Structures of the essential efflux pump EfpA from Mycobacterium tuberculosis reveal the mechanisms of substrate transport and small-molecule inhibition.

As the first identified multidrug efflux pump in Mycobacterium tuberculosis (Mtb), EfpA is an essential protein and promising drug target. However, the functional and inhibitory mechanisms of EfpA are poorly understood. Herein we report cryo-EM structures of EfpA in outward-open conformation, either bound to three endogenous lipids or the inhibitor BRD-8000.3. Three lipids inside EfpA span from the inner leaflet to the outer leaflet of the membrane. BRD-8000.3 occupies one lipid site at the level of inner membrane leaflet, competitively inhibiting lipid binding. EfpA resembles the related lysophospholipid transporter MFSD2A in both overall structure and lipid binding sites, and may function as a lipid flippase. Combining AlphaFold-predicted EfpA structure, which is inward-open, we propose a complete conformational transition cycle for EfpA. Together, our results provide a structural and mechanistic foundation to comprehend EfpA function and develop EfpA-targeting anti-TB drugs.

Potent inhibitors targeting cyclin-dependent kinase 9 discovered via virtual high-throughput screening and absolute binding free energy calculations.

Due to the crucial regulatory mechanism of cyclin-dependent kinase 9 (CDK9) in mRNA transcription, the development of kinase inhibitors targeting CDK9 holds promise as a potential treatment strategy for cancer. A structure-based virtual screening approach has been employed for the discovery of potential novel CDK9 inhibitors. First, compounds with kinase inhibitor characteristics were identified from the ZINC15 database via virtual high-throughput screening. Next, the predicted binding modes were optimized by molecular dynamics simulations, followed by precise estimation of binding affinities using absolute binding free energy calculations based on the free energy perturbation scheme. The binding mode of molecule 006 underwent an inward-to-outward flipping, and the new binding mode exhibited binding affinity comparable to the small molecule T6Q in the crystal structure (PDB ID: 4BCF), highlighting the essential role of molecular dynamics simulation in capturing a plausible binding pose bridging docking and absolute binding free energy calculations. Finally, structural modifications based on these findings further enhanced the binding affinity with CDK9. The results revealed that enhancing the molecule's rigidity through ring formation, while maintaining the major interactions, reduced the entropy loss during the binding process and, thus, enhanced binding affinities.

Training machine learning potentials for reactive systems: A Colab tutorial on basic models.

In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.

Rivaroxaban for the treatment of heparin-induced thrombocytopenia with thrombosis in a patient undergoing artificial hip arthroplasty: A case report.

BACKGROUND: Anticoagulation treatment after lower limb surgery is one of the key methods to avoid thrombosis, and low-molecular-weight heparin is the treatment that is most frequently used in clinical practice. But one uncommon side effect of low-molecular-weight heparin is heparin-induced thrombocytopenia (HIT), which can develop into thrombosis if not caught early or managed incorrectly. CASE SUMMARY: We present a case of a patient who underwent hip arthroplasty and experienced thrombocytopenia due to HIT on the 9th d following the application of low-molecular-weight heparin anticoagulation. We did not diagnose HIT in time and applied 1 unit of platelets to the patient, which led to thrombosis. Luckily, the patient recovered following effective and timely surgery and treatment with rivaroxaban. CONCLUSION: Patients using low-molecular-weight heparin after lower limb surgery need to have their platelet counts regularly checked. If HIT develops, platelet treatment should be given with caution.

Tracking the Early Stage of Triplet-Induced Photodamage in a DNA Dimer and Oligomer Containing 5-Methylcytosine.

