COMPUTATIONAL APPROACHES RELATED TO DRUG DISPOSITION

Drug disposition connects with the movement of drug molecules inside the body after administration irrespective with the route of administration. After entering the system, drug molecule and internal body systems comes under various pharmacokinetic interactions followed by observation of suitable biological activity. In this exhaustive process, physicochemical nature of the chemical substance and physiological nature of system makes this movement competitive. In this view, pharmacokinetic and toxic properties of the molecule regulates the destination of the molecule. Various computational processes are available for in silico pharmacokinetic assessment of drug molecule after absorption through biological membrane, distributed throughout the system based on the percent ionization or partition coefficient factors followed by biologically transformed into an another entity in presence of microsomal enzymes and finally excrete out from system using various cellular transport systems as well as related cellular toxicity behavior. In this chapter, we ensemble all the possible information related with the drug movement and related computational tools to understand the possible chemical and pathophysiological changes. Here detailed knowledge on database expedition, establishment of pharmacophore model, homology modelling based on sequence similarity, molecular docking study (rigid and flexible docking) and QSAR/QSPR study (with detailed process and available softwares) are provided. These diversely united informations actually helps a researcher to understand the factual movement of a drug molecule inside the system

Computational analysis of CpG site DNA methylation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Epigenetics is the study of factors that can change DNA and passed to next generation without change to DNA sequence. DNA methylation is one of the categories of epigenetic change. DNA methylation is the attachment of methyl group (CH3) to DNA. Most of the time it occurs in the sequences that G is followed by C known as CpG sites and by addition of methyl to the cytosine residue. As science and technology progress new data are available about individual’s DNA methylation profile in different conditions. Also new features discovered that can have role in DNA methylation. The availability of new data on DNA methylation and other features of DNA provide challenge to bioinformatics and the opportunity to discover new knowledge from existing data. In this research multiple data series were used to identify classes of methylation DNA to CpG sites. These classes are a) Never methylated CpG sites,b) Always methylated CpG sites, c) Methylated CpG sites in cancer/disease samples and non-methylated in normal samples d) Methylated CpG sites in normal samples and non-methylated in cancer/disease samples. After identification of these sites and their classes, an analysis was carried out to find the features which can better classify these sites a matrix of features was generated using four applications in EMBOSS software suite. Features matrix was also generated using the gUse/WS-PGRADE portal workflow system. In order to do this each of the four applications were grid enabled and ported to BOINC platform. The gUse portal was connected to the BOINC project via 3G-bridge. Each node in the workflow created portion of matrix and then these portions were combined together to create final matrix. This final feature matrix used in a hill climbing workflow. Hill climbing node was a JAVA program ported to BOINC platform. A Hill climbing search workflow was used to search for a subset of features that are better at classifying the CpG sites using 5 different measurements and three different classification methods: support vector machine, naïve bayes and J48 decision tree. Using this approach the hill climbing search found the models which contain less than half the number of features and better classification results. It is also been demonstrated that using gUse/WS-PGRADE workflow system can provide a modular way of feature generation so adding new feature generator application can be done without changing other parts. It is also shown that using grid enabled applications can speedup both feature generation and feature subset selection. The approach used in this research for distributed workflow based feature generation is not restricted to this study and can be applied in other studies that involve feature generation. The approach also needs multiple binaries to generate portions of features. The grid enabled hill climbing search application can also be used in different context as it only requires to follow the same format of feature matrix

Recruitment of RNA molecules by connexin RNA-binding motifs: Implication in RNA and DNA transport through microvesicles and exosomes

Connexins (Cxs) are integral membrane proteins that form high-conductance plasma membrane channels, allowing communication from cell to cell (via gap junctions) and from cells to the extracellular environment (via hemichannels). Initially described for their role in joining excitable cells (nerve and muscle), gap junctions (GJs) are found between virtually all cells in solid tissues and are essential for functional coordination by enabling the direct transfer of small signalling molecules, metabolites, ions, and electrical signals from cell to cell. Several studies have revealed diverse channel-independent functions of Cxs, which include the control of cell growth and tumourigenicity. Connexin43 (Cx43) is the most widespread Cx in the human body. The myriad roles of Cx43 and its implication in the development of disorders such as cancer, inflammation, osteoarthritis and Alzheimer's disease have given rise to many novel questions. Several RNA- and DNA-binding motifs were predicted in the Cx43 and Cx26 sequences using different computational methods. This review provides insights into new, ground-breaking functions of Cxs, highlighting important areas for future work such as transfer of genetic information through extracellular vesicles. We discuss the implication of potential RNA- and DNA-binding domains in the Cx43 and Cx26 sequences in the cellular communication and control of signalling pathwaysThis work was supported in part through funding from the Society for Research on Bone and Mineral Metabolism - Grant number FEIOMM2016 (to M.D.M.), by grant PRECIPITA-2015-000139 from the FECYT-Ministry of Economy and Competitiveness (to M.D.M), by grants PI13/00591 and PI16/00035 from the Health Institute “Carlos III” (ISCIII, Spain) and co-financed by the European Regional Development Fund, “A way of making Europe” from the European Union (to M.D.M.), by a grant from Xunta de Galicia (pre-doctoral fellowship) to M.V.-E., and by a grant from the Ministry of Education, Culture and Sports, Spain (FPU grant to M.R.-C.M.)S

