Protein-ligand Docking Simulation Research Articles

A Novel Fireworks Algorithm for the Protein-Ligand Docking on the AutoDock

The AutoDock is a widely used protein-ligand docking simulation platform which is a simulator to bring the field of computer-aided drug design (CADD) conveniences. The protein-ligand docking problem is of great significance to design more effective and ideal drugs. In order to solve the protein-ligand docking problem more effectively, we propose an improved fireworks algorithm called FWADOCK. FWADOCK utilizes the position updating information of fireworks with the best fitness value to determine the shape of the fireworks and the distribution of sparks. During the process of searching for the optimal solution, it is also considered that the diversity of understanding and how to avoid premature convergence of the algorithm. The proposed algorithm is tested on some benchmark test cases of protein-ligand docking problems and compared with some other related algorithms from different perspectives on the AutoDock platform. The results show that FWADOCK has a competitive performance in solving the protein-ligand docking problem.

Computationally Guided Discovery and Experimental Validation of Indole-3-acetic Acid Synthesis Pathways.

Elucidating the interaction networks associated with secondary metabolite production in microorganisms is an ongoing challenge made all the more daunting by the rate at which DNA sequencing technology reveals new genes and potential pathways. Developing the culturing methods, expression conditions, and genetic systems needed for validating pathways in newly discovered microorganisms is often not possible. Therefore, new tools and techniques are needed for defining complex metabolic pathways. Here, we describe an in vitro computationally assisted pathway description approach that employs bioinformatic searches of genome databases, protein structural modeling, and protein-ligand-docking simulations to predict the gene products most likely to be involved in a particular secondary metabolite production pathway. This information is then used to direct in vitro reconstructions of the pathway and subsequent confirmation of pathway activity using crude enzyme preparations. As a test system, we elucidated the pathway for biosynthesis of indole-3-acetic acid (IAA) in the plant-associated microbe Pantoea sp. YR343. This organism is capable of metabolizing tryptophan into the plant phytohormone IAA. BLAST analyses identified a likely three-step pathway involving an amino transferase, an indole pyruvate decarboxylase, and a dehydrogenase. However, multiple candidate enzymes were identified at each step, resulting in a large number of potential pathway reconstructions (32 different enzyme combinations). Our approach shows the effectiveness of crude extracts to rapidly elucidate enzymes leading to functional pathways. Results are compared to affinity purified enzymes for select combinations and found to yield similar relative activities. Further, in vitro testing of the pathway reconstructions revealed the "underground" nature of IAA metabolism in Pantoea sp. YR343 and the various mechanisms used to produce IAA. Importantly, our experiments illustrate the scalable integration of computational tools and cell-free enzymatic reactions to identify and validate metabolic pathways in a broadly applicable manner.

Molecular Modeling and Docking of Aquaporin Inhibitors to Reveal New Insights into Schistosomiasis Treatment.

Schistosomiasis (snail fever/bilharzia), a disease caused by parasitic flatworms (schistosomes), infects millions of people worldwide. Aquaporins from these organisms were found to be a potent drug target. We investigate the possible mechanism of inhibition of Aquaporin (AQP) from S.mansoni by 5 drug molecules (Praziquantel, Metrifonate, Artimisinin, Albendazole, and Amoscanate). 3D molecular structure of Aquaporin was obtained through homology modeling and further protein-ligand docking and MD simulation were performed. VAL-75, ASN-91, ALA-220, ASN-222, ARG-225 amino acids were found to play crucial role in ligand binding. TRP-71 and other important residues play major role in hydrophobic interactions stabilizing protein-ligand complexes. We hope that this study (with the newly identified aquaporin target) will support the development of structure and pharmacophore-based novel S. mansoni drugs to control and curb Schistosomiasis.

