KNIME Workflow Research Articles

Monitoring nucleolar-nucleoplasmic protein shuttling in living cells by high-content microscopy and automated image analysis.

The nucleolus has corefunctions in ribosome biosynthesis, but also acts as a regulatory hub in a plethora of non-canonical processes, including cellular stress. Upon DNA damage, several DNA repair factors shuttle between the nucleolus and the nucleoplasm. Yet, the molecular mechanisms underlying such spatio-temporal protein dynamics remain to be deciphered. Here, we present a novel imaging platform to investigate nucleolar-nucleoplasmic protein shuttling in living cells. For image acquisition, we used a commercially available automated fluorescence microscope and for image analysis, we developed a KNIME workflow with implementation of machine learning-based tools. We validated the method with different nucleolar proteins, i.e., PARP1, TARG1 and APE1, by monitoring their shuttling dynamics upon oxidative stress. As a paradigm, we analyzed PARP1 shuttling upon H2O2 treatment in combination with a range of pharmacological inhibitors in a novel reporter cell line. These experiments revealed that inhibition of SIRT7 results in a loss of nucleolar PARP1 localization. Finally, we unraveled specific differences in PARP1 shuttling dynamics after co-treatment with H2O2 and different clinical PARP inhibitors. Collectively, this work delineates a highly sensitive and versatile bioimaging platform to investigate swift nucleolar-nucleoplasmic protein shuttling in living cells, which can be employed for pharmacological screening and in-depth mechanistic analyses.

KNIME workflows for applications in medicinal and computational chemistry

Artificial intelligence (AI) has huge potential to accelerate drug discovery, but challenges remain in implementing AI algorithms that can be used by the broad scientific community. Identification of molecular features and their subsequent use in training of machine learning models may permit prediction of new molecules with enhanced properties. Predictive modeling is particularly applicable to analysis of structure-activity relationships (SARs) and would be a useful tool in the hands of laboratory medicinal chemists. This requires a software platform that is chemically intuitive while providing the user with access to AI methods. The KNIME platform provides such an environment through inclusion of broad chemical toolsets and a user-friendly approach for utilization of machine learning for analysis of SAR data. Here, we illustrate use of KNIME for this purpose, with a focus on discovery of features of highly potent tau inhibitors from a series of structurally diverse polyphenols. Workflows are described that enable implementation of AI tools in KNIME for diverse SAR projects.

Examining evolutionary scale modeling-derived different-dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow.

Molecular features play an important role in different bio-chem-informatics tasks, such as the Quantitative Structure-Activity Relationships (QSAR) modeling. Several pre-trained models have been recently created to be used in downstream tasks, either by fine-tuning a specific model or by extracting features to feed traditional classifiers. In this regard, a new family of Evolutionary Scale Modeling models (termed as ESM-2 models) was recently introduced, demonstrating outstanding results in protein structure prediction benchmarks. Herein, we studied the usefulness of the different-dimensional embeddings derived from the ESM-2 models to classify antimicrobial peptides (AMPs). To this end, we built a KNIME workflow to use the same modeling methodology across experiments in order toguarantee fair analyses. As a result, the 640- and 1280-dimensional embeddings derived from the 30- and 33-layer ESM-2 models, respectively, are the most valuablesince statistically better performances were achieved by the QSAR models built from them. We also fused features of the different ESM-2 models, and it was concluded that the fusion contributes to getting better QSAR models than using features of a single ESM-2 model. Frequency studies revealed that only a portion of the ESM-2 embeddings is valuable for modeling tasks since between 43% and 66% of the features were never used. Comparisons regarding state-of-the-art deep learning (DL) models confirm that when performing methodologically principled studies in the prediction of AMPs, non-DL based QSAR models yield comparable-to-superior performances to DL-based QSAR models. The developed KNIME workflow is available-freely at https://github.com/cicese-biocom/classification-QSAR-bioKom. This workflow can be valuable to avoid unfair comparisons regarding new computational methods, as well as to propose new non-DL based QSAR models.

The macrocycle inhibitor landscape of SLC-transporter.

In the past years the interest in Solute Carrier Transporters (SLC) has increased due to their potential as drug targets. At the same time, macrocycles demonstrated promising activities as therapeutic agents. However, the overall macrocycle/SLC-transporter interaction landscape has not been fully revealed yet. In this study, we present a statistical analysis of macrocycles with measured activity against SLC-transporter. Using a data mining pipeline based on KNIME retrieved in total 825 bioactivity data points of macrocycles interacting with SLC-transporter. For further analysis of the SLC inhibitor profiles we developed an interactive KNIME workflow as well as an interactive map of the chemical space coverage utilizing parametric t-SNE models. The parametric t-SNE models provide a good discrimination ability among several corresponding SLC subfamilies' targets. The KNIME workflow, the dataset, and the visualization tool are freely available to the community.

Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling

The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional “QSAR-ready” forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the “QSAR-ready” workflow to generate “MS-ready structures” to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.

Design of small molecules for CDK-2 inhibition in colorectal cancer based on substructure search

The global frequency of colorectal cancer motivates extensive drug discovery efforts. CDK2, a key member of the CDK family, has been linked to tumor progression, unregulated cell proliferation, and growth promotion. Water-soluble flavonoids with a fast metabolism called anthocyanins have been shown to have a variety of pharmacological properties, including anti-cancer properties. This study aims to find possible CDK2 inhibitors from Anthocyanin-like molecules. Anthocyanins sourced from PubChem were screened using a virtual screening approach that included a KNIME workflow, QSAR-model, Pharmacophore hypothesis, and a structure-based screening to identify compounds with a better binding affinity and predicted bioactivity compared to the standard, Sorafenib. The top compounds were subjected to a 100 ns MD simulation to confirm their stability at the active site. Compounds 1–5 were shown to have higher binding affinity and bioactivity in this study. These substances interacted with the critical amino acids (LEU 83, ASP 145 and LYS 89) at CDK2's active site. Compared to the reference with a pIC50 value of 6.003 nM, the top compounds listed have superior predicted bioactivity ranging from 6.539 to 6.36 nM. Also, ADMET predictions predicted that Compounds 1–5 were not carcinogenic and not a p-glycoprotein substrate. MD simulation also validated Compound 1's stability at the active site compared to the standard. This study uncovers potential CDK2 inhibitors with good binding affinities, shedding light on their interactions with the target protein. While promising, further in vivo and in vitro investigations are essential to validate the anticancer potential of these compounds. Communicated by Ramaswamy H. Sarma

Using Read-Across to build Physiologically-Based Kinetic models: Part 2. Case studies for atenolol and flumioxazin

Read-across, wherein information from a data-rich chemical is used to make a prediction for a similar chemical that lacks the relevant data, is increasingly being accepted as an alternative to animal testing. Identifying chemicals that can be considered as similar (analogues) is crucial to the process. Two resources have been developed previously to address the issue of analogue selection and facilitate physiologically-based kinetic (PBK) model development, using read-across. Chemical-specific PBK models, available in the literature, were collated to form a PBK model dataset (PMD) of over 7,500 models. A KNIME workflow was created to accompany this PMD that can aid the selection of appropriate chemical analogues from chemicals within this dataset (i.e. chemicals that are similar to a target of interest and are known to have an existing PBK model). Information from the PBK model for the source chemical can then be used in a read-across approach to inform the development of a new PBK model for the target. The application of these resources is tested here using two case studies (i) for the drug atenolol and (ii) for the plant protection product, flumioxazin. New PBK models were constructed for these two target chemicals using data obtained from source chemicals, identified by the workflow as being similar (analogues). In each case, the published PBK model for the source chemical was initially reproduced, as accurately as possible, before being adapted and used as a template for the target chemical. The performance of the new PBK models was assessed by comparing simulation outputs to existing data on key kinetic properties for the targets. The results demonstrate that a read-across approach can be successfully applied to develop new PBK models for data-poor chemicals, thus enabling their deployment during early-stage risk assessment. This assists prediction of internal exposure whilst reducing reliance on animal testing.

Using read-across to build physiologically-based kinetic models: Part 1. Development of a KNIME workflow to assist analogue selection for PBK modelling

Read-across refers to the process by which information from one (source) chemical is used to infer information about another similar (target) chemical. This method can be used to fill data gaps and so inform safety assessment where data are lacking for chemicals of interest. As one chemical cannot be considered as absolutely similar to another, only similar with respect to a given property, it is essential to justify the selection of similar chemicals (analogues) for the purposes of read-across. A previously created dataset of available physiologically-based kinetic (PBK) models (referred to as the PBK modelling dataset or PMD) was used in the development of a KNIME workflow. KNIME is a freely-available, open-source analytics platform that allows users to create workflows to analyse and visualise data. The KNIME workflow described here was designed to identify chemical analogues with a corresponding model in the PMD. The PMD combined with the KWAAS enables PBK model information from source chemical(s) to be used in a read-across approach to help develop new PBK models for target chemicals. This KNIME workflow was applied to six chemicals, representing different types of chemical classes (drugs, cosmetics, botanicals, industrial chemicals, pesticides, and food additives) to assess its applicability across various industries. Information acquired from these PBK models can be used to support safety assessment of chemicals and reduce reliance on animal testing.