Exploring the effects of polymorphic variation on the stability and function of human cytochrome P450 enzymes in silico and in vitro

Includes bibliographical references.Cytochrome P450s are highly polymorphic enzymes responsible for the Phase I metabolism of over 80% of pharmaceutical drugs. Polymorphic variation can result in altered drug efficacy as well as adverse drug reactions so the lack of understanding of the effects of single amino acid substitutions on cytochrome P450 drug metabolism is a major problem for drug development. In order to begin to address this problem, this thesis describes an in silico analysis of over 300 nonsynonymous single nucleotide polymorphisms found across nine of the major human drug metabolising cytochrome P450 isoforms. Information from functional studies - in which regions of the cytochrome P450 structure important for substrate recognition, substrate and product access and egress and interaction with the cytochrome P450 reductase were delineated - was combined with in silico calculations on the effect of mutations on protein stability in order to establish the likely causes of altered drug metabolism observed for cytochrome P450 variants in functional assays carried out to date. This study revealed that 75% of all cytochrome P450 mutations showing altered activity in vitro are either predicted to be damaging to protein structure or are found within regions predicted to be important for catalytic activity. Furthermore, this study showed that 70% of the mutations that showed similar activity to the wild-type enzyme in in vitro studies lie outside of functional regions important for catalytic activity and are predicted to have no effect on protein stability. Based on these results, a cytochrome P450 polymorphic variant map was created that should find utility in predicting the functional effect of uncharacterised variants on drug metabolism. To further test the accuracy of the in silico predictions, in vitro assays were performed on a panel of CYP3A4 and CYP2C9 variants heterogeneously expressed in E.coli. All mutations predicted to alter protein function by stabilising or destabilising the apo-protein structure in silico were found to significantly alter the thermostability of the holo-protein in solution. Thermostability assays also suggest that other mutations may affect stability by disrupting haem binding, changing protein conformation or altering oligomer formation. The utility of a fluorescence-based functional P450 protein microarray platform, previously developed in our laboratory, for generating kinetic data for multiple CYP450 variants in parallel was also examined. Since the microarray platform in its current stage of development was found to be unsuitable for this purpose, kinetic data for the full panel of CYP3A4 and CYP2C9 variants was generated using solution phase assays, revealing several variants with altered catalytic turnover and/or binding affinity for fluorescent substrates

Development of novel synthetic and systems biology tools for investigating and obviating the effect of atherogenic blood flow on vascular cells

The high mortality from cardiovascular diseases is caused by atherosclerosis. Atherosclerosis develops due to multiple factors, including biomechanical factors, such as shear stress generated by the flow of blood on the inner lining of blood vessels. In order to tackle this serious life-threatening condition, we aimed to develop synthetic and systems biology tools for studying the effect of atherogenic shear stress regimes on vascular cells. Our tools could potentially also lead to the identification of drug targets and of drug candidates against cardiovascular disease. The first tool that we developed is a network of genes consisting of a shear stress sensor module, a reporter module and a linker module which signals from the sensor to the reporter module. This circuit is capable of processing the shear stress or ligand activation signal into a fluorescent readout, allowing screening for drug candidate compounds that modify the activity of the shear stress sensor. Instead of the reporter module, the gene network could be coupled to therapeutic genes in order to express these genes under atherogenic shear stress conditions. Our second developed tool is a flow chamber which facilitates exposure of vascular cells to linearly increasing shear stress along the length of the channel floor for in vitro cellular biomechanical studies. This device outperforms currently available linear shear stress inducing devices in terms of the magnitude of shear stress range, linearity of shear stress along the channel length, and the large sampling area granted by the uniformity of shear stress across the channel width. The third tool that we developed is an electroporation and flow device capable of inserting genetic material into primary vascular cells in their adherent state, exposing these cells to fluid flow. This device allows investigations into cardiovascular mechanotransduction pathways under relevant physiological flow conditions.Open Acces

