Interior pH-sensing residue of human voltage-gated proton channel Hv1 is histidine 168.

The molecular mechanisms governing the human voltage-gated proton channel hHv1 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 hHv1 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. Among them, residue H168 at the intracellular end of the S3 helix switches from the deprotonated to the protonated state and its protonation is correlated with the increased tilting of the S3 helix during the transition from the closed to the open state. Thus, the simulation data suggest H168 as an interior pH sensor, in support of a recent finding based on electrophysiological experiments of Hv1 mutants. We propose that protonation of H168 acts as a key that unlocks the closed channel configuration by increasing the flexibility of the S2-S3 linker, which increases the tilt angle of S3 and enhances the mobility of the S4 helix, thus promoting channel opening. Our work represents an important step toward deciphering the pH-dependent gating mechanism of hHv1.

DeepKa Web Server: High-Throughput Protein pKa Prediction.

DeepKa is a deep-learning-based protein pKa predictor proposed in our previous work. In this study, a web server was developed that enables online protein pKa 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. Two case studies have been attached in order to explain how pKa's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.

Basis for Accurate Protein pKa 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 pKa's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pKa prediction is crucial. Here we present a theoretical pKa data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pKa orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pKa prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pKa predictor and thus can be applied immediately to, for example, pKa database construction, protein design, drug discovery, and so on.

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

Escherichia coli 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. Simulations suggested that K300 mutants maintain the electrogenic transport by utilizing an alternative proton-binding residue Asp133. Surprisingly, while Asp133 is solely responsible for binding the second proton in K300R, Asp133 and Asp163 jointly bind the second proton in K300A, and Asp133 and Asp164 jointly bind two protons in K300Q/D163N. Intriguingly, the coupling between Asp133 and Asp163 or Asp164 is enabled through the proton-coupled hydrogen-bonding network at the flexible intersection of two disrupted helices. These data resolve the controversy and highlight the intricacy of the compensatory transport mechanism of NhaA mutants. Alternative proton-binding site and proton sharing between distant aspartates may represent important general mechanisms of proton-coupled transport in secondary active transporters.

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. 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 p Ka'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 p Ka'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 p Ka's of the coupled carboxylic dyads. We quantified the sampling errors, which revealed that prolonged simulation is needed to converge p Ka'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 p Ka predictions.

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. In this review, we discuss the Langevin-like approaches, including the channel-based and simplified subunit-based stochastic differential equations proposed by Fox and Lu, and the effective Langevin approaches in which colored noise is added to deterministic differential equations. In the framework of Fox and Lu's classical models, several variants of numerical algorithms, which have been recently developed to improve accuracy as well as efficiency, are also discussed. Through the comparison of different simulation algorithms of ion-channel noise with the standard Markovian simulation, we aim to reveal the extent to which the existing Langevin-like methods approximate results using Markovian methods. Open questions for future studies are also discussed.

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. Furthermore, the simulations indicate that the blip fraction among all release events and the blip frequency are increasing with larger basal [Ca(2+)], with blips in turn giving a growing contribution to basal [Ca(2+)]. This result suggests that blips are not just lapses to trigger puffs, but they may also possess a biological function to contribute to the initiation of calcium waves by a preceding increase of basal [Ca(2+)] in cells that have small IP3R clusters.

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. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.

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. The feedback loop of circ-POLA2/miR-138-5p/SEMA4C was surveyed. As measured, circ-POLA2 and SEMA4C were highly expressed, while miR-138-5p was poorly expressed. Meanwhile, circ-POLA2 could mediate SEMA4C through miR-138-5p targeting. Circ-POLA2 knockdown caused the blockade for cell activities, but this effect was alleviated by miR-138-5p inhibition or SEMA4C overexpression. Overall, circ-POLA2 is tumorigenic for CC through miR-138-5p/SEMA4C axis, which may provide a promising molecular target for CC therapy.

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. This is a significant limitation, as the assigned protonation states may be wrong and they may change during dynamics. In this tutorial, we guide the reader in learning and using the various continuous constant pH MD methods in Amber and CHARMM packages, which have been applied to predict pK a values and elucidate proton-coupled conformational dynamics of a variety of proteins including enzymes and membrane transporters.

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

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.

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. 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.

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. Example protocols from our recent application studies of proton channels and ion/substrate transporters are discussed.

Protein pK a Prediction with Machine Learning.

Protein pK 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 pK a predictor DeepKa, which is trained and validated with the pK 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). 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 pK a predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning-based protein pK 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.

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. 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.

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 [G2+ = Mn2+ (d5), Fe2+ (d6), Co2+ (d7), Ni2+ (d8), Cu2+ (d9), or Zn2+ (d10)] 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 Fe2+ < Co2+ < Ni2+ < Mn2+ < Cu2+ ≈ Zn2+ 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. Furthermore, we used theoretical calculations to optimize the favorable conformations of the complexes and found the same interaction structure of G2+ coordination with the five carbonyl oxygens of LE/ME that have different bond lengths. Finally, we calculated the proton affinity (PA) values of the optimized complexes in order to interpret the HDX observations that the higher the PA values, the more difficult it is for the complex to undergo HDX. Overall, both the experiments and the theoretical calculations show that the six metal ions have different effects on the LE/ME conformation, with the low-energy stability of the G2+ 3d orbitals corresponding to more dramatic effects on the LE/ME conformation. In addition, the hardness of the ionic acid corresponding to the fully filled Mn2+ and half-filled Zn2+ orbitals also contributes strongly to the coordination effect; the conformation effect of Fe2+/Co2+/Ni2+ on LE is greater than that on ME, whereas the conformation effect of Mn2+/Cu2+/Zn2+ on ME is greater than that on LE.

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. We show that, although the gating dynamics for each subunit between open and closed states is Markovian, the channel can show a memory behavior of weak anti-cross-correlation between the adjacent open and closed durations. Our study indicates that a non-Markovian binary time series can be obtained from a linear superposition of some independent channel subunits with Markovian gating dynamics.

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 Ka's of the buried and hydrogen bonded catalytic dyads in five enzymes. We asked the question what determines the p Ka order, i.e., what makes a residue proton donor vs a nucleophile. The continuous constant pH molecular dynamics simulations captured the experimental p Ka orders and revealed that the negative nucleophile is stabilized by increased hydrogen bonding and solvent exposure as compared to the proton donor. Surprisingly, this simple trend is not apparent from crystal structures and the static structure-based calculations. While the generality of the findings awaits further testing via a larger set of data, they underscore the role of dynamics in bridging enzyme structures and functions.

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. 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.

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. 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 pKa'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 pKa 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.