Trends in Medicinal Chemistry: KNIME Workflows, QSAR Models, LLMs and Chemical Search Strategies

Through a lens encompassing KNIME workflows, QSAR models, LLMs, and chemical substructure search strategies, the article navigates the essential considerations driving innovation and progress in industrial cheminformatics for medicinal chemistry and drug discovery.

Accurate Sphingolipid Quantification Reducing Fragmentation Bias by Nonlinear Models.

Quantitative sphingolipid analysis is crucial for understanding the roles of these bioactive molecules in various physiological and pathological contexts. Molecular sphingolipid species are typically quantified using sphingoid base-derived fragments relative to a class-specific internal standard. However, the commonly employed "one standard per class" strategy fails to account for fragmentation differences presented by the structural diversity of sphingolipids. To address this limitation, we developed a novel approach for quantitative sphingolipid analysis. This approach utilizes fragmentation models to correct for structural differences and thus overcomes the limitations associated with using a limited number of standards for quantification. Importantly, our method is independent of the internal standard, instrumental setup, and collision energy. Furthermore, we integrated this method into a user-friendly KNIME workflow. The validation results illustrate the effectiveness of our approach in accurately quantifying ceramide subclasses from various biological matrices. This breakthrough opens up new avenues for exploring sphingolipid metabolism and gaining insights into its implications.

A machine learning-based KNIME workflow to predict VEGFR-2 inhibitors.

Vascular endothelial growth factors (VEGFs) are specific cytokines involved in angiogenesis and do so via binding to vascular endothelial growth factor receptors (VEGFRs), a type of receptor tyrosine kinase. VEGFs are reported to facilitate angiogenesis in physiological (embryogenesis) and pathological (tumor) conditions. The overexpression of VEGFs and consequently VEGFRs is reported in tumorigenic conditions. Several VEGFR inhibitors currently used as anticancer drugs to prevent angiogenesis are sunitinib, sorafenib, etc. To identify new potential candidates as VEGFR inhibitors, a classification study using a large and diverse dataset of VEGFR inhibitors from the BindingDB database has been conducted. The KNIME platform was used to calculate molecular and fingerprint-based descriptors and several classification algorithms viz. linear regression (LR), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), and gradient boosted tree (GBT) were employed to build the classification model. The model performance was evaluated by accuracy, precision, recall, and F1 score of the test set. The best LR, kNN, DT, RF, and GBT classifiers had the F1 score of 0.81, 0.87, 0.82, 0.87, and 0.87, respectively. The assorted 5120 VEGFR inhibitors were clustered into 10 subsets, and the structural features of each subset were assessed along with the identification of significant fragments in active and inactive compounds. The automated classifier model developed using the KNIME platform could serve as an important platform for screening and designing molecules as VEGFR inhibitors.

NeRNA: A negative data generation framework for machine learning applications of noncoding RNAs

Many supervised machine learning based noncoding RNA (ncRNA) analysis methods have been developed to classify and identify novel sequences. During such analysis, the positive learning datasets usually consist of known examples of ncRNAs and some of them might even have weak or strong experimental validation. On the contrary, there are neither databases listing the confirmed negative sequences for a specific ncRNA class nor standardized methodologies developed to generate high quality negative examples. To overcome this challenge, a novel negative data generation method, NeRNA (negative RNA), is developed in this work. NeRNA uses known examples of given ncRNA sequences and their calculated structures for octal representation to create negative sequences in a manner similar to frameshift mutations but without deletion or insertion. NeRNA is tested individually with four different ncRNA datasets including microRNA (miRNA), transfer RNA (tRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA). Furthermore, a species-specific case analysis is performed to demonstrate and compare the performance of NeRNA for miRNA prediction. The results of 1000 fold cross-validation on Decision Tree, Naïve Bayes and Random Forest classifiers, and deep learning algorithms such as Multilayer Perceptron, Convolutional Neural Network, and Simple feedforward Neural Networks indicate that models obtained by using NeRNA generated datasets, achieves substantially high prediction performance. NeRNA is released as an easy-to-use, updatable and modifiable KNIME workflow that can be downloaded with example datasets and required extensions. In particular, NeRNA is designed to be a powerful tool for RNA sequence data analysis.

