Proteochemometric Modeling of the Bioactivity Spectra of HIV-1 Protease Inhibitors by Introducing Protein-Ligand Interaction Fingerprint

HIV-1 protease is one of the main therapeutic targets in HIV. However, a major problem in treatment of HIV is the rapid emergence of drug-resistant strains. It should be particularly helpful to clinical therapy of AIDS if one method can be used to predict antivirus capability of compounds for different variants. In our study, proteochemometric (PCM) models were created to study the bioactivity spectra of 92 chemical compounds with 47 unique HIV-1 protease variants. In contrast to other PCM models, which used Multiplication of Ligands and Proteins Descriptors (MLPD) as cross-term, one new cross-term, i.e. Protein-Ligand Interaction Fingerprint (PLIF) was introduced in our modeling. With different combinations of ligand descriptors, protein descriptors and cross-terms, nine PCM models were obtained, and six of them achieved good predictive abilities (Q2 test>0.7). These results showed that the performance of PCM models could be improved when ligand and protein descriptors were complemented by the newly introduced cross-term PLIF. Compared with the conventional cross-term MLPD, the newly introduced PLIF had a better predictive ability. Furthermore, our best model (GD & P & PLIF: Q2test = 0.8271) could select out those inhibitors which have a broad antiviral activity. As a conclusion, our study indicates that proteochemometric modeling with PLIF as cross-term is a potential useful way to solve the HIV-1 drug-resistant problem.

[1]  Arun K. Ghosh,et al.  A Potent Human Immunodeficiency Virus Type 1 Protease Inhibitor, UIC-94003 (TMC-126), and Selection of a Novel (A28S) Mutation in the Protease Active Site , 2002, Journal of Virology.

[2]  Liangpei Zhang,et al.  A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Brian K Shoichet,et al.  Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. , 2006, Journal of medicinal chemistry.

[4]  A. Bergamini,et al.  Indolyl Aryl Sulphones as HIV-1 Non-Nucleoside Reverse Transcriptase Inhibitors: Synthesis, Biological Evaluation and Binding Mode Studies of New Derivatives at Indole-2-carboxamide , 2006, Antiviral chemistry & chemotherapy.

[5]  Yong-tang Zheng,et al.  Mangiferin, an Anti-HIV-1 Agent Targeting Protease and Effective against Resistant Strains , 2011, Molecules.

[6]  Jarl E. S. Wikberg,et al.  Interaction Model Based on Local Protein Substructures Generalizes to the Entire Structural Enzyme-Ligand Space , 2008, J. Chem. Inf. Model..

[7]  A. Wensing,et al.  Fifteen years of HIV Protease Inhibitors: raising the barrier to resistance. , 2010, Antiviral research.

[8]  Celia A Schiffer,et al.  Decomposing the energetic impact of drug resistant mutations in HIV-1 protease on binding DRV. , 2010, Journal of chemical theory and computation.

[9]  P. Labute.  A widely applicable set of descriptors. , 2000, Journal of molecular graphics & modelling.

[10]  E. Freire,et al.  A major role for a set of non-active site mutations in the development of HIV-1 protease drug resistance. , 2003, Biochemistry.

[11]  Gunnar Rätsch,et al.  Classifying 'Drug-likeness' with Kernel-Based Learning Methods , 2005, J. Chem. Inf. Model..

[12]  Ruixin Zhu,et al.  Multi-target QSAR Study in the Analysis and Design of HIV-1 Inhibitors† , 2010 .

[13]  Z. Deng,et al.  Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. , 2004, Journal of medicinal chemistry.

[14]  Parimal Kar,et al.  Origin of decrease in potency of darunavir and two related antiviral inhibitors against HIV-2 compared to HIV-1 protease. , 2012, The journal of physical chemistry. B.

[15]  Jarl E. S. Wikberg,et al.  Proteochemometric Modeling of Drug Resistance over the Mutational Space for Multiple HIV Protease Variants and Multiple Protease Inhibitors , 2009, J. Chem. Inf. Model..

[16]  K. Fidelis,et al.  Generalized modeling of enzyme–ligand interactions using proteochemometrics and local protein substructures , 2006, Proteins.

[17]  Hiroaki Mitsuya,et al.  Darunavir, a conceptually new HIV-1 protease inhibitor for the treatment of drug-resistant HIV. , 2007, Bioorganic & medicinal chemistry.

[18]  Haifeng Chen,et al.  Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression , 2004, J. Chem. Inf. Model..

[19]  T. Lundstedt,et al.  Proteochemometrics modeling of the interaction of amine G-protein coupled receptors with a diverse set of ligands. , 2002, Molecular pharmacology.

[20]  P. Lam,et al.  Counteracting HIV-1 protease drug resistance: structural analysis of mutant proteases complexed with XV638 and SD146, cyclic urea amides with broad specificities. , 1998, Biochemistry.

[21]  J. Doucet,et al.  QSAR models for 2-amino-6-arylsulfonylbenzonitriles and congeners HIV-1 reverse transcriptase inhibitors based on linear and nonlinear regression methods. , 2009, European journal of medicinal chemistry.

[22]  Richard H. Henchman,et al.  Revisiting free energy calculations: a theoretical connection to MM/PBSA and direct calculation of the association free energy. , 2004, Biophysical journal.

