DDT - Drug Discovery Tool: a fast and intuitive graphics user interface for docking and molecular dynamics analysis

Inhibition of dipeptidyl peptidase-4 (DPP4) activity has emerged as a promising therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM). Bioinformatics-driven approaches have emerged as crucial tools in drug discovery. Molecular docking and molecular dynamics (MD) simulations are effective tools in drug discovery, as they reduce the time and cost associated with experimental screening. In this study, we employed structure-assisted in-silico methods, including molecular docking and MD simulations, to identify SRT2183, a small molecule that may potentially inhibit the activity of DPP4 enzyme. The interaction between the small molecule "SRT2183" and DPP4 exhibited a binding affinity of −9.9 Kcal/Mol, leading to the formation of hydrogen bonds with the amino acid residues MET348, SER376, and THR351 of DPP4. The MD simulations over a period of 100 ns indicated stable protein-ligand interactions, with no significant conformational rearrangements observed within the simulated timeframe. In conclusion, our results suggest that the small molecule SRT2183 may have the potential to inhibit the DPP4 enzyme and pave the way for the therapeutics of T2DM.

The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macromolecules could in principle lead to important scientific advances and provide a powerful new tool for drug discovery. A wide range of biologically interesting phenomena, however, occur over time scales on the order of a millisecond - several orders of magnitude beyond the duration of the longest current MD simulations. Our research group is currently building a specialized, massively parallel machine, called Anton, which should soon be capable of executing millisecond-scale MD simulations of proteins at an atomic level of detail. Antonpsilas highly accelerated execution of such simulations is attributable in large part to specialized logic for the high-speed calculation of pairwise interactions between particles and/or gridpoints separated by no more than some specified cutoff radius.In particular, each of Anton's 512 ASICs, which are implemented using 90-nm technology,includes a "high-throughput interaction subsystem" incorporating 32 highly specialized pipelines running at 800 MHz. During every cycle, each of these pipelines produces a pairwise-interaction result that would require approximately 50 arithmetic operations to calculate on a general-purpose processor. Novel algorithms and architectural features are used to greatly reduce the requirements for inter- and intra-chip communication, allowing Anton to feed these pipelines and collect their results at a speed sufficient to take advantage of the machine's computational power. The ASIC also includes a "flexible subsystem" based on eight programmable "geometry cores", each containing eight arithmetic pipelines. This talk will provide an overview of our work on parallel algorithms and machine architectures for high-speed MD simulation, with special attention to the respective roles of specialized vs. general-purpose hardware, and to the techniques used to minimize communication at various levels within the system.

Molecular dynamics simulations of biomolecules, proteins especially, have emerged as an important tool in the study of the conformational change, flexibility, and dynamics. These simulations, especially when combined with virtual screening, have been a tool in drug discovery. Herein, we cover the basics of molecular dynamics simulation, in the hopes that a reader would be able to intelligently conduct a simulation of their favorite protein(s), analyze the results in order to make hypotheses about the links between protein dynamics and conformation. We also discuss the integration between molecular dynamics and virtual screening, so that a reader could use the results of simulations to perform virtual screening for lead identification. Finally, we review several case studies to show what sort of information can be gained by simulation of biomedically interesting proteins, and how that may impact drug discovery, as well as a discussion of some areas in which simulation may prove more useful in the near future.

Functional and computational studies reveal that the allosteric inhibition mechanism is conserved between glutamate and neutral amino acid transporters of the SLC1 family, and identify a novel allosteric inhibitor.

ObjectivesTo evaluate the drug-target interaction of the antibacterial compounds from the Streptomyces chrestomyceticus ADP4.MethodsStaphylococcus aureus is of the WHO priority pathogen, causative agent for multiple soft tissue and skin infections, gastroenteritis etc. making it a suitable candidate for investigation of the drug target based studies. Computational biology has emerged as promising, sustainable and efficient technique for shortlisting, screening and to predict drug target interaction and possible antibacterial mechanisms involved. The study involves molecular docking studies of compounds with the bacterial antibacterial targets of S. aureus which includes topoisomerase, metabolic enzymes such as malate dehydrogenase, dihydrofolate reductase etc. The protein structures were collected from protein data bank and homology models were constructed using templates with high sequence identity to get reliable 3D structure. The blind docking was performed using the PyRx software using the AutoDock Vina. Molecular dynamic (MD) simulation analysis was carried out using GROMACS tool. The drug-target complexes were screened and selected based on the threshold binding energy from molecular docking. The trajectory obtained from MD simulation were analysed for the time dependent interaction, conformational changes, stability, residue flexibility, compactness, solvent accessible surface area (SASA) of the drug- target complexes.ResultsMolecular Docking analysis showed that the compounds interact and bind with target enzyme with intermediate binding affinity upto-7kcal/mol with decent stability and interaction in MD analysis. It further indicates the compounds as promising antibacterial agents with further scope of lead optimization for better antimicrobial potential.ConclusionsThe study also highlights on the mode of action of the compounds by predicting the inhibition of the key metabolic enzymes and therefore metabolic pathways leading to bactericidal activity. This approach helps fast tracking the drug discovery initiatives time bound and early stage identification of the antimicrobial molecules.

