Neural Network and Bioinformatic Methods for Predicting HIV-1 Protease Inhibitor Resistance

This article presents a new method for predicting viral resistance to seven protease inhibitors from the HIV-1 genotype, and for identifying the positions in the protease gene at which the specific nature of the mutation affects resistance. The neural network Analog ARTMAP predicts protease inhibitor resistance from viral genotypes. A feature selection method detects genetic positions that contribute to resistance both alone and through interactions with other positions. This method has identified positions 35, 37, 62, and 77, where traditional feature selection methods have not detected a contribution to resistance. At several positions in the protease gene, mutations confer differing degress of resistance, depending on the specific amino acid to which the sequence has mutated. To find these positions, an Amino Acid Space is introduced to represent genes in a vector space that captures the functional similarity between amino acid pairs. Feature selection identifies several new positions, including 36, 37, and 43, with amino acid-specific contributions to resistance. Analog ARTMAP networks applied to inputs that represent specific amino acids at these positions perform better than networks that use only mutation locations.Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Nucleic acid sequence-based amplification: Relative performance and applications in HIV-1 disease monitoring and patient management

In recent years there have been significant advances in the understanding of the pathogenesis of HIV-1 infection. Central to this progress has been the development of accurate and reproducible methods of measuring HIV-1 RNA in plasma. This has been pivotal in studies of viral dynamics, disease progression and antiretroviral drug efficacy. This thesis describes evaluations of the performance of Nucleic Acid Sequence-Based Amplification (NASBA) technologies in quantifying HIV-1 RNA, relative to other commercial systems, investigating reproducibility, sensitivity and reliability across subtypes using a number of panels designed with either plasma dilutions of laboratory strains of HIV-1, or by selected clinical viruses of interest. Quantification of HIV-1 B and non B subtypes was investigated to study the impact of increasing diversity of HIV-1 subtypes in our clinic population. The evaluations described here involve close collaborations with assay manufacturers (as an alpha testing site) and clinical users, and have encouraged the continual improvement of viral load quantification systems, supporting the development of new versions of RT-PCR and NASBA assays. Further, attempts were made to develop an economic in house viral quantification system based on immunocapture and RT-PCR using an internal biological standard. To investigate the versatility of viral load measurement in different clinical settings, studies were performed in primary HIV-1 infection and established HIV-1 infection. A well characterised cohort of patients undergoing primary HIV-1 infection (n = 47), were evaluated for the relative performance of NASBA quantification during the initial stages of infection and followed for up to 3 years. In addition, this enabled a comparison of virological responses to therapy in early HIV-1 disease and analysis for antiretroviral resistance mutations to investigate the prevalence of resistance transmission in this recently infected group. A clinical trial of patients with established infection was undertaken to determine the practical significance of virological measures relative to immunological responses and therapeutic drug monitoring. The performance of NASBA in quantifying viral load in a wide range of studies was shown to be less sensitive than the bDNA and RT-PCR assays and also less able to amplify viruses of more diverse subtypes. In a number of studies the quantification of samples with a low viral load (>5,000 copies/ml) by NASBA was shown have greater variance. However, at higher viral copy numbers (>5,000 copies/ml), the reproducibility of results was equivalent to alternative viral load systems and other markers of disease monitoring and drug efficacy. NASBA performed adequately in characterising patients undergoing primary HIV-1 infection relative to other disease markers such as p24 antigen, anti-HIV-1 serology, and the conventional understanding of virological events during this early infection period. Further, NASBA viral load measures demonstrated that although antiretroviral therapy was very effective during this period, it was not significantly more effective than therapy initiated later. It was noted in this cohort that the frequency of resistance transmission was low for nucleoside (15%) and non-nucleoside (6%) inhibitor resistance, and absent for protease inhibitors. The investigations into an in house system showed that a modified immunocapture using latex microparticle produced inconsistent results, and a variable high background in the detection system precluded further evaluation. By comparison, the reverse transcription and amplification step was modified successfully and further optimisation of this method was undertaken within the department to allow routine use. Throughout this study, the objective has been to evaluate the current and future use of viral load measurement. This thesis has gone some way towards validating the expanding use of viral load since the identification of HIV-1 RNA as a disease marker. As long as the reliability of viral quantification systems supports the prevailing clinical environment and is evaluated in studies such as this, it may continue to act as a principal tool in HIV-1 clinical research and patient care

