Identification of potential inhibitors of Mycobacterium tuberculosis shikimate kinase: molecular docking, in silico toxicity and in vitro experiments.

Tuberculosis (TB) is one of the main causes of death from a single pathological agent, Mycobacterium tuberculosis (Mtb). In addition, the emergence of drug-resistant TB strains has exacerbated even further the treatment outcome of TB patients. It is thus needed the search for new therapeutic strategies to improve the current treatment and to circumvent the resistance mechanisms of Mtb. The shikimate kinase (SK) is the fifth enzyme of the shikimate pathway, which is essential for the survival of Mtb. The shikimate pathway is absent in humans, thereby indicating SK as an attractive target for the development of anti-TB drugs. In this work, a combination of in silico and in vitro techniques was used to identify potential inhibitors for SK from Mtb (MtSK). All compounds of our in-house database (Centro de Pesquisas em Biologia Molecular e Funcional, CPBMF) were submitted to in silico toxicity analysis to evaluate the risk of hepatotoxicity. Docking experiments were performed to identify the potential inhibitors of MtSK according to the predicted binding energy. In vitro inhibitory activity of MtSK-catalyzed chemical reaction at a single compound concentration was assessed. Minimum inhibitory concentration values for in vitro growth of pan-sensitive Mtb H37Rv strain were also determined. The mixed approach implemented in this work was able to identify five compounds that inhibit both MtSK and the in vitro growth of Mtb.

Molecular Human Targets of Bioactive Alkaloid-Type Compounds from Tabernaemontana cymose Jacq.

Alkaloids are a group of secondary metabolites that have been widely studied for the discovery of new drugs due to their properties on the central nervous system and their anti-inflammatory, antioxidant and anti-cancer activities. Molecular docking was performed for 10 indole alkaloids identified in the ethanol extract of Tabernaemontana cymosa Jacq. with 951 human targets involved in different diseases. The results were analyzed through the KEGG and STRING databases, finding the most relevant physiological associations for alkaloids. The molecule 5-oxocoronaridine proved to be the most active molecule against human proteins (binding energy affinity average = -9.2 kcal/mol) and the analysis of the interactions between the affected proteins pointed to the PI3K/ Akt/mTOR signaling pathway as the main target. The above indicates that indole alkaloids from T. cymosa constitute a promising source for the search and development of new treatments against different types of cancer.

Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2.

BACKGROUND: The main protease of SARS-CoV-2 (Mpro) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies. OBJECTIVE: Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the Mpro using the program AutoDock4. METHODS: We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for Mpro to analyze intermolecular interactions and reviewed the methods used to search for inhibitors. RESULTS: The application of docking against the structures available for the Mpro found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of Mpro that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity. CONCLUSION: The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of Mpro. Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to Mpro. This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19.

Biased Docking for Protein-Ligand Pose Prediction.

The interaction between a protein and its ligands is one of the basic and most important processes in biological chemistry. Docking methods aim to predict the molecular 3D structure of protein-ligand complexes starting from coordinates of the protein and the ligand separately. They are widely used in both industry and academia, especially in the context of drug development projects. AutoDock4 is one of the most popular docking tools and, as for any docking method, its performance is highly system dependent. Knowledge about specific protein-ligand interactions on a particular target can be used to successfully overcome this limitation. Here, we describe how to apply the AutoDock Bias protocol, a simple and elegant strategy that allows users to incorporate target-specific information through a modified scoring function that biases the ligand structure towards those poses (or conformations) that establish selected interactions. We discuss two examples using different bias sources. In the first, we show how to steer dockings towards interactions derived from crystal structures of the receptor with different ligands; in the second example, we define and apply hydrophobic biases derived from Molecular Dynamics simulations in mixed solvents. Finally, we discuss general concepts of biased docking, its performance in pose prediction, and virtual screening campaigns as well as other potential applications.

Are vanadium complexes druggable against the main protease Mpro of SARS-CoV-2? - A computational approach.