Sargahydroquinoic Acid in Sargassum Serratifolium Activates Lipid Catabolic Pathways in 3T3-L1 Preadipocytes by Inducing White Adipocyte Browning (P06-108-19)

ObjectivesSargassum serratifolium, a marine brown alga consumed in Asian countries, has shown anti-obesity effect by stimulating white adipose tissue browning. Therefore, the purpose of this study was to investigate lipid catabolic effects of sargahydroquinoic acid (SHQA), which is one of the major bioactive compounds of S. serratifolium by white adipocyte browning effect. MethodsIsolated and purified SHQA from S. serratifolium was used to treat 3T3-L1 preadipocytes to see the effects of lipid catabolism and white adipocyte browning. Glycerol concentration measurement, Oil Red O lipid staining, and triglyceride quantification were conducted to examine the lipid catabolism by SHQA in the 3T3-L1 cells. In addition, real-time PCR and Western Blot assays were used to examine the expressions of genes related with lipid catabolic pathway, mitochondria biogenesis and white adipocyte browning. ResultsSHQA significantly upregulated non-shivering thermogenic gene expressions in 3T3-L1 cells including uncoupling protein 1, peroxisome proliferator-activated receptor γ and PR domain containing 16. SHQA also notably reduced cellular lipid accumulation. Interestingly, peroxisome proliferator-activated receptor (PPAR)γ, which is involved in lipid uptake and adipogenesis was upregulated by SHQA treatment. However, Western Blotting and protein-ligand docking simulation revealed that SHQA activates not only PPARγ but also PPARα and AMP-activated protein kinase (AMPK) α, which are lipid-catabolic proteins. In addition, SHQA treatment markedly elevated lipolysis and the amounts of mitochondria in 3T3-L1 cells. ConclusionsOur results indicate that SHQA may combat obesity and associated metabolic syndromes through lipid catabolic pathways, mitochondria biogenesis and adipocyte browning by activating PPARγ, PPARα, and AMPKα pathways. Funding SourcesThis study was a part of the project, “Development of functional food products with natural materials derived from marine resources” funded by the Ministry of Oceans and Fisheries, Republic of Korea.

Sargahydroquinoic acid, a major compound in Sargassum serratifolium (C. Agardh) C. Agardh, widely activates lipid catabolic pathways, contributing to the formation of beige-like adipocytes

Abstract Sargassum serratifolium (C. Agardh) C. Agardh, a marine brown alga, has been consumed as food and traditional medicine in Asia. Previous study showed that the meroterpenoid-rich fraction of an ethanolic extract of Sargassum serratifolium (C. Agardh) C. Agardh (MES) stimulated adipose tissue browning and inhibited diet-induced obesity and metabolic syndrome. Sargahydroquinoic acid (SHQA) is a major component in MES. It is investigated whether SHQA is a contributor for the anti-obesity effect of MES and whether SHQA directly acts on adipocytes using 3T3-L1 differentiated under the classical and beige adipocyte-inducing conditions. Interestingly, SHQA notably reduced lipid accumulation despite the upregulation of adipogenic transcription factor PPARγ in adipocytes differentiated under both conditions. Western blotting and protein-ligand docking simulation further revealed that SHQA also activates PPARα and AMPKα, lipid-catabolic proteins. Thus, it is further focused on the signaling pathways mediated by these proteins. SHQA markedly elevated lipolysis and mitochondria number, but paradoxically reduced ATP production. When uncoupled metabolic signaling was examined, SHQA significantly increased UCP1-positive adipocytes as well as beige adipocyte markers. In conclusion, SHQA may activate PPARγ, PPARα, and AMPKα, contributing to the stimulation of lipid catabolic pathways and adipocyte browning. Therefore, SHQA may be used as a novel pharmaceutical agent to combat obesity and associated metabolic syndrome.

The Effect of Cofactor Binding on the Conformational Plasticity of the Biological Receptors in Artificial Metalloenzymes: The Case Study of LmrR.

