Bimetallic doped carbon dot nanozymes for enhanced sonodynamic and nanocatalytic therapy.

Conventional inorganic semiconductors are not suitable for acting as nanozymes or sonosensitizers for therapeutic nanomedicine owing to the lack of excellent biocompatibility. Biocompatible carbon dots (CDs) exhibit a variety of biological activities due to their adjustable size and surface chemical modification; however, the simultaneous sonodynamic activity and multiple enzyme-mimicking catalytic activity of a single CD have not been reported. Herein, we report the development of bimetallic doped CDs as a high-efficiency nanozyme and sonosensitizer for enhanced sonodynamic therapy (SDT) and nanocatalytic therapy (NCT).

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.

Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published.

Interior pH-sensing residue of human voltage-gated proton channel H1 is histidine 168.

The molecular mechanisms governing the human voltage-gated proton channel hH1 remain elusive. Here, we used membrane-enabled hybrid-solvent continuous constant pH molecular dynamics (CpHMD) simulations with pH replica exchange to further evaluate the structural models of hH1 in the closed (hyperpolarized) and open (depolarized) states recently obtained with MD simulations and explore potential pH-sensing residues. The CpHMD titration at a set of symmetric pH conditions revealed three residues that can gain or lose protons upon channel depolarization.

DeepKa Web Server: High-Throughput Protein p Prediction.

DeepKa is a deep-learning-based protein p predictor proposed in our previous work. In this study, a web server was developed that enables online protein p prediction driven by DeepKa. The web server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job.

Establishment and preliminary application of personalized three-dimensional reconstruction of thyroid gland with automatic detection of thyroid nodules based on ultrasound videos.

Purpose: A well display of the spatial location of thyroid nodules in the thyroid is important for surgical path planning and surgeon-patient communication. The aim of this study was to establish a three-dimensional (3D) reconstruction method of the thyroid gland, thyroid nodule, and carotid artery with automatic detection based on two-dimensional (2D) ultrasound videos, and to evaluate its clinical value.

Methods: Ultrasound videos, including the thyroid gland with nodule, isthmus of thyroid gland, and ipsilateral carotid artery, were recorded.

Circ-POLA2-mediated miR-138-5p/SEMA4C axis affects colon cancer cell activities.

This study aimed to investigate the mechanism of circ-POLA2 in colon cancer (CC). Circ-POLA2, miR-138-5p, and SEMA4C levels in CC tissues and cells were recorded. The influences mediated by circ-POLA2, miR-138-5p or SEMA4C on cell proliferation, migration, invasion, and apoptosis were determined.

Basis for Accurate Protein p Prediction with Machine Learning.

pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by p's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate p prediction is crucial. Here we present a theoretical p data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work.

A Guide to the Continuous Constant pH Molecular Dynamics Methods in Amber and CHARMM [Article v1.0].

Like temperature and pressure, solution pH is an important environmental variable in biomolecular simulations. Virtually all proteins depend on pH to maintain their structure and function. In conventional molecular dynamics (MD) simulations of proteins, pH is implicitly accounted for by assigning and fixing protonation states of titratable sidechains.

circ_0052184 Promotes Colorectal Cancer Progression via Targeting miR-604/HOXA9 Axis.

Background: The mortality rate of colorectal cancer (CRC) ranks second. circRNAs are abnormal expression in some diseases, and their dysregulation is associated with cancer progression. Recent studies have shown that the malignant progression of colorectal cancer is inseparable from the abnormal expression of circRNAs.

Association between polymorphism and susceptibility of osteoporosis in Chinese Mulao people.

To explore the association between the single nucleotide polymorphism (SNP) of leptin receptor () gene and the susceptibility to osteoporosis (OP) among Chinese Mulao people. A total of 738 people were involved. Bone mineral density (BMD) was examined by calcaneus ultrasound attenuation measurement.

Protein p Prediction with Machine Learning.

