Current Medicinal Chemistry - Volume 27, Issue 38, 2020

Background: The KNIME platform offers several tools for the analysis of chem- and pharmacoinformatics data. Unless one has sufficient in-house data available for the analysis of interest, it is necessary to fetch third party data into KNIME. Many data sources offer valuable data, but including this data in a workflow is not always straightforward. Objective: Here we discuss different ways of accessing public data sources. We give an overview of KNIME nodes for different sources, with references to available example workflows. For data sources with no individual KNIME node available, we present a general approach of accessing a web interface via KNIME. In addition, we discuss necessary steps before the data can be analysed, such as data curation, chemical standardisation and the merging of datasets.

In recent years there has been a paradigm shift in how data is being used to progress early drug discovery campaigns from hit identification to candidate selection. Significant developments in data mining methods and the accessibility of tools for research scientists have been instrumental in reducing drug discovery timelines and in increasing the likelihood of a chemical entity achieving drug development milestones. KNIME, the Konstanz Information Miner, is a leading open source data analytics platform and has supported drug discovery endeavours for over a decade. KNIME provides a rich palette of tools supported by an extensive community of contributors to enable ligandand structure-based drug design. This review will examine recent developments within the KNIME platform to support small-molecule drug design and provide a perspective on the challenges and future developments within this field.

Drug discovery is a challenging and expensive field. Hence, novel in silico tools have been developed in early discovery stage to identify and prioritize novel molecules with suitable physicochemical properties. In many in silico drug design projects, molecular databases are screened by virtual screening tools to search for potential bioactive molecules. The preparation of the molecules is therefore a key step in the success of well-established techniques such as docking, similarity or pharmacophore searching. We review here the lists of several toolkits used in different steps during the cleaning of molecular databases, integrated within a KNIME workflow. During the first step of the automatic workflow, salts are removed, and mixtures are split to get one compound per entry. Then compounds with unwanted features are filtered. Duplicated entries are then deleted while considering stereochemistry. As a compromise between exhaustiveness and computational time, most distributed tautomers at physiological pH are computed. Additionally, various flags are applied to molecules by using either classical molecular descriptors, similarity search to known libraries or substructure search rules. Moreover, stereoisomers are enumerated depending on the unassigned chiral centers. Then, three-dimensional coordinates, and optionally conformers, are generated. This workflow has been already applied to several drug design projects and can be used for molecular database preparation upon request.

Since the official release as a KNIME Community Contribution in June 2013, the Vernalis KNIME nodes have increased from a single node (the ‘PDB Connector’ node) to around 126 nodes (November 2017; Version 1.12.0); furthermore, a number of nodes have been adopted into the core KNIME product. In this review, we provide a brief timeline of the development of the current public release and an overview of the current nodes. We will focus in more detail on three particular areas: nodes accessing publicly available information via web services, nodes providing cheminformatics functionality without recourse to a cheminformatics toolkit, and nodes using one of the cheminformatics toolkits present in KNIME. We will conclude with a number of case studies demonstrating the use of KNIME at Vernalis.

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

A number of clinical studies have revealed that there is an association between major depression (MD) and gastroesophageal reflux disease (GERD). Both the diseases are shown to affect a large proportion of the global population. More advanced studies for understanding the comorbidity mechanism of these two diseases can shed light on developing new therapies of both diseases. To the best of our knowledge, there has not been any research work in the literature investigating the relationship between MD and GERD using their miRNA biomarkers. We adopt a phylogenetic analysis to analyze their miRNA biomarkers. From our analyzed results, the association between these two diseases can be explored through miRNA phylogeny. In addition to evidence from the phylogenetic analysis, we also demonstrate epidemiological evidence for the relationship between MD and GERD based on Taiwan biobank data.

Background: Gangliosides are an essential component of eukaryotic plasma membranes implicated in multiple physiological processes. Little is known about molecular mechanisms underlying the distribution and functions of membrane gangliosides. The overwhelmingly complex organization of glycocalyx impedes the structural analysis on cell surface and the interplay between the lipid components. Advanced X-ray analytical tools applicable to studying biological interfaces call for the simplistic models that mimic ganglioside-enriched cellular membranes. Objective: To summarize the mechanistic evidences of ganglioside interactions with lipid environment and biologically active ligands using high-resolution synchrotron X-ray scattering. Methods: A comprehensive review of studies published over the last decade was done to discuss recent accomplishments and future trends. Results: Langmuir monolayers represent an adequate model system to assess the effect of gangliosides on membrane structure. Grazing incidence X-ray diffraction reveals a condensation effect by gangliosides on zwitterionic phospholipids with the cooperative packing of sialo- and phosphate groups. In turn, the arrangement of negatively charged lipids in ganglioside mixture remains unchanged due to the stretched conformation of carbohydrate moieties. Upon interaction with biological ligands, such as cholera toxin and galectins, the ganglioside redistribution within the ordered regions of monolayer follows distinct mechanistic patterns. The cholera toxin pentamer attached to the oligosaccharide core induces local transition from oblique to the hexagonal lattice resulting in phase coexistence. The incorporation of the A subunit responsible for endocytosis is further promoted by the acidic environment characteristic for endosomal space. X-ray reflectivity shows in-plane orientation of galectin dimers with the spatial mismatch between the lectin binding sites and ganglioside carbohydrates to perturb ceramide alkyl chains. Recent data also demonstrate sialic acid groups to be potential targets for novel peptide mimicking anticancer therapeutics. Conclusion: Coupled with surface X-ray scattering, the membrane mimetic approach allows for better understanding the biological role of gangliosides and their potential applications.