Computational Studies of Drug Repurposing Targeting P-Glycoprotein-Mediated Multidrug Resistance Phenotypes in Priority Infectious Agents

ABCB1 P-glycoprotein (P-gp) is an ATP-dependent efflux pump with broad substrate specificity associated with cellular drug resistance. Homologous to role in mammalian biology, P-glycoproteins of bacterial and fungal pathogens mediate the emergence of multidrug resistance phenotypes, with widespread clinical/socioeconomic implications. This work aims to characterize P-gp homologues in certain WHO-prioritized infectious agents, namely (1) bacteria: Acinetobacter baumannii and Staphylococcus aureus and (2) fungi: Aspergillus fumigatus, Candida albicans, and Cryptococcus neoformans. PSI-BLAST searches against the genome of each of these organisms confirmed the presence of P-gp homologues. Each homologue was aligned against five known P-gp structures, for structural modeling. FDA-approved antibiotics used in the current line of therapy were retrieved from PubChem, and potential antibiotics were identified based on similarity and repurposing of the existing drugs. The most tenable target-ligand conformations from docking studies of the respective modeled P-gp structures and the antibiotic ligands were assessed for interacting residues within 4.5 Å of the ligand, probable binding pockets and relative efficacies of the new drugs. Our studies could lay the foundation for the development of effective synergistic or new therapies against these pathogens

Computational Studies of Drug Repurposing Targeting P-Glycoprotein-Mediated Multidrug-Resistance Phenotypes in Agents of Neglected Tropical Diseases

Mammalian ABCB1 P-glycoprotein is an ATP- dependent efflux pump with broad substrate specificity associated with cellular drug resistance. Homologous to this role in mammalian biology, the P-glycoprotein of agents of neglected tropical diseases (NTDs) mediates the emergence of multidrug-resistance phenotypes. The clinical and socioeconomic implications of NTDs are exacerbated by the lack of research interest among Big Pharma for treating such conditions. This work aims to characterise P-gp homologues in certain agents of key NTDs, namely (1) Protozoa: Leishmania major, Trypanosoma cruzi; (2) Helminths: Onchocerca volvulus, Schistosoma mansoni. Based on structural modelling of the organismal P-gp homologues, potential antibiotics targeting these structures were identified based on similarity and repurposing of existing drugs. Docking studies of the Pgp receptor—antibiotic ligand complexes were carried out and the most tenable target-ligand conformations assessed. The interacting residues were identified, and binding pockets studied. The in silico studies yielded measurements of the relative efficacy of the new drugs, which need experimental validation. Our studies could lay the foundation for the development of effective synergistic or new therapies against key neglected tropical diseases. The potential mechanisms of multidrug resistance emergence in E. coli were examined

PromoterPredict: sequence-based modelling of Escherichia coli σ70 promoter strength yields logarithmic dependence between promoter strength and sequence

We present PromoterPredict, a dynamic multiple regression approach to predict the strength of Escherichia coli promoters binding the σ70 factor of RNA polymerase. σ70 promoters are ubiquitously used in recombinant DNA technology, but characterizing their strength is demanding in terms of both time and money. We parsed a comprehensive database of bacterial promoters for the −35 and −10 hexamer regions of σ70-binding promoters and used these sequences to construct the respective position weight matrices (PWM). Next we used a well-characterized set of promoters to train a multivariate linear regression model and learn the mapping between PWM scores of the −35 and −10 hexamers and the promoter strength. We found that the log of the promoter strength is significantly linearly associated with a weighted sum of the −10 and −35 sequence profile scores. We applied our model to 100 sets of 100 randomly generated promoter sequences to generate a sampling distribution of mean strengths of random promoter sequences and obtained a mean of 6E-4 ± 1E-7. Our model was further validated by cross-validation and on independent datasets of characterized promoters. PromoterPredict accepts −10 and −35 hexamer sequences and returns the predicted promoter strength. It is capable of dynamic learning from user-supplied data to refine the model construction and yield more robust estimates of promoter strength. PromoterPredict is available as both a web service (https://promoterpredict.com) and standalone tool (https://github.com/PromoterPredict). Our work presents an intuitive generalization applicable to modelling the strength of other promoter classes

Theory-Based Investigations of the Potassium -Selective Ion Channel Protein Family

