Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.

Many MAS (magic angle spinning) solid-state NMR investigations of biologically relevant protein samples are hampered by poor resolution, particularly in the 15N chemical shift dimension. We show that dynamics in the nanosecond-microsecond time scale in solid-state samples can induce significant line broadening of 15N resonances in solid-state NMR experiments. Averaging of 15NH(alpha/beta) multiplet components due to 1H decoupling induces effective relaxation of the 15N coherence in case the N-H spin pair undergoes significant motion. High resolution solid-state NMR spectra can then only be recorded by application of TROSY (Transverse Relaxation Optimized Spectroscopy) type techniques which select the narrow component of the multiplet pattern. We speculate that this effect has been the major obstacle to the NMR spectroscopic characterization of many membrane proteins and fibrillar aggregates so far. Only in very favorable cases, where dynamics are either absent or very fast (picosecond), high-resolution spectra were obtained. We expect that this approach which requires intense deuteration will have a significant impact on the quality and the rate at which solid-state NMR spectroscopic investigations will emerge in the future.

Suberoylanilide hydroxamic acid (SAHA) is a potent histone deacetylase (HDAC) inhibitor. Inhibitors of HDACs are used in cancer therapy based on the role HDACs play in transcription by regulating chromatin compaction and non-histone proteins such as transcription factors. Profiling of HDAC expression is of interest in the functional proteomics analysis of cancer. Also, non-HDAC proteins may interact with HDAC inhibitor drugs and contribute to the drug mode of action. We here present a tool for the unbiased chemical proteomic profiling of proteins that specifically interact with SAHA. We designed and synthesized a trifunctional Capture Compound containing SAHA as selectivity and identified HDACs1, 2, 3 and 6, known and predicted HDAC interactors from human-derived HepG2 cell lysate, as well as a set of new potential non-HDAC targets of SAHA. One of these non-HDAC targets, isochorismatase domain-containing protein 2 (ISOC2) is putative hydrolase associated with the negative regulation of the tumor-suppressor p16(INK4a). We demonstrated the direct and dose-dependent interaction of SAHA to the purified recombinant ISOC2 protein. Using SAHA Capture Compound mass spectrometry, we thus identified potential new SAHA target proteins in an entirely unbiased chemical proteomics approach.

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