Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock

<p>Abstract</p> <p>Background</p> <p>Molecular docking methods are commonly used for predicting binding modes and energies of ligands to proteins. For accurate complex geometry and binding energy estimation, an appropriate method for calculating partial charges is essential. AutoDockTools software, the interface for preparing input files for one of the most widely used docking programs AutoDock 4, utilizes the Gasteiger partial charge calculation method for both protein and ligand charge calculation. However, it has already been shown that more accurate partial charge calculation - and as a consequence, more accurate docking- can be achieved by using quantum chemical methods. For docking calculations quantum chemical partial charge calculation as a routine was only used for ligands so far. The newly developed Mozyme function of MOPAC2009 allows fast partial charge calculation of proteins by quantum mechanical semi-empirical methods. Thus, in the current study, the effect of semi-empirical quantum-mechanical partial charge calculation on docking accuracy could be investigated.</p> <p>Results</p> <p>The docking accuracy of AutoDock 4 using the original AutoDock scoring function was investigated on a set of 53 protein ligand complexes using Gasteiger and PM6 partial charge calculation methods. This has enabled us to compare the effect of the partial charge calculation method on docking accuracy utilizing AutoDock 4 software. Our results showed that the docking accuracy in regard to complex geometry (docking result defined as accurate when the RMSD of the first rank docking result complex is within 2 Å of the experimentally determined X-ray structure) significantly increased when partial charges of the ligands and proteins were calculated with the semi-empirical PM6 method.</p> <p>Out of the 53 complexes analyzed in the course of our study, the geometry of 42 complexes were accurately calculated using PM6 partial charges, while the use of Gasteiger charges resulted in only 28 accurate geometries. The binding affinity estimation was not influenced by the partial charge calculation method - for more accurate binding affinity prediction development of a new scoring function for AutoDock is needed.</p> <p>Conclusion</p> <p>Our results demonstrate that the accuracy of determination of complex geometry using AutoDock 4 for docking calculation greatly increases with the use of quantum chemical partial charge calculation on both the ligands and proteins.</p

Docking glycosaminoglycans to proteins: analysis of solvent inclusion

Glycosaminoglycans (GAGs) are anionic polysaccharides, which participate in key processes in the extracellular matrix by interactions with protein targets. Due to their charged nature, accurate consideration of electrostatic and water-mediated interactions is indispensable for understanding GAGs binding properties. However, solvent is often overlooked in molecular recognition studies. Here we analyze the abundance of solvent in GAG-protein interfaces and investigate the challenges of adding explicit solvent in GAG-protein docking experiments. We observe PDB GAG-protein interfaces being significantly more hydrated than protein–protein interfaces. Furthermore, by applying molecular dynamics approaches we estimate that about half of GAG-protein interactions are water-mediated. With a dataset of eleven GAG-protein complexes we analyze how solvent inclusion affects Autodock 3, eHiTs, MOE and FlexX docking. We develop an approach to de novo place explicit solvent into the binding site prior to docking, which uses the GRID program to predict positions of waters and to locate possible areas of solvent displacement upon ligand binding. To investigate how solvent placement affects docking performance, we compare these results with those obtained by taking into account information about the solvent position in the crystal structure. In general, we observe that inclusion of solvent improves the results obtained with these methods. Our data show that Autodock 3 performs best, though it experiences difficulties to quantitatively reproduce experimental data on specificity of heparin/heparan sulfate disaccharides binding to IL-8. Our work highlights the current challenges of introducing solvent in protein-GAGs recognition studies, which is crucial for exploiting the full potential of these molecules for rational engineering

Neurochemical and Neuropharmacological Studies on a Range of Novel Psychoactive Substances