[23]  A. Fauci,et al.  Thirty Years of HIV and AIDS: Future Challenges and Opportunities , 2011, Annals of Internal Medicine.

[24]  Dino Isa,et al.  Intellectual Property Management System for the Super-Capacitor Pilot Plant , 2009, IC-AI.

[25]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[26]  N. S. Hari Narayana Moorthy,et al.  QSAR analysis of some 5-amino-2-mercapto-1,3,4-thiadiazole based inhibitors of matrix metalloproteinases and bacterial collagenase. , 2006, Bioorganic & medicinal chemistry letters.

[27]  Jon Cohen.  HIV prevention. Halting HIV/AIDS epidemics. , 2011, Science.

[28]  Obdulia Rabal,et al.  APIF: A New Interaction Fingerprint Based on Atom Pairs and Its Application to Virtual Screening , 2009, J. Chem. Inf. Model..

[29]  Christoph A Sotriffer,et al.  "In situ cross-docking" to simultaneously address multiple targets. , 2005, Journal of medicinal chemistry.

[30]  Maris Lapins,et al.  Towards Proteome–Wide Interaction Models Using the Proteochemometrics Approach , 2010, Molecular informatics.

[31]  Silvio Massa,et al.  Computer-aided design, synthesis, and anti-HIV-1 activity in vitro of 2-alkylamino-6-[1-(2,6-difluorophenyl)alkyl]-3,4-dihydro-5-alkylpyrimidin-4(3H)-ones as novel potent non-nucleoside reverse transcriptase inhibitors, also active against the Y181C variant. , 2004, Journal of medicinal chemistry.

[32]  Teruki Honma,et al.  Combining Machine Learning and Pharmacophore-Based Interaction Fingerprint for in Silico Screening , 2010, J. Chem. Inf. Model..

[33]  Parimal Kar,et al.  Energetic basis for drug resistance of HIV-1 protease mutants against amprenavir , 2012, Journal of Computer-Aided Molecular Design.

[34]  Ian Witten,et al.  Data Mining , 2000 .

[35]  R. Zauhar,et al.  Computational studies on HIV-1 protease inhibitors: influence of calculated inhibitor-enzyme binding affinities on the statistical quality of 3D-QSAR CoMFA models. , 2000, Journal of medicinal chemistry.

[36]  Jarl E. S. Wikberg,et al.  Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques , 2010, BMC Bioinformatics.

[37]  Qi Liu,et al.  Virtual Drug Screen Schema Based on Multiview Similarity Integration and Ranking Aggregation , 2012, J. Chem. Inf. Model..

[38]  M. Ghate,et al.  QSAR modeling of some substituted alkylidenepyridazin-3-one as a non-cAMP-based antiplatelet agent , 2011, Medicinal Chemistry Research.

[39]  Ola Spjuth,et al.  Proteochemometric Modeling of the Susceptibility of Mutated Variants of the HIV-1 Virus to Reverse Transcriptase Inhibitors , 2010, PloS one.

[40]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[41]  Quantitative Structure-Activity Relationship of IOPY/ISPY Analogues as HIV-1 Non-Nucleoside Reverse Transcriptase Inhibitors , 2011 .

[42]  Peteris Prusis,et al.  Proteochemometric modeling of HIV protease susceptibility , 2008, BMC Bioinformatics.

[43]  Jürgen Bajorath,et al.  Virtual screening methods that complement HTS. , 2004, Combinatorial chemistry & high throughput screening.

[44]  S. Wold,et al.  New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. , 1998, Journal of medicinal chemistry.

[45]  Peteris Prusis,et al.  Improved approach for proteochemometrics modeling: application to organic compound - amine G protein-coupled receptor interactions , 2005, Bioinform..

[46]  Jun Xu,et al.  Drug-like Index: A New Approach To Measure Drug-like Compounds and Their Diversity , 2000, J. Chem. Inf. Comput. Sci..

[47]  W Patrick Walters,et al.  A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance , 2004, Proteins.

[48]  Gerard J. P. van Westen,et al.  Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets , 2011 .

[49]  L. Bourgon,et al.  Selection and characterization of HIV-1 showing reduced susceptibility to the non-peptidic protease inhibitor tipranavir. , 2005, Antiviral research.

[50]  C. E. Peishoff,et al.  A critical assessment of docking programs and scoring functions. , 2006, Journal of medicinal chemistry.

[51]  Xi Chen,et al.  Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study , 2011, BMC Bioinformatics.

[52]  Fabrizio Manetti,et al.  HIV-reverse transcriptase inhibition: inclusion of ligand-induced fit by cross-docking studies. , 2005, Journal of medicinal chemistry.

[53]  V. B. Silva,et al.  Virtual Screening and Toxicology Prediction of Novel Potential Non- Nucleoside Reverse Transcriptase Inhibitors , 2009 .

[54]  Christopher R. Corbeil,et al.  Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go , 2008, British journal of pharmacology.

[55]  3,4,5-Trisubstituted-1,2,4-4H-triazoles as WT and Y188L mutant HIV-1 non-nucleoside reverse transcriptase inhibitors: docking-based CoMFA and CoMSIA analyses , 2011, Journal of molecular modeling.

[56]  Qi Kang,et al.  Comparison of Ligand-, Target Structure-, and Protein-Ligand Interaction Fingerprint-based Virtual Screening Methods , 2011 .