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Digles, D.

Publications and source records attributed to Digles, D..

3 recordsLinked to original sources

Mutation hot spots for clinical pathogenicity across the SLC6 transporter family

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.

bioinformatics↗

Data- and knowledge-derived functional landscape of human solute carriers

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.

systems biology↗

Proteochemometric modeling strengthens the role of Q299 for GABA transporter subtype selectivity

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.

pharmacology and toxicology↗