Leveraging Structural Flexibility to Predict Protein Function

Proteins are essentially versatile and flexible molecules and understanding protein function plays a fundamental role in understanding biological systems. Protein structure comparisons are widely used for revealing protein function. However,with rigidity or partial rigidity assumption, most existing comparison methods do not consider conformational flexibility in protein structures. To address this issue, this thesis seeks to develop algorithms for flexible structure comparisons to predict one specific aspect of protein function, binding specificity. Given conformational samples as flexibility representation, we focus on two predictive problems related to specificity: aggregate prediction and individual prediction.For aggregate prediction, we have designed FAVA (Flexible Aggregate Volumetric Analysis). FAVA is the first conformationally general method to compare proteins with identical folds but different specificities. FAVA is able to correctly categorize members of protein superfamilies and to identify influential amino acids that cause different specificities. A second method PEAP (Point-based Ensemble for Aggregate Prediction) employs ensemble clustering techniques from many base clustering to predict binding specificity. This method incorporates structural motions of functional substructures and is capable of mitigating prediction errors.For individual prediction, the first method is an atomic point representation for representing flexibilities in the binding cavity. This representation is able to predict binding specificity on each protein conformation with high accuracy, and it is the first to analyze maps of binding cavity conformations that describe proteins with different specificities. Our second method introduces a volumetric lattice representation. This representation localizes solvent-accessible shape of the binding cavity by computing cavity volume in each user-defined space. It proves to be more informative than point-based representations. Last but not least, we discuss a structure-independent representation. This representation builds a lattice model on protein electrostatic isopotentials. This is the first known method to predict binding specificity explicitly from the perspective of electrostatic fields.The methods presented in this thesis incorporate the variety of protein conformations into the analysis of protein ligand binding, and provide more views on flexible structure comparisons and structure-based function annotation of molecular design

Structure-activity approaches for prediction of chemical reactivity and pharmacological properties of some heterocyclic compounds

Benzodiazepine drugs are widely prescribed to treat many psychiatric and neurologic disorders. As its pharmacological action is exerted in a sensitive area of the brain; ''the central nervous system'', it is crucial to provide detailed reports on the chemistry of benzodiazepines, model the mechanism of action that occurs with GABAA receptors, identify the overlap with other modulators, as well as explore the structural requirements that better potentiate the receptor response to benzodiazepines. This dissertation consists of two original studies that consider the new lines of research related to benzodiazepines, particularly the identification of three new TMD binding sites. The first study provides, on the one hand, an overview of the chemistry of six Benzodiazepine basic rings starting from structural characteristics, electronic properties, Global/local reactivities, up to intermolecular interactions with long-range nucleophilic/electrophilic reactants. This was achieved by combining a DFT investigation with a quantitative MEP analysis on the vdW surface. On the other hand, the performed molecular docking simulations allowed identifying the best binding modes, binding interactions, and binding affinities with residues, which helped to validate the quantitative MEP analysis predictions. The second study was conducted on a dataset of [3H]diazepam derivatives. First, molecular docking simulation was used to initially screen the dataset and select the best ligand/target complexes. Afterwise, the best-docked complexes were refined by performing molecular dynamics simulation for 1000 picoseconds. For both simulations, the binding modes, binding interactions, and binding affinities were thoroughly discussed and compared with each other and with outcomes collected from the literature. Additionally, the good pharmacokinetic properties (ADME prediction) as well as compliance with all druglikeness rules were checked via in silico tools for all the dataset compounds. Finally, a QSAR analysis was carried out using an improved version of PLS regression. Briefly, the dataset is randomly split into 10 000 training and test sets that involve, respectively, 80% and 20% of chemicals. Subsequently, 10 000 statistical simulations were conducted that; after excluding outlying observations, yielded 10 000 best training models following the Bayesian Information Criterion. Among these 10 000 best models, the best predictors with the highest probability of occurrence were selected. As a consequence, the derived PLS regression equation explains 63.2% of the variability in BDZ activity around its mean. Furthermore, Internal and external validation metrics assure the robustness and predictability of the developed model. The developed model was interpreted based on literature investigations and a combination of implemented approaches

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Advances in Molecular Simulation

Molecular simulations are commonly used in physics, chemistry, biology, material science, engineering, and even medicine. This book provides a wide range of molecular simulation methods and their applications in various fields. It reflects the power of molecular simulation as an effective research tool. We hope that the presented results can provide an impetus for further fruitful studies