Introduction: Over recent decades, there has been an increase in the availability and use of Novel Psychoactive Substances (NPS) all over the world. They include several classes of chemicals that mimic the effects of illicit drugs and have been purposefully introduced into the market to circumvent or undermine the purpose of legal regulation. Currently, there is information lacking on the pharmacology of these substances; however, the increasing number of cases and outbreaks of intoxications/deaths is becoming a cause for deepening concern. Multi-disciplinary research in the fields of biology, chemistry, clinical medicine and web analysis is needed to develop responses against this tidal wave. Aim: The overall aim of this project is to gain insights into pharmacological, neurochemical and molecular properties of selected NPS to provide a reliable background needed for detection, assessment, and management of NPS-related harms. A range of approaches and methodologies was employed and a spectrum of different fields of knowledge has been engaged to gain some understanding into the complex multi-faceted phenomenon of NPS. Methods: Different substances have been selected as targets for the present project according to the clinical pattern of toxicity raised by their worldwide use and the lack of scientific knowledge available about them. The methods employed were: in vitro quantitative autoradiography (to evaluate the binding properties of the novel SCs BB-22, 5F-PB-22, 5F-AKB-48 and STS-135 at the cannabinoid receptor type 1 and N-methyl-D-aspartate receptor; and the binding properties of the synthetic stimulants 5-IT and 2-DPMP at the dopamine transporter in rat brain slices); in vitro Fast Scan Cyclic Voltammetry (to assess the effects of BB-22 on evoked dopamine efflux and dopamine re-uptake half-life in nucleus accumbens brain slices); in vivo microdialysis (to monitor dopamine release in terminal areas of the reward system after acute administration of the synthetic cannabinoids BB-22, 5F-PB-22, 5F-AKB-48 and STS-135; the dieting aid compound 2,4-DNP; the synthetic stimulants 2-DPMP and D2PM in freely moving animals); in silico molecular docking (to investigate the intermolecular interactions of the SCs BB-22, 5F-PB-22, 5F-AKB-48 and STS-135, and other referent compounds, with a homology model of the rodent cannabinoid receptor type 1 (CB1R) and the crystal structure of the human CB1R); and a web-based analysis approach (to analyse the information provided by a range of fora communities on 4,4’-DMAR use, additionally critical reviewing the available evidence-based literature on this topic). Results: Our in vitro quantitative autoradiography studies, confirmed that the index compounds BB-22, 5F-PB-22, 5F-AKB-48 and STS-135, behave as highly potent CB1R ligands able to compete with the radioligand [3H]CP-55,940 in cortical and striatal brain slices. On the other hand, all synthetic cannabinoids tested were unable to compete with the radioligand [3H]MK-801 in the same cerebral areas, rejecting the hypothesis of their potential binding to the N-methyl-D-aspartate receptor (NMDAR) at all concentrations investigated. Consistent with previous in vitro studies, 5-IT and 2-DPMP behaved as highly potent dopamine transporter (DAT) ligands able to compete with the radioligand [125 I]RTI-121 in a concentration-dependent way in the Caudate Putamen (CPu) and Nucleus Accumbens (NAc) brain slices. Notably, 2-DPMP was able to displace the radioligand in both cerebral regions, starting from lower concentrations compared to 5-IT. In vitro Fast Scan Cyclic Voltammetry findings demonstrated that local application of the synthetic cannabinoid BB-22 in brain slices, was unable to change evoked dopamine efflux and dopamine reuptake time-constant in the NAc shell at any doses tested. The results obtained would suggest the relative contributions of complex neuronal circuits, either within or outside the NAc, whose modulation would interfere with the interactions between BB-22 and dopaminergic neurons and represent critical pathways accounting for some of the rewarding properties of BB-22 exposure. In vivo microdialysis outcomes suggested that all SCs tested could increase dopamine release in the NAc shell at specific doses, while no changes in dopamine output were observed in other areas of the reward system, namely NAc core and medial prefrontal cortex (mPFCx) after BB-22 administration. These outcomes provided a circumstantial pre-clinical evidence for a greater putative abuse liability of SCs compared to the natural compound found in cannabis (Δ9‐THC). Furthermore, the acute treatment with 2,4-DNP did not cause any change in dopamine release in the NAc shell and CPu rejecting the hypothesis of psychoactivity of this substance at the dose tested. On the other hand, the synthetic stimulant 2-DPMP elicited a comparable increase of dopamine (DA) release in the NAc shell and CPu at the higher doses tested, while D2PM caused a selective increase of DA release in the NAc shell, providing a circumstantial preclinical evidence for a putative abuse liability of this compound at the highest dose assessed. The in silico molecular docking studies demonstrated that the SCs BB-22, 5F-PB-22, 5F-AKB-48 and STS-135 interact with CB1 receptor residues that, according to previous mutation and computational studies, are considered crucial for synthetic cannabinoid binding recognition. Additionally, they share some interacting residues with other aminoalkylindole derivatives (e.g. WIN-55,212-2). The web-based analysis focused on 4,4’-DMAR, suggested that fora members co-operate in exchanging an extensive body of knowledge about this drug, and the recurring topics of discussion include: routes of administration and dosages; desired and undesired effects; comparison and association with other drugs and medications; overall impression; provision of harm reduction advice. This approach has been useful to better understand some of the clinical and psychopharmacological issues pertaining to 4,4’-DMAR. Conclusions: Overall, these studies provided new pharmacological, neurochemical and molecular knowledge on a range of Novel Psychoactive Substances essential for identifying potential therapeutical approaches against their use/abuse. The novelty of this project lies in the adoption of a multi-disciplinary approach involving a range of methodologies from different areas of expertise (neurobiology, pharmacology, chemistry, netnography) all integrated to clarify some aspects of the index NPS, which were not yet available in the current literature. Additional studies are needed to better explain short and long-term effects of the index NPS, their abuse potential, and their interactions with other drugs of abuse