Methylation at the C5 position of cytosine, a naturally occurring epigenetic modification on DNA, shows a high correlation with mutational hotspots in disease such as skin cancer. Due to its essential biological relevance, numerous studies were devoted to confirming that the methylated sites favor the formation of the cyclobutane pyrimidine dimer (CPD), a well-known UV-induced lesion. However, photophysical and photochemical properties of dinucleotides and polynucleotides containing 5-methylcytosine (5mC) remain elusive. Herein, a charge transfer (CT) triplet state, generated via intersystem crossing (ISC) from a CT singlet state that enhanced after methylation on cytosine, is directly observed by using femtosecond transient absorption (TA) and time-resolved mid-infrared (TRIR) spectroscopy together with quantum chemical calculations for the first time in the T5mC dimer. Such an ISC process is quenched due to limitations of the ground-state geometries in 5mC-containing single-strand oligomer d(T5mC)9. This mechanistic information is important for understanding the early stage of triplet state-induced CPD formation in 5mC containing DNA.

Perspective: Reference-Potential Methods for the Study of Thermodynamic Properties in Chemical Processes: Theory, Applications, and Pitfalls.

In silico investigations of enzymatic reactions and chemical reactions in condensed phases often suffer from formidable computational costs due to a large number of degrees of freedom and enormous important volume in phase space. Usually, accuracy must be compromised to trade for efficiency by lowering the reliability of the Hamiltonians employed or reducing the sampling time. Reference-potential methods (RPMs) offer an alternative approach to reaching high accuracy of simulation without much loss of efficiency. In this Perspective, we summarize the idea of RPMs and showcase some recent applications. Most importantly, the pitfalls of these methods are also discussed, and remedies to these pitfalls are presented.

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.

Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol-1 Å-1 from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol-1. Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for ab initio-QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute.

Ursolic acid augments the chemosensitivity of drug-resistant breast cancer cells to doxorubicin by AMPK-mediated mitochondrial dysfunction.

Multidrug resistance remains the major obstacle to successful therapy for breast carcinoma. Ursolic acid (UA), a triterpenoid compound, has been regarded as a potential neoplasm chemopreventive drug in some preclinical studies since it exerts multiple biological activities. In this research, we investigated the role of UA in augmenting the chemosensitivity of drug-resistant breast carcinoma cells to doxorubicin (DOX), and we further explored the possible molecular mechanisms. Notably, we found that UA treatment led to inhibition of cellular proliferation and migration and cell cycle arrest in DOX-resistant breast cancers. Furthermore, combination treatment with UA and DOX showed a stronger inhibitory effect on cell viability, colony formation, and cell migration; induced more cell apoptosis in vitro; and generated a more potent inhibitory effect on the growth of the MCF-7/ADR xenograft tumor model than DOX alone. Mechanistically, UA effectively increased p-AMPK levels and concomitantly reduced p-mTOR and PGC-1α protein levels, resulting in impaired mitochondrial function, such as mitochondrial respiration inhibition, ATP depletion, and excessive reactive oxygen species (ROS) generation. In addition, UA induced a DNA damage response by increasing intracellular ROS production, thus causing cell cycle arrest at the G0/G1 phase. UA also suppressed aerobic glycolysis by prohibiting the expression and function of Glut1. Considered together, our data demonstrated that UA potentiated the susceptibility of DOX-resistant breast carcinoma cells to DOX by targeting energy metabolism through the AMPK/mTOR/PGC-1α signaling pathway, and it is a potential adjuvant chemotherapeutic candidate in MDR breast cancer.

Understanding of chiral site-dependent enantioselective identification on a plasmon-free semiconductor based SERS substrate.

Chiral differentiation is an important topic in diverse fields ranging from pharmaceutics to chiral synthesis. The improvement of sensitivity and the elucidation of the mechanism of chiral recognition are still the two main challenges. Herein, a plasmon-free semiconductive surface-enhanced Raman spectroscopy (SERS) substrate with sensitive chiral recognition ability is proposed for the discrimination of enantiomers. A homochiral environment is constructed by typical π-π stacking between l-tryptophan (l-Trp) and phenyl rings on well-aligned TiO2 nanotubes (TiO2 NTs). Using 3,4-dihydroxyphenylalanine (DOPA) enantiomers as the targets and the chelating interaction of Fe3+-DOPA for the onsite growth of Prussian blue (PB), the enantioselectivity difference between l-DOPA and d-DOPA on the homochiral substrate can be directly monitored from PB signals in the Raman-silent region. By combining the experimental results with molecular dynamic (MD) simulations, it is found that satisfactory enantioselective identification not only requires a homochiral surface but also largely depends on the chiral center environment-differentiated hydrogen-bond formation availability.

