Daniela Rodrigues Silva

Computational Enzymology and Organophosphorus Degrading Enzymes: Promising Approaches Toward Remediation Technologies of Warfare Agents and Pesticides

The re-emergence of chemical weapons as a global threat in hands of terrorist groups, together with an increasing number of pesticides intoxications and environmental contaminations worldwide, has called the attention of the scientific community for the need of improvement in the technologies for detoxification of organophosphorus (OP) compounds. A compelling strategy is the use of bioremediation by enzymes that are able to hydrolyze these molecules to harmless chemical species. Several enzymes have been studied and engineered for this purpose. However, their mechanisms of action are not well understood. Theoretical investigations may help elucidate important aspects of these mechanisms and help in the development of more efficient bio-remediators. In this review, we point out the major contributions of computational methodologies applied to enzyme based detoxification of OPs. Furthermore, we highlight the use of PTE, PON, DFP, and BuChE as enzymes used in OP detoxification process and how computational tools such as molecular docking, molecular dynamics simulations and combined quantum mechanical/molecular mechanics have and will continue to contribute to this very important area of research.

QSAR Models Guided by Molecular Dynamics Applied to Human Glucokinase Activators

In this study, quantitative structure–activity relationship studies which make use of molecular dynamics trajectories were performed on a set of 54 glucokinase protein activators. The conformations obtained by molecular dynamics simulation were superimposed according to the twelve alignments tested in a virtual three‐dimensional box comprised of 2 Å cells. The models were generated by the technique that combines genetic algorithms and partial least squares. The best alignment models generated with a determination coefficient (r2) between 0.674 and 0.743 and cross‐validation (q2) between 0.509 and 0.610, indicating good predictive capacity. The 4D‐QSAR models developed in this study suggest novel molecular regions to be explored in the search for better glucokinase activators.

4D-QSAR Model for Compounds with Binding Affinity Towards Dopamine D 2 Receptors

Dopamine is an abundant neurotransmitter in the brain, and acts as a regulator of many physiological functions in the central nervous system, such as motor activity, cognition, and positive reinforcement, and in the periphery, as a modulator of cardiovascular function, among others. Dopamine receptors belong to a superfamily of G protein-coupled receptors, and to date five sequences in the human body have been reported with various isoforms each. Disturbances in the dopaminergic systems are associated with several diseases, such as, for example, Parkinsons disease and schizophre- nia. Since the disturbances affecting the locomotor activity are related to dopamine D2 receptors. Quantitative structure- activity relationship analysis in four-dimensional (4D-QSAR) studies was applied on a series of 73 tetracyclic tetrahydro- furan derivatives containing a substituted cyclic amine side chain with binding affinity towards dopamine D2 receptors. The 4D-QSAR models were developed using 60 compounds, the training set, and externally validated using 13 com- pounds, the test set. We tested three different alignments, and the Model 3, generated from Equation 3, showed the best statistical results, the same being chosen to represent the data set. The model developed in this work shows descriptors with important pharmacophoric groups for inhibiting dopamine D2 receptors, suggesting structural changes for new in- hibitors.

Multi-Objective Optimization of Benzamide Derivatives as Rho Kinase Inhibitors

Despite recent advances in Computer Aided Drug Discovery and High Throughput Screening, the attrition rates of drug candidates continue to be high, underscoring the inherent complexity of the drug discovery paradigm. Indeed, a compromise between several objectives is often required to obtain successful clinical drugs. The present manuscript details a multi‐objective workflow that integrates the 4D‐QSAR and molecular docking methods in the simultaneous modeling of the Rho Kinase inhibitory activity and acute toxicity of Benzamide derivatives. To this end, the pIC50/pLD50 ratio is considered as the response variable, permitting the concurrent modeling of both properties and representing a shift from classical step‐by‐step evaluations. The 4D‐QSAR strategy is used to generate the Grid Cell Occupancy Descriptors (GCODs), and Stochastic Gradient Boosting (SGB) and Partial Least Squares (PLS) methods as the model fitting techniques. While the statistical parameters for the PLS model do not meet established criteria for acceptability, the SGB model yields satisfactory performance, with correlation coefficients r2=0.95 and r2pred=0.65 for the training and test set, respectively. Posteriorly, the structural interpretation of the most relevant GCODs according to the SGB model is performed, allowing for the proposal of 139 novel benzamide derivatives, which are then screened using the same model. Of these 9 compounds were predicted to possess pIC50/pLD50 ratio values higher than those for the employed dataset. Finally, in order to corroborate the results obtained with the SGB model, a docking simulation was formed to evaluate the binding affinity of the proposed molecules to the ROCK2 active site and 3 chemical structures (i. e. p6, p14 and p131) showed higher binding affinity than the most active compound in the training set, while the rest generally demonstrated comparable behavior. It may therefore be concluded that the consensus models that intertwine the 4D‐QSAR and molecular docking methods contribute to more reliable virtual screening and compound optimization experiments. Additionally, the use of multi‐objective modeling schemes permits the simultaneous evaluation of different chemical and biological profiles, which should contribute to the control a priori of causative factors for the high attrition rates in later drug discovery phases.

