How to find simple and accurate rules for viral protease cleavage specificities

<p>Abstract</p> <p>Background</p> <p>Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way.</p> <p>Results</p> <p>A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods.</p> <p>Conclusion</p> <p>A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.</p

The Structural Basis for the Interdependence of Drug Resistance in the HIV-1 Protease

The human immunodeficiency virus type 1 (HIV-1) protease (PR) is a critical drug target as it is responsible for virion maturation. Mutations within the active site (1°) of the PR directly interfere with inhibitor binding while mutations distal to the active site (2°) to restore enzymatic fitness. Increasing mutation number is not directly proportional to the severity of resistance, suggesting that resistance is not simply additive but that it is interdependent. The interdependency of both primary and secondary mutations to drive protease inhibitor (PI) resistance is grossly understudied. To structurally and dynamically characterize the direct role of secondary mutations in drug resistance, I selected a panel of single-site mutant protease crystal structures complexed with the PI darunavir (DRV). From these studies, I developed a network hypothesis that explains how mutations outside the active site are able to perpetuate changes to the active site of the protease to disrupt inhibitor binding. I then expanded the panel to include highly mutated multi-drug resistant variants. To elucidate the interdependency between primary and secondary mutations I used statistical and machine-learning techniques to determine which specific mutations underlie the perturbations of key inter-molecular interactions. From these studies, I have determined that mutations distal to the active site are able to perturb the global PR hydrogen bonding patterns, while primary and secondary mutations cooperatively perturb hydrophobic contacts between the PR and DRV. Discerning and exploiting the mechanisms that underlie drug resistance in viral targets could proactively ameliorate both current treatment and inhibitor design for HIV-1 targets

COVID-19 and SARS-CoV-2. Modeling the present, looking at the future

Since December 2019 the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has produced an outbreak of pulmonary disease which has soon become a global pandemic, known as COronaVIrus Disease-19 (COVID-19). The new coronavirus shares about 82% of its genome with the one which produced the 2003 outbreak (SARS CoV-1). Both coronaviruses also share the same cellular receptor, which is the angiotensin-converting enzyme 2 (ACE2) one. In spite of these similarities, the new coronavirus has expanded more widely, more faster and more lethally than the previous one. Many researchers across the disciplines have used diverse modeling tools to analyze the impact of this pandemic at global and local scales. This includes a wide range of approaches – deterministic, data-driven, stochastic, agent-based, and their combinations – to forecast the progression of the epidemic as well as the effects of non-pharmaceutical interventions to stop or mitigate its impact on the world population. The physical complexities of modern society need to be captured by these models. This includes the many ways of social contacts – (multiplex) social contact networks, (multilayers) transport systems, metapopulations, etc. – that may act as a framework for the virus propagation. But modeling not only plays a fundamental role in analyzing and forecasting epidemiological variables, but it also plays an important role in helping to find cures for the disease and in preventing contagion by means of new vaccines. The necessity for answering swiftly and effectively the questions: could existing drugs work against SARS CoV-2? and can new vaccines be developed in time? demands the use of physical modeling of proteins, protein-inhibitors interactions, virtual screening of drugs against virus targets, predicting immunogenicity of small peptides, modeling vaccinomics and vaccine design, to mention just a few. Here, we review these three main areas of modeling research against SARS CoV-2 and COVID-19: (1) epidemiology; (2) drug repurposing; and (3) vaccine design. Therefore, we compile the most relevant existing literature about modeling strategies against the virus to help modelers to navigate this fast-growing literature. We also keep an eye on future outbreaks, where the modelers can find the most relevant strategies used in an emergency situation as the current one to help in fighting future pandemics

Drug Repurposing for COVID-19 Using Molecular Docking Tools

Since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious and mortal, finding a treatment is time critical. Drug repurposing is probably the quickest and safest approach in our arsenal. However, testing every drug in a brute force manner would require a lot of resources, and a more sophisticated method is required to filter possible candidates. Since several molecules have already been shown to be effective against SARS-CoV-2 in wet-lab experiments, choosing drugs with similar characteristics would increase our chances of success. In this study, we compare the molecular docking results of FDA-approved drugs from the ZINC database against the molecules with positive experimental results. AutoDock Vina was used to dock the molecules against the SARS-CoV-2 spike receptor bound to the ACE2 receptor (6M0J). Results were pre-filtered to 50 candidates according to their binding affinities and the 10 most promising molecules that have similar interactions with the experimental drugs were identified. Then, the 10 molecules were docked against B.1.1.7, B.1.351, and P.1 variants, and their inhibition potentials were discussed. According to the results, we conclude that some molecules that inhibit the wild type also have the potential to inhibit the variants as well. However, further experimental and clinical studies are needed