In silico techniques helped explore the binding capacities of the SARS-CoV-2 main protease (Mpro) for a series of metalloorganic compounds. Along with small size vanadium complexes a vanadium-containing derivative of the peptide-like inhibitor N3 (N-[(5-methylisoxazol-3-yl)carbonyl]alanyl-l-valyl-N1-((1R,2Z)-4-(benzyloxy)-4-oxo-1-{[(3R)-2-oxopyrrolidin-3-yl] methyl }but-2-enyl)-l-leucinamide) was designed from the crystal structure with PDB entry code 6LU7. On theoretical grounds our consensus docking studies evaluated the binding affinities at the hitherto known binding site of Chymotrypsin-like protease (3CLpro) of SARS-CoV-2 for existing and designed vanadium complexes. This main virus protease (Mpro) has a Cys-His dyad at the catalytic site that is characteristic of metal-dependent or metal-inhibited hydrolases. Mpro was compared to the human protein-tyrosine phosphatase 1B (hPTP1B) with a comparable catalytic dyad. HPTP1B is a key regulator at an early stage in the signalling cascade of the insulin hormone for glucose uptake into cells. The vanadium-ligand binding site of hPTP1B is located in a larger groove on the surface of Mpro. Vanadium constitutes a well-known phosphate analogue. Hence, its study offers possibilities to design promising vanadium-containing binders to SARS-CoV-2. Given the favourable physicochemical properties of vanadium nuclei, such organic vanadium complexes could become drugs not only for pharmacotherapy but also diagnostic tools for early infection detection in patients. This work presents the in silico design of a potential lead vanadium compound. It was tested along with 20 other vanadium-containing complexes from the literature in a virtual screening test by docking to inhibit Mpro of SARS-CoV-2.

Electrostatic Potential Energy in Protein-Drug Complexes.

BACKGROUND: Electrostatic interactions are one of the forces guiding the binding of molecules to proteins. The assessment of this interaction through computational approaches makes it possible to evaluate the energy of protein-drug complexes. OBJECTIVE: Our purpose here is to review some of the methods used to calculate the electrostatic energy of protein-drug complexes and explore the capacity of these approaches for the generation of new computational tools for drug discovery using the abstraction of scoring function space. METHODS: Here, we present an overview of the AutoDock4 semi-empirical scoring function used to calculate binding affinity for protein-drug complexes. We focus our attention on electrostatic interactions and how to explore recently published results to increase the predictive performance of the computational models to estimate the energetics of protein- drug interactions. Public data available at Binding MOAD, BindingDB, and PDBbind were used to review the predictive performance of different approaches to predict binding affinity. RESULTS: A comprehensive outline of the scoring function used to evaluate potential energy available in docking programs is presented. Recent developments of computational models to predict protein-drug energetics were able to create targeted-scoring functions to predict binding to these proteins. These targeted models outperform classical scoring functions and highlight the importance of electrostatic interactions in the definition of the binding. CONCLUSION: Here, we reviewed the development of scoring functions to predict binding affinity through the application of a semi-empirical free energy scoring function. Our studies show the superior predictive performance of machine learning models when compared with classical scoring functions and the importance of electrostatic interactions for binding affinity.

Computational Evaluation of Interaction Between Curcumin Derivatives and Amyloid-ß Monomers and Fibrils: Relevance to Alzheimer's Disease.

BACKGROUND: The most important hallmark in the neuropathology of Alzheimer's disease (AD) is the formation of amyloid-ß (Aß) fibrils due to the misfolding/aggregation of the Aß peptide. Preventing or reverting the aggregation process has been an active area of research. Naturally occurring products are a potential source of molecules that may be able to inhibit Aß42 peptide aggregation. Recently, we and others reported the anti-aggregating properties of curcumin and some of its derivatives in vitro, presenting an important therapeutic avenue by enhancing these properties. OBJECTIVE: To computationally assess the interaction between Aß peptide and a set of curcumin derivatives previously explored in experimental assays. METHODS: The interactions of ten ligands with Aß monomers were studied by combining molecular dynamics and molecular docking simulations. We present the in silico evaluation of the interaction between these derivatives and the Aß42 peptide, both in the monomeric and fibril forms. RESULTS: The results show that a single substitution in curcumin could significantly enhance the interaction between the derivatives and the Aß42 monomers when compared to a double substitution. In addition, the molecular docking simulations showed that the interaction between the curcumin derivatives and the Aß42 monomers occur in a region critical for peptide aggregation. CONCLUSION: Results showed that a single substitution in curcumin improved the interaction of the ligands with the Aß monomer more so than a double substitution. Our molecular docking studies thus provide important insights for further developing/validating novel curcumin-derived molecules with high therapeutic potential for AD.