A KNIME Workflow to Assist the Analogue Identification for Read-Across, Applied to Aromatase Activity.

The reduction and replacement of in vivo tests have become crucial in terms of resources and animal benefits. The read-across approach reduces the number of substances to be tested, exploiting existing experimental data to predict the properties of untested substances. Currently, several tools have been developed to perform read-across, but other approaches, such as computational workflows, can offer a more flexible and less prescriptive approach. In this paper, we are introducing a workflow to support analogue identification for read-across. The implementation of the workflow was performed using a database of azole chemicals with in vitro toxicity data for human aromatase enzymes. The workflow identified analogues based on three similarities: structural similarity (StrS), metabolic similarity (MtS), and mechanistic similarity (McS). Our results showed how multiple similarity metrics can be combined within a read-across assessment. The use of the similarity based on metabolism and toxicological mechanism improved the predictions in particular for sensitivity. Beyond the results predicting a large population of substances, practical examples illustrate the advantages of the proposed approach.

Virtual screening for novel FGFR2 inhibitors: Exploring Gefitinib-like compounds as promising therapeutic candidates

Gefitinib, an established anticancer medication targeting Epidermal Growth Factor Receptor (EGFR), has faced challenges due to tumor resistance and relapse over time. A notable factor in Gefitinib resistance is the elevated expression of FGFR2. Combining Gefitinib with an FGFR2 inhibitor has been proposed to counteract this resistance and prolong the efficacy of EGFR-targeted therapy.To identify potential FGFR2 inhibitors, our study adopted a virtual screening approach using the PubChem database and a KNIME Workflow to filter compounds for Lipinski Violations. These selected compounds underwent evaluation for their binding affinity to FGFR2, with the aid of the AutoQSAR module to predict bioactivity. For comparison, Derazantinib, a known FGFR2-targeting anticancer agent, was used as a benchmark. Moreover, bioisosteric replacements were conducted for the top two compounds to enhance ADMET profiles and binding affinity.Results indicated that the leading compounds exhibited notably improved binding affinities toward FGFR2, surpassing Derazantinib's performance. These compounds engaged in significant interactions with specific amino acids, such as ASP 644, LYS 157, ASN 157, and TYR 566, forging unique connections. Additionally, ADMET predictions highlighted favorable oral absorption rates exceeding 75% for all tested compounds, including Derazantinib. Also, the bioisosteres demonstrated enhanced binding affinity compared to the original structures.This study uncovers potential FGFR2 inhibitors with good binding affinities, shedding light on their interactions with the target protein. While promising, further in vivo and in vitro investigations are essential to validate the anticancer potential of these compounds.

Q-raKtion: A Semiautomated KNIME Workflow for Bioactivity Data Points Curation.

The recent increase of bioactivity data freely available to the scientific community and stored as activity data points in chemogenomic repositories provides a huge amount of ready-to-use information to support the development of predictive models. However, the benefits provided by the availability of such a vast amount of accessible information are strongly counteracted by the lack of uniformity and consistency of data from multiple sources, requiring a process of integration and harmonization. While different automated pipelines for processing and assessing chemical data have emerged in the last years, the curation of bioactivity data points is a less investigated topic, with useful concepts provided but no tangible tools available. In this context, the present work represents a first step toward the filling of this gap, by providing a tool to meet the needs of end-user in building proprietary high-quality data sets for further studies. Specifically, we herein describe Q-raKtion, a systematic, semiautomated, flexible, and, above all, customizable KNIME workflow that effectively aggregates information on biological activities of compounds retrieved by two of the most comprehensive and widely used repositories, PubChem and ChEMBL.

GediNET for discovering gene associations across diseases using knowledge based machine learning approach

The most common approaches to discovering genes associated with specific diseases are based on machine learning and use a variety of feature selection techniques to identify significant genes that can serve as biomarkers for a given disease. More recently, the integration in this process of prior knowledge-based approaches has shown significant promise in the discovery of new biomarkers with potential translational applications. In this study, we developed a novel approach, GediNET, that integrates prior biological knowledge to gene Groups that are shown to be associated with a specific disease such as a cancer. The novelty of GediNET is that it then also allows the discovery of significant associations between that specific disease and other diseases. The initial step in this process involves the identification of gene Groups. The Groups are then subjected to a Scoring component to identify the top performing classification Groups. The top-ranked gene Groups are then used to train a Machine Learning Model. The process of Grouping, Scoring and Modelling (G-S-M) is used by GediNET to identify other diseases that are similarly associated with this signature. GediNET identifies these relationships through Disease–Disease Association (DDA) based machine learning. DDA explores novel associations between diseases and identifies relationships which could be used to further improve approaches to diagnosis, prognosis, and treatment. The GediNET KNIME workflow can be downloaded from: https://github.com/malikyousef/GediNET.git or https://kni.me/w/3kH1SQV_mMUsMTS.