The context-dependence of mutations: a linkage of formalisms

Defining the extent of epistasis - the non-independence of the effects of mutations - is essential for understanding the relationship of genotype, phenotype, and fitness in biological systems. The applications cover many areas of biological research, including biochemistry, genomics, protein and systems engineering, medicine, and evolutionary biology. However, the quantitative definitions of epistasis vary among fields, and its analysis beyond just pairwise effects remains obscure in general. Here, we show that different definitions of epistasis are versions of a single mathematical formalism - the weighted Walsh-Hadamard transform. We discuss that one of the definitions, the backgound-averaged epistasis, is the most informative when the goal is to uncover the general epistatic structure of a biological system, a description that can be rather different from the local epistatic structure of specific model systems. Key issues are the choice of effective ensembles for averaging and to practically contend with the vast combinatorial complexity of mutations. In this regard, we discuss possible approaches for optimally learning the epistatic structure of biological systems.Comment: 6 pages, 3 figures, supplementary informatio

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

NMR RELAXATION METHODS TO DETECT PROTEIN DYNAMICS: EVALUATION OF ACCURACY, IMPROVEMENT OF THE METHODOLOGY, AND ITS APPLICATION

Proteins are dynamic molecules whose ability to undergo conformational changes and fluctuations can impact their biological function, such as enzyme catalysis and substrate recognition. Mutations or perturbations that do not significantly change protein structure can often have a significant effect on the function by disrupting important internal motions and conformational states. Due to the importance of protein dynamics on function, dynamics have been extensively studied by many different biophysical methods as well as computational means. One of the methods to characterize protein dynamics is by nuclear magnetic resonance (NMR) spectroscopy. NMR spectroscopy is a powerful tool for the characterization of the structure as well as the dynamics of biological molecules. In particular, NMR relaxation experiments have been used to characterize protein motion in a wide range of timescales ranging from sub-nano second (ns) motions up to millisecond (ms) and above. Recent advances in instrumentation, such as the introduction of commercially available cryogenic probes and higher field static magnets (with 1H Larmor frequency of 900 MHz and above), have increased the sensitivity of NMR experiments. However, reevaluation of the methods used in NMR relaxation experiments and the analysis of the data is required to confirm whether the same experimental methods remain valid for the improved instrumentation. In this thesis, the NMR relaxation experiments were evaluated and improvements in the experimental aspects and the analysis of the NMR relaxation data are made for high resolution NMR experiments. In addition, NMR relaxation experiments were used to investigate the dynamics of the Sarcoplasmic Reticulum Ca2+ ATPase and the Human Immunodeficiency Virus Type 1 Protease wild-type (WT) and mutant forms

Kinetics of the viral cycle influence pharmacodynamics of antiretroviral therapy

<p>Abstract</p> <p>Background</p> <p>More and more antiretroviral therapies are being developed for treatment of HIV infection. The <it>in-vivo </it>efficacy of these drugs is commonly predicted based on <it>in-vitro </it>measures of antiviral effect. One primary <it>in-vitro </it>measure is the IC50, the amount of drug required for 50% inhibition of viral replication. We have previously shown that HIV life-cycle kinetics impact clinically observed HIV viral dynamics. Here we present a mathematical model of how they affect the pharmacodynamics of antiretroviral drugs.</p> <p>Results</p> <p>We find that experimentally measured antiretroviral IC50s are determined by three factors: (i) intrinsic drug properties (e.g. drug-target binding), (ii) kinetics of the HIV life cycle, and (iii) kinetics of drug-inhibited infected cells. Our model predicts that the IC50 is a declining function of the duration of the drug-susceptible stage in the host cell. We combine our model with known viral life-cycle kinetics to derive a measure of intrinsic properties, reflecting drug action, for known antiretroviral drugs from previously measured IC50s. We show that this measure of intrinsic drug property correlates very well with <it>in vitro</it>-measured antiviral activity, whereas experimentally measured IC50 does not.</p> <p>Conclusions</p> <p>Our results have implications for understanding pharmacodynamics of and improving activity of antiretroviral drugs. Our findings predict that drug activity can be improved through co-administration of synergistic drugs that delay the viral life cycle but are not inhibitory by themselves. Moreover, our results may easily extend to treatment of other pathogens.</p> <p>This article was reviewed by Dr. Ruy Ribeiro, Dr. Ha Youn Lee, Dr. Alan Perelson and Dr. Christoph Adami.</p

An update on feline infectious peritonitis: diagnostics and therapeutics.

