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.

Aggregation of Apo/Glycated Human Serum Albumins and Aptamer-Saturated Graphene Quantum Dot: A Simulation Study.

Human serum albumin (HSA) is a protein carrier that transports a wide range of drugs and nutrients. The amount of glycated HSA (GHSA) is used as a diabetes biomarker. To quantify the GHSA amount, the fluorescent graphene-based aptasensor has been a successful method. In aptasensors, the key mechanism is the adsorption/desorption of albumin from the aptamer-graphene complex. Recently, the graphene quantum dot (GQD) has been reported to be an aptamer sorbent. Due to its comparable size to aptamers, it is attractive enough to explore the possibility of GQD as a part of an albumin aptasensor. Therefore, molecular dynamics (MD) simulations were performed here to reveal the binding mechanism of albumin to an aptamer-GQD complex in molecular detail. GQD saturated by albumin-selective aptamers (GQDA) is studied, and GHSA and HSA are studied in comparison to understand the effect of glycation. Fast and spontaneous albumin-GQDA binding was observed. While no specific GQDA-binding site on both albumins was found, the residues used for binding were confined to domains I and III for HSA and domains II and III for GHSA. Albumins were found to bind preferably to aptamers rather than to GQD. Lysines and arginines were the main contributors to binding. We also found the dissociation of GLC from all GHSA trajectories, which highlights the role of GQDA in interfering with the ligand binding affinity in Sudlow site I. The binding of GQDA appears to impair albumin structure and function. The insights obtained here will be useful for the future design of diabetes aptasensors.

Cocrystalline Matrices for Hyperpolarization at Room Temperature Using Photoexcited Electrons.

We propose using cocrystals as effective polarization matrices for triplet dynamic nuclear polarization (DNP) at room temperature. The polarization source can be uniformly doped into cocrystals formed through acid-acid, amide-amide, and acid-amide synthons. The dense-packing crystal structures, facilitated by multiple hydrogen bonding and π-π interactions, result in extended T1 relaxation times, enabling efficient polarization diffusion within the crystals. Our study demonstrates the successful polarization of a DNP-magnetic resonance imaging molecular probe, such as urea, within a cocrystal matrix at room temperature using triplet-DNP.

A machine learning potential construction based on radial distribution function sampling.

Sampling reference data is crucial in machine learning potential (MLP) construction. Inadequate coverage of local configurations in reference data may lead to unphysical behaviors in MLP-based molecular dynamics (MLP-MD) simulations. To address this problem, this study proposes a new on-the-fly reference data sampling method called radial distribution function (RDF)-based data sampling for MLP construction. This method detects and extracts anomalous structures from the trajectories of MLP-MD simulations by focusing on the shapes of RDFs. The detected structures are added to the reference data to improve the accuracy of the MLP. This method allows us to realize a reasonable MLP construction for liquid water with minimal additional data. We prepare data from an H2O molecular cluster system and verify whether the constructed MLPs are practical for bulk water systems. MLP-MD simulations without RDF-based data sampling show unphysical behaviors, such as atomic collisions. In contrast, after applying this method, we obtain MLP-MD trajectories with features, such as RDF shapes and angle distributions, that are comparable to those of ab initio MD simulations. Our simulation results demonstrate that the RDF-based data sampling approach is useful for constructing MLPs that are robust to extrapolations from molecular cluster systems to bulk systems without any specialized know-how.

Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery.

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy ( ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.

Grinding-Induced Water Solubility Exhibited by Mechanochromic Luminescent Supramolecular Fibers.

Most mechanochromic luminescent compounds are crystalline and highly hydrophobic; however, mechanochromic luminescent molecular assemblies comprising amphiphilic molecules have rarely been explored. This study investigated mechanochromic luminescent supramolecular fibers composed of dumbbell-shaped 9,10-bis(phenylethynyl)anthracene-based amphiphiles without any tetraethylene glycol (TEG) substituents or with two TEG substituents. Both amphiphiles formed water-insoluble supramolecular fibers via linear hydrogen bond formation. Both compounds acquired water solubility when solid samples composed of supramolecular fibers are ground. Grinding induces the conversion of 1D supramolecular fibers into micellar assemblies where fluorophores can form excimers, thereby resulting in a large redshift in the fluorescence spectra. Excimer emission from the ground amphiphile without TEG chains is retained after dissolution in water. The micelles are stable in water because hydrophilic dendrons surround the hydrophobic luminophores. By contrast, when water is added to a ground amphiphile having TEG substituents, fragmented supramolecular fibers with the same molecular arrangement as the initial supramolecular fibers are observed, because fragmented fibers are thermodynamically preferable to micelles as the hydrophobic arrays of fluorophores are covered with hydrophilic TEG chains. This leads to the recovery of the initial fluorescent properties for the latter amphiphile. These supramolecular fibers can be used as practical mechanosensors to detect forces at the mesoscale.

