75 research outputs found
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 β<sub>2</sub> Adrenergic Receptor and Gs Protein by Molecular Dynamics Simulations
The β<sub>2</sub> adrenergic receptor (β<sub>2</sub>AR) 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 β<sub>2</sub>AR–Gs protein complex was reported, which facilitates study
of the activation mechanism of the β<sub>2</sub>AR and G-protein-coupled
receptors (GPCRs). In this work, we perform 20 ns molecular dynamics
(MD) simulations of the β<sub>2</sub>AR–Gs protein complex
with its agonist in an explicit lipid and water environment to investigate
the activation mechanism of β<sub>2</sub>AR. We find that during
20 ns MD simulation with a nanobody bound the interaction between
the β<sub>2</sub>AR 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 β<sub>2</sub>AR 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 β<sub>2</sub>AR–Gs protein complex makes conformational
changes in the following sequence: (1) an agonist-bound part of β<sub>2</sub>AR, (2) the intracellular region of β<sub>2</sub>AR,
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, -NH<sub>2</sub> and CH<sub>3</sub>) 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
H-bond interactions between crizotinib and ROS1 tyrosine kinase.
<p>Two and three stable H-bonds were found in WT-ROS1 (A) and G2032R-ROS1 (B), respectively. The mutated residue was colored in yellow, and it can be found that a new hydrogen bond was formed between R2032 and crizotinib in G2032R-ROS1 (green dot line). The time evolutions of the H-bond distance changes were plotted in panels C, D, and E, where the H-bond in WT-ROS1 and G2032R-ROS1 were colored in gray and purple, respectively.</p
Calculated RMSF of Cα atoms vs protein residue number for (a) cytochrome c (103 residues), (b) RNase A (124 residues), and (c) lysozyme (130 residues) during the MD simulation.
<p>A comparison between the RMSF plot for natural structures of proteins, proteins adsorbed onto the surface of 4 nm SNPs, and the proteins adsorbed onto the surface of 11 ns SNPs.</p
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><sub>exp</sub> = −<i>RT</i>ln<i>K</i><sub>Kd</sub> at 310 K.</p
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