AMDock: a versatile graphical tool for assisting molecular docking with Autodock Vina and Autodock4.

AMDock (Assisted Molecular Docking) is a user-friendly graphical tool to assist in the docking of protein-ligand complexes using Autodock Vina and AutoDock4, including the option of using the Autodock4Zn force field for metalloproteins. AMDock integrates several external programs (Open Babel, PDB2PQR, AutoLigand, ADT scripts) to accurately prepare the input structure files and to optimally define the search space, offering several alternatives and different degrees of user supervision. For visualization of molecular structures, AMDock uses PyMOL, starting it automatically with several predefined visualization schemes to aid in setting up the box defining the search space and to visualize and analyze the docking results. One particularly useful feature implemented in AMDock is the off-target docking procedure that allows to conduct ligand selectivity studies easily. In summary, AMDock's functional versatility makes it a very useful tool to conduct different docking studies, especially for beginners. The program is available, either for Windows or Linux, at https://github.com/Valdes-Tresanco-MS . REVIEWERS: This article was reviewed by Alexander Krah and Thomas Gaillard.

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.

Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data?

Molecular docking is the most frequently used computational method for studying the interactions between organic molecules and biological macromolecules. In this context, docking allows predicting the preferred pose of a ligand inside a receptor binding site. However, the selection of the "best" solution is not a trivial task, despite the widely accepted selection criterion that the best pose corresponds to the best energy score. Here, several rigid-target docking methods were evaluated on the same dataset with respect to their ability to reproduce crystallographic binding orientations, to test if the best energy score is a reliable criterion for selecting the best solution. For this, two experiments were performed: (A) to reconstruct the ligand-receptor complex by performing docking of the ligand in its own crystal structure receptor (defined as self-docking), and (B) to reconstruct the ligand-receptor complex by performing docking of the ligand in a crystal structure receptor that contains other ligand (defined as cross-docking). Root-mean square deviation (RMSD) was used to evaluate how different the obtained docking orientation is from the corresponding co-crystallized pose of the same ligand molecule. We found that docking score function is capable of predicting crystallographic binding orientations, but the best ranked solution according to the docking energy is not always the pose that reproduces the experimental binding orientation. This happened when self-docking was achieved, but it was critical in cross-docking. Taking into account that docking is typically used with predictive purposes, during cross-docking experiments, our results indicate that the best energy score is not a reliable criterion to select the best solution in common docking applications. It is strongly recommended to choose the best docking solution according to the scoring function along with additional structural criteria described for analogue ligands to assure the selection of a correct docking solution.

Practices in Molecular Docking and Structure-Based Virtual Screening.

Drug discovery has evolved significantly over the past two decades. Progress in key areas such as molecular and structural biology has contributed to the elucidation of the three-dimensional structure and function of a wide range of biological molecules of therapeutic interest. In this context, the integration of experimental techniques, such as X-ray crystallography, and computational methods, such as molecular docking, has promoted the emergence of several areas in drug discovery, such as structure-based drug design (SBDD). SBDD strategies have been broadly used to identify, predict and optimize the activity of small molecules toward a molecular target and have contributed to major scientific breakthroughs in pharmaceutical R&D. This chapter outlines molecular docking and structure-based virtual screening (SBVS) protocols used to predict the interaction of small molecules with the phosphatidylinositol-bisphosphate-kinase PI3Kδ, which is a molecular target for hematological diseases. A detailed description of the molecular docking and SBVS procedures and an evaluation of the results are provided.

Insights into cytochrome bc1 complex binding mode of antimalarial 2-hydroxy-1, 4-naphthoquinones through molecular modelling

BACKGROUND Malaria persists as a major public health problem. Atovaquone is a drug that inhibits the respiratory chain of Plasmodium falciparum, but with serious limitations like known resistance, low bioavailability and high plasma protein binding. OBJECTIVES The aim of this work was to perform molecular modelling studies of 2-hydroxy-1,4-naphthoquinones analogues of atovaquone on the Qo site of P. falciparum cytochrome bc1 complex (Pfbc1) to suggest structural modifications that could improve their antimalarial activity. METHODS We have built the homology model of the cytochrome b (CYB) and Rieske iron-sulfur protein (ISP) subunits from Pfbc1 and performed the molecular docking of 41 2-hydroxy-1,4-naphthoquinones with known in vitro antimalarial activity and predicted to act on this target. FINDINGS Results suggest that large hydrophobic R2 substituents may be important for filling the deep hydrophobic Qo site pocket. Moreover, our analysis indicates that the H-donor 2-hydroxyl group may not be crucial for efficient binding and inhibition of Pfbc1 by these atovaquone analogues. The C1 carbonyl group (H-acceptor) is more frequently involved in the important hydrogen bonding interaction with His152 of the Rieske ISP subunit. MAIN CONCLUSIONS Additional interactions involving residues such as Ile258 and residues required for efficient catalysis (e.g., Glu261) could be explored in drug design to avoid development of drug resistance by the parasite.