Technologies to study protein oxidation in ageing Investigating the effect of protein oxidation on protein function

Protein oxidation can cause aggregation, fragmentation, and affect enzymatic activity and binding partner interactions. Protein oxidation is implicated in a range of agerelated pathologies including neurodegeneration and cancer. The VHR and PTEN phosphatases studied are sensitive to oxidation and regulated by protein-protein interactions. PTEN acts by dephosphorylating phosphatidylinositol (3,4,5)-triphosphate, negatively regulating the Akt pathway as part of a signalling control network that can protect against apoptosis, and is involved in the regulation of cell fate regulation and cancer. VHR is involved in neural development and cancer. A technology workflow for detecting protein oxidation and to correlate oxidative modifications to enzymatic activity and protein-protein interaction was developed; which may contribute towards the advancement of fundamental science as well as potential therapeutic and biomarker target identification in proteins. The technology platform consists of the mass spectrometric technique MS2 to detect, validate, map and quantify oxidative modifications. The technology workflow consists of enzymatic activity assays to correlate modification with changes in activity, targeted MS2 and statistical analysis. The fundamental and distinct contribution to knowledge in this thesis is a systematic mapping of protein oxidative modifications over a range of oxidants and concentrations of hypochlorous acid (HOCl), 3-morpholino-sydnonimine (sin-1) and tetranitromethane for VHR (vaccinia H1 related) and PTEN (phosphatase and tensin homolog on chromosome 10), including modification identification including active site residues and putative binding domain, mapping the relative abundances of those modification and statistically correlating them to changes in enzymatic activity. Additional contributions to knowledge have been i) the nonspecificity and complexity of oxidation profiles and oxidant damage of nitrating agents, that have largely been proposed to be specific without substantial oxidative capacity and ii) expanding the known interactome of VHR (vaccinia H1 related) through array and co-immunoprecipitation

ANALYSIS AND SIMULATION OF TANDEM MASS SPECTROMETRY DATA

This dissertation focuses on improvements to data analysis in mass spectrometry-based proteomics, which is the study of an organism’s full complement of proteins. One of the biggest surprises from the Human Genome Project was the relatively small number of genes (~20,000) encoded in our DNA. Since genes code for proteins, scientists expected more genes would be necessary to produce a diverse set of proteins to cover the many functions that support the complexity of life. Thus, there is intense interest in studying proteomics, including post-translational modifications (how proteins change after translation from their genes), and their interactions (e.g. proteins binding together to form complex molecular machines) to fill the void in molecular diversity. The goal of mass spectrometry in proteomics is to determine the abundance and amino acid sequence of every protein in a biological sample. A mass spectrometer can determine mass/charge ratios and abundance for fragments of short peptides (which are subsequences of a protein); sequencing algorithms determine which peptides are most likely to have generated the fragmentation patterns observed in the mass spectrum, and protein identity is inferred from the peptides. My work improves the computational tools for mass spectrometry by removing limitations on present algorithms, simulating mass spectroscopy instruments to facilitate algorithm development, and creating algorithms that approximate isotope distributions, deconvolve chimeric spectra, and predict protein-protein interactions. While most sequencing algorithms attempt to identify a single peptide per mass spectrum, multiple peptides are often fragmented together. Here, I present a method to deconvolve these chimeric mass spectra into their individual peptide components by examining the isotopic distributions of their fragments. First, I derived the equation to calculate the theoretical isotope distribution of a peptide fragment. Next, for cases where elemental compositions are not known, I developed methods to approximate the isotope distributions. Ultimately, I created a non-negative least squares model that deconvolved chimeric spectra and increased peptide-spectrum-matches by 15-30%. To improve the operation of mass spectrometer instruments, I developed software that simulates liquid chromatography-mass spectrometry data and the subsequent execution of custom data acquisition algorithms. The software provides an opportunity for researchers to test, refine, and evaluate novel algorithms prior to implementation on a mass spectrometer. Finally, I created a logistic regression classifier for predicting protein-protein interactions defined by affinity purification and mass spectrometry (APMS). The classifier increased the area under the receiver operating characteristic curve by 16% compared to previous methods. Furthermore, I created a web application to facilitate APMS data scoring within the scientific community.Doctor of Philosoph

