Genetic mutations of the Solute Carrier 6 (SLC6) family can lead to a diversity of clinal syndromes, such as creatine deficiency. Studying the impact of genetic mutations at the SLC6 family level is valuable not only for their medical significance but also for their conserved sequence and structural features. Within this work, we aim to link the disease-related mutations to their clinical significance from protein-protein interactions (PPIs) perspective, addressing how particular mutations may affect these critical interaction hotspots. In this study, we integrated both curated mutation data from previous work and predictive output from AlphaMissense to examine the entire SLC6 family. The mutations were mapped both onto the sequences and structures of SLC6 transporters. Thereby, a clustering of pathogenic mutations appeared on the surface regions that are likely involved in PPIs. After modeling complexes of SLC6s with potential shared interactors, we assessed these models overall and interface quality. By analyzing the complex interfaces together with the pathogenic mutations, we identified specific hotspots in the interfaces enriched with pathogenic mutations. In-depth examinations of selected PPIs offered insights into how particular mutations may affect these critical interaction hotspots. The hotspots were identified on the ECL3 and ECL4. For instance, Thr394Lys in SLC6A8 was found in the model interfaces, supported by experimental data showing the significant enrichment of the mutated proteins in the ER. Understanding these mutation hotspots can shed light on the broader structure-function relationships of SLC6 transporters and encourage therapeutic interventions targeting protein-protein interactions affected by pathogenic mutations. ### Competing Interest Statement The authors have declared no competing interest.

The human solute carrier (SLC) superfamily of ~460 membrane transporters remains the largest understudied protein family despite its therapeutic potential. To advance SLC research, we developed a comprehensive knowledgebase that integrates systematic multi-omics data sets with selected curated information from public sources. We annotated SLC substrates through literature curation, compiled SLC disease associations using data mining techniques, and determined the subcellular localization of SLCs by combining annotations from public databases with an immunofluorescence imaging approach. This SLC-centric knowledge is made accessible to the scientific community via a web portal featuring interactive dashboards and visualization tools. Utilizing this systematically collected and curated resource, we computationally derived an integrated functional landscape for the entire human SLC superfamily. We identified clusters with distinct properties and established functional distances between transporters. Based on all available data sets and their integration, we assigned biochemical/biological functions to each SLC, making this study one of the largest systematic annotations of human gene function and a potential blueprint for future research endeavors.

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. image

Predict SLC6 mutation clinical pathogenicity by calculating the amino acid descriptors in different ranges with rationalization analysis of the prediction.

With over 450 genes, solute carriers (SLCs) constitute the largest transporter superfamily responsible for the uptake and efflux of nutrients, metabolites, and xenobiotics in human cells. SLCs are associated with a wide variety of human diseases, including cancer, diabetes, and metabolic and neurological disorders. They represent an important therapeutic target class that remains only partly exploited as therapeutics that target SLCs are scarce. Additionally, many small molecules reported in the literature to target SLCs are poorly characterized. Both features may be due to the difficulty of developing SLC transport assays that fulfill the quality criteria for high-throughput screening. Here, we report one of the main limitations hampering assay development within the RESOLUTE consortium: the lack of a resource providing high-quality information on SLC tool compounds. To address this, we provide a systematic annotation of tool compounds targeting SLCs. We first provide an overview on RESOLUTE assays. Next, we present a list of SLC-targeting compounds collected from the literature and public databases; we found that most data sources lacked specificity data. Finally, we report on experimental tests of 19 selected compounds against a panel of 13 SLCs from seven different families. Except for a few inhibitors, which were active on unrelated SLCs, the tested inhibitors demonstrated high selectivity for their reported targets. To make this knowledge easily accessible to the scientific community, we created an interactive dashboard displaying the collected data in the RESOLUTE web portal (https://re-solute.eu). We anticipate that our open-access resources on assays and compounds will support the development of future drug discovery campaigns for SLCs.