Natural Products as Chemopreventive Agents by Potential Inhibition of the Kinase Domain in ErbB Receptors.

Small molecules found in natural products provide therapeutic benefits due to their pharmacological or biological activity, which may increase or decrease the expression of human epidermal growth factor receptor (HER), a promising target in the modification of signaling cascades involved in excessive cellular growth. In this study, in silico molecular protein-ligand docking protocols were performed with AutoDock Vina in order to evaluate the interaction of 800 natural compounds (NPs) from the NatProd Collection (http://www.msdiscovery.com/natprod.html), with four human HER family members: HER1 (PDB: 2ITW), HER2 (PDB: 3PP0), HER3 (PDB: 3LMG) and HER4 (PDB: 2R4B). The best binding affinity values (kcal/mol) for docking pairs were obtained for HER1-podototarin (-10.7), HER2-hecogenin acetate (-11.2), HER3-hesperidin (-11.5) and HER4-theaflavin (-10.7). The reliability of the theoretical calculations was evaluated employing published data on HER inhibition correlated with in silico binding calculations. IC50 values followed a significant linear relationship with the theoretical binding Affinity data for HER1 (R = 0.656, p < 0.0001) and HER2 (R = 0.543, p < 0.0001), but not for HER4 (R = 0.364, p > 0.05). In short, this methodology allowed the identification of several NPs as HER inhibitors, being useful in the discovery and design of more potent and selective anticancer drugs.

Mechanisms of interaction between persistent organic pollutants (POPs) and CYP2B6: An in silico approach.

Human Cytochrome P450s (CYP450) are a group of heme-containing metalloenzymes responsible for recognition and metabolism of numerous xenobiotics, including drugs and environmental contaminants. CYP2B6, a member of CYP450, is well known for being a highly inducible and polymorphic enzyme and for its important role in the oxidative metabolism of environmental pollutants, such as the Polybrominated Diphenyl Ethers (PBDEs) and Polychlorinated Biphenyls (PCBs). However the mechanisms of interaction of PBDEs and PCBs with CYP2B6 is not entirely known. In this work, a computational approach was carried out to study the interactions of 41 POPs (17 PBDEs, 17 PCBs, and 7 Dioxins) with four CYP2B6 protein structures downloaded from PDB data base (PDB: 3UA5, 3QOA, 3QU8 and 4I91) using molecular docking protocols with AutoDock Vina. The best binding affinity values (kcal/mol) were obtained for PBDE-99 (-8.5), PCB-187 (-9.6), and octachloro-dibenzo-dioxin (-9.8) that can be attributed to the hydrophobic interactions with important residues, such as Phe-363, in the catalytic site of CYP2B6. Molecular docking validation revealed the best values for PDB: 3UA5 (R = 0.622, p = 0.001) demonstrating the reliability of molecular docking predictions. The information obtained in this work can be useful in evaluating the modes of interaction of xenobiotic compounds with the catalytic site of CYP2B6 and provide insights on the important role of these enzymes in the metabolism of potentially toxic compounds in humans.

Potential interaction of components from essential oils with dengue virus proteins / Potencial interacción de componentes de aceites esenciales con proteínas del virus del dengue

An antiviral drug for treatment of dengue is an urgent necessity. In this study in silico activities of essential oils components on dengue virus (DENV) were evaluated, and beta-Caryophyllene was subjected to biological examination to assess inhibition of DENV-2 replication. Components previously optimized were coupled with viral proteins prepared, using AutoDock Vina. Theoretical affinity values varied between -4.0 and -7.3 kcal/mol. alpha-copaene, beta-bourbonene, germacrene D, spathulenol, beta-caryophyllene, caryophyllene oxide and (+)- epi-bicyclosesquiphellandrene showed the greatest interaction with viral proteins. beta-caryophyllene inhibits DENV-2 in vitro (50 percent inhibitory concentration [IC50] = 22.5 +/- 5.6 uM [4.6 +/- 1.1 ug/mL] and resulted non-cytostatic with a selectivity index value of 71.1. The in silico results permit infer that DENV proteins are potential targets for the concomitant docking of various essential oils components. Biological examination suggest that beta-caryophyllene acts on very early steps of the viral replication cycle and it might prove virucidal.

