Concerted Movement in pH-Dependent Gating of FocA from Molecular Dynamics Simulations

FocA, a member of the formate-nitrite transporter (FNT) family, transports formate and nitrite across biological membranes in cellular organisms. The export and uptake of formate in bacteria are both mediated by FocA, which undergoes a pH-dependent functional switch. Recently, the crystal structures of <i>Escherichia coli</i> FocA (EcFocA), <i>Vibrio cholerae</i> FocA (VcFocA), and <i>Salmonella typhimurium</i> FocA (StFocA) were reported. We performed molecular dynamics (MD) on StFocA and EcFocA with different states of His209 (protonated and unprotonated), representing different pH conditions of FocA. The N-terminal helix in each protomer of StFocA covers and blocks the formate channel. At neutral or high pH (MD simulations with unprotonated His209), the concerted movement of the N-terminal helices of pairs of protomers of StFocA opens its formate channel. At low pH (MD simulations with protonated His209), protonated His209 interacts tightly with its neighboring residue Asn262, and the channel becomes narrower, so that the formate can hardly pass through the channel. We obtained similar results for EcFocA. Our study shows that pairs of protomers of FocA move in a concerted way to achieve its pH-dependent gating function, which provides information on the dynamics of the gating mechanism of FNT proteins and aquaporins

Studies on the Interactions between β₂ Adrenergic Receptor and Gs Protein by Molecular Dynamics Simulations

The β₂ adrenergic receptor (β₂AR) plays a key role in the control of smooth muscle relaxation in airways, the therapy of asthma, and a series of other basic physiological functions. Recently, the crystal structure of the β₂AR–Gs protein complex was reported, which facilitates study of the activation mechanism of the β₂AR and G-protein-coupled receptors (GPCRs). In this work, we perform 20 ns molecular dynamics (MD) simulations of the β₂AR–Gs protein complex with its agonist in an explicit lipid and water environment to investigate the activation mechanism of β₂AR. We find that during 20 ns MD simulation with a nanobody bound the interaction between the β₂AR and the Gs protein is stable and the whole system is equilibrated within 6 ns. However, without a nanobody stabilizing the complex, the agonist triggers conformational changes of β₂AR sequentially from the extracellular region to the intracellular region, especially the intracellular parts of TM3, TM5, TM6, and TM7, which directly interact with the Gs protein. Our results show that the β₂AR–Gs protein complex makes conformational changes in the following sequence: (1) an agonist-bound part of β₂AR, (2) the intracellular region of β₂AR, and (3) the Gs protein

Feasibility of Using Molecular Docking-Based Virtual Screening for Searching Dual Target Kinase Inhibitors

Multitarget agents have been extensively explored for solving limited efficacies, poor safety, and resistant profiles of an individual target. Theoretical approaches for searching and designing multitarget agents are critically useful. Here, the performance of molecular docking to search dual-target inhibitors for four kinase pairs (CDK2-GSK3B, EGFR-Src, Lck-Src, and Lck-VEGFR2) was assessed. First, the representative structures for each kinase target were chosen by structural clustering of available crystal structures. Next, the performance of molecular docking to distinguish inhibitors from noninhibitors for each individual kinase target was evaluated. The results show that molecular docking-based virtual screening illustrates good capability to find known inhibitors for individual targets, but the prediction accuracy is structurally dependent. Finally, the performance of molecular docking to identify the dual-target kinase inhibitors for four kinase pairs was evaluated. The analyses show that molecular docking successfully filters out most noninhibitors and achieves promising performance for identifying dual-kinase inhibitors for CDK2-GSK3B and Lck-VEGFR2. But a high false-positive rate leads to low enrichment of true dual-target inhibitors in the final list. This study suggests that molecular docking serves as a useful tool in searching inhibitors against dual or even multiple kinase targets, but integration with other virtual screening tools is necessary for achieving better predictions

Characterization of Domain–Peptide Interaction Interface: Prediction of SH3 Domain-Mediated Protein–Protein Interaction Network in Yeast by Generic Structure-Based Models

Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein–protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue–residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain–peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein–protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain–peptide interactions

Prediction of chemical biodegradability using computational methods

<p>Biodegradability is a key factor to describe the long-time effects of chemicals to be decomposed in the environment. Compared with time-consuming and laborious experimental testing, the use of <i>in silico</i> approaches for assessing chemical biodegradability is highly encouraged by the legislators. In this study, based on an extensive data-set with 547 ready biodegradation (RB) and 1178 non-ready biodegradation (NRB) chemicals, we first examined the differences of the important physico-chemical properties and scaffold architectures between the RB and NRB molecules. We found that compared with the NRB molecules, the RB molecules are usually smaller, more flexible and hydrophilic, and have less polar groups and more complicated structural patterns (ring systems). However, the RB and NRB molecules cannot be well distinguished by any simple property-based or substructure-based rules. Then, the naïve Bayesian classification (NBC) approach was employed to develop classifiers for discriminating the RB and NRB molecules. Based on the 21 physico-chemical properties, 76 <i>VolSurf</i> descriptors and LPFP_4 structural fingerprints, the Bayesian classifier can achieve a sensitivity of 0.877, a specificity of 0.864, a global accuracy of 0.869, a <i>C</i> value of 0.720 and a AUC value of 0.890 for the training set. Besides, the best predictions can be achieved for the classifiers based on the combinations of simple physico-chemical properties, <i>VolSurf</i> descriptors, and LPFP_6 fingerprints for the test set I (AUC = 0.921), and any of the three fingerprint classes (ECFC_6, ECFC_8 or LPFC_4) for the test set II (AUC = 0.901). In addition, 20 structural fragments favourable and unfavourable for ready biodegradation, which were directly generated from the best naive Bayesian classifier, were highlighted and discussed. The results provide useful guidelines/tools for designing promising chemical compounds with good chemical biodegradability.</p