Hydrogen Abstraction by Alkoxyl Radicals: Computational Studies of Thermodynamic and Polarity Effects on Reactivities and Selectivities.

Density functional theory calculations (ωB97X-D) are reported for the reactions of methoxy, tert-butoxy, trichloroethoxy, and trifluoroethoxy radicals with a series of 26 C-H bonds in different environments characteristic of a variety of hydrocarbons and substituted derivatives. The variations in activation barriers are analyzed with modified Evans-Polanyi treatments to account for polarity and unsaturation effects. The treatments by Roberts and Steel and by Mayer have inspired the development of a simple treatment involving the thermodynamics of reactions, the difference between the reactant radical and product radical electronegativities, and the absence or presence of α-unsaturation. The three-parameter equation (ΔH⧧ = 0.52ΔHrxn(1 - d) - 0.35ΔχAB2 + 10.0, where d = 0.44 when there is α-unsaturation to the reacting C-H bond), correlates well with quantum mechanically computed barriers and shows the quantitative importance of the thermodynamics of reactions (dictated by the reactant and the product bond dissociation energies) and polar effects.

Near Infrared Light-Driven Photothermal Effect on Homochiral Au/TiO2 Nanotube Arrays for Enantioselective Desorption.

Chiral enantiomers have different effects on biological processes. Enantiomer separation is significant and necessary. Herein, a photothermal (PT) effect-derived enantioselective desorption strategy based on homochiral Au/TiO2 nanotubes (NTs) is developed. Using 3,4-dihydroxyphenylalanine (DOPA) as the model enantiomer, an obvious selective desorption of L/D-DOPA can be achieved by the NIR light-triggered local temperature enhancement. Molecular docking simulation further verifies that the distinct affinity precipitated by the different hydrogen bonds between homochiral sorbent and target enantiomers is the origin of enantioselective desorption. This desorption strategy provides a green and alternative approach for the selective separation of chiral molecules.

Affordable Ab Initio Path Integral for Thermodynamic Properties via Molecular Dynamics Simulations Using Semiempirical Reference Potential.

Path integral molecular dynamics (PIMD) is becoming a routinely applied method for incorporating the nuclear quantum effect in computer simulations. However, direct PIMD simulations at an ab initio level of theory are formidably expensive. Using the protonated 1,8-bis(dimethylamino)naphthalene molecule as an example, we show in this work that the computational expense for the intramolecular proton transfer between the two nitrogen atoms can be remarkably reduced by implementing the idea of reference-potential methods. The simulation time can be easily extended to a scale of nanoseconds while maintaining the accuracy on an ab initio level of theory for thermodynamic properties. In addition, postprocessing can be carried out in parallel on massive computer nodes. A 545-fold reduction in the total CPU time can be achieved in this way as compared to a direct PIMD simulation at the same ab initio level of theory.

Potential effects of the nursing work environment on the work-family conflict in operating room nurses.

BACKGROUND: The nursing working environment is an important subsystem in the hospital environment. A good working environment could have a positive impact on nurses. However, the work-family conflict and unsatisfactory working environment could significantly reduce their working enthusiasm, efficacy as well as the overall quality of the nursing, increase their fatigue, and thereby compromise their career status. AIM: To explore the possible status quo and to analyze the correlation between work environment perception and the work-family conflict among nurses in the operating room. METHODS: A total of 312 operating room nurses from two first-class hospitals at Grade 2 and two first-class hospitals at Grade 3 in China from May to September 2017 were included in this research using the cluster sampling method. The data, including the general information questionnaire, the practice environment scale of the nursing work index (PES-NWI), and the work-family conflict scale, were systematically collected. Pearson correlation analysis was applied to analyze the correlation between the two scores, with influencing factors analyzed by hierarchical regression analysis. RESULTS: A total of 312 questionnaires were issued, and the response rate and effective questionnaire rate were both 96.15% (300/312). The total scores of the PES-NWI scale and the work-family conflict scale were 3.07 ± 0.43 (vs maximum up to 4 points) and 52.32 ± 8.79 (vs maximum up to 90 points), respectively. The scores of the PES-NWI scale were negatively correlated with that of work-family conflict scale (all P < 0.05). The perception of the nursing work environment and the number of night shifts per month were the major factors contributing to the work-family conflict (all P < 0.05). CONCLUSION: The nursing work environment and the work-family conflict among nurses in the operating room were both found at a medium level with a negative correlation between the two.