Una droga antiviral para tratamiento del dengue es una necesidad urgente. En este estudio se evaluó la actividad in silico de componentes de aceites esenciales sobre el virus del dengue (VDEN) y el beta-cariofileno se seleccionó para evaluar la inhibición sobre la replicación in vitro del VDEN-2. Los componentes previamente optimizados fueron acoplados con proteínas virales preparadas, utilizando AutoDock Vina. Los valores de afinidad variaron entre -4.0 and -7.3 kcal/mol. alfa-Copaeno, beta-bourboneno, germacreno D, spatulenol, beta- cariofileno, óxido de cariofileno y (+)-epi-biciclosesquifellandreno presentaron la mayor interacción con las proteínas virales. beta-Cariofileno inhibió VDEN-2 in vitro (Concentración inhibitoria 50 [IC50] = 22.5 +/- 5.6 uM [4.6 +/- 1.1 ug/mL] y resultó no-citostático con índice de selectividad de 71.1. Los resultados in silico indican que proteínas del VDEN son blancos potenciales para varios componentes. El análisis biológico sugiere que el beta-cariofileno actúa en etapas tempranas de la replicación viral y podría ser virucida.

Método acoplado Autodock-PM6 para seleccionar la mejor pose en estudios de acoplamiento molecular / Autodock-PM6 method to choose the better pose in molecular docking studies / A Autodock-PM6 metodo pra seleccionar o melhor pose en estudios de acoplamiento molecular

El acoplamiento molecular (conocido como docking) es una técnica de mecánica molecular ampliamente utilizada para predecir energías y modos de enlace entre ligandos y proteínas, información de gran utilidad en el estudio de nuevos compuestos con efectos terapéuticos. No obstante, los resultados obtenidos mediante esta técnica tienden a la subjetividad, debido a que los programas utilizados para llevarla a cabo proporcionan más de un criterio de selección de la mejor pose. En la presente investigación se aplicó el método semiempírico PM6 a los resultados del acoplamiento, obteniendo con ello mejorías en el proceso de selección de la mejor pose pues se obtuvieron poses con alta probabilidad de unión al sitio activo de su receptor y con energías de unión menores a las reportadas por los criterios de selección ofrecidos por el programa de docking.

The acoplamiento molecular (conocido como docking) es una técnica de mecánica molecular ampliamente utilizada para predecir energías y modos de enlace entre ligandos y proteínas, lo que proporciona información de gran utilidad para el estudio de nuevos compuestos con efectos terapéuticos. No obstante, los resultados obtenidos mediante esta técnica tienden a la subjetividad, debido a que los programas utilizados para llevarla a cabo proporcionan más de un criterio de selección de la mejor pose. En la presente investigación, se aplicó el método semiempírico PM6 a los resultados del acoplamiento, obteniendo con ello mejorías en el proceso de selección de la mejor pose al observarse finalmente poses con alta probabilidad de unión al sitio activo de su receptor y con energías de unión menores a las reportadas por los criterios de selección ofrecidos por el programa de docking.

El acoplamiento molecular (conocido como docking) es una técnica de mecánica molecular ampliamente utilizada para predecir energías y modos de enlace entre ligandos y proteínas, lo que proporciona información de gran utilidad para el estudio de nuevos compuestos con efectos terapéuticos. No obstante, los resultados obtenidos mediante esta técnica tienden a la subjetividad, debido a que los programas utilizados para llevarla a cabo proporcionan más de un criterio de selección de la mejor pose. En la presente investigación, se aplicó el método semiempírico PM6 a los resultados del acoplamiento, obteniendo con ello mejorías en el proceso de selección de la mejor pose al observarse finalmente poses con alta probabilidad de unión al sitio activo de su receptor y con energías de unión menores a las reportadas por los criterios de selección ofrecidos por el programa de docking.