119 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.Potassium (K+) channels are important in many life-sustaining processes. The prime motivation of my research has been to develop and expand our knowledge and understanding of K+ channels through hypothesis and computational validation. The research was organized under four dominant themes: uncovering of phylogenetic relationships, conceptualization of relationships of structure, elucidation of relationships through function and physiology, and detailing of the influence of co-evolutionary relationships on modularity. Two principal tools were used: one, theory to generate hypotheses consistent with wide-ranging experimental results, accompanied by mathematical validation: two, recruitment of comparative analysis to sort information inherent in evolutionary processes. The principal findings are: (1) the catalytic domain of potassium channels, namely the permeation pathway, has co-evolved with its regulatory domain. This bears the following important implications: (i) the catalytic domain is not functionally modular; (ii) the catalytic domain is subfamily-specific, which promises a revolutionary technique for the function annotation of a protein family based on evolutionary similarity in the structural scaffold of the active site. Also detailed is a method for the characterization of genome-complements of protein families. (2) identification of numerous residue segments which underlie individual conduction events and impart subfamily-specific phenotypes. In particular, we analyzed differences in the structurally important elements in the pore helix and the inner helix of various K+ channel subfamilies, and theorized the importance of each observation with regard to physiology and channel function. (3) analysis of evolutionary relationships has revealed the order of emergence of various classes of subfamilies, explained the origin of the two-pore channels, and raised an interesting research avenue for exploring beta subunits. We also demonstrated the conservation of K+ channels across all life, and a method for visualizing large phylogenies. (4) structural modeling of hERG K+ channels which are the subject of great pharmacological interest. (5) discovery of new intracellular locations, namely the mitochondrion, for a specific isoform of plant sucrose synthases, namely the SH1 isoform. This discovery seems to explain the pattern of their altered localization in anoxic conditions, and suggests an important role for sucrose synthases in plant cell adaptation to oxygen availability.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Fourier Analysis of Conservation Patterns in Protein Secondary Structure

Residue conservation is a common observation in alignments of protein families, underscoring positions important in protein structure and function. Though many methods measure the level of conservation of particular residue positions, currently we do not have a way to study spatial oscillations occurring in protein conservation patterns. It is known that hydrophobicity shows spatial oscillations in proteins, which is characterized by computing the hydrophobic moment of the protein domains. Here, we advance the study of moments of conservation of protein families to know whether there might exist spatial asymmetry in the conservation patterns of regular secondary structures. Analogous to the hydrophobic moment, the conservation moment is defined as the modulus of the Fourier transform of the conservation function of an alignment of related protein, where the conservation function is the vector of conservation values at each column of the alignment. The profile of the conservation moment is useful in ascertaining any periodicity of conservation, which might correlate with the period of the secondary structure. To demonstrate the concept, conservation in the family of potassium ion channel proteins was analyzed using moments. It was shown that the pore helix of the potassium channel showed oscillations in the moment of conservation matching the period of the α-helix. This implied that one side of the pore helix was evolutionarily conserved in contrast to its opposite side. In addition, the method of conservation moments correctly identified the disposition of the voltage sensor of voltage-gated potassium channels to form a 310 helix in the membrane. Keywords: Periodicity, Secondary structure, Evolution, Moment of conservation, Fourier transform, Potassium channe

CESCProg: a compact prognostic model and nomogram for cervical cancer based on miRNA biomarkers

Cervical squamous cell carcinoma, more commonly cervical cancer, is the fourth common cancer among women worldwide with substantial burden of disease, and less-invasive, reliable and effective methods for its prognosis are necessary today. Micro-RNAs are increasingly recognized as viable alternative biomarkers for direct diagnosis and prognosis of disease conditions, including various cancers. In this work, we addressed the problem of systematically developing an miRNA-based nomogram for the reliable prognosis of cervical cancer. Towards this, we preprocessed public-domain miRNA -omics data from cervical cancer patients, and applied a cascade of filters in the following sequence: (i) differential expression criteria with respect to controls; (ii) significance with univariate survival analysis; (iii) passage through dimensionality reduction algorithms; and (iv) stepwise backward selection with multivariate Cox modeling. This workflow yielded a compact prognostic DEmiR signature of three miRNAs, namely hsa-miR-625-5p, hs-miR-95-3p, and hsa-miR-330-3p, which were used to construct a risk-score model for the classification of cervical cancer patients into high-risk and low-risk groups. The risk-score model was subjected to evaluation on an unseen test dataset, yielding a one-year AUROC of 0.84 and five-year AUROC of 0.71. The model was validated on an out-of-domain, external dataset yielding significantly worse prognosis for high-risk patients. The risk-score was combined with significant features of the clinical profile to establish a predictive prognostic nomogram. Both the miRNA-based risk score model and the integrated nomogram are freely available for academic and not-for-profit use at CESCProg, a web-app (https://apalania.shinyapps.io/cescprog)