The design of Artificial Metalloenzymes (ArMs), which result from the incorporation of organometallic cofactors into biological structures, has grown steadily in the last two decades and important new-to-Nature reactions have been reached. These type of exercises could greatly benefit from an understanding of the structural impact that the inclusion of organometallic moieties may have on the biological host. To date though, our understanding of this phenomenon is highly partial. This lack of knowledge is one of the elements that condition that first-generation ArMs generally display relatively poor catalytic profiles. In this work, we approach this matter by assessing the dynamics and stability of a series of ArMs resulting from the inclusion, via different anchoring strategies, of a variety of organometallic cofactors into the Lactococcal multidrug resistance regulator (LmrR) protein. To this aim, we coupled standard force field-based techniques such as Protein-Ligand Docking and Molecular Dynamics simulations with a variety of trajectory convergence analyses, capable of assessing both the stability and flexibility of the different systems under study upon the binding of cofactors. Together with the experimental evidence obtained in other studies, we provide an overview on how these changes can affect the catalytic outcomes obtained from the different ArMs. Fundamentally, our results show that the convergence analysis used in this work can assess how the inclusion of synthetic metallic cofactors in proteins can condition different structural modulations of their host. Those conformational modifications are key to the success of the desired catalytic activity and their proper identification can be wisely used to improve the quality and the rate of success of the ArMs.

Structural insights into the binding behavior of isoflavonoid glabridin with human serum albumin

Glabridin (GB), a prenylated isoflavonoid in licorice root has been used as foods and medicines with beneficial biological activities. Human serum albumin (HSA) is the most abundant blood plasma protein binding and transporting ligands such as GB. The present study investigated the interaction between GB and HSA using multi-spectroscopic and molecular docking techniques. Fluorescence spectroscopy demonstrated that GB strongly quenched the tryptophan fluorescence emission of HSA, suggesting that GB-HSA interaction followed a static mode of quenching with a binding constant (K, 105-104 M−1, 25-37 °C) reflecting strong-to-moderate affinity of GB to HSA. Molecular displacement and protein-ligand docking simulations showed that the probable GB-binding positions of HSA exist near the site I of HSA. The hydrophobic interaction is a dominant binding force for forming GB-HSA complexes, and the binding resulted in conformational changes of HSA protein with more β-sheet structures. Furthermore, the complex formation of GB with HSA changed the hydrodynamic size and zeta-potential of HSA with enhanced solubility of GB. The elucidation of GB-HSA binding behavior will provide insights into the application of GB in food and pharmaceutical industries.

Docking with SwissDock.

Protein-ligand docking simulation is central in drug design and development. Therefore, the development of web servers intended to docking simulations is of pivotal importance. SwissDock is a web server dedicated to carrying out protein-ligand docking simulation intuitively and elegantly. SwissDock is based on the protein-ligand docking program EADock DSS and has a simple and integrated interface. The SwissDock allows the user to upload structure files for a protein and a ligand, and returns the results by e-mail. To facilitate the upload of the protein and ligand files, we can prepare these input files using the program UCSF Chimera. In this chapter, we describe how to use UCSF Chimera and SwissDock to perform protein-ligand docking simulations. To illustrate the process, we describe the molecular docking of the competitive inhibitor roscovitine against the structure of human cyclin-dependent kinase 2.

Docking with GemDock.

GEMDOCK is a protein-ligand docking software that makes use of an elegant biologically inspired computational methodology based on the differential evolution algorithm. As any docking program, GEMDOCK has two major features to predict the binding of a small-molecule ligand to the binding site of a protein target: the search algorithm and the scoring function to evaluate the generated poses. The GEMDOCK scoring function uses a piecewise potential energy function integrated into the differential evolutionary algorithm. GEMDOCK has been applied to a wide range of protein systems with docking accuracy similar to other docking programs such as Molegro Virtual Docker, AutoDock4, and AutoDock Vina. In this chapter, we explain how to carry out protein-ligand docking simulations with GEMDOCK. We focus this tutorial on the protein target cyclin-dependent kinase 2.

SAnDReS: A Computational Tool for Docking.

Since the early 1980s, we have witnessed considerable progress in the development and application of docking programs to assess protein-ligand interactions. Most of these applications had as a goal the identification of potential new binders to protein targets. Another remarkable progress is taking place in the determination of the structures of protein-ligand complexes, mostly using X-ray diffraction crystallography. Considering these developments, we have a favorable scenario for the creation of a computational tool that integrates into one workflow all steps involved in molecular docking simulations. We had these goals in mind when we developed the program SAnDReS. This program allows the integration of all computational features related to modern docking studies into one workflow. SAnDReS not only carries out docking simulations but also evaluates several docking protocols allowing the selection of the best approach for a given protein system. SAnDReS is a free and open-source (GNU General Public License) computational environment for running docking simulations. Here, we describe the combination of SAnDReS and AutoDock4 for protein-ligand docking simulations. AutoDock4 is a free program that has been applied to over a thousand receptor-ligand docking simulations. The dataset described in this chapter is available for downloading at https://github.com/azevedolab/sandres.

Docking with AutoDock4.

AutoDock is one of the most popular receptor-ligand docking simulation programs. It was first released in the early 1990s and is in continuous development and adapted to specific protein targets. AutoDock has been applied to a wide range of biological systems. It has been used not only for protein-ligand docking simulation but also for the prediction of binding affinity with good correlation with experimental binding affinity for several protein systems. The latest version makes use of a semi-empirical force field to evaluate protein-ligand binding affinity and for selecting the lowest energy pose in docking simulation. AutoDock4.2.6 has an arsenal of four search algorithms to carry out docking simulation including simulated annealing, genetic algorithm, and Lamarckian algorithm. In this chapter, we describe a tutorial about how to perform docking with AutoDock4. We focus our simulations on the protein target cyclin-dependent kinase 2.

Molecular Docking Simulations with ArgusLab.

Molecular docking is the major computational technique employed in the early stages of computer-aided drug discovery. The availability of free software to carry out docking simulations of protein-ligand systems has allowed for an increasing number of studies using this technique. Among the available free docking programs, we discuss the use of ArgusLab ( http://www.arguslab.com/arguslab.com/ArgusLab.html ) for protein-ligand docking simulation. This easy-to-use computational tool makes use of a genetic algorithm as a search algorithm and a fast scoring function that allows users with minimal experience in the simulations of protein-ligand simulations to carry out docking simulations. In this chapter, we present a detailed tutorial to perform docking simulations using ArgusLab.

Web Services for Molecular Docking Simulations.

Docking process is one of the most significant activities for the analysis of protein-protein or protein-ligand complexes. These tools have become of unique importance when allocated in web services, collaborating scientifically with several areas of knowledge in an interdisciplinary way. Among the several web services dedicated to carrying out molecular docking simulations, we selected the DockThor web service. To illustrate the application of DockThor to protein-ligand docking simulations, we analyzed the docking of a ligand against the structure of epidermal growth factor receptor, an essential molecular marker in cancer research.

Molegro Virtual Docker for Docking.

Molegro Virtual Docker is a protein-ligand docking simulation program that allows us to carry out docking simulations in a fully integrated computational package. MVD has been successfully applied to hundreds of different proteins, with docking performance similar to other docking programs such as AutoDock4 and AutoDock Vina. The program MVD has four search algorithms and four native scoring functions. Considering that we may have water molecules or not in the docking simulations, we have a total of 32 docking protocols. The integration of the programs SAnDReS ( https://github.com/azevedolab/sandres ) and MVD opens the possibility to carry out a detailed statistical analysis of docking results, which adds to the native capabilities of the program MVD. In this chapter, we describe a tutorial to carry out docking simulations with MVD and how to perform a statistical analysis of the docking results with the program SAnDReS. To illustrate the integration of both programs, we describe the redocking simulation focused the cyclin-dependent kinase 2 in complex with a competitive inhibitor.