Cancer is a complex and life‐threatening disease characterized by uncontrolled tumor cell growth. Targeting the MDM2 protein to reactivate the tumor suppressor p53 presents a promising therapeutic strategy. This study employed an energy‐optimized pharmacophore model to perform a virtual screening of 47,451 compounds from the comprehensive marine natural product database (CMNPD), leveraging the rich diversity of marine‐derived bioactives with high efficacy and low side effects. The screening workflow combined ADMET profiling and molecular docking using HTVS, SP and XP methods to identify potential MDM2 inhibitors. Six top hits exhibited strong binding affinities and favorable interactions compared to standard drugs. Molecular dynamics (MD) simulations and density function theory (DFT) studies were conducted to evaluate the structural stability and electronic properties of the protein–ligand complexes. MM‐GBSA calculations quantified the binding free energies, confirming the stability of these complexes. Specifically, comp‐4 and 6 showed low energy gaps in DFT analysis, indicating strong ligand affinity. Principal component analysis (PCA) and free energy landscape (FEL) evaluations further demonstrated stable conformational behavior throughout the MD simulation trajectories. Collectively, these integrated computational approaches identified potent and stable MDM2 inhibitors, providing promising candidates for experimental validation and further development as anticancer therapeutics.

Dynamics of Topology-dependent Water Purification by Siliceous Zeolite Membranes

A new formulation of the boundary conditions in Couette flow geometry was used to enforce a no-slip condition at the boundaries. This condition was used employing two counter-moving boundaries to enforce the symmetry of the flow. Molecular dynamics (MD) simulations employing Lenard–Jones potential were performed to derive shear force, the gradient of the velocity, and consequently, the shear velocity. MD simulations were preformed for high pressures, up to 1 GPa, and low temperatures, up to 500 K, i.e. for the region dominated by the interaction potential. The obtained results were compared with the existing experimental data [E.W. Lemmon and R.T. Jacobsen, Viscosity and thermal conductivity equations for nitrogen, oxygen, argon, and air, Int. J. Thermophys. 25(1) (2004), pp. 21–69]. It is shown that MD results are in very good agreement with the experiments, having a difference not higher than 4% for all the considered results. From the analysis of these results, it also follows that the dominant portion of the error is due to insufficiently precise determination of the velocity gradient in MD simulations.

This Ph.D. thesis describes three research projects in theoretical and computational biophysics aimed at studying the dynamics of water and lipid molecules in two distinct biomolecular systems. In the first project the energetics and dynamics of water transport through the aquaglyceroporin (GlpF) channel protein is studied by a combination of all-atom molecular dynamics (MD) simulations (both equilibrium and non-equilibrium MD) and stochastic modeling. The MD results are used to determine the potential of mean force and the diffusion coefficient of water molecules crossing the channel. Then, the latter quantities are used to estimate the intrinsic water flux through GlpF, and the corresponding channel permeability. In the second project, by employing a 0.1 micro second long, all-atom MD simulation, the self and correlated dynamics of lipid atoms and molecules in a fully hydrated DMPC lipid bilayer is investigated. The MD simulation results are used to develop a memory function based approach for accurately calculating the lateral self-diffusion coefficient of lipids. In some cases, the proposed memory function method provides a better approach than the currently used ones for determining the lateral diffusion coefficient of lipids in lipid bilayers from the dynamic structure factor measured in inelastic neutron scattering experiments. The purpose of the third project is to investigate the dynamics of water molecules in a hydrated lipid membrane. Using the same MD simulation as in the second project, the anomalous properties of buried and hydration waters (located at the proximity of the fluctuating surface of the lipid membrane) are revealed and contrasted to the properties of bulk water.

CC chemokine receptor type-2 (CCR2) is a member of G-protein coupled receptors superfamily, expressed on the cell surface of monocytes and macrophages. It binds to the monocyte chemoattractant protein-1, a CC chemokine, produced at the sites of inflammation and infection. A homology model of human CCR2 receptor based on the recently available C-X-C chemokine recepor-4 crystal structure has been reported. Ligand information was used as an essential element in the homology modeling process. Six known CCR2 antagonists were docked into the model using simple and induced fit docking procedure. Docked complexes were then subjected to visual inspection to check their suitability to explain the experimental data obtained from site directed mutagenesis and structure-activity relationship studies. The homology model was refined, validated, and assessed for its performance in docking-based virtual screening on a set of CCR2 antagonists and decoys. The docked complexes of CCR2 with the known antagonists, TAK779, a dual CCR2/CCR5 antagonist, and Teijin-comp1, a CCR2 specific antagonist were subjected to molecular dynamics (MD) simulations, which further validated the binding modes of these antagonists. B-factor analysis of 20 ns MD simulations demonstrated that Cys190 is helpful in providing structural rigidity to the extracellular loop (EL2). Residues important for CCR2 antagonism were recognized using free energy decomposition studies. The acidic residue Glu291 from TM7, a conserved residue in chemokine receptors, is favorable for the binding of Teijin-comp1 with CCR2 by ΔG of −11.4 kcal/mol. Its contribution arises more from the side chains than the backbone atoms. In addition, Tyr193 from EL2 contributes −0.9 kcal/mol towards the binding of the CCR2 specific antagonist with the receptor. Here, the homology modeling and subsequent molecular modeling studies proved successful in probing the structure of human CCR2 chemokine receptor for the structure-based virtual screening and predicting the binding modes of CCR2 antagonists.