Theory and computation show that Asp463 is the catalytic proton donor in human endoplasmic reticulum α-(1→2)-mannosidase I

It has been difficult to identify the proton donor and nucleophilic assistant/base of endoplasmic reticulum α-(1→2)-mannosidase I, a member of glycoside hydrolase Family 47, which cleaves the glycosidic bond between two α-(1→2)-linked mannosyl residues by the inverting mechanism, trimming Man9GlcNAc2 to Man8GlcNAc2 isomer B. Part of the difficulty is caused by the enzyme’s use of a water molecule to transmit the proton that attacks the glycosidic oxygen atom. We earlier used automated docking to conclusively determine that Glu435 in the yeast enzyme (Glu599 in the corresponding human enzyme) is the nucleophilic assistant. The commonly accepted proton donor has been Glu330 in the human enzyme (Glu132 in the yeast enzyme). However, for theoretical reasons this conclusion is untenable. Theory, automated docking of α-d-3S1-mannopyranosyl-(1→2)-α-d-4C1-mannopyranose and water molecules associated with candidate proton donors, and estimation of dissociation constants of the latter have shown that the true proton donor is Asp463 in the human enzyme (Asp275 in the yeast enzyme)

Bioactive compounds as potential angiotensin-converting enzyme II inhibitors against COVID-19: a scoping review

Objective and design The current study aimed to summarize the evidence of compounds contained in plant species with the ability to block the angiotensin-converting enzyme 2 (ACE-II), through a scoping review. Methods PubMed and Scopus electronic databases were used for the systematic search and a manual search was performed Results Studies included were characterized as in silico. Among the 200 studies retrieved, 139 studies listed after the exclusion of duplicates and 74 were included for the full read. Among them, 32 studies were considered eligible for the qualitative synthesis. The most evaluated class of secondary metabolites was flavonoids with quercetin and curcumin as most actives substances and terpenes (isothymol, limonin, curcumenol, anabsinthin, and artemisinin). Other classes that were also evaluated were alkaloid, saponin, quinone, substances found in essential oils, and primary metabolites as the aminoacid l-tyrosine and the lipidic compound 2-monolinolenin. Conclusion This review suggests the most active substance from each class of metabolites, which presented the strongest affinity to the ACE-II receptor, what contributes as a basis for choosing compounds and directing the further experimental and clinical investigation on the applications these compounds in biotechnological and health processes as in COVID-19 pandemic

Optimización multi-objetivo en las ciencias de la vida.

