Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME

Abstract

Today’s large, public databases of protein–small molecule interaction data are creating important new opportunities for data mining and integration. At the same time, new graphical user interface-based workflow tools offer facile alternatives to custom scripting for informatics and data analysis. Here, we illustrate how the large protein-ligand database BindingDB may be incorporated into KNIME workflows as a step toward the integration of pharmacological data with broader biomolecular analyses. Thus, we describe a collection of KNIME workflows that access BindingDB data via RESTful webservices and, for more intensive queries, via a local distillation of the full BindingDB dataset. We focus in particular on the KNIME implementation of knowledge-based tools to generate informed hypotheses regarding protein targets of bioactive compounds, based on notions of chemical similarity. A number of variants of this basic approach are tested for seven existing drugs with relatively ill-defined therapeutic targets, leading to replication of some previously confirmed results and discovery of new, high-quality hits. Implications for future development are discussed.Database URL: www.bindingdb.org

Towards reproducible computational drug discovery

“…Pipeline Pilot has been used in several studies, including to design screening libraries [197], for high-content screening [198], and for compound design [199]. KNIME has been used, for example, for virtual screening [200], target identification [201]; more in-depth coverage of applications are provided elsewhere [202].…”

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

“…Several primers on connecting proteins with compounds using workflows and distributed web services exist, for example using BindingDB and KNIME [28, 45]. One of our goals here is to provide a primer on data mining in drug discovery for modulating and inhibiting protein-protein interactions.…”

Scientific workflows: Past, present and future

“…KNIME KNIME [67] has focused on usability for life sciences, chemistry, finance and media through integration (with other tools and multiple data types and sources) and on reproducibility (KNIME workflows from version 1.0 still run and produce exactly the same results in today's version). This has delivered convenience so that one customer automatically runs nightly training for tens of thousands of predictive models, but tuning to meet application community needs also yields discoveries [68,69]. MOTEUR MOTEUR [70,71] is a scientific (data-driven) workflow manager designed to efficiently enact large data flow-based computations, leveraging the capacity of various distributed computing infrastructures.…”