Multi-Body Interactions in Molecular Docking Program Devised with Key Water Molecules in Protein Binding Sites.

Water molecules play an important role in modeling protein-ligand interactions. However, traditional molecular docking methods often ignore the impact of the water molecules by removing them without any analysis or keeping them as a static part of the proteins or the ligands. Hence, the accuracy of the docking simulations will inevitably be damaged. Here, we introduce a multi-body docking program which incorporates the fixed or the variable number of the key water molecules in protein-ligand docking simulations. The program employed NSGA II, a multi-objective optimization algorithm, to identify the binding poses of the ligand and the key water molecules for a protein. To this end, a force-field-based hydration-specific scoring function was designed to favor estimate the binding affinity considering the key water molecules. The program was evaluated in aspects of the docking accuracy, cross-docking accuracy, and screening efficiency. When the numbers of the key water molecules were treated as fixed-length optimization variables, the docking accuracy of the multi-body docking program achieved a success rate of 80.58% for the best RMSD values for the recruit of the ligands smaller than 2.0 Å. The cross-docking accuracy was investigated on the presence and absence of the key water molecules by four protein targets. The screening efficiency was assessed against those protein targets. Results indicated that the proposed multi-body docking program was with good performance compared with the other programs. On the other side, when the numbers of the key water molecules were treated as variable-length optimization variables, the program obtained comparative performance under the same three evaluation criterions. These results indicated that the multi-body docking with the variable numbers of the water molecules was also efficient. Above all, the multi-body docking program developed in this study was capable of dealing with the problem of the water molecules that explicitly participating in protein-ligand binding.

Exploring Binding Mechanisms between Curcumin and Silkworm 30Kc19 Protein Using Spectroscopic Analyses and Computational Simulations

The curry compound, curcumin exerts multiple health-promotive functions; however, its poor solubility and stability limits its biological applications. In this study, we illuminate intermolecular binding mechanisms in the nano-sized complex of curcumin with silkworm protein, 30Kc19. The intrinsic fluorescence of 30Kc19 was gradually quenched by the increase of curcumin concentrations, which demonstrates molecule-molecule complexations mediated by the fluorophore amino acid residues (Tyr, Trp) in the protein. The fluorescence quenching showed that the binding occurred at 1:1 molar ratio with binding constant of 3.28 × 104 M-1. The results from scanning electron microscopy and dynamic light scattering indicate that the complexes were formed with cubicle shapes and sizes of 200–250 nm at pH 8.0 (zeta-potential < −20 mV). Along with Fourier transform infrared analysis, computational studies of protein-ligand docking simulation suggest a mechanism that curcumin and 30Kc19 forms complexes through specific amino acid residues (Trp174, Trp180, and Trp225) with minimum binding distance (4 A). The complexation of curcumin with 30Kc19 protein effectively suppressed the degradation of curcumin over 10 h and improved its antioxidant activity up to 30%. These findings suggest an application of 30Kc19 for the delivery of waterinsoluble bioactive medicines.

Mechanism of Action of Magnesium Lithospermate B against Aging and Obesity-Induced ER Stress, Insulin Resistance, and Inflammsome Formation in the Liver.

Magnesium lithospermate B (MLB) is the biologically active compound of the water-soluble fraction of Salvia miltiorrhiza. Magnesium lithospermate B exhibits various biological functions, including antidiabetic, neuroprotective, and antioxidant effects. However, its beneficial effects on insulin sensitivity and related signaling pathways in the liver need to be elucidated. Our previous study reported that MLB is a PPARβ/δ agonist in fibroblasts. Because insulin-sensitizing and anti-inflammatory effects of PPARβ/δ has been reported in the liver, we investigated whether MLB has a beneficial effect on insulin-, ER stress- and inflammasome-related signaling in the livers of aging and obese animal models. Western blotting and protein-ligand docking simulation showed that MLB activated PPARβ/δ and improved glucose tolerance in the livers of aging and obese animal models. MLB supplementation ameliorated aging or obesity-induced disruption of insulin signaling in the liver. Consistently, aging and obesity-induced increase in the protein levels of a gluconeogenic phosphoenolpyruvate carboxykinase was decreased by MLB. When molecular signaling pathways related to insulin signaling were examined in the liver, MLB supplementation suppressed ER stress- and inflammasome-related signaling molecules induced by aging and obesity. These results suggest that MLB may improve insulin resistance in the liver at least partially by suppressing ER stress and inflammasome formation in aging and obese animal models.