This review is concerned with what has been learned about feline infectious peritonitis (FIP) diagnostics and therapeutics since the publication of an extensive overview of literature covering the period 1963-2009. Although progress has been made in both areas, obtaining a definitive diagnosis of FIP remains a problem for those veterinarians and/or cat owners who require absolute certainty. This review will cover both indirect and direct diagnostic tests for the disease and will emphasize their limitations, as well as their specificity and sensitivity. There is still no effective treatment for FIP, although there are both claims that such therapies exist and glimmers of hope coming from new therapies that are under research. FIP has also been identified in wild felids and FIP-like disease is now a growing problem among pet ferrets

The use of machine learning to improve the effectiveness of ANRS in predicting HIV drug resistance.

Master of TeleHealth in Medical Informatics. University of KwaZulu-Natal, Durban, 2016.BACKGROUD HIV has placed a large burden of disease in developing countries. HIV drug resistance is inevitable due to selective pressure. Computer algorithms have been proven to help in determining optimal treatment for HIV drug resistance patients. One such algorithm is the ANRS gold standard interpretation algorithm developed by the French National Agency for AIDS Research AC11 Resistance group. OBJECTIVES The aim of this study is to investigate the possibility of improving the accuracy of the ANRS gold standard in predicting HIV drug resistance. METHODS Data consisting of genome sequence and a HIV drug resistance measure was obtained from the Stanford HIV database. Machine learning factor analysis was performed to determine sequence positions where mutations lead to drug resistance. Sequence positions not found in ANRS were added to the ANRS rules and accuracy was recalculated. RESULTS The machine learning algorithm did find sequence positions, not associated with ANRS, but the model suggests they are important in the prediction of HIV drug resistance. Preliminary results show that for IDV 10 sequence positions where found that were not associated with ANRS rules, 4 for LPV, and 8 for NFV. For NFV, ANRS misclassified 74 resistant profiles as being susceptible to the ARV. Sixty eight of the 74 sequences (92%) were classified as resistance with the inclusion of the eight new sequence positions. No change was found for LPV and a 78% improvement was associated with IDV. CONCLUSION The study shows that there is a possibility of improving ANRS accuracy

Antiviral Resistance and Correlates of Virologic Failure in the first Cohort of HIV-Infected Children Gaining Access to Structured Antiretroviral Therapy in Lima, Peru: A Cross-Sectional Analysis

Background: The impact of extended use of ART in developing countries has been enormous. A thorough understanding of all factors contributing to the success of antiretroviral therapy is required. The current study aims to investigate the value of cross-sectional drug resistance monitoring using DNA and RNA oligonucleotide ligation assays (OLA) in treatment cohorts in low-resource settings. The study was conducted in the first cohort of children gaining access to structured ART in Peru. Methods: Between 2002–5, 46 eligible children started the standard regimen of AZT, 3TC and NFV Patients had a median age of 5.6 years (range: 0.7-14y), a median viral load of 1.7·105 RNA/ml (range: 2.1·103 – 1.2·106), and a median CD4-count of 232 cells/μL (range: 1–1591). Of these, 20 patients were classified as CDC clinical category C and 31/46 as CDC immune category 3. At the time of cross-sectional analysis in 2005, adherence questionnaires were administered. DNA OLAs and RNA OLAs were performed from frozen PBMC and plasma, RNA genotyping from dried blood spots. Results: During the first year of ART, 44% of children experienced virologic failure, with an additional 9% failing by the end of the second year. Virologic failure was significantly associated with the number of resistance mutations detected by DNA-OLA (p < 0.001) during cross-sectional analysis, but also with low immunologic CDC-scores at baseline (p < 0.001). Children who had been exposed to unsupervised short-term antiretrovirals before starting structured ART showed significantly higher numbers of resistance mutations by DNA-OLA (p = 0.01). Detection of M184V (3TC resistance) by RNA-OLA and DNA-OLA demonstrated a sensitivity of 0.93 and 0.86 and specificity of 0.67 and 0.7, respectively, for the identification of virologic failure. The RT mutations N88D and L90M (NFV resistance) detected by DNA-OLA correlated with virologic failure, whereas mutations at RT position 215 (AZT resistance) were not associated with virologic failure. Conclusions: Advanced immunosuppression at baseline and previous exposures to unsupervised brief cycles of ART significantly impaired treatment outcomes at a time when structured ART was finally introduced in his cohort. Brief maternal exposures to with AZT +/− NVP for the prevention of mother-to-child transmission did not affect treatment outcomes in this group of children. DNA-OLA from frozen PBMC provided a highly specific tool to detect archived drug resistance. RNA consensus genotyping from dried blood spots and RNA-OLA fromplasma consistently detected drug resistance mutations, but merely in association with virologic failur