Phase-error-compensation-based surface recovery algorithm using spectrum selection for white light interferometry.

White light interferometry is a well-established surface recovery technique. In this paper, a white light signal processing algorithm based on phase error compensation using spectrum selection is proposed. The derived nonlinear phase distribution from the correlogram is modeled as the combination of random errors and systemic deviations. By developing a new, to the best of our knowledge, recovery algorithm, the phase noise can be separated from the linear map and significantly attenuated. Based on the proposed algorithm, the spectrum features of white light LEDs and halogen lamps are investigated in detail. The inner products defined by three selected points are employed to generate a coefficient to evaluate the linearity of an unwrapped phase map within a certain spectrum region. The optimal spectrum range corresponding to the best measurement performance can then be located where the coefficient approximates 1 and the spectrum energy stays relatively high. The simulations are carried out under different levels of SNR and scan step noises, which show that the new method can effectively reduce additional disturbance from the recovered topography. In experiments, the system with the proposed method is first calibrated by a step height standard (VLSI, 182.7±2.0nm) with the repeatability of 0.44 nm. A silicon wafer and three roughness standards are also tested to further verify the robustness of the new method.

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions.

Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the ai-QM/MM and semiempirical (se) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the ai-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol-1 Å-1, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results at only a fraction of the computational cost.

Theoretical Study of the Spectral Differences of the Fenna-Matthews-Olson Protein from Different Species and Their Mutants.

The structural basis for the spectral differences between the Fenna-Matthews-Olson (FMO) proteins from Chlorobaculum tepidum (C. tepidum) and Prosthecochloris aestuarii 2K (P. aestuarii) is yet to be fully understood. Mutation-induced perturbation to the exciton structure and the optical spectra of the complex provide a suitable means to investigate the critical role played by the protein scaffold. In this work, we have performed quantum-mechanics/molecular-mechanics calculations over the molecular dynamics simulation trajectories with the polarized protein-specific charge scheme for both wild-type FMOs and two mutants. Our result reveals that a single-point mutation in the vicinity of BChl 6, namely, W183F of C. tepidum, significantly affects the absorption spectrum, resulting in a switch of the absorption spectrum from type 2, for which the 806 nm band is more pronounced than the 815 nm band, to type 1, for which the 815 nm band is pronounced. Our observations agree with the single-point mutation experiments reported by Saer et al. (Biochim. Biophys. Acta, Bioenerg. 2017, 1858, 288-296) and Khmelnitskiy et al. (J. Phys. Chem. Lett. 2018, 9, 3378-3386). In contrast, the absorption spectrum of the P. aestuarii experiences the opposite transition (from type 1 to type 2) upon the same mutation. Furthermore, by comparing the contributions of individual pigments to the spectra in the wild type and its mutant, we find that a single-point mutation near BChl 6 not only induces changes in excitation energy of BChl 6 per se but also affects the excitonic structures of the neighboring BChls 5 and 7 through strong interpigment electronic couplings, resulting in a significant change in the absorption spectra.

Research on Vibration Reduction Method of Nonpneumatic Tire Spoke Based on the Mechanical Properties of Domestic cat's Paw Pads.

