Bioinformatics Techniques for Studying Drug Resistance In HIV and Staphylococcus Aureus

The worldwide HIV/AIDS pandemic has been partly controlled and treated by antivirals targeting HIV protease, integrase and reverse transcriptase, however, drug resistance has become a serious problem. HIV-1 drug resistance to protease inhibitors evolves by mutations in the PR gene. The resistance mutations can alter protease catalytic activity, inhibitor binding, and stability. Different machine learning algorithms (restricted boltzmann machines, clustering, etc.) have been shown to be effective machine learning tools for classification of genomic and resistance data. Application of restricted boltzmann machine produced highly accurate and robust classification of HIV protease resistance. They can also be used to compare resistance profiles of different protease inhibitors. HIV drug resistance has also been studied by enzyme kinetics and X-ray crystallography. Triple mutant HIV-1 protease with resistance mutations V32I, I47V and V82I has been used as a model for the active site of HIV-2 protease. The effects of four investigational antiviral inhibitors was measured for Triple mutant. The tested compounds had significantly worse inhibition of triple mutant with Ki values of 17-40 nM compared to 2-10 pM for wild type protease. The crystal structure of triple mutant in complex with GRL01111 was solved and showed few changes in protease interactions with inhibitor. These new inhibitors are not expected to be effective for HIV-2 protease or HIV-1 protease with changes V32I, I47V and V82I. Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen that causes hospital and community-acquired infections. Antibiotic resistance occurs because of newly acquired low-affinity penicillin-binding protein (PBP2a). Transcriptome analysis was performed to determine how MuM (mutated PBP2 gene) responds to spermine and how Mu50 (wild type) responds to spermine and spermine–β-lactam synergy. Exogenous spermine and oxacillin were found to alter some significant gene expression patterns with major biochemical pathways (iron, sigB regulon) in MRSA with mutant PBP2 protein

HIV Drug Resistant Prediction and Featured Mutants Selection using Machine Learning Approaches

HIV/AIDS is widely spread and ranks as the sixth biggest killer all over the world. Moreover, due to the rapid replication rate and the lack of proofreading mechanism of HIV virus, drug resistance is commonly found and is one of the reasons causing the failure of the treatment. Even though the drug resistance tests are provided to the patients and help choose more efficient drugs, such experiments may take up to two weeks to finish and are expensive. Because of the fast development of the computer, drug resistance prediction using machine learning is feasible. In order to accurately predict the HIV drug resistance, two main tasks need to be solved: how to encode the protein structure, extracting the more useful information and feeding it into the machine learning tools; and which kinds of machine learning tools to choose. In our research, we first proposed a new protein encoding algorithm, which could convert various sizes of proteins into a fixed size vector. This algorithm enables feeding the protein structure information to most state of the art machine learning algorithms. In the next step, we also proposed a new classification algorithm based on sparse representation. Following that, mean shift and quantile regression were included to help extract the feature information from the data. Our results show that encoding protein structure using our newly proposed method is very efficient, and has consistently higher accuracy regardless of type of machine learning tools. Furthermore, our new classification algorithm based on sparse representation is the first application of sparse representation performed on biological data, and the result is comparable to other state of the art classification algorithms, for example ANN, SVM and multiple regression. Following that, the mean shift and quantile regression provided us with the potentially most important drug resistant mutants, and such results might help biologists/chemists to determine which mutants are the most representative candidates for further research