Para conseguir este objetivo, en lugar de intentar incorporar nuevos algoritmos directamente en el código fuente de AutoDock, se utilizó un framework orientado a la resolución de problemas de optimización con metaheurísticas. Concretamente, se usó jMetal, que es una librería de código libre basada en Java. Ya que AutoDock está implementado en C++, se desarrolló una versión en C++ de jMetal (posteriormente distribuida públicamente). De esta manera, se consiguió integrar ambas herramientas (AutoDock 4.2 y jMetal) para optimizar la energía libre de unión entre compuesto químico y receptor. Después de disponer de una amplia colección de metaheurísticas implementadas en jMetalCpp, se realizó un detallado estudio en el cual se aplicaron un conjunto de metaheurísticas para optimizar un único objetivo minimizando la energía libre de unión, el cual es el resultado de la suma de todos los términos de energía de la función objetivo de energía de AutoDock 4.2. Por lo tanto, cuatro metaheurísticas tales como dos variantes de algoritmo genético gGA (Algoritmo Genético generacional) y ssGA (Algoritmo Genético de estado estacionario), DE (Evolución Diferencial) y PSO (Optimización de Enjambres de Partículas) fueron aplicadas para resolver el problema del acoplamiento molecular. Esta fase se dividió en dos subfases en las que se usaron dos conjuntos de instancias diferentes, utilizando como receptores HIV-proteasas con cadenas laterales de aminoacidos flexibles y como ligandos inhibidores HIV-proteasas flexibles. El primer conjunto de instancias se usó para un estudio de configuración de parámetros de los algoritmos y el segundo para comparar la precisión de las conformaciones ligando-receptor obtenidas por AutoDock y AutoDock+jMetalCpp. La siguiente fase implicó aplicar una formulación multi-objetivo para resolver problemas de acoplamiento molecular dados los resultados interesantes obtenidos en estudios previos existentes en los que dos objetivos como la energía intermolecular y la energía intramolecular fueron minimizados. Por lo tanto, se comparó y analizó el rendimiento de un conjunto de metaheurísticas multi-objetivo mediante la resolución de complejos flexibles de acoplamiento molecular minimizando la energía inter- e intra-molecular. Estos algoritmos fueron: NSGA-II (Algoritmo Genético de Ordenación No dominada) y su versión de estado estacionario (ssNSGA-II), SMPSO (Optimización Multi-objetivo de Enjambres de Partículas con Modulación de Velocidad), GDE3 (Tercera versión de la Evolución Diferencial Generalizada), MOEA/D (Algoritmo Evolutivo Multi-Objetivo basado en la Decomposición) y SMS-EMOA (Optimización Multi-objetivo Evolutiva con Métrica S). Después de probar enfoques multi-objetivo ya existentes, se probó uno nuevo. En concreto, el uso del RMSD como un objetivo para encontrar soluciones similares a la de la solución de referencia. Se replicó el estudio previo usando este conjunto diferente de objetivos. Por último, se analizó de forma detallada el algoritmo que obtuvo mejores resultados en los estudios previos. En concreto, se realizó un estudio de variantes del SMPSO minimizando la energía intermolecular y el RMSD. Este estudio proporcionó algunas pistas sobre cómo nuevos algoritmos basados en SMPSO pueden ser adaptados para mejorar los resultados de acoplamiento molecular para aquellas simulaciones que involucren ligandos y receptores flexibles. Esta tesis demuestra que la inclusión de técnicas metaheurísticas de jMetalCpp en la herramienta de acoplamiento molecular AutoDock incrementa las posibilidades a los usuarios de ámbito biológico cuando resuelven el problema del acoplamiento molecular. El uso de técnicas de optimización mono-objetivo diferentes aparte de aquéllas ampliamente usadas en las comunidades de acoplamiento molecular podría dar lugar a soluciones de mayor calidad. En nuestro caso de estudio mono-objetivo, el algoritmo de evolución diferencial obtuvo mejores resultados que aquellos obtenidos por AutoDock. También se propone diferentes enfoques multi-objetivo para resolver el problema del acoplamiento molecular, tales como la decomposición de los términos de la energía de unión o el uso del RMSD como un objetivo. Finalmente, se demuestra que el SMPSO, una metaheurística de optimización multi-objetivo de enjambres de partículas, es una técnica remarcable para resolver problemas de acoplamiento molecular cuando se usa un enfoque multi-objetivo, obteniendo incluso mejores soluciones que las técnicas mono-objetivo.Las herramientas de acoplamiento molecular han llegado a ser bastante eficientes en el descubrimiento de fármacos y en el desarrollo de la investigación de la industria farmacéutica. Estas herramientas se utilizan para elucidar la interacción de una pequeña molécula (ligando) y una macro-molécula (diana) a un nivel atómico para determinar cómo el ligando interactúa con el sitio de unión de la proteína diana y las implicaciones que estas interacciones tienen en un proceso bioquímico dado. En el desarrollo computacional de las herramientas de acoplamiento molecular los investigadores de este área se han centrado en mejorar los componentes que determinan la calidad del software de acoplamiento molecular: 1) la función objetivo y 2) los algoritmos de optimización. La función objetivo de energía se encarga de proporcionar una evaluación de las conformaciones entre el ligando y la proteína calculando la energía de unión, que se mide en kcal/mol. En esta tesis, se ha usado AutoDock, ya que es una de las herramientas de acoplamiento molecular más citada y usada, y cuyos resultados son muy precisos en términos de energía y valor de RMSD (desviación de la media cuadrática). Además, se ha seleccionado la función de energía de AutoDock versión 4.2, ya que permite realizar una mayor cantidad de simulaciones realistas incluyendo flexibilidad en el ligando y en las cadenas laterales de los aminoácidos del receptor que están en el sitio de unión. Se han utilizado algoritmos de optimización para mejorar los resultados de acoplamiento molecular de AutoDock 4.2, el cual minimiza la energía libre de unión final que es la suma de todos los términos de energía de la función objetivo de energía. Dado que encontrar la solución óptima en el acoplamiento molecular es un problema de gran complejidad y la mayoría de las veces imposible, se suelen utilizar algoritmos no exactos como las metaheurísticas, para así obtener soluciones lo suficientemente buenas en un tiempo razonable

