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. The latter is triggered by charging of Asp164, the first proton carrier. The second proton carrier Lys300 forms a salt bridge with Asp163 in the inactive state, and releases a proton when a sodium ion binds Asp163. These data reconcile current models and illustrate the power of state-of-the-art molecular dynamics simulations in providing atomic details of proton-coupled transport across membrane which is challenging to elucidate by experimental techniques.
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. BC‐UNet, MTN‐Net, and RDPA‐U‐Net network models were innovatively employed for segmentation of the thyroid glands, the thyroid nodules, and the carotid artery respectively. Marching Cubes algorithm was used for reconstruction, while Laplacian smoothing algorithm was employed to smooth the 3D model surface. Using this model, 20 patients and 15 surgeons completed surveys on the effectiveness of this model for the pre‐surgery demonstration of nodule location as well as surgeon‐patient communication.
Results
The thyroid gland with nodule, isthmus of gland, and carotid artery were reconstructed and displayed. With the 3D model, the understanding of the spatial location of thyroid nodules improved in all three surgeon groups, eliminating the influence of professional levels. In the patient survey, the patients’ understanding of the thyroid nodule location and procedure for surgery were significantly improved. In addition, with the 3D model, the time for doctors to explain to patients was significantly reduced (16.75 vs. 8.85 min, p = 0.001).
Conclusion
To our knowledge, this is the first report of constructing a 3D thyroid model using a deep learning technique for personalized thyroid segmentation based on 2D ultrasound videos. The preliminary clinical application showed that it was conducive to the comprehension of the location of thyroid nodules for surgeons and patients, with significant improvement on the efficiency of surgeon‐patient communication.
Protein p
K
a
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
K
a
...predictor DeepKa, which is
trained and validated with the p
K
a
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.
J. Chem. Inf. Model.
2018
,
58
,
1372
−
1383
29949356
). Notably, to avoid discontinuities at the boundary,
grid charges are proposed to represent protein electrostatics. We
show that the prediction accuracy by DeepKa is close to that by CpHMD
benchmarking simulations, validating DeepKa as an efficient protein
p
K
a
predictor. In addition, the training
and validation sets created in this study can be applied to the development
of machine learning-based protein p
K
a
predictors
in the future. Finally, the grid charge representation is general
and applicable to other topics, such as the protein–ligand
binding affinity prediction.
To explore the association between the single nucleotide polymorphism (SNP) of leptin receptor (LEPR) 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. Six SNPs of LEPR were detected. The genotypes, allele frequencies, linkage disequilibrium, and haplotype were analyzed. BMD decreased with age and males had higher BMD than women. The proportion of normal bone mass decreased with age, and morbidity of OP increased. Three out of six SNPs showed a difference between OP and normal group. Individuals with AA genotype of rs1137100 in OP group outnumber the normal group, AA increased the risk of OP. In rs2767485, CT increased the risk of OP, C allele may be susceptible to OP. TT genotype of rs465555 was susceptible genotype of OP, T locus may be associated with OP. Strong linkage disequilibrium was detected among rs1137100, rs1137101, and rs4655555. Four haplotypes were constructed, among which, AACGCT and GGTGTA increased the risk of OP by 3.9 and 4.2 times, respectively, whereas, GGCGTA reduced 74% of OP susceptibility. The rs1137100, rs2767485, and rs465555 of LEPR were associated with OP in Chinese Mulao people.
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. The prediction accuracy by the MLPRNN on the publicly available benchmark CB513 data set is comparable with those by other state-of-the-art models. More importantly, taking into account the reductive architecture, MLPRNN could be a baseline for future developments.
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. Methods. First, the circ_0052184 expression in clinical tissue and cell samples was analyzed by qRT-PCR. Then, we constructed circ_0052184-silenced CRC cells and detected by qRT-PCR. Furthermore, the proliferation ability of cells was detected by colony formation assay. Cell migration ability was tested by wound healing assay and transwell assay. Cell invasion ability was detected by transwell assay. Results. Expression of circ_0052184 was significantly increased in colorectal cancer cell lines and tissues. Silencing circ_0052184 affected the proliferation, migration, and invasion of colorectal cancer cells. miR-604 was targeted by circ_0052184. The downstream target of miR-604 was HOXA9, and silencing circ_0052184 inhibited HOXA9 expression. The existence of the circ_0052184/miR-604/HOXA9 regulatory network in colorectal cancer was validated. circ_0052184 promoted the occurrence and development of colorectal cancer by targeting the miR-604/HOXA9 axis. Conclusions. Our study revealed that the molecular mechanism of circ_0052184 regulated the miR-604/HOXA9 axis, which might promote the malignant progression of colorectal cancer cells.
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. Starting from the closed crystal structure, simulation reveals a pH-dependent conformational switch to an activated state that resembles the open crystal structure. Importantly, simulation affords the free energy of channel opening coupled to the titration of a histidine tetrad, thereby providing a thermodynamic mechanism for M2 activation, that is consistent with NMR data and resolves the controversy with crystal structures obtained at different pH values. This work illustrates the utility of CpHMD in offering previously unattainable conformational details and thermodynamic information for proton-coupled transmembrane channels and transporters.
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. The performance of the method was tested using pH replica-exchange titration simulations of Asp, Glu and His side chains in 4 miniproteins and 7 enzymes with experimentally known pK a’s, some of which are significantly shifted from the model values. The added computational cost due to CpHMD titration ranges from 11 to 33% for the data set and scales roughly linearly as the ratio between the titrable sites and number of solute atoms. Comparison of the experimental and calculated pK a’s using 2 ns per replica sampling yielded a mean unsigned error of 0.70, a root-mean-squared error of 0.91, and a linear correlation coefficient of 0.79. Though this level of accuracy is similar to the GBSW-based CpHMD in CHARMM, in contrast to the latter, the current implementation was able to reproduce the experimental orders of the pK a’s of the coupled carboxylic dyads. We quantified the sampling errors, which revealed that prolonged simulation is needed to converge pK a’s of several titratable groups involved in salt-bridge-like interactions or deeply buried in the protein interior. Our benchmark data demonstrate that GBNeck2-CpHMD is an attractive tool for protein pK a predictions.
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. Here we report the implementation of the particle mesh Ewald (PME) scheme into the all-atom continuous constant pH molecular dynamics (CpHMD) method, enabling CpHMD to be performed with a standard MD engine at a fractional added computational cost. We demonstrate the performance using pH replica-exchange CpHMD simulations with titratable water for a stringent test set of proteins, HP36, BBL, HEWL, and SNase. With the sampling time of 10 ns per replica, most pK a’s are converged, yielding the average absolute and root-mean-square deviations of 0.61 and 0.77, respectively, from experiment. Linear regression of the calculated vs experimental pK a shifts gives a correlation coefficient of 0.79, a slope of 1, and an intercept near 0. Analysis reveals inadequate sampling of structure relaxation accompanying a protonation-state switch as a major source of the remaining errors, which are reduced as simulation prolongs. These data suggest PME-based CpHMD can be used as a general tool for pH-controlled simulations of macromolecular systems in various environments, enabling atomic insights into pH-dependent phenomena involving not only soluble proteins but also transmembrane proteins, nucleic acids, surfactants, and polysaccharides.
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