Pocket to concavity: a tool for the refinement of protein-ligand binding site shape from alpha spheres.

SUMMARY: Understanding the binding site of the target protein is essential for rational drug design. Pocket detection software predicts the ligand binding site of the target protein; however, the predicted protein pockets are often excessively estimated in comparison with the actual volume of the bound ligands. This study proposes a refinement tool for the pockets predicted by an alpha sphere-based approach, Pocket to Concavity (P2C). P2C is divided into two modes: Ligand-Free (LF) and Ligand-Bound (LB) modes. The LF mode provides the shape of the deep and druggable concavity where the core scaffold can bind. The LB mode searches the deep concavity around the bound ligand. Thus, P2C is useful for identifying and designing desirable compounds in Structure-Based Drug Design (SBDD). AVAILABILITY AND IMPLEMENTATION: Pocket to Concavity is freely available at https://github.com/genki-kudo/Pocket-to-Concavity. This tool is implemented in Python3 and Fpocket2.

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.

Ribosome Tunnel Environment Drives the Formation of α-Helix during Cotranslational Folding.

Protein conformations in cells are not solely determined by amino acid sequences; they also depend on cellular environments. For instance, the ribosome tunnel induces its specific α-helix formation during cotranslational folding. Owing to the link between these temporally α-helix and biological functions, the mechanism of α-helix formation inside the ribosome tunnel has been previously explored. Consequently, the conformational restrictions of the tunnel were considered one of the driving forces of α-helix formation. Conversely, the ribosomal tunnel environment, including its chemical properties, appears to influence the α-helix formation. However, a comprehensive analysis of the ribosome tunnel environment's impact on the α-helix formation has not been conducted yet due to challenges in experimentally controlling it. Therefore, as a new computational approach, we proposed a ribosome environment-mimicking model (REMM) based on the radius and components of the experimentally determined ribosome tunnel structures. Using REMM, we assessed the impact of the ribosome tunnel environment on α-helix formation. Herein, we employed carbon nanotubes (CNT) as a reference model alongside REMM because CNT reproduce conformational restrictions rather than the ribosome tunnel environment. Quantitatively, the ability to reproduce the α-helix of nascent peptides in the experimental structure was compared between the CNT and REMM using enhanced all-atom molecular dynamics simulations. Consequently, the REMM more accurately reproduced the α-helix of the nascent peptides than the CNT, highlighting the significance of the ribosome tunnel environment in α-helix formation. Additionally, we analyzed the properties of the peptide inside each model to reveal the mechanism of ribosome tunnel-specific α-helix formation. Consequently, we revealed that the chemical diversities of the tunnel are essential for the formation of backbone-to-backbone hydrogen bonds in the peptides. In conclusion, the ribosome tunnel environment, with the diverse chemical properties, drives its specific α-helix formation. By proposing REMM, we newly provide the technical basis for investigating the protein conformations in various cellular environments.

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization.

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

Rational Design of Cyclopentadiene-Based Super- and Hyperacids Based on Aromaticity.