A Novel Atypical PKC-Iota Inhibitor, Echinochrome A, Enhances Cardiomyocyte Differentiation from Mouse Embryonic Stem Cells.

Echinochrome A (EchA) is a marine bioproduct extracted from sea urchins having antioxidant, antimicrobial, anti-inflammatory, and chelating effects, and is the active component of the clinical drug histochrome. We investigated the potential use of Ech A for inducing cardiomyocyte differentiation from mouse embryonic stem cells (mESCs). We also assessed the effects of Ech A on mitochondrial mass, inner membrane potential (Δψm), reactive oxygen species generation, and levels of Ca2+. To identify the direct target of Ech A, we performed in vitro kinase activity and surface plasmon resonance binding assays. Ech A dose-dependently enhanced cardiomyocyte differentiation with higher beating rates. Ech A (50 μM) increased the mitochondrial mass and membrane potential but did not alter the mitochondrial superoxide and Ca2+ levels. The in vitro kinase activity of the atypical protein kinase C-iota (PKCι) was significantly decreased by 50 μM of Ech A with an IC50 for PKCι activity of 107 μM. Computational protein-ligand docking simulation results suggested the direct binding of Ech A to PKCι, and surface plasmon resonance confirmed the direct binding with a low KD of 6.3 nM. Therefore, Ech A is a potential drug for enhancing cardiomyocyte differentiation from mESCs through direct binding to PKCι and inhibition of its activity.

Combinatorially Screened Peptide as Targeted Covalent Binder: Alteration of Bait-Conjugated Peptide to Reactive Modifier.

A peptide-type covalent binder for a target protein was obtained by combinatorial screening of fluoroprobe-conjugated peptide libraries on bacteriophage T7. The solvatochromic fluoroprobe works as a bait during the affinity selection process of phage display. To obtain the targeted covalent binder, the bait in the selected consensus peptide was altered into a reactive warhead possessing a sulfonyl fluoride. The reaction efficiency and site/position specificity of the covalent conjugation between the binder and the target protein were evaluated by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and rationalized by a protein-ligand docking simulation.

KRDS: a web server for evaluating drug resistance mutations in kinases by molecular docking

Kinases are major targets of anti-cancer therapies owing to their importance in signaling processes that regulate cell growth and proliferation. However, drug resistance has emerged as a major obstacle to cancer therapy. Resistance to drugs has various underlying mechanisms, including the acquisition of mutations at drug binding sites and the resulting reduction in drug binding affinity. Therefore, the identification of mutations that are relevant to drug resistance may be useful to overcome this issue. We hypothesized that these mutations can be identified by combining recent advances in computational methods for protein structure modeling and ligand docking simulation. Hence, we developed a web-based tool named the Kinase Resistance Docking System (KRDS) that enables the assessment of the effects of mutations on kinase-ligand interactions. KRDS receives a list of mutations in kinases, generates structural models of the mutants, performs docking simulations, and reports the results to users. The changes in docking scores and docking conformations can be analyzed to infer the effects of mutations on drug binding and drug resistance. We expect our tool to improve our understanding of drug binding mechanisms and facilitate the development of effective new drugs to overcome resistance related to kinases; it may be particularly useful for biomedical researchers who are not familiar with computational environments. Our tool is available at http://bcbl.kaist.ac.kr/KRDS/.