Bioinformatic analysis of bacterial and eukaryotic amino- terminal signal peptides

Ph.DDOCTOR OF PHILOSOPH

Exploration of urine and plasma biomarkers in liver fibrosis and hepatocellular carcinoma

Liver fibrosis is a major risk factor for development of hepatocellular carcinoma. Both liver fibrosis and hepatocellular carcinoma are associated with molecular pathogenic mechanisms involving alterations in the hepatocellular proteome, metabolome, and genome. Both liver fibrosis and hepatocellular carcinoma lack suitable biological predictive biomarkers in clinical practice. Therefore, to aid in identifying suitable biomarkers, three approaches were employed in patients with liver fibrosis and hepatocellular carcinoma. Firstly, proteomic analysis was applied to identify post-translational enzymatic protein modifications peripherally present in the urine. Secondly, metabolic profiling was applied to characterise small volatile organic compounds present in the urine. Thirdly, DNA methylation detection technology was applied, to identify methylated SEPTIN9 patterns among the circulating hepatocellular carcinoma DNA molecules within the cell-free DNA pool present peripherally in the plasma. Urinary proteomic analysis identified novel specific peptides for liver fibrosis and hepatocellular carcinoma. Additionally, proteases potentially involved in liver fibrosis and hepatocellular carcinoma were predicted from the peptides sequence with further demonstration of these proteases by immunohistochemistry in human normal liver tissue, liver fibrosis and hepatocellular carcinoma. The identified urinary peptides showed good diagnostic and prognostic performance in liver fibrosis and hepatocellular carcinoma. Urinary metabolic profiling technologies demonstrated that volatile organic compounds patterns can be used noninvasively to detect hepatocellular carcinoma and they also revealed chemical composition of novel volatile organic compounds related to liver fibrosis and hepatocellular carcinoma. DNA methylation analysis showed that methylated SEPTIN9 has good sensitivity and specificity for hepatocellular carcinoma. It was also a prognostic indicator in patients with liver disease and hepatocellular carcinoma. The methylated SEPTIN9 was also associated with other surrogate biomarkers for liver function, liver fibrosis and inflammation. Additionally, methylated SEPTIN9 was noted to incrementally increase in various stages of liver disease. The researched biomarkers in this work provided some insight into the pathogenic mechanisms of liver fibrosis and hepatocellular carcinoma. If further validated, the identified biomarkers in this work could offer cost-effective tools for screening, diagnosis, prognosis and/or surveillance, particularly in low resource settings where access to advanced imaging and invasive biopsy is not feasible

Novel bioinformatics tools for epitope-based peptide vaccine design

BACKGROUND T-cells are essential in the mediation of immune responses, helping clear bacteria, viruses and cancerous cells. T-cells recognise anomalies in the cellular proteome associated with infection and neoplasms through the T-cell receptor (TCR). The most common TCRs in humans, αβ TCRs, engage processed peptide epitopes presented on the major histocompatibility complex (pMHC). TCR-pMHC interaction is critical to vaccination. In this thesis I will discuss three pieces of software and outcomes derived from them that contribute to epitope-based vaccine design. RESULTS Three pieces of software were developed to help scientists study and understand T-cell responses. The first, STACEI allows users to interrogate the TCR-pMHC crystal structures. The time consuming, error-prone analysis that previously would have to be ran manually, is replaced by a single, flexible package. The second development is the introduction of general-purpose computing on the GPU (GP-GPU) in aiding the prediction of T-cell epitopes by scanning protein datasets using data derived from combinatorial peptide libraries (CPLs). Finally, I introduce RECIPIENT, a reverse vaccinology tool (RV) that combines pangenomic and population genetics methods to predict good vaccine targets across multiple pathogen samples. CONCLUSION Across this thesis, I introduce three different methods that aid the study of T-cells that will hopefully improve future vaccine design. These methods range across data types and methodologies, with methods focusing on mechanistic understanding of the TCR-pMHC binding event; the application of GP-GPU to CPLs and using microbial genomics to aid the study and understanding of antigen-specific T-cell responses. These three methods have a significant potential for further integration, especially the structural methods

Concept and application of a computational vaccinology workflow

BACKGROUND : The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders. RESULTS : We introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage. CONCLUSION : Based on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges