4-Pregnen-21-ol-3,20-dione-21-(4-bromobenzenesulfonate) (NSC 88915) and related novel steroid derivatives as tyrosyl-DNA phosphodiesterase (Tdp1) inhibitors

Tyrosyl-DNA phosphodiesterase 1 (Tdp1) is an enzyme that catalyzes the hydrolysis of 3'-phosphotyrosyl bonds. Such linkages form in vivo when topoisomerase I (Top1) processes DNA. For this reason, Tdp1 has been implicated in the repair of irreversible Top1-DNA covalent complexes. Tdp1 inhibitors have been regarded as potential therapeutics in combination with Top1 inhibitors, such as the camptothecin derivatives, topotecan and irinotecan, which are used to treat human cancers. Using a novel high-throughput screening assay, we have identified the C21-substituted progesterone derivative, NSC 88915 (1), as a potential Tdp1 inhibitor. Secondary screening and cross-reactivity studies with related DNA processing enzymes confirmed that compound 1 possesses specific Tdp1 inhibitory activity. Deconstruction of compound 1 into discrete functional groups reveals that both components are required for inhibition of Tdp1 activity. Moreover, the synthesis of analogues of compound 1 has provided insight into the structural requirements for the inhibition of Tdp1. Surface plasmon resonance shows that compound 1 binds to Tdp1, whereas an inactive analogue fails to interact with the enzyme. Based on molecular docking and mechanistic studies, we propose that these compounds are competitive inhibitors, which mimics the oligonucleotide-peptide Tdp1 substrate. These steroid derivatives represent a novel chemotype and provide a new scaffold for developing small molecule inhibitors of Tdp1

Development and Implementation of (Q)SAR Modeling Within the CHARMMing Web-user Interface

Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a web-based tool for structure activity relationship and quantitative structure activity relationship modeling to add to the services provided by CHARMMing (www.charmming.org). This new module implements some of the most recent advances in modern machine learning algorithms—Random Forest, Support Vector Machine, Stochastic Gradient Descent, Gradient Tree Boosting, so forth. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity. © 2014 Wiley Periodicals, Inc

PDB Ligand Conformational Energies Calculated Quantum-Mechanically

We present here a greatly updated version of an earlier study on the conformational energies of protein–ligand complexes in the Protein Data Bank (PDB) [Nicklaus et al. <i>Bioorg. Med. Chem</i>. <b>1995</b>, <i>3</i>, 411–428], with the goal of improving on all possible aspects such as number and selection of ligand instances, energy calculations performed, and additional analyses conducted. Starting from about 357,000 ligand instances deposited in the 2008 version of the Ligand Expo database of the experimental 3D coordinates of all small-molecule instances in the PDB, we created a “high-quality” subset of ligand instances by various filtering steps including application of crystallographic quality criteria and structural unambiguousness. Submission of 640 Gaussian 03 jobs yielded a set of about 415 successfully concluded runs. We used a stepwise optimization of internal degrees of freedom at the DFT level of theory with the B3LYP/6-31G­(d) basis set and a single-point energy calculation at B3LYP/6-311++G­(3df,2p) after each round of (partial) optimization to separate energy changes due to bond length stretches vs bond angle changes vs torsion changes. Even for the most “conservative” choice of all the possible conformational energiesthe energy difference between the conformation in which all internal degrees of freedom except torsions have been optimized and the fully optimized conformersignificant energy values were found. The range of 0 to ∼25 kcal/mol was populated quite evenly and independently of the crystallographic resolution. A smaller number of “outliers” of yet higher energies were seen only at resolutions above 1.3 Å. The energies showed some correlation with molecular size and flexibility but not with crystallographic quality metrics such as the Cruickshank diffraction-component precision index (DPI) and R_free-R, or with the ligand instance-specific metrics such as occupancy-weighted B-factor (OWAB), real-space R factor (RSR), and real-space correlation coefficient (RSCC). We repeated these calculations with the solvent model IEFPCM, which yielded energy differences that were generally somewhat lower than the corresponding vacuum results but did not produce a qualitatively different picture. Torsional sampling around the crystal conformation at the molecular mechanics level using the MMFF94s force field typically led to an increase in energy