Structure based prediction of a novel GPR120 antagonist based on pharmacophore screening and molecular dynamics simulations

Proteins are highly dynamic macromolecules. To analyze their inherent flexibility, computational biologists often use molecular dynamics (MD) simulations. The quantification of protein flexibility is based on various methods such as Root Mean Square Fluctuations (RMSF) that rely on multiple MD snapshots or Normal Mode Analysis (NMA) that rely on a single structure and focus on quantifying large movements. Alternative in silico approaches assess protein motions through the protein residue network or dynamical correlations from MD simulations. An alternative yet powerful approach based on small prototypes or “structural alphabets” (SAs) can be used. SAs approximate conformations of protein backbones and code the local structures of proteins as one-dimensional sequences. Protein Blocks (PBs) are one of these SAs. Applying PB-based approaches to biological systems such as the DARC protein, the human αIIb β3 integrin and the KISSR1 protein highlighted the major interest of PBs in understanding local deformations of large protein structures. Specifically, these analyses have shown that a region considered as highly flexible through RMSF quantifications can be seen using PBs as locally highly rigid. This unexpected behavior is explained by a local rigidity surrounded by deformable regions. To go further, we used PBs to analyze long-range allosteric interactions in the Calf-1 domain of αIIb integrin and will also present new unpublished results. Our tool named PBxplore allows the analyses of MDs in terms of PBs and quantification with entropy index and also different types of visualization. The presentation of this research will be done in 5 successive parts: (i) the presentation and interest of the PBs, (ii) the MD of the integrins, (iii) the analysis of the results using PBs with the PBxplore software, (iv) recent developments based on PBxplore to compare reference and variant proteins, i.e. providing information about potential allosteric events and (v) the extension of the analyses to the disordered protein.

The dynamics of α-amylase inhibitors has been investigated using molecular dynamics (MD) simulations and two analytical approaches, the Gaussian network model (GNM) and anisotropic network model (ANM). MD simulations use a full atomic approach with empirical force fields, while the analytical approaches are based on a coarse-grained single-site-per-residue model with a single-parameter harmonic potential between sufficiently close (r ≤ 7 Å) residue pairs. The major difference between the GNM and the ANM is that no directional preferences can be obtained in the GNM, all residue fluctuations being theoretically isotropic, while ANM does incorporate directional preferences. The dominant modes of motions are identified by (i) the singular value decomposition (SVD) of the MD trajectory matrices, and (ii) the similarity transformation of the Kirchhoff matrices of inter-residue contacts in the GNM or ANM. The mean-square fluctuations of individual residues and the cross-correlations between domain movements retain the same characteristics, in all approaches—although the dispersion of modes and detailed amplitudes of motion obtained in the ANM conform more closely with MD results. The major weakness of the analytical approaches appears, on the other hand, to be their inadequacy to account for the anharmonic motions or multimeric transitions driven by the slowest collective mode observed in MD. Such motions usually suffer, however, from MD sampling inefficiencies, and multiple independent runs should be tested before making conclusions about their validity and detailed mechanisms. Overall this study invites attention to (i) the robustness of the average properties (mean-square fluctuations, cross-correlations) controlled by the low frequency motions, which are invariably reproduced in all approaches, and (ii) the utility and efficiency of the ANM, the computational time cost of which is of the order of “minutes” (real time), as opposed to “days” for MD simulations. Proteins 2000;40:512–524. © 2000 Wiley-Liss, Inc.

Sigma-2 (σ2) receptor is a transmembrane protein shown to be linked with neurodegenerative diseases and cancer development. Thus, it emerges as a potential biological target for the advancement of anticancer and anti-Alzheimer’s agents. The current study was aimed to identify potential σ2 receptor ligands using integrated computational approaches including homology modelling, combined pharmacophore- and docking-based virtual screening, and molecular dynamics (MD) simulation. Pharmacophore-based screening was conducted against a database composed of 20,523 small natural and natural-like products. In total, 1200 structures were found to satisfy the required pharmacophore features and were then exposed to docking-based screening against the generated homology model of σ2 receptor. On the basis of the pharmacophore fit scores, docking scores, and mechanism of binding interaction, 20 potential hits were retained. Five promising candidates were selected (SR84, SR823, SR300, SR413, and SR530) on the basis of their binding score and interaction. Further, in silico ADMET profiling of these compounds showed that the selected compounds possess favourable ADME properties with low toxicity risk. The mechanism of interaction of these compounds with σ2 receptor as well as their binding stability were characterized by MD simulation.

Exploration of the structural mechanism of hydrogen (H2)-promoted horseradish peroxidase (HRP) activity via multiple spectroscopic and molecular dynamics simulation techniques