Protein p prediction is essential for the investigation of the pH-associated relationship between protein structure and function. In this work, we introduce a deep learning-based protein p predictor DeepKa, which is trained and validated with the p values derived from continuous constant-pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here, the CpHMD implemented in the Amber molecular dynamics package has been employed (Huang Y.

Protein Secondary Structure Prediction With a Reductive Deep Learning Method.

Protein secondary structures have been identified as the links in the physical processes of primary sequences, typically random coils, folding into functional tertiary structures that enable proteins to involve a variety of biological events in life science. Therefore, an efficient protein secondary structure predictor is of importance especially when the structure of an amino acid sequence fragment is not solved by high-resolution experiments, such as X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance spectroscopy, which are usually time consuming and expensive. In this paper, a reductive deep learning model MLPRNN has been proposed to predict either 3-state or 8-state protein secondary structures.

Continuous Constant pH Molecular Dynamics Simulations of Transmembrane Proteins.

Many membrane channels, transporters, and receptors utilize a pH gradient or proton coupling to drive functionally relevant conformational transitions. Conventional molecular dynamics simulations employ fixed protonation states, thus neglecting the coupling between protonation and conformational equilibria. Here we describe the membrane-enabled hybrid-solvent continuous constant pH molecular dynamics method for capturing atomic details of proton-coupled conformational dynamics of transmembrane proteins.

Alternative proton-binding site and long-distance coupling in sodium-proton antiporter NhaA.

NhaA is a prototypical sodium-proton antiporter responsible for maintaining cellular ion and volume homeostasis by exchanging two protons for one sodium ion; despite two decades of research, the transport mechanism of NhaA remains poorly understood. Recent crystal structure and computational studies suggested Lys300 as a second proton-binding site; however, functional measurements of several K300 mutants demonstrated electrogenic transport, thereby casting doubt on the role of Lys300. To address the controversy, we carried out state-of-the-art continuous constant pH molecular dynamics simulations of NhaA mutants K300A, K300R, K300Q/D163N, and K300Q/D163N/D133A.

Anti-cross-correlation between the adjacent open and closed durations of Markovian channels.

We show that a non-Markovian behavior can appear in a type of Markovian multimeric channel. Such a channel consists of N independent subunits, and each subunit has at least one open state and more than one closed state. Suppose the open state of the channel is defined as M out of N subunits in the open state with N>M>0.

Effect of Transition-Metal Ions on the Conformation of Encephalin Investigated by Hydrogen/Deuterium Exchange and Theoretical Calculations.

We have studied the effects of different 3d orbitals in divalent transition-metal ions [G = Mn (d), Fe (d), Co (d), Ni (d), Cu (d), or Zn (d)] on the conformations of leucine encephalin (LE) and methionine encephalin (ME) in the gas phase using hydrogen/deuterium exchange mass spectrometry (HDX-MS) and theoretical calculations at the molecular level. The HDX-MS reveals a 1:1 stoichiometric monovalent complex of [LE/ME + G - H] and observed that the different HDX reactivities follow the trend Fe < Co < Ni < Mn < Cu ≈ Zn and that [ME + Mn/Cu/Zn - H] > [LE + Mn/Cu/Zn - H], while [LE + Fe/Co/Ni - H] > [ME + Fe/Co/Ni - H]. We cross-correlated the collision-induced dissociation energies of the complexes with the HDX results and found that the more stable the complex, the harder it is for it to undergo HDX.

Generalized Born Based Continuous Constant pH Molecular Dynamics in Amber: Implementation, Benchmarking and Analysis.

Solution pH plays an important role in structure and dynamics of biomolecular systems; however, pH effects cannot be accurately accounted for in conventional molecular dynamics simulations based on fixed protonation states. Continuous constant pH molecular dynamics (CpHMD) based on the λ-dynamics framework calculates protonation states on the fly during dynamical simulation at a specified pH condition. Here we report the CPU-based implementation of the CpHMD method based on the GBNeck2 generalized Born (GB) implicit-solvent model in the pmemd engine of the Amber molecular dynamics package.