Mechanism of Graphene Oxide as an Enzyme Inhibitor from Molecular Dynamics Simulations

Graphene and its water-soluble derivative, graphene oxide (GO), have attracted huge attention because of their interesting physical and chemical properties, and they have shown wide applications in various fields including biotechnology and biomedicine. Recently, GO has been shown to be the most efficient inhibitor for α-chymotrypsin (ChT) compared with all other artificial inhibitors. However, how GO interacts with bioactive proteins and its potential in enzyme engineering have been rarely explored. In this study, we investigate the interactions between ChT and graphene/GO by using molecular dynamics (MD) simulation. We find that ChT is adsorbed onto the surface of GO or graphene during 100 ns MD simulations. The α-helix of ChT plays as an important anchor to interact with GO. The cationic and hydrophobic residues of ChT form strong interactions with GO, which leads to the deformation of the active site of ChT and the inhibition of ChT. In comparison, the active site of ChT is only slightly affected after ChT adsorbed onto the graphene surface. In addition, the secondary structure of ChT is not affected after it is adsorbed onto GO or graphene surface. Our results illustrate the mechanism of the interaction between GO/graphene and enzyme and provide guidelines for designing efficient artificial inhibitors

The Selective Interaction between Silica Nanoparticles and Enzymes from Molecular Dynamics Simulations

<div><p>Nanoscale particles have become promising materials in many fields, such as cancer therapeutics, diagnosis, imaging, drug delivery, catalysis, as well as biosensors. In order to stimulate and facilitate these applications, there is an urgent need for the understanding of the interaction mode between the nano-particles and proteins. In this study, we investigate the orientation and adsorption between several enzymes (cytochrome c, RNase A, lysozyme) and 4 nm/11 nm silica nanoparticles (SNPs) by using molecular dynamics (MD) simulation. Our results show that three enzymes are adsorbed onto the surfaces of both 4 nm and 11 nm SNPs during our MD simulations and the small SNPs induce greater structural stabilization. The active site of cytochrome c is far away from the surface of 4 nm SNPs, while it is adsorbed onto the surface of 11 nm SNPs. We also explore the influences of different groups (-OH, -COOH, -NH₂ and CH₃) coated onto silica nanoparticles, which show significantly different impacts. Our molecular dynamics results indicate the selective interaction between silicon nanoparticles and enzymes, which is consistent with experimental results. Our study provides useful guides for designing/modifying nanomaterials to interact with proteins for their bio-applications.</p></div

Free energy decomposition of the absolute binding free energy (kcal/mol).

a<p>the deviations were estimated based on the last 4 ns US simulation of each window.</p>b<p>the deviations were estimated based on the minimized pathway of metadynamics from 15 to 22 Å.</p>c<p>the experimental binding free energy was estimated by Δ<i>G</i>_exp = −<i>RT</i>ln<i>K</i>_Kd at 310 K.</p

Base Stable Pyrrolidinium Cations for Alkaline Anion Exchange Membrane Applications

The synthesis and characterization of pyrrolidinium cation based anion exchange membranes (AEMs) are reported. Pyrrolidinium cations with various N-substituents (including methyl, ethyl, butyl, octyl, isopropyl, 2-hydroxylethyl, benzyl, and cyclohexylmethyl groups) were synthesized and investigated with respect to their chemical stability in alkaline media. The influence of substitutions on alkaline stability of pyrrolidinium cations was investigated by quantitative ¹H nuclear magnetic resonance spectroscopy (NMR) and theoretical approaches. <i>N</i>,<i>N</i>-Ethylmethyl-substituted pyrrolidinium cation ([EMPy]⁺) exhibited the highest alkaline stability in this study. The synthesized AEMs based on [EMPy]⁺ show promising alkaline stability in strongly basic solution. The study of this work should provide a feasible way for improving the alkaline stability of pyrrolidinium cation based AEMs

Highly Stable N3-Substituted Imidazolium-Based Alkaline Anion Exchange Membranes: Experimental Studies and Theoretical Calculations

Imidazolium cations with various N3-substituents (including methyl, butyl, heptyl, dodecyl, isopropyl, and diphenylmethyl groups) were synthesized and investigated in terms of their alkaline stability. The effect of the N3-substituent on the alkaline stability was studied by quantitative ¹H NMR spectra and density functional theory (GGA-BLYP) calculations. The isopropyl substituted imidazolium cation ([DMIIm]⁺) with the highest LUMO energy value exhibited the highest alkaline stability in aqueous NaOH. The [DMIIm]⁺ cation also exhibited higher alkaline stability than that of a quaternary ammonium cation, benzyltrimethyl-ammonium ([BTMA]⁺), in CD₃OD/D₂O NaOH solution at elevated temperatures. This observation inspired the preparation of [DMIIm]⁺-based alkaline anion exchange membranes (AEMs) which showed high alkaline stability in alkaline solution