The design and synthesis of neutral organic superacids have been of interest recently due to their vast applications in chemistry and material sciences, for example, olefinic polymerization, isolation of highly reactive short-lived cations, etc. Cyclopentadiene behaves as a mild organic acid, producing a stable conjugate base by gaining aromaticity and conjugation after deprotonation. To stabilize conjugate bases of organic acids to show superacidities and hyperacidities, we considered aromatic phenyl substituents with cyclopentadiene (mono-, di-, and triphenyl-substituted cyclopentadiene and their cyano derivatives). The MP2, DFT (B3LYP, M06-2X), and CBS-QB3 methods were used to calculate the gas-phase proton affinities of the parent cyclopentadiene, and the DFT methods were chosen for the substituted cyclopentadiene as they yield an experimental proton affinity of cyclopentadiene of 253.66 kcal/mol (Expt. 253.6 ± 1.3 kcal/mol). The stable trisubstituted cyclopentadiene derivative shows gas-phase enthalpies of deprotonation (ΔHacid) of 245 and 239 kcal/mol with DFT B3LYP and M06-2X methods, respectively, with values in the range of hyperacidity. Some of the tautomers of cyclopentadiene derivatives show hyperacidity, with proton affinity values of 205-240 kcal/mol. Triphenyl-substituted cyclopentadiene behaves as a moderate acid but transforms into a superacid after replacing the phenyl group with nitrobenzene, which is a stronger acid than H2SO4 (ΔHacid = 298 kcal/mol). The nucleus-independent chemical shift (NICS) shows the extent of conjugation in the derivatives of cyclopentadienyl anions after deprotonation, which affects the stabilities of conjugate bases, i.e., the acidities of the protonated species. The calculated harmonic oscillator model of aromaticity and NICS indices reveal that the stability of conjugate bases as well as their acidities increase with increasing aromaticity of cyclopentadiene rings after deprotonation in all molecules.

Synthesis and biological evaluation of 2'-hydroxychalcone derivatives as AMPK activators.

A series of 2'-hydroxychalcone derivatives with various substituents on B-ring were synthesized and evaluated for AMP-activated protein kinase (AMPK) activation activity in podocyte cells. The results displayed that hydroxy, methoxy and methylenedioxy groups on B-ring could enhance the activitiy better than O-saturated alkyl, O-unsaturated alkyl or other alkoxy groups. Compounds 27 and 29 possess the highest fold change of 2.48 and 2.73, respectively, which were higher than those of reference compound (8) (1.28) and metformin (1.88). Compounds 27 and 29 were then subjected to a concentration-response study to obtain the EC50 values of 2.0 and 4.8 µM, respectively and MTT assays also showed that cell viability was not influenced by the exposure of podocytes to compounds 27 and 29 at concentrations up to 50 µM. In addition, compound 27 was proved to activate AMPK via calcium/calmodulin-dependent protein kinase kinase ß (CaMKKß)-dependent pathway without affecting intracellular calcium levels. The computational study showed that the potent compounds exhibited stronger ligand-binding strength to CaMKKß, particularly compounds 27 (-8.4 kcal/mol) and 29 (-8.0 kcal/mol), compared to compound 8 (-7.5 kcal/mol). Fragment molecular orbital (FMO) calculation demonstrated that compound 27 was superior to compound 29 due to the presence of methyl group, which amplified the binding by hydrophobic interactions. Therefore, compound 27 would represent a promising AMPK activator for further investigation of the treatment of diabetes and diabetic nephropathy.

Manipulation of photosynthetic energy transfer by vibrational strong coupling.

Uncovering the mystery of efficient and directional energy transfer in photosynthetic organisms remains a critical challenge in quantum biology. Recent experimental evidence and quantum theory developments indicate the significance of quantum features of molecular vibrations in assisting photosynthetic energy transfer, which provides the possibility of manipulating the process by controlling molecular vibrations. Here, we propose and theoretically demonstrate efficient manipulation of photosynthetic energy transfer by using vibrational strong coupling between the vibrational state of a Fenna-Matthews-Olson (FMO) complex and the vacuum state of an optical cavity. Specifically, based on a full-quantum analytical model to describe the strong coupling effect between the optical cavity and molecular vibration, we realize efficient manipulation of energy transfer efficiency (from 58% to 92%) and energy transfer time (from 20 to 500 ps) in one branch of FMO complex by actively controlling the coupling strength and the quality factor of the optical cavity under both near-resonant and off-resonant conditions, respectively. Our work provides a practical scenario to manipulate photosynthetic energy transfer by externally interfering molecular vibrations via an optical cavity and a comprehensible conceptual framework for researching other similar systems.

Development of an FeII Complex Exhibiting Intermolecular Proton Shifting Coupled Spin Transition.

In this study, we investigated reversible intermolecular proton shifting (IPS) coupled with spin transition (ST) in a novel FeII complex. The host FeII complex and the guest carboxylic acid anion were connected by intermolecular hydrogen bonds (IHBs). We extended the intramolecular proton transfer coupled ST phenomenon to the intermolecular system. The dynamic phenomenon was confirmed by variable-temperature single-crystal X-ray diffraction, neutron crystallography, and infrared spectroscopy. The mechanism of IPS was further validated using density functional theory calculations. The discovery of IPS-coupled ST in crystalline molecular materials provides good insights into fundamental processes and promotes the design of novel multifunctional materials with tunable properties for various applications, such as optoelectronics, information storage, and molecular devices.