Predicting Catalytic Proton Donors and Nucleophiles in Enzymes: How Adding Dynamics Helps Elucidate the Structure-Function Relationships.

Despite the relevance of understanding structure-function relationships, robust prediction of proton donors and nucleophiles in enzyme active sites remains challenging. Here we tested three types of state-of-the-art computational methods to calculate the p K's of the buried and hydrogen bonded catalytic dyads in five enzymes. We asked the question what determines the p K order, i.

All-Atom Continuous Constant pH Molecular Dynamics With Particle Mesh Ewald and Titratable Water.

Development of a pH stat to properly control solution pH in biomolecular simulations has been a long-standing goal in the community. Toward this goal recent years have witnessed the emergence of the so-called constant pH molecular dynamics methods. However, the accuracy and generality of these methods have been hampered by the use of implicit-solvent models or truncation-based electrostatic schemes.

Mechanism of pH-dependent activation of the sodium-proton antiporter NhaA.

Escherichia coli NhaA is a prototype sodium-proton antiporter, which has been extensively characterized by X-ray crystallography, biochemical and biophysical experiments. However, the identities of proton carriers and details of pH-regulated mechanism remain controversial. Here we report constant pH molecular dynamics data, which reveal that NhaA activation involves a net charge switch of a pH sensor at the entrance of the cytoplasmic funnel and opening of a hydrophobic gate at the end of the funnel.

Conformational Activation of a Transmembrane Proton Channel from Constant pH Molecular Dynamics.

Proton-coupled transmembrane proteins play important roles in human health and diseases; however, detailed mechanisms are often elusive. Experimentally resolving proton positions and structural details is challenging, and conventional molecular dynamics simulations are performed with preassigned and fixed protonation states. To address this challenge, here we illustrate the use of the state-of-the-art continuous constant pH molecular dynamics (CpHMD) to directly describe the activation of the M2 channel of influenza virus, for which abundant experimental data are available.

Channel based generating function approach to the stochastic Hodgkin-Huxley neuronal system.

Internal and external fluctuations, such as channel noise and synaptic noise, contribute to the generation of spontaneous action potentials in neurons. Many different Langevin approaches have been proposed to speed up the computation but with waning accuracy especially at small channel numbers. We apply a generating function approach to the master equation for the ion channel dynamics and further propose two accelerating algorithms, with an accuracy close to the Gillespie algorithm but with much higher efficiency, opening the door for expedited simulation of noisy action potential propagating along axons or other types of noisy signal transduction.

Accurate Langevin approaches to simulate Markovian channel dynamics.

The stochasticity of ion-channels dynamic is significant for physiological processes on neuronal cell membranes. Microscopic simulations of the ion-channel gating with Markov chains can be considered to be an accurate standard. However, such Markovian simulations are computationally demanding for membrane areas of physiologically relevant sizes, which makes the noise-approximating or Langevin equation methods advantageous in many cases.

Frequency and relative prevalence of calcium blips and puffs in a model of small IP₃R clusters.

In this work, we model the local calcium release from clusters with a few inositol 1,4,5-trisphosphate receptor (IP3R) channels, focusing on the stochastic process in which an open channel either triggers other channels to open (as a puff) or fails to cause any channel to open (as a blip). We show that there are linear relations for the interevent interval (including blips and puffs) and the first event latency against the inverse cluster size. However, nonlinearity is found for the interpuff interval and the first puff latency against the inverse cluster size.

Induced dipoles incorporated into all-atom Zn protein simulations with multiscale modeling.

Zinc is found saturated in the deposited Amyloid-beta (Aβ) peptide plaques in Alzheimer's disease (AD) patients' brains. Binding of zinc promotes aggregation of Aβ, including the pathogenic aggregates. Up to now, only the region 1-16 of Aβ complexed with zinc (Aβ(1-16)-Zn) is defined structurally in experiment.