Discovery of Novel Tankyrase Inhibitors through Molecular Docking-Based Virtual Screening and Molecular Dynamics Simulation Studies.

Tankyrase enzymes (TNKS), a core part of the canonical Wnt pathway, are a promising target in the search for potential anti-cancer agents. Although several hundreds of the TNKS inhibitors are currently known, identification of their novel chemotypes attracts considerable interest. In this study, the molecular docking and machine learning-based virtual screening techniques combined with the physico-chemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) profile prediction and molecular dynamics simulations were applied to a subset of the ZINC database containing about 1.7 M commercially available compounds. Out of seven candidate compounds biologically evaluated in vitro for their inhibition of the TNKS2 enzyme using immunochemical assay, two compounds have shown a decent level of inhibitory activity with the IC50 values of less than 10 nM and 10 µM. Relatively simple scores based on molecular docking or MM-PBSA (molecular mechanics, Poisson-Boltzmann, surface area) methods proved unsuitable for predicting the effect of structural modification or for accurate ranking of the compounds based on their binding energies. On the other hand, the molecular dynamics simulations and Free Energy Perturbation (FEP) calculations allowed us to further decipher the structure-activity relationships and retrospectively analyze the docking-based virtual screening performance. This approach can be applied at the subsequent lead optimization stages.

Time-Domain Analysis of Molecular Dynamics Trajectories Using Deep Neural Networks: Application to Activity Ranking of Tankyrase Inhibitors.

Molecular dynamics simulations provide valuable insights into the behavior of molecular systems. Extending the recent trend of using machine learning techniques to predict physicochemical properties from molecular dynamics data, we propose to consider the trajectories as multidimensional time series represented by 2D tensors containing the ligand-protein interaction descriptor values for each time step. Similar in structure to the time series encountered in modern approaches for signal, speech, and natural language processing, these time series can be directly analyzed using long short-term memory (LSTM) recurrent neural networks or convolutional neural networks (CNNs). The predictive regression models for the ligand-protein affinity were built for a subset of the PDBbind v.2017 database and applied to inhibitors of tankyrase, an enzyme of the poly(ADP-ribose)-polymerase (PARP) family that can be used in the treatment of colorectal cancer. As an additional test set, a subset of the Community Structure-Activity Resource (CSAR) data set was used. For comparison, the random forest and simple neural network models based on the crystal pose or the trajectory-averaged descriptors were used, as well as the commonly employed docking and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) scores. Convolutional neural networks based on the 2D tensors of ligand-protein interaction descriptors for short (2 ns) trajectories provide the best accuracy and predictive power, reaching the Spearman rank correlation coefficient of 0.73 and Pearson correlation coefficient of 0.70 for the tankyrase test set. Taking into account the recent increase in computational power of modern GPUs and relatively low computational complexity of the proposed approach, it can be used as an advanced virtual screening filter for compound prioritization.

Machine Learning Classification Models to Improve the Docking-based Screening: A Case of PI3K-Tankyrase Inhibitors.

One of the major challenges in the current drug discovery is the improvement of the docking-based virtual screening performance. It is especially important in the rational design of compounds with desired polypharmacology or selectivity profiles. To address this problem, we present a methodology for the development of target-specific scoring functions possessing high screening power. These scoring functions were built using the machine learning methods for the dual target inhibitors of PI3Kα and tankyrase, promising targets for colorectal cancer therapy. The Deep Neural Network models achieve the external test AUC ROC values of 0.96 and 0.93 for the random split and 0.90 and 0.84 for the time-based split of the PI3Kα and tankyrase inhibitors, respectively. In addition, the impact of the training set size and the actives/decoys ratio on the model quality was assessed. The study demonstrates that the optimized scoring functions could significantly improve the docking screening power for each individual target. This is very useful in the design of multitarget or selective drugs wherein the screening filters are applied in sequence.