Deprotonation-Induced and Ion-Pairing-Modulated Diradical Properties of Partially Conjugated Pyrrole-Quinone Conjunction.

Quinoidal molecules based on dipyrrolyldiketone boron complexes (QPBs), in which pyrrole units were connected by a partially conjugated system as a singlet spin coupler, were synthesized. QPB, which was stabilized by the introduction of a benzo unit at the pyrrole ß-positions, formed a closed-shell tautomer conformation that showed near-infrared absorption. The deprotonated species, monoanion QPB- and dianion QPB2-, showing over 1000 nm absorption, were formed by the addition of bases, providing ion pairs in combination with countercations. Diradical properties were observed in QPB2-, whose hyperfine coupling constants were modulated by ion-pairing with π-electronic and aliphatic cations, demonstrating cation-dependent diradical properties. VT NMR and ESR along with a theoretical study revealed that the singlet diradical was more stable than the triplet diradical.

Development of an Iron(II) Complex Exhibiting Thermal- and Photoinduced Double Proton-Transfer-Coupled Spin Transition in a Short Hydrogen Bond.

Multiple proton transfer (PT) controllable by external stimuli plays a crucial role in fundamental chemistry, biological activity, and material science. However, in crystalline systems, controlling multiple PT, which results in a distinct protonation state, remains challenging. In this study, we developed a novel tridentate ligand and iron(II) complex with a short hydrogen bond (HB) that exhibits a PT-coupled spin transition (PCST). Single-crystal X-ray and neutron diffraction measurements revealed that the positions of the two protons in the complex can be controlled by temperature and photoirradiation based on the thermal- and photoinduced PCST. The obtained results suggest that designing molecules that form short HBs is a promising approach for developing multiple PT systems in crystals.

Characterization of the Geometrical and Electronic Structures of the Active Site and Its Effects on the Surrounding Environment in Reduced High-Potential Iron-Sulfur Proteins Investigated by the Density Functional Theory Approach.

The high-potential iron-sulfur protein (HiPIP) is an electron-transporting protein that functions in the photosynthetic electron-transfer system and possesses a cubane-type [4Fe-4S] cluster in the active center. Characterization of the geometrical and electronic structures of the [4Fe-4S] cluster leads to an understanding of the functions in HiPIP, which are expected to be influenced by the environment surrounding the [4Fe-4S] cluster. This work characterized the geometrical and electronic structures of the [4Fe-4S] cluster in the reduced HiPIP and evaluated their effects on the protein environment using the density functional theory (DFT) approach. DFT calculations showed that the structural asymmetry and spin delocalization between iron atoms allowed for the acquisition of a unique stable geometrical and electronic structure in the open-shell singlet. In addition, the formation of an Fe-Fe bond accompanying the spin delocalization was found to depend on the interatomic distance. A comparison of the calculated stable structures with and without consideration of the amino acids around the [4Fe-4S] cluster demonstrated that the surrounding amino acids stabilized the unique geometrical and electronic structure of the [4Fe-4S] cluster in HiPIP.

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.

Identification of Promising Sulfonamide Chalcones as Inhibitors of SARS-CoV-2 3CLpro through Structure-Based Virtual Screening and Experimental Approaches.

3CLpro is a viable target for developing antiviral therapies against the coronavirus. With the urgent need to find new possible inhibitors, a structure-based virtual screening approach was developed. This study recognized 75 pharmacologically bioactive compounds from our in-house library of 1052 natural product-based compounds that satisfied drug-likeness criteria and exhibited good bioavailability and membrane permeability. Among these compounds, three promising sulfonamide chalcones were identified by combined theoretical and experimental approaches, with SWC423 being the most suitable representative compound due to its competitive inhibition and low cytotoxicity in Vero E6 cells (EC50 = 0.89 ± 0.32 µM; CC50 = 25.54 ± 1.38 µM; SI = 28.70). The binding and stability of SWC423 in the 3CLpro active site were investigated through all-atom molecular dynamics simulation and fragment molecular orbital calculation, indicating its potential as a 3CLpro inhibitor for further SARS-CoV-2 therapeutic research. These findings suggested that inhibiting 3CLpro with a sulfonamide chalcone such as SWC423 may pave the effective way for developing COVID-19 treatments.

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.