiles for the membrane permeation and to compute the membrane permeation coefficients. Moreover, we present an example of application of the free energy reaction network method as a versatile way for incorporating explicitly into reaction coordinates the degrees of freedom related to internal motion.

Latrunculin resistance mechanism of non-conventional actin NAP1 uncovered by molecular dynamics simulations.

Monomeric G-actin polymerizes into F-actin to perform various cellular functions. Actin depolymerization drugs, such as latrunculin-A (Lat-A), inhibit filament formation and disrupt the cytoskeleton. Interestingly, the green algae Chlamydomonas alternatively produces a non-conventional actin, NAP1, that responds to inhibition by latrunculin. However, the molecular mechanism underlying latrunculin resistance of NAP1 remains unclear because of the difficulty due to its low in vitro polymerizability. Instead of biochemical experiments, we performed molecular dynamics (MD) simulations to investigate whether NAP1 has a lower affinity for Lat-A than the conventional actins. Our phylogenetic comparison of the binding free energies shows that Lat-A is evolutionarily optimized for skeletal muscles. By decomposing the binding free energy into each amino acid residue, we found that some residues in NAP1 play an important role in latrunculin resistance, suggesting that the primary mechanism of latrunculin resistance is the loss of affinity for Lat-A due to substitutions. In conclusion, our binding-free-energy calculations using MD simulations provide the critical insight that loss of affinity is the direct mechanism of latrunculin resistance.

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.

Design of electron-donating group substituted 2-PAM analogs as antidotes for organophosphate insecticide poisoning.

The use of organophosphate (OPs) pesticides is widespread in agriculture and horticulture, but these chemicals can be lethal to humans, causing fatalities and deaths each year. The inhibition of acetylcholinesterase (AChE) by OPs leads to the overstimulation of cholinergic receptors, ultimately resulting in respiratory arrest, seizures, and death. Although 2-pralidoxime (2-PAM) is the FDA-approved drug for treating OP poisoning, there is difficulty in blood-brain barrier permeation. To address this issue, we designed and evaluated a series of 2-PAM analogs by substituting electron-donating groups on the para and/or ortho positions of the pyridinium core using in silico techniques. Our PCM-ONIOM2 (MP2/6-31G*:PM7//B3LYP/6-31G*:UFF) binding energy results demonstrated that 13 compounds exhibited higher binding energy than 2-PAM. The analog with phenyl and methyl groups substituted on the para and ortho positions, respectively, showed the most favorable binding characteristics, with aromatic residues in the active site (Y124, W286, F297, W338, and Y341) and the catalytic residue S203 covalently bonding with paraoxon. The results of DS-MD simulation revealed a highly favorable apical conformation of the potent analog, which has the potential to enhance reactivation of AChE. Importantly, newly designed compound demonstrated appropriate drug-likeness properties and blood-brain barrier penetration. These results provide a rational guide for developing new antidotes to treat organophosphate insecticide toxicity.

Determination of the Association between Mesotrione Sensitivity and Conformational Change of 4-Hydroxyphenylpyruvate Dioxygenase via Free-Energy Analyses.

One widely known herbicide target is 4-hydroxyphenylpyruvate dioxygenase (HPPD). Avena sativa HPPD is less sensitive to mesotrione (herbicide) than Arabidopsis thaliana HPPD. HPPD inhibitor-sensitivity is governed by the dynamic behavior of the C-terminal α-helix of HPPD (H11) in closed and open forms. However, the specific relationship between the plant inhibitor sensitivity and H11 dynamic behavior remains unclear. Herein, we determined the conformational changes in H11 to understand the inhibitor-sensitivity mechanism based on free-energy calculations using molecular dynamics simulations. The calculated free-energy landscapes revealed that Arabidopsis thaliana HPPD preferred the open form of H11 in the apo form and the closed-like form in complex with mesotrione, whereas Avena sativa HPPD exhibited the opposite tendency. We also identified some important residues involved in the dynamic behavior of H11. Therefore, inhibitor sensitivity is governed by indirect interactions due to the protein flexibility caused by the conformational changes of H11.