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches.

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

KNIME workflow for retrieving causal drug and protein interactions, building networks, and performing topological enrichment analysis demonstrated by a DILI case study

As an alternative to one drug-one target approaches, systems biology methods can provide a deeper insight into the holistic effects of drugs. Network-based approaches are tools of systems biology, that can represent valuable methods for visualizing and analysing drug-protein and protein–protein interactions. In this study, a KNIME workflow is presented which connects drugs to causal target proteins and target proteins to their causal protein interactors. With the collected data, networks can be constructed for visualizing and interpreting the connections. The last part of the workflow provides a topological enrichment test for identifying relevant pathways and processes connected to the submitted data. The workflow is based on openly available databases and their web services. As a case study, compounds of DILIRank were analysed. DILIRank is the benchmark dataset for Drug-Induced Liver Injury by the FDA, where compounds are categorized by their likeliness of causing DILI. The study includes the drugs that are most likely to cause DILI (“mostDILI”) and the ones that are not likely to cause DILI (“noDILI”). After selecting the compounds of interest, down- and upregulated proteins connected to the mostDILI group were identified; furthermore, a liver-specific subset of those was created. The downregulated sub-list had considerably more entries, therefore, network and causal interactome were constructed and topological pathway enrichment analysis was performed with this list. The workflow identified proteins such as Prostaglandin G7H synthase 1 and UDP-glucuronosyltransferase 1A9 as key participants in the potential toxic events disclosing the possible mode of action. The topological network analysis resulted in pathways such as recycling of bile acids and salts and glucuronidation, indicating their involvement in DILI. The KNIME pipeline was built to support target and network-based approaches to analyse any sets of drug data and identify their target proteins, mode of actions and processes they are involved in. The fragments of the pipeline can be used separately or can be combined as required.

A toolkit for covalent docking with GOLD: from automated ligand preparation with KNIME to bound protein-ligand complexes.

Current covalent docking tools have limitations that make them difficult to use for performing large-scale structure-based covalent virtual screening (VS). They require time-consuming tasks for the preparation of proteins and compounds (standardization, filtering according to the type of warheads), as well as for setting up covalent reactions. We have developed a toolkit to help accelerate drug discovery projects in the phases of hit identification by VS of ultra-large covalent libraries and hit expansion by exploration of the binding of known covalent compounds. With this application note, we offer the community a toolkit for performing automated covalent docking in a fast and efficient way. The toolkit comprises a KNIME workflow for ligand preparation and a Python program to perform the covalent docking of ligands with the GOLD docking engine running in a parallelized fashion. The KNIME workflow entitled 'Evotec_Covalent_Processing_forGOLD.knwf' for the preparation of the ligands is available in the KNIME Hub https://hub.knime.com/emilie_pihan/spaces. Supplementary data are available at Bioinformatics Advances online.

BeerMIMS: Exploring the Use of Membrane-Inlet Mass Spectrometry (MIMS) Coupled to KNIME for the Characterization of Danish Beers.

Beer is a complex mix of more than 7700 compounds, around 800 of which are volatile. While GC-MS has been actively employed in the analysis of the volatome of beer, this method is challenged by the complex nature of the sample. Herein, we explored the possible of using membrane-inlet mass spectrometry (MIMS) coupled to KNIME to characterize local Danish beers. KNIME stands for Konstanz Information Miner and is a free open-source data processing software which comes with several prebuilt nodes, that, when organized, result in data processing workflows allowing swift analysis of data with outputs that can be visualized in the desired format. KNIME has been shown to be promising in automation of large datasets and requires very little computing power. In fact, most of the computations can be carried out on a regular PC. Herein, we have utilized a KNIME workflow for data visualization of MIMS data to understand the global volatome of beers. Feature identification was not possible as of now but with a combination of MIMS and a KNIME workflow, we were able to distinguish beers from different micro-breweries located in Denmark, laying the foundation for the use of MIMS in future analysis of the beer volatome.