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Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data.

ABSTRACT: Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Current models are based only on sequence data derived from viral genotyping; chemical similarity of drugs is not considered. To explore the added value of chemical similarity inclusion we applied proteochemometric models, combining chemical and protein target properties in a single bioactivity model. Our dataset was a large scale clinical database of genotypic and phenotypic information (in total ca. 300,000 drug-mutant bioactivity data points, 4 (NNRTI), 8 (NRTI) or 9 (PI) drugs, and 10,700 (NNRTI) 10,500 (NRTI) or 27,000 (PI) mutants). Our models achieved a prediction error below 0.5 Log Fold Change. Moreover, when directly compared with previously published sequence data, derived models PCM performed better in resistance classification and prediction of Log Fold Change (0.76 log units versus 0.91). Furthermore, we were able to successfully confirm both known and identify previously unpublished, resistance-conferring mutations of HIV Reverse Transcriptase (e.g. K102Y, T216M) and HIV Protease (e.g. Q18N, N88G) from our dataset. Finally, we applied our models prospectively to the public HIV resistance database from Stanford University obtaining a correct resistance prediction rate of 84% on the full set (compared to 80% in previous work on a high quality subset). We conclude that proteochemometric models are able to accurately predict the phenotypic resistance based on genotypic data even for novel mutants and mixtures. Furthermore, we add an applicability domain to the prediction, informing the user about the reliability of predictions.

SUBMITTER: van Westen GJ

PROVIDER: S-EPMC3578754 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data.

van Westen Gerard J P GJ Hendriks Alwin A Wegner Jörg K JK Ijzerman Adriaan P AP van Vlijmen Herman W T HW Bender Andreas A

PLoS computational biology 20130221 2

Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Curr ...[more]

PMID: 23436985

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Project description:BackgroundThe nematode Caenorhabditis briggsae has been used as a model in comparative genomics studies with Caenorhabditis elegans because of their striking morphological and behavioral similarities. However, the potential of C. briggsae for comparative studies is limited by the quality of its genome resources. The genome resources for the C. briggsae laboratory strain AF16 have not been developed to the same extent as C. elegans. The recent publication of a new chromosome-level reference genome for QX1410, a C. briggsae wild strain closely related to AF16, has provided the first step to bridge the gap between C. elegans and C. briggsae genome resources. Currently, the QX1410 gene models consist of software-derived gene predictions that contain numerous errors in their structure and coding sequences. In this study, a team of researchers manually inspected over 21,000 gene models and underlying transcriptomic data to repair software-derived errors.ResultsWe designed a detailed workflow to train a team of nine students to manually curate gene models using RNA read alignments. We manually inspected the gene models, proposed corrections to the coding sequences of over 8,000 genes, and modeled thousands of putative isoforms and untranslated regions. We exploited the conservation of protein sequence length between C. briggsae and C. elegans to quantify the improvement in protein-coding gene model quality and showed that manual curation led to substantial improvements in the protein sequence length accuracy of QX1410 genes. Additionally, collinear alignment analysis between the QX1410 and AF16 genomes revealed over 1,800 genes affected by spurious duplications and inversions in the AF16 genome that are now resolved in the QX1410 genome.ConclusionsCommunity-based, manual curation using transcriptome data is an effective approach to improve the quality of software-derived protein-coding genes. The detailed protocols provided in this work can be useful for future large-scale manual curation projects in other species. Our manual curation efforts have brought the QX1410 gene models to a comparable level of quality as the extensively curated AF16 gene models. The improved genome resources for C. briggsae provide reliable tools for the study of Caenorhabditis biology and other related nematodes.

Dataset Information

Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data.

Publications

Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data.

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