Quantitative structure–activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries
28 Feb 2017-Sar and Qsar in Environmental Research (SAR QSAR Environ Res)-Vol. 28, Iss: 3, pp 199-220
TL;DR: This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.
Abstract: Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immune disorders. Computational modelling approaches have been widely applied for the virtual screening and rational design of novel HDAC inhibitors. In this study, different machine learning (ML) techniques were applied for the development of models that accurately discriminate HDAC2 inhibitors form non-inhibitors. The obtained models showed encouraging results, with the global accuracy in the external set ranging from 0.83 to 0.90. Various aspects related to the comparison of modelling techniques, applicability domain and descriptor interpretations were discussed. Finally, consensus predictions of these models were used for screening HDAC2 inhibitors from four chemical libraries whose bioactivities against HDAC1, HDAC3, HDAC6 and HDAC8 have been known. According to the results of virtual screening assays, structures of some hits with pair-isoform-selective activity (between HDAC2 and other HDACs) were revealed. This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.
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TL;DR: This work introduces a novel approach for epigenetic quantitative structure–activity relationship (QSAR) modelling using conformal prediction and discusses the development of models for 11 sets of inhibitors of histone deacetylases, which are one of the major epigenetic target families that have been screened.
Abstract: The growing interest in epigenetic probes and drug discovery, as revealed by several epigenetic drugs in clinical use or in the lineup of the drug development pipeline, is boosting the generation of screening data. In order to maximize the use of structure-activity relationships there is a clear need to develop robust and accurate models to understand the underlying structure-activity relationship. Similarly, accurate models should be able to guide the rational screening of compound libraries. Herein we introduce a novel approach for epigenetic quantitative structure-activity relationship (QSAR) modelling using conformal prediction. As a case study, we discuss the development of models for 11 sets of inhibitors of histone deacetylases (HDACs), which are one of the major epigenetic target families that have been screened. It was found that all derived models, for every HDAC endpoint and all three significance levels, are valid with respect to predictions for the external test sets as well as the internal validation of the corresponding training sets. Furthermore, the efficiencies for the predictions are above 80% for most data sets and above 90% for four data sets at different significant levels. The findings of this work encourage prospective applications of conformal prediction for other epigenetic target data sets.
13 citations
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"Quantitative structure–activity rel..." refers methods in this paper
...In order to gain an insight into the structure–activity relationship and to find variable balanced levels that produce desirable predicted HDAC inhibitory potency, a desirability analysis was performed based on a multi-response experiment proposed by Deringer and Suich [69]....
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Research Institute of Molecular Pathology1, Ludwig Maximilian University of Munich2, University of Colorado Boulder3, University of Cambridge4, Centro Nacional de Investigaciones Cardiovasculares5, Autonomous University of Madrid6, University of Navarra7, University of Barcelona8, Howard Hughes Medical Institute9
TL;DR: It is found that cancer cells had a loss of monoacetylated and trimethylated forms of histone H4 early and accumulated during the tumorigenic process, which is a common hallmark of human tumor cells.
Abstract: CpG island hypermethylation and global genomic hypomethylation are common epigenetic features of cancer cells. Less attention has been focused on histone modifications in cancer cells. We characterized post-translational modifications to histone H4 in a comprehensive panel of normal tissues, cancer cell lines and primary tumors. Using immunodetection, high-performance capillary electrophoresis and mass spectrometry, we found that cancer cells had a loss of monoacetylated and trimethylated forms of histone H4. These changes appeared early and accumulated during the tumorigenic process, as we showed in a mouse model of multistage skin carcinogenesis. The losses occurred predominantly at the acetylated Lys16 and trimethylated Lys20 residues of histone H4 and were associated with the hypomethylation of DNA repetitive sequences, a well-known characteristic of cancer cells. Our data suggest that the global loss of monoacetylation and trimethylation of histone H4 is a common hallmark of human tumor cells.
1,807 citations
University of British Columbia1, National Academy of Sciences of Ukraine2, University of North Carolina at Chapel Hill3, University of Strasbourg4, Moscow State University5, Liverpool John Moores University6, University of Insubria7, University of Milano-Bicocca8, Istituto Superiore di Sanità9, Ohio State University10, Research Triangle Park11
TL;DR: In this paper, the authors provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive quantitative structure-activity relationship models.
Abstract: Quantitative structure–activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR stu...
1,314 citations