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Comprehensive model of wild-type and mutant HIV-1 reverse transciptases.
Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ` di Roma, P. le A. Moro 5, 00185 Rome, Italy.ORCID iD: 0000-0002-4831-3423
Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ` di Roma, P. le A. Moro 5, 00185 Rome, Italy.
Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ` di Roma, P. le A. Moro 5, 00185 Rome, Italy; Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110, USA.
Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ` di Roma, P. le A. Moro 5, 00185 Rome, Italy.
2012 (English)In: Journal of Computer-Aided Molecular Design, ISSN 0920-654X, E-ISSN 1573-4951, Vol. 26, no 8, 907-19 p.Article in journal (Refereed) Published
Abstract [en]

An enhanced version of COMBINE that uses both ligand-based and structure-based alignment of ligands has been used to build a comprehensive 3-D QSAR model of wild-type HIV-1 reverse transcriptase and drug-resistant mutants. The COMBINEr model focused on 7 different RT enzymes complexed with just two HIV-RT inhibitors, niverapine (NVP) and efavirenz (EFV); therefore, 14 inhibitor/enzyme complexes comprised the training set. An external test set of chiral 2-(alkyl/aryl)amino-6-benzylpyrimidin-4(3H)-ones (DABOs) was used to test predictability. The COMBINEr model MC4, although developed using only two inhibitors, predicted the experimental activities of the test set with an acceptable average absolute error of prediction (0.89 pK (i)). Most notably, the model was able to correctly predict the right eudismic ratio for two R/S pairs of DABO derivatives. The enhanced COMBINEr approach was developed using only software freely available to academics.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2012. Vol. 26, no 8, 907-19 p.
National Category
Medicinal Chemistry Chemical Sciences Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-321306DOI: 10.1007/s10822-012-9586-6PubMedID: 22833004OAI: oai:DiVA.org:uu-321306DiVA: diva2:1094672
Available from: 2017-05-10 Created: 2017-05-10 Last updated: 2017-05-12

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