The highly diversified composition of lipid bilayers across living cells is crucial for many biological processes. Lipid bilayers mainly consist of phosphatidylcholines (PC), phosphatidylethanolamines (PE), sphingomyelin (SM), and cholesterol, with eukaryotic membranes containing high percentage of sphingomyelin and cholesterol. In this study, we have modeled bilayers with different concentration of PC, PE, and SM to understand the changes in bilayer properties with varied SM concentrations. In addition, membrane models with 33% cholesterol have been simulated to understand the influence of cholesterol. To quantitatively access the structure and dynamics of membranes, deuterium order parameters (S), mass density profiles, lipid relaxation times, clustering analysis, and radial distribution functions are calculated. The Ss compare favorably with past NMR experiments and increase with an increase in SM content. The surface area calculations showed that on addition of 50% palmitoyl-SM (PSM) surface area decreases (60.0 ± 0.6 Å) from that of pure POPC (64.7 Å), which is further lowered in the presence of cholesterol (44.4 ± 0.2 Å). The lipid axial relaxation time decreases with increase in concentration of glycerophospholipids. The accuracy of these lipid membranes allows for future studies with more complex lipid mixtures containing SM to represent the diversity of lipids in natural membranes.
Background: Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process. Objective: The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design. Method: This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained. Results: This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations. Conclusion: The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.
Selective modulators of GABA(A) alpha(3) (gamma amino butyric acid alpha(3)) receptor are known to alleviate the side effects associated with nonspecific modulators. A follow up study was undertaken on a series of functionally selective phthalazines with an ideological credo of identifying more potent isofunctional chemotypes. A bioisosteric database enumerated using the combichem approach endorsed mining in a lead-like chemical space. Primary screening of the massive library was undertaken using the "Miscreen" toolkit, which uses sophisticated bayesian statistics for calculating bioactivity score. The resulting subset, thus, obtained was mined using a novel proteo-chemometric method that integrates molecular docking and QSAR formalism termed CoIFA (comparative interaction fingerprint analysis). CoIFA encodes protein-ligand interaction terms as propensity values based on a statistical inference to construct categorical QSAR models that assist in decision making during virtual screening. In the absence of an experimentally resolved structure of GABA(A) alpha(3) receptor, standard comparative modeling techniques were employed to construct a homology model of GABA(A) alpha(3) receptor. A typical docking study was then carried out on the modeled structure, and the interaction fingerprints generated based on the docked binding mode were used to derive propensity values for the interacting atom pairs that served as pseudo-energy variables to generate a CoIFA model. The classification accuracy of the CoIFA model was validated using different metrics derived from a confusion matrix. Further predictive lead mining was carried out using a consensus two-dimensional QSAR approach, which offers a better predictive protocol compared to the arbitrary choice of a single QSAR model. The predictive ability of the generated model was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. Few analogs designed using the concept of bioisosterism were found to be promising and could be considered for synthesis and subsequent screening.
Recent disclosure of high resolution crystal structures of Gloeobacter violaceus (GLIC) in open state and Erwinia chrysanthemii (ELIC) in closed state provides newer avenues to advance our knowledge and understanding of the physiologically and pharmacologically important ionotropic GABA(A) ion channel. The present modeling study envisions understanding the complex molecular transitions involved in ionic conductance, which were not evident in earlier disclosed homology models. In particular, emphasis was put on understanding the structural basis of gating, gating transition from the closed to the open state on an atomic scale. Homology modeling of two different physiological states of GABA(A) was carried out using their respective templates. The ability of induced fit docking in breaking the critical inter residue salt bridge (Glu155β(2) and Arg207β(2)) upon endogenous GABA docking reflects the perceived side chain rearrangements that occur at the orthosteric site and consolidate the quality of the model. Biophysical calculations like electrostatic mapping, pore radius calculation, ion solvation profile, and normal-mode analysis (NMA) were undertaken to address pertinent questions like the following: How the change in state of the ion channel alters the electrostatic environment across the lumen; How accessible is the Cl(-) ion in the open state and closed state; What structural changes regulate channel gating. A "Twist to Turn" global motion evinced at the quaternary level accompanied by tilting and rotation of the M2 helices along the membrane normal rationalizes the structural transition involved in gating. This perceived global motion hints toward a conserved gating mechanism among pLGIC. To paraphrase, this modeling study proves to be a reliable framework for understanding the structure function relationship of the hitherto unresolved GABA(A) ion channel. The modeled structures presented herein not only reveal the structurally distinct conformational states of the GABA(A) ion channel but also explain the biophysical difference between the respective states.
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