Although there is no risk of puncture, the vibration problem caused by discontinuous structures limits nonpneumatic tire development (NPT). The vibration reduction of nonpneumatic tires is a solvable urgent problem. This current study analyzed the dynamic grounding characteristics and the vibration reduction mechanism of the cat's paw pads and then applied the mechanical properties to the bionic design of nonpneumatic tire spokes to solve the vibration problem. Domestic cats' paw pads' dynamic grounding characteristics were determined using the pressure-sensitive walkway, high-speed camera, and VIC-2D. The results indicated that the mechanical characteristics of swing deformation of paw pads during the grounding process attenuated the grounding stress and buffered the energy storage to achieve the vibration reduction effect. According to the similarity transformation, a finite element model of NPT that could accurately reconstruct the structure and realistically reflect the load deformation was employed. The structure design of asymmetric arcs on the spokes' side edges was proposed, and it can effectively reduce the radial excitation force of NPT. The three parameters, the asymmetric arc, the thickness, and the curvature of spokes, were used as design variables to maximize the vibration reduction. The orthogonal experimental, the Kriging approximate model, and the genetic algorithm were carefully selected for optimal solutions. Compared with the original tire, the results showed that peak amplitude 1, peak amplitude 2, and the root square of the optimized tire's amplitudes were reduced by 76.07%, 52.88%, and 51.65%, respectively. These research results offer great potential guidance in the design of low-vibration NPT.

Free energy change estimation: The Divide and Conquer MBAR method.

In the present study, the Divide and Conquer MBAR (DC-MBAR) method is proposed to predict the free energies based on the data sampled by multi-states simulations. For DC-MBAR method, the overlap between any two alchemical states is calculated first and those with sufficient overlap are defined as the adjacent states. Unlike the traditional MBAR method, which calculates the free energy of each state using all the data at once, DC-MBAR focuses on predicting the free energy changes between adjacent states. To estimate the free energy changes accurately, the other states with overlaps with the two adjacent states bigger than the defined threshold are included in the MBAR equation. At a specific threshold, the free energies predicted by DC-MBAR are very close to those calculated by the traditional MABR method. Furthermore, DC-MBAR scheme can reduce both the computation and memory cost. One important characteristic of DC-MBAR method is linear scaling, which means the CPU time with the change of the number of states is a straight-line relation. As the pair-based calculations are mutually independent and parallelizable, all accessible CPU cores on the HPC cluster could be utilized, which makes DC-MBAR strategy more efficient.

Inhibitor of DNA-binding family regulates the prognosis of ovarian cancer.

Aim: The mechanistic role of inhibitor of DNA binding or differentiation (ID) family in ovarian cancer (OC) has remained unclear. Materials & methods: We used the Oncomine, GEPIA, Kaplan-Meier Plotter, cBioPortal, SurvExpress, PROGgene V2, TIMER, and FunRich to evaluate the prognostic value of IDs in patients with OC. Results: the mRNA transcripts of all IDs were markedly downregulated in OC compared with normal tissue. The prognostic value of IDs was also explored within the subtypes, pathological stages, clinical stages and TP53 mutational status. The group with low-risk IDs showed relatively good overall survival (OS) compared with the high-risk group. Conclusion: ID1/3/4 may be exploited as promising prognostic biomarkers and therapeutic targets in OC patients.

Accelerated Computation of Free Energy Profile at Ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semiempirical Reference Potential. 4. Adaptive QM/MM.

Although quantum mechanical/molecular mechanics (QM/MM) methods are now routinely applied to the studies of chemical reactions in condensed phases and enzymatic reactions, they may experience technical difficulties when the reactive region is varying over time. For instance, when the solvent molecules are directly participating in the reaction, the exchange of water molecules between the QM and MM regions may occur on a time scale comparable to the reaction time. To cope with this situation, several adaptive QM/MM schemes have been proposed. However, these methods either add significantly to the computational cost or introduce artificial restraints to the system. In this work, we developed a novel adaptive QM/MM scheme and applied it to the study of a nucleophilic addition reaction. In this scheme, the configuration sampling was performed with a small QM region (without solvent molecules), and the thermodynamic properties under another potential energy function with a larger QM region (with a certain number of solvent molecules and/or different levels of QM theory) are computed via extrapolation using the reference-potential method. Our simulation results show that this adaptive QM/MM scheme is numerically stable, at least for the case studied in this work. Furthermore, this method also offers an inexpensive way to examine the convergence of the QM/MM calculation with respect to the size of the QM region.