Insights into the pharmaceuticals and mechanisms of neurological orphan diseases: Current Status and future expectations

ABSTRACT Several rare or orphan diseases have been characterized that singly affect low numbers of people, but cumulatively reach ˜6%–10% of the population in Europe and in the United States. Human genetics has shown to be broadly effective when evaluating subjacent genetic defects such as orphan genetic diseases, but on the other hand, a modest progress has been achieved toward comprehending the molecular pathologies and designing new therapies. Chemical genetics, placed at the interface of chemistry and genetics, could be employed to understand the molecular mechanisms of subjacent illnesses and for the discovery of new remediation processes. This review debates current progress in chemical genetics, and how a variety of compounds and reaction mechanisms can be used to study and ultimately treat rare genetic diseases. We focus here on a study involving Amyotrophic lateral sclerosis (ALS), Duchenne Muscular Dystrophy (DMD), Spinal muscular atrophy (SMA) and Familial Amyloid Polyneuropathy (FAP), approaching different treatment methods and the reaction mechanisms of several compounds, trying to elucidate new routes capable of assisting in the treatment profile.

Design of Novel N-Miristoiltransferase Inhibitors of Leishmania donovani Using Four-Dimensional Quantitative Structure-Activity Relationship Analysis

N-Myristoylation protein is catalyzed by N-myristoyltransferase (NMT), an essential target in Leishmania donovani, the causative agent of kala-azar. Four-dimensional quantitative structureactivity relationship (4D-QSAR) analysis was applied to a series of 77 Leishmania donovani NMT inhibitors. Then, three new compounds were proposed using QSAR models. In addition, molecular docking was performed to predict the binding affinities and interaction modes among the proposed compounds and the NMT active site. In silico absorption, distribution, metabolism and excretion (ADME) evaluation was performed and potential inhibitors demonstrated satisfactory pharmacokinetic properties.

Asymmetric biodegradation of the nerve agents Sarin and VX by human dUTPase: Chemometrics, Molecular Docking and Hybrid QM/MM calculations

Abstract Organophosphorus compounds (OP) nerve agents are among the most toxic chemical substances known. Their toxicity is due to their ability to bind to acetylcholinesterase. Currently, some enzymes, such as phosphotriesterase, human serum paraoxonase 1 and diisopropyl fluorophosphatase, capable of degrading OP, have been characterized. Regarding the importance of bioremediation methods for detoxication of OP, this work aims to study the interaction modes between the human human deoxyuridine triphosphate nucleotidohydrolase (dUTPase) and Sarin and VX, considering their Rp and Sp enantiomers, to evaluate the asymmetric catalysis of those compounds. In previous work, this enzyme has shown good potential to degrade phosphotriesters, and based on this characteristic, we have applied the human dUTPase to the OP degradation. Molecular docking, chemometrics and mixed quantum and molecular mechanics calculations have been employed, showing a good interaction between dUTPase and OP. Two possible reaction mechanisms were tested, and according to our theoretical results, the catalytic degradation of OP by dUTPase can take place via both mechanisms, beyond being stereoselective, that is, dUTPase cleaves one enantiomer preferentially in relation to other. Chemometric techniques provided excellent assistance for performing this theoretical investigation. The dUTPase study shows importance by the fact of it being a human enzyme. Communicated by Ramaswamy H. Sarma

QSPR Models of β-dihydroagarofuran Derivatives: Exploring Lead Compounds for Pesticides

The use of chemical pesticides, although the most effective method for controlling insects, may in the long-term result in pest resistance development as well as it may impact on food quality, the environment and human health. Therefore, the botanical insecticides are interesting alternatives to minimize these undesirable effects, including a secondary metabolite in the Celastraceae family. Thus, a QSPR study was conducted for β-dihidroagarofuran derivatives with pesticide properties in order to identify features that may improve the potency thereof. The best model obtained from alignment 3 showed values of Q2=0.657, R2=0.757, R2p=0672 and R2m(test)=0.509, indicating good predictive ability and statistical robustness. Moreover, the descriptors presented important pharmacophore groups for the development of new pesticides. KEywoRDS β-dihidroagarofuran, Mortality, Natural Pesticides, QSPR