Literature DB >> 19391634

Proteochemometric modeling of drug resistance over the mutational space for multiple HIV protease variants and multiple protease inhibitors.

Maris Lapins1, Jarl E S Wikberg.   

Abstract

The main therapeutic targets in HIV are its protease and reverse transcriptase. A major problem in treatment of HIV is the ability of the virus to develop drug resistance by accumulating mutations in its targets. Acquiring detailed understanding of the molecular mechanisms for the interactions of drugs with mutated variants of the HIV virus is mandatory to be able to design inhibitors that can evade the resistance. Here we have used proteochemometric modeling to simultaneously analyze the interactions of 21 protease inhibitors with 72 unique protease variants. Inhibition data (pK(i)) were correlated to descriptions of chemical and structural properties of the inhibitors and proteases. The proteochemometric model obtained showed excellent fit and predictive ability (R(2)=0.92, Q(2)=0.83, Q(2)(inh)=0.78) and provided quantitative assessments for the contribution of each mutation and their combinations to the decrease in inhibitor activity, both for the whole compounds series as well as for individual compounds. The model revealed the most deleterious mutations in the protease to be D30N, V32I, G48V, I50V, I54V, V82A, I84V, and L90M. The model was further used to identify molecular properties of chemical compounds that are important for their inhibition of multimutated protease variants. Our results give directions how to design novel improved inhibitors.

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Year:  2009        PMID: 19391634     DOI: 10.1021/ci800453k

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  13 in total

1.  Proteochemometric modeling of the antigen-antibody interaction: new fingerprints for antigen, antibody and epitope-paratope interaction.

Authors:  Tianyi Qiu; Han Xiao; Qingchen Zhang; Jingxuan Qiu; Yiyan Yang; Dingfeng Wu; Zhiwei Cao; Ruixin Zhu
Journal:  PLoS One       Date:  2015-04-22       Impact factor: 3.240

2.  Proteochemometric model for predicting the inhibition of penicillin-binding proteins.

Authors:  Sunanta Nabu; Chanin Nantasenamat; Wiwat Owasirikul; Ratana Lawung; Chartchalerm Isarankura-Na-Ayudhya; Maris Lapins; Jarl E S Wikberg; Virapong Prachayasittikul
Journal:  J Comput Aided Mol Des       Date:  2014-10-26       Impact factor: 3.686

Review 3.  Structure-based methods for predicting target mutation-induced drug resistance and rational drug design to overcome the problem.

Authors:  Ge-Fei Hao; Guang-Fu Yang; Chang-Guo Zhan
Journal:  Drug Discov Today       Date:  2012-07-10       Impact factor: 7.851

4.  A unified proteochemometric model for prediction of inhibition of cytochrome p450 isoforms.

Authors:  Maris Lapins; Apilak Worachartcheewan; Ola Spjuth; Valentin Georgiev; Virapong Prachayasittikul; Chanin Nantasenamat; Jarl E S Wikberg
Journal:  PLoS One       Date:  2013-06-17       Impact factor: 3.240

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

Authors:  Gerard J P van Westen; Alwin Hendriks; Jörg K Wegner; Adriaan P Ijzerman; Herman W T van Vlijmen; Andreas Bender
Journal:  PLoS Comput Biol       Date:  2013-02-21       Impact factor: 4.475

6.  Linking the Resource Description Framework to cheminformatics and proteochemometrics.

Authors:  Egon L Willighagen; Jonathan Alvarsson; Annsofie Andersson; Martin Eklund; Samuel Lampa; Maris Lapins; Ola Spjuth; Jarl Es Wikberg
Journal:  J Biomed Semantics       Date:  2011-03-07

7.  Which compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development.

Authors:  Gerard J P van Westen; Jörg K Wegner; Peggy Geluykens; Leen Kwanten; Inge Vereycken; Anik Peeters; Adriaan P Ijzerman; Herman W T van Vlijmen; Andreas Bender
Journal:  PLoS One       Date:  2011-11-23       Impact factor: 3.240

8.  High-Dimensional descriptor selection and computational QSAR modeling for antitumor activity of ARC-111 analogues Based on Support Vector Regression (SVR).

Authors:  Wei Zhou; Zhijun Dai; Yuan Chen; Haiyan Wang; Zheming Yuan
Journal:  Int J Mol Sci       Date:  2012-01-20       Impact factor: 6.208

9.  Proteochemometric modeling of the bioactivity spectra of HIV-1 protease inhibitors by introducing protein-ligand interaction fingerprint.

Authors:  Qi Huang; Haixiao Jin; Qi Liu; Qiong Wu; Hong Kang; Zhiwei Cao; Ruixin Zhu
Journal:  PLoS One       Date:  2012-07-27       Impact factor: 3.240

10.  Screening of selective histone deacetylase inhibitors by proteochemometric modeling.

Authors:  Dingfeng Wu; Qi Huang; Yida Zhang; Qingchen Zhang; Qi Liu; Jun Gao; Zhiwei Cao; Ruixin Zhu
Journal:  BMC Bioinformatics       Date:  2012-08-22       Impact factor: 3.169

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