Human immunode_ciency virus reverse transcriptase and protease sequence database.

Division of Infectious Diseases, Department of Medicine, Stanford University, Stanford, CA 94305, USA.
Nucleic Acids Research (Impact Factor: 9.11). 02/2003; 31(1):298-303.
Source: PubMed


The HIV reverse transcriptase and protease sequence database is an on-line relational database that catalogues evolutionary and drug-related sequence variation in the human immunodeficiency virus (HIV) reverse transcriptase (RT) and protease enzymes, the molecular targets of antiretroviral therapy ( The database contains a compilation of nearly all published HIV RT and protease sequences, including submissions to GenBank, sequences published in journal articles and sequences of HIV isolates from persons participating in clinical trials. Sequences are linked to data about the source of the sequence, the antiretroviral drug treatment history of the person from whom the sequence was obtained and the results of in vitro drug susceptibility testing. Sequence data on two new molecular targets of HIV drug therapy--gp41 (cell fusion) and integrase--will be added to the database in 2003.

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    • "The design of novel or rewired signaling and metabolic networks [78], for instance, inevitably invokes complex tradeoffs between different molecules and their properties, and these can also be encoded within the framework presented here. Recent advances in the application of deep sequencing to libraries of natural protein variants [79] [80] and of in vitro mutational repertoires selected for complex combinations of physical features, including stability , binding, and specificity profiles [34,36,81–83] are generating datasets comprising thousands of mutants that relate sequence changes to function at unprecedented resolution and coverage [84]. The fuzzy-logic design framework described here can be used to test hypotheses relating sequence, structure , and energetics to function, as well as in turn to fitness. "
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    ABSTRACT: To carry out their activities biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often-opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a ‘fuzzy’-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial-backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand, and multispecificity on the other. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics.
    Journal of Molecular Biology 10/2014; 426(24). DOI:10.1016/j.jmb.2014.10.002 · 4.33 Impact Factor
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    • "All the datasets were retrieved from Genotype-Phenotype Data on the Stanford HIV drug resistance database [37] ( In this experiment, the proposed algorithm was tested on two different systems: HIV-1 PR and HIV-1 RT resistance data. "
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    ABSTRACT: Background Drug resistance has become a severe challenge for treatment of HIV infections. Mutations accumulate in the HIV genome and make certain drugs ineffective. Prediction of resistance from genotype data is a valuable guide in choice of drugs for effective therapy. Results In order to improve the computational prediction of resistance from genotype data we have developed a unified encoding of the protein sequence and three-dimensional protein structure of the drug target for classification and regression analysis. The method was tested on genotype-resistance data for mutants of HIV protease and reverse transcriptase. Our graph based sequence-structure approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers. Cross-validated regression analysis with the sparse dictionary gave excellent correlation between predicted and observed resistance. Conclusion The approach of encoding the protein structure and sequence as a 210-dimensional vector, based on Delaunay triangulation, has promise as an accurate method for predicting resistance from sequence for drugs inhibiting HIV protease and reverse transcriptase.
    BMC Genomics 07/2014; 15 Suppl 5(Suppl 5):S1. DOI:10.1186/1471-2164-15-S5-S1 · 3.99 Impact Factor
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    • "The PCR products were purified by Gel Purification kit (Bioneer, Global Genomics Partner, Daejeon, Republic of Korea) according to manufacturer's instructions and sequenced in an automated DNA sequencer (ABI PRISM 3730 version 3.0, Applied Biosystems, Foster City, CA). The reverse transcriptase and protease sequences were analyzed by BioEdit software (version 5.0.6), and the Stanford University HIV Drug Resistance Database was used for drug resistance interpretation [Rhee et al., 2003; Campbell et al., 2005; Gallant et al., 2006]. This database categorized resistance as either susceptible or as low, intermediate, or high-level resistance using a mutation penalty score based on published drug resistance and treatment outcome studies as well as in-vitro susceptibility data. "
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    ABSTRACT: Resistance to antiretroviral therapy (ART) threatens the success of programs to reduce HIV morbidity and mortality, particularly in countries with few treatment options. In the present study, genotype and phenotype data from ART-naïve and experienced hospitalized patients infected with HIV in Tehran, Iran were used to assess the prevalence and types of transmitted (TDR) and acquired drug resistance (ADR) mutations. All 30 participants naïve to ART and 62 of 70 (88.6%) participants receiving ART had detectable viral loads. Among participants receiving ART with sequencing data available (n = 62), 36 (58.1%) had at least one drug resistance mutation; the most common mutations were K103N (21.0%), M184V (19.4%), and the thymidine analogue mutations. Seven (11.3%), 27 (43.5%), and two (3.2%) of these participants had resistance to one, two, and three drug classes, respectively. High-level resistance to efavirenz (EFV) was more common among participants on EFV-based regimens than high-level lopinavir/ritonivar (LPV/r) resistance among those on LPV/r-based regimens (55.3% vs. 6.7%, P < 0.0001). Two (6.7%) antiretroviral-naïve participants had K103N mutations. These findings document an alarmingly high frequency of multiple HIV drug class resistance in Iran, confirm the presence of TDR, and highlight the need for systematic viral load monitoring and drug resistance testing, including at diagnosis. Expanded access to new antiretroviral medications from additional drug classes is needed. J. Med. Virol. © 2014 Wiley Periodicals, Inc.
    Journal of Medical Virology 07/2014; 86(7). DOI:10.1002/jmv.23898 · 2.35 Impact Factor
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