Decrypting strong and weak single-walled carbon nanotubes interactions with mitochondrial voltage-dependent anion channels using molecular docking and perturbation theory

[Abstract] The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p  (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio rerio when compared with VDAC1-Mus musculus and VDAC1-Homo sapiens (p  r2 > 0.97) was observed between n-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73–98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R2 of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity.Brasil. Conselho Nacional de Desenvolvimento Científico e Tecnológico; 552131/2011-3Brasil. Conselho Nacional de Desenvolvimento Científico e Tecnológico; 454332/2014-9Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/03

Identification of bioflavonoid as fusion inhibitor of dengue virus using molecular docking approach

AbstractDengue virus with four distinct serotypes belongs to Flavivirus, poses a significant threat to human health and becomes an emerging global problem. Membrane fusion is a central molecular event during viral entry into host cell. To prevent viral infection it is necessary to interrupt the virus replication at an early stage of attachment. Dengue Virus (DENV) envelope protein experiences conformational changes and it causes the virus to fuse with host cell. Hinge region movement of domain I and II in envelope protein facilitates the fusion process. Small molecules that bind in this pocket may have the ability to interrupt the conformational changes that trigger fusion process. We chose different flavonoids (baicalein, fisetin, hesperetin, naringenin/ naringin, quercetin and rutin) that possess anti dengue activity. Molecular docking analysis was done to examine the inhibitory effect of flavonoids against envelope protein of DENV-2. Results manifest quercetin (flavonoid found in Carica papaya, apple and even in lemon) as the only flavone that can interrupt the fusion process of virus by inhibiting the hinge region movement and by blocking the conformational rearrangement in envelope protein. These novel findings using computational approach are worthwhile and will be a bridge to check the efficacy of compounds using appropriate animal model under In vivo studies. This information can be used by new techniques and provides a way to control dengue virus infection

MDM2 Case Study: Computational Protocol Utilizing Protein Flexibility Improves Ligand Binding Mode Predictions

Recovery of the P53 tumor suppressor pathway via small molecule inhibitors of onco-protein MDM2 highlights the critical role of computational methodologies in targeted cancer therapies. Molecular docking programs in particular, provide a quantitative ranking of predicted binding geometries based on binding free energy allowing for the screening of large chemical libraries in search of lead compounds for cancer therapeutics. This study found improved binding mode predictions of medicinal compounds to MDM2 using the popular docking programs AutoDock and AutoDock Vina, while adopting a rigid-ligand/flexible-receptor protocol. Crystal structures representing small molecule inhibitors bound to MDM2 were selected and a total of 12 rotatable bonds was supplied to each complex and distributed systematically between the ligand and binding site residues. Docking results were evaluated in terms of the top ranked binding free energy and corresponding RMSD values from the experimentally known binding site. Results show lowest RMSD values coincide with a rigid ligand, while the protein retained the majority of flexibility. This study suggests the future implementation of a rigid-ligand/flexible-receptor protocol may improve accuracy of high throughput screenings of potential cancer drugs targeting the MDM2 protein, while maintaining manageable computational costs