Research on the understudied solute carrier (SLC) superfamily of membrane transporters would greatly profit from a comprehensive knowledgebase, synthesizing data and knowledge on different aspects of SLC function. We consolidated multi-omics data sets with selected curated information from the public domain, such as structure prediction, substrate annotation, disease association and subcellular localization. This SLC-centric knowledge is made accessible to the scientific community via a web portal, featuring interactive dashboards and a tool for family-wide, tree-based visualization of SLC properties. Making use of the systematically collected and curated data sets, we selected eight feature-dimensions to compute an integrated functional landscape of human SLCs. This landscape represents various functional aspects, harmonizing local and global features of the underlying data sets, as demonstrated by inspecting structural folds and subcellular locations of selected transporters. Based on all available data sets and their integration, we assigned a biochemical/biological function to each SLC, making it one of the largest systematic annotations of human gene function and likely acting as a blueprint for future endeavours. ### Competing Interest Statement G.S-F. is co-founder and owns shares of Solgate GmbH, an SLC-focused company. D.H. and A.M. are employees and stockholders of Pfizer.

WikiPathways (wikipathways.org) is an open-source biological pathway database. Collaboration and open science are pivotal to the success of WikiPathways. Here we highlight the continuing efforts supporting WikiPathways, content growth and collaboration among pathway researchers. As an evolving database, there is a growing need for WikiPathways to address and overcome technical challenges. In this direction, WikiPathways has undergone major restructuring, enabling a renewed approach for sharing and curating pathway knowledge, thus providing stability for the future of community pathway curation. The website has been redesigned to improve and enhance user experience. This next generation of WikiPathways continues to support existing features while improving maintainability of the database and facilitating community input by providing new functionality and leveraging automation.

Proteochemometric modeling (PCM) combines ligand information as well as target information in order to predict an output variable of interest (e.g. activity of a compound). The big advantage of PCM compared to conventional Quantitative Structure-Activity Relationship (QSAR) modeling is, that by creating a single model one can not only predict the affinity of a diverse set of compounds to a diverse set of targets, but also extrapolate the specific ligand-protein interactions that might be relevant for activity. In this study, we compiled a dataset of 323 compounds and their bioactivity data regarding the inhibition of the four GABA-transporter (GAT1/BGT1/GAT2/GAT3) subtypes, which are potential new drug targets for treating epilepsy. Proteochemometric modeling using partial least squares and random forest provided models which performed equally well than conventional QSAR models for each individual transporter. However, by analyzing the importance of the protein descriptors used in the PCM models, we identified the amino acid Leu300/Q299/L294/L314/ in GAT1/BGT1/GAT2/GAT3 to be relevant for binding and subtype selectivity. ### Competing Interest Statement The authors have declared no competing interest.

The solute carrier (SLC) superfamily represents the biggest family of transporters with important roles in health and disease. Despite being attractive and druggable targets, the majority of SLCs remains understudied. One major hurdle in research on SLCs is the lack of tools, such as cell-based assays to investigate their biological role and for drug discovery. Another challenge is the disperse and anecdotal information on assay strategies that are suitable for SLCs. This review provides a comprehensive overview of state-of-the-art cellular assay technologies for SLC research and discusses relevant SLC characteristics enabling the choice of an optimal assay technology. The Innovative Medicines Initiative consortium RESOLUTE intends to accelerate research on SLCs by providing the scientific community with high-quality reagents, assay technologies and data sets, and to ultimately unlock SLCs for drug discovery.

1 Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, California, United States of America, 2 Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands, 3 Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands, 4 Department of Pharmaceutical Sciences, Division of Pharmaceutical Sciences, University of Vienna, Vienna, Austria, 5 Instituto de Ciencias Biologicas, Departamento de Bioquimica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil, 6 Micelio, Antwerp, Belgium

The Innovative Medicines Initiative Consortium RESOLUTE has started to develop tools and produce data sets to de-orphanize transporters in the solute carrier protein (SLC) superfamily, thereby lowering the barrier for the scientific community to explore SLCs as an attractive drug target class.

WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.

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.

Open PHACTS is a pre-competitive project to answer scientific questions developed recently by the pharmaceutical industry. Having high quality biological interaction information in the Open PHACTS Discovery Platform is needed to answer multiple pathway related questions. To address this, updated WikiPathways data has been added to the platform. This data includes information about biological interactions, such as stimulation and inhibition. The platform's Application Programming Interface (API) was extended with appropriate calls to reference these interactions. These new methods of the Open PHACTS API are available now.

The Open PHACTS Discovery Platform integrates several public databases, which can be of interest when annotating the results of a phenotypic screening campaign. Workflow tools provide easy-to-customize possibilities to access the platform. Here, we describe how to create such workflows for two different workflow tools (KNIME and Pipeline Pilot), including a protocol to annotate compounds (e.g., phenotypic screening hits) with compound classification, known protein targets, and classifications of the targets.