Prediction of Covid-19 Multiparametric Biomarkers and Drug Target of Patients for Risk Stratification Using Machine Learning Approach

In the situation of Coronavirus disease 2019 (COVID-19), forecasting disease progression and identifying therapeutic drug targets is critical, especially given the nonattendance of a viable approach for treating severe cases. The preparation cohort revealed promising biomarkers, which were then precisely measured and employed to assess prediction accuracy across validation cohorts. This approach holds significant potential in enhancing understanding of severe COVID-19 and aiding the development of effective treatments. However, ultrasound-guided MRI (US-MRI) is an emerging modality that can noninvasively acquire multi-parametric information on COVID-19 and function without the need for contrast agents. This shows that neural network analysis of US-MRI transports exclusive prognosis data and this significantly improved prognosis performance. Consequently, the research proposed a deep neural network model of an Ensemble Multi-Relational Graph Neural Network (EMR-GNN) to determine the optimal model for predicting vascular biomarkers (CRP, IL-6, ferritin). In the nonappearance of a tailored treatment for this emerging virus, scientists are actively investigating various strategies to curb its replication. This work focuses on identifying potential drug targets, drawing from proteins abundant in lung material and those targeted by FDA-approved drugs as catalogued in HPA. This effort reflects a broader initiative within the methodical unrestricted to develop effective means of limiting virus replication. Accordingly, recognized five lung-improved proteins, comprising MRC1, SG3A1, CCL18, histone H4, and CLEC3B, were annotated as “drug targets”. For this, the researcher proposes a Heterogeneous Graph Structural Attention Neural Network (HGS-ANN) model to learn topological information of composite molecules and a Dilated Causal CNN-LSTM model with U-Net layers for modelling spatial-sequential information in Simplified Molecular-Input Line-Entry System (SMILES) sequences of drug data. The COVID-19 datasets are downloaded from the GEO database. These data are evaluated using Matlab software. The proposed work evaluated that the AUC of the work is 0.995, however, the AUC is measured based on sex, age, and chronic diseases. This model has a 0.933 accuracy in the subgroup of slices thicker than 1mm. However, the AUC curve and the classification outcome of the proposed method are compared with the existing rad model, deeper, and KNN models. In comparison to existing methods, the proposed model demonstrates superior performance. This research not only identifies potential therapeutic targets nonetheless also serves to uncover biomarkers crucial for comprehending the pathogenesis of undecorated COVID-19

Aquaporin Gating: A New Twist to Unravel Permeation through Water Channels

Aquaporins (AQPs) are small transmembrane tetrameric proteins that facilitate water, solute and gas exchange. Their presence has been extensively reported in the biological membranes of almost all living organisms. Although their discovery is much more recent than ion transport systems, different biophysical approaches have contributed to confirm that permeation through each monomer is consistent with closed and open states, introducing the term gating mechanism into the field. The study of AQPs in their native membrane or overexpressed in heterologous systems have experimentally demonstrated that water membrane permeability can be reversibly modified in response to specific modulators. For some regulation mechanisms, such as pH changes, evidence for gating is also supported by high-resolution structures of the water channel in different configurations as well as molecular dynamics simulation. Both experimental and simulation approaches sustain that the rearrangement of conserved residues contributes to occlude the cavity of the channel restricting water permeation. Interestingly, specific charged and conserved residues are present in the environment of the pore and, thus, the tetrameric structure can be subjected to alter the positions of these charges to sustain gating. Thus, is it possible to explore whether the displacement of these charges (gating current) leads to conformational changes? To our knowledge, this question has not yet been addressed at all. In this review, we intend to analyze the suitability of this proposal for the first time.Fil: Ozu, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; ArgentinaFil: Alvear Arias, Juan José. Universidad de Valparaíso; ChileFil: Fernandez, Miguel. Universidad de Valparaíso; ChileFil: Caviglia, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; ArgentinaFil: Peña Pichicoi, Antonio. Universidad de Valparaíso; ChileFil: Carrillo, Christian. Universidad de Valparaíso; ChileFil: Carmona, Emerson. No especifíca;Fil: Otero Gonzalez, Anselmo. Universidad de La Habana; CubaFil: Garate, José Antonio. Universidad de Valparaíso; ChileFil: Amodeo, Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; ArgentinaFil: Gonzalez, Carlos. Universidad de Valparaíso; Chil