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.

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.

Identifying antibiotics based on structural differences in the conserved allostery from mitochondrial heme-copper oxidases.

Antimicrobial resistance (AMR) is a global health problem. Despite the enormous efforts made in the last decade, threats from some species, including drug-resistant Neisseria gonorrhoeae, continue to rise and would become untreatable. The development of antibiotics with a different mechanism of action is seriously required. Here, we identified an allosteric inhibitory site buried inside eukaryotic mitochondrial heme-copper oxidases (HCOs), the essential respiratory enzymes for life. The steric conformation around the binding pocket of HCOs is highly conserved among bacteria and eukaryotes, yet the latter has an extra helix. This structural difference in the conserved allostery enabled us to rationally identify bacterial HCO-specific inhibitors: an antibiotic compound against ceftriaxone-resistant Neisseria gonorrhoeae. Molecular dynamics combined with resonance Raman spectroscopy and stopped-flow spectroscopy revealed an allosteric obstruction in the substrate accessing channel as a mechanism of inhibition. Our approach opens fresh avenues in modulating protein functions and broadens our options to overcome AMR.

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.

Promising SARS-CoV-2 main protease inhibitor ligand-binding modes evaluated using LB-PaCS-MD/FMO.

Parallel cascade selection molecular dynamics-based ligand binding-path sampling (LB-PaCS-MD) was combined with fragment molecular orbital (FMO) calculations to reveal the ligand path from an aqueous solution to the SARS-CoV-2 main protease (Mpro) active site and to customise a ligand-binding pocket suitable for delivering a potent inhibitor. Rubraxanthone exhibited mixed-inhibition antiviral activity against SARS-CoV-2 Mpro, relatively low cytotoxicity, and high cellular inhibition. However, the atomic inhibition mechanism remains ambiguous. LB-PaCS-MD/FMO is a hybrid ligand-binding evaluation method elucidating how rubraxanthone interacts with SARS-CoV-2 Mpro. In the first step, LB-PaCS-MD, which is regarded as a flexible docking, efficiently samples a set of ligand-binding pathways. After that, a reasonable docking pose of LB-PaCS-MD is evaluated by the FMO calculation to elucidate a set of protein-ligand interactions, enabling one to know the binding affinity of a specified ligand with respect to a target protein. A possible conformation was proposed for rubraxanthone binding to the SARS-CoV-2 Mpro active site, and allosteric inhibition was elucidated by combining blind docking with k-means clustering. The interaction profile, key binding residues, and considerable interaction were elucidated for rubraxanthone binding to both Mpro sites. Integrated LB-PaCS-MD/FMO provided a more reasonable complex structure for ligand binding at the SARS-CoV-2 Mpro active site, which is vital for discovering and designing antiviral drugs.

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.

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.

Ligand Binding Path Sampling Based on Parallel Cascade Selection Molecular Dynamics: LB-PaCS-MD.

Parallel cascade selection molecular dynamics (PaCS-MD) is a rare-event sampling method that generates transition pathways between a reactant and product. To sample the transition pathways, PaCS-MD repeats short-time MD simulations from important configurations as conformational resampling cycles. In this study, PaCS-MD was extended to sample ligand binding pathways toward a target protein, which is referred to as LB-PaCS-MD. In a ligand-concentrated environment, where multiple ligand copies are randomly arranged around the target protein, LB-PaCS-MD allows for the frequent sampling of ligand binding pathways. To select the important configurations, we specified the center of mass (COM) distance between each ligand and the relevant binding site of the target protein, where snapshots generated by the short-time MD simulations were ranked by their COM distance values. From each cycle, snapshots with smaller COM distance values were selected as the important configurations to be resampled using the short-time MD simulations. By repeating conformational resampling cycles, the COM distance values gradually decreased and converged to constants, meaning that a set of ligand binding pathways toward the target protein was sampled by LB-PaCS-MD. To demonstrate relative efficiency, LB-PaCS-MD was applied to several proteins, and their ligand binding pathways were sampled more frequently than conventional MD simulations.