Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria

Identification of novel antiplasmodial compound by hierarquical virtual screening and in vitro assays

Malaria is an infectious disease caused by protozoa of the genus Plasmodium spp. with approximately 219 million cases in 2017. P. falciparum is main responsible for the most severe form of the disease, cerebral malaria. Despite of public health impacts, chemotherapy against malaria is still limited due to the emergence of drug resistance cases used in monotherapy and combination therapies. Thus, the development of new antimalarial drugs becomes emergency. One way of achieve this goal is to explore essential and/or unique therapeutic targets of the parasite, or at least sufficiently different to ensure selective inhibition. Enoil-ACP reductase (ENR) is a NADH-dependent enzyme responsible for the limiting step of the type II fatty acid biosynthetic pathway (FAS II). Thus, pharmacophore and docking based virtual screening were applied to prioritize molecules for in vitro assays against P. falciparum W2 strain. The application of successive filters at OOCC database (n = 618) resulted in the identification of one molecule (13) (EC₅₀ = 0.098 ± 0.021 μM) with similar biological activity to artemether. The molecule 13 is a typical drug repurposing case due to previous other approved therapeutic uses on Chinese medicine as a non-specific cholinergic antagonist, thus it could be accelerated the drug development process. Additionally, molecular dynamics studies were used to confirm stability of the molecular interactions identified by molecular docking. Thus, representative structures of P. falciparum ENR can be used in a study to propose new derivatives for evaluation of biological activity in vitro and in vivo. Communicated by Ramaswamy H. Sarma.

TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data

Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines. While documentation for such tools is available, there are only a few freely accessible examples that teach the underlying concepts focused on CADD, especially addressing users new to the field. Here, we present TeachOpenCADD, a teaching platform developed by students for students, using open source compound and protein data as well as basic and CADD-related Python packages. We provide interactive Jupyter notebooks for central CADD topics, integrating theoretical background and practical code. TeachOpenCADD is freely available on GitHub: https://github.com/volkamerlab/TeachOpenCADD .

3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery

eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure-based bioactivity data mapping, ii) structure-based identification of scaffold replacement strategies for ligand design, iii) ligand-based target prediction, iv) protein sequence-based binding site identification and ligand repurposing, and v) structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.

Optimal Design of Experiments by Combining Coarse and Fine Measurements

In many contexts, it is extremely costly to perform enough high-quality experimental measurements to accurately parametrize a predictive quantitative model. However, it is often much easier to carry out large numbers of experiments that indicate whether each sample is above or below a given threshold. Can many such categorical or "coarse" measurements be combined with a much smaller number of high-resolution or "fine" measurements to yield accurate models? Here, we demonstrate an intuitive strategy, inspired by statistical physics, wherein the coarse measurements are used to identify the salient features of the data, while the fine measurements determine the relative importance of these features. A linear model is inferred from the fine measurements, augmented by a quadratic term that captures the correlation structure of the coarse data. We illustrate our strategy by considering the problems of predicting the antimalarial potency and aqueous solubility of small organic molecules from their 2D molecular structure.