Prediction of virus-host protein-protein interactions mediated by short linear motifs

Table S4. Supplement information describing the previous files. (Supplement - Prediction of Virus-Host Protein-Protein interactions based on Short Linear Motifs.pdf) available at https://figshare.com/articles/Supplement_-_Prediction_of_Virus-Host_Protein-Protein_interactions_based_on_Short_Linear_Motifs/4667461 . (PDF 166 kb

Prediction of host-virus interaction networks

As with other viral pathogens, HIV-1 and dengue virus (DENV) are dependent on their hosts for the bulk of the functions necessary for viral survival and replication. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects, while avoiding elimination by the immune system. Protein-protein interactions are one avenue through which viruses can connect with and exploit these host cellular pathways and processes. We developed a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2,000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence. We then applied this approach to predict interactions between (DENV) and both of its hosts, Homo sapiens and the insect vector Aedes aegypti. We predict over 4,000 interactions between DENV and humans, as well as 176 interactions between DENV and A. aegypti. Additional filtering based on shared Gene Ontology cellular component annotation reduced the number of predictions to approximately 2,000 for humans and 18 for A. aegypti. Of 19 experimentally validated interactions between DENV and humans extracted from the literature, this method was able to predict nearly half. Our results suggest specific interactions between virus and host proteins relevant to interferon signaling, transcriptional regulation, stress, and the unfolded protein response. Viruses manipulate cellular processes to their advantage through specific interactions with the host's protein interaction network. The interaction networks presented here provide a set of hypothesis for further experimental investigation into viral life cycles and potential therapeutic targets.Doctor of Philosoph

Joint learning from multiple information sources for biological problems

Thanks to technological advancements, more and more biological data havebeen generated in recent years. Data availability offers unprecedented opportunities to look at the same problem from multiple aspects. It also unveils a more global view of the problem that takes into account the intricated inter-play between the involved molecules/entities. Nevertheless, biological datasets are biased, limited in quantity, and contain many false-positive samples. Such challenges often drastically downgrade the performance of a predictive model on unseen data and, thus, limit its applicability in real biological studies. Human learning is a multi-stage process in which we usually start with simple things. Through the accumulated knowledge over time, our cognition ability extends to more complex concepts. Children learn to speak simple words before being able to formulate sentences. Similarly, being able to speak correct sentences supports our learning to speak correct and meaningful paragraphs, etc. Generally, knowledge acquired from related learning tasks would help boost our learning capability in the current task. Motivated by such a phenomenon, in this thesis, we study supervised machine learning models for bioinformatics problems that can improve their performance through exploiting multiple related knowledge sources. More specifically, we concern with ways to enrich the supervised models’ knowledge base with publicly available related data to enhance the computational models’ prediction performance. Our work shares commonality with existing works in multimodal learning, multi-task learning, and transfer learning. Nevertheless, there are certain differences in some cases. Besides the proposed architectures, we present large-scale experiment setups with consensus evaluation metrics along with the creation and release of large datasets to showcase our approaches’ superiority. Moreover, we add case studies with detailed analyses in which we place no simplified assumptions to demonstrate the systems’ utilities in realistic application scenarios. Finally, we develop and make available an easy-to-use website for non-expert users to query the model’s generated prediction results to facilitate field experts’ assessments and adaptation. We believe that our work serves as one of the first steps in bridging the gap between “Computer Science” and “Biology” that will open a new era of fruitful collaboration between computer scientists and biological field experts

A single AKH neuropeptide activating three different fly AKH-receptors: an insecticide study via computational methods

Flies are a widely distributed pest insect that poses a significant threat to food security. Flight is essential for the dispersal of the adult flies to find new food sources and ideal breeding spots. The supply of metabolic fuel to power the flight muscles of insects is regulated by adipokinetic hormones (AKHs). The fruit fly, Drosophila melanogaster, the flesh fly, Sarcophaga crassipalpis, and the oriental fruit fly, Bactrocera dorsalis all have the same AKH that is present in the blowfly, Phormia terraenovae; this AKH has the code-name Phote-HrTH. Binding of the AKH to the extracellular binding site of a G protein-coupled receptor causes its activation. In this thesis, the structure of Phote-HrTH in SDS micelle solution was determined using NMR restrained molecular dynamics. The peptide was found to bind to the micelle and be reasonably rigid, with an S 2 order parameter of 0.96. The translated protein sequence of the AKH receptor from the fruit fly, Drosophila melanogaster, the flesh fly, Sarcophaga crassipalpis, and the oriental fruit fly, Bactrocera dorsalis were used to construct two models for each receptor: Drome-AKHR, Sarcr-AKHR, and Bacdo-AKHR. It is proposed that these two models represent the active and inactive state of the receptor. The models based on the crystal structure of the β-2 adrenergic receptor were found to bind Phote-HrTH with a predicted binding free energy of –107 kJ mol–1 for Drome-AKHR, –102 kJ mol–1 for Sarcr-AKHR and –102 kJ mol–1 for Bacdo-AKHR. Under molecular dynamics simulation, in a POPC membrane, the β-2AR receptor-like complexes transformed to rhodopsin-like. The identification and characterisation of the ligand-binding site of each receptor provide novel information on ligand-receptor interactions, which could lead to the development of species-specific control substances to use discriminately against these pest flies

Doctor of Philosophy

dissertationSurface-enhanced Raman scattering (SERS) is a technique that can be used for the detection of materials down to the single molecule level. The development of the extrinsic Raman Label (ERL) that incorporates biorecognition, a Raman reporter molecule, and gold nanoparticles (AuNPs), is the base for an extensible, sensitive, and selective SERS-based sandwich immunoassay. The use of SERS as a quantitative detection platform, however, has not progressed past the laboratory. Research presented here in describes how the analysis and production of a SERS immunoassay substrate has a significant role in the reliability and reproducibility of SERS substrates. First, the analysis of the SERS immunoassay platform and can be simulated as a random distribution of points on a surface. Simulation results indicated the best method to improve the accuracy of the analysis was through increasing the number of measurements or increasing the area measured by a single measurement. The precision of the measurement, however, was only improved by increasing the analysis area. This indicates that a larger laser spot used for analysis improves the accuracy and precision of SERS measurements. Second, to produce a SERS substrate with a random distribution of ERLs, the adsorption of ERLs should follow diffusional transport to increase the uniformity of ERLs on the substrate. By inverting the substrates during the ERL incubation step, sedimentation of the ERLs is directed away from the substrate and stable ERLs left in suspension diffuse to the substrate. Diffusional transport and a more even distribution of ERLs increased the reliability and reproducibility of the SERS substrates. Improved SERS immunoassay techniques implemented in conjunction with a novel pretreatment of serum samples for tuberculosis (TB) diagnostics was used to validate the SERS method. The TB marker mannose-capped lipoarabinomannan (ManLAM) is a lipopolysaccharide cell wall component that is constantly sloughed off the surface of the virulent bacterium, Mycobacterium tuberculosis. Normally ManLAM complexes with serum protein inhibiting detection but the use of a simple five step pretreatment method frees ManLAM improving the limit of detection (LoD). Improved SERS methodologies and sample pretreatment provide promising sensitivity and specificity for set of patient samples