Background: As in many different areas of science and technology, most important problems in bioinformatics rely on the proper development and assessment of binary classifiers. A generalized assessment of the performance of binary classifiers is typically carried out through the analysis of their receiver operating characteristic (ROC) curves. The area under the ROC curve (AUC) constitutes a popular indicator of the performance of a binary classifier. However, the assessment of the statistical significance of the difference between any two classifiers based on this measure is not a straightforward task, since not many freely available tools exist. Most existing software is either not free, difficult to use or not easy to automate when a comparative assessment of the performance of many binary classifiers is intended. This constitutes the typical scenario for the optimization of parameters when developing new classifiers and also for their performance validation through the comparison to previous art. Results: In this work we describe and release new software to assess the statistical significance of the observed difference between the AUCs of any two classifiers for a common task estimated from paired data or unpaired balanced data. The software is able to perform a pairwise comparison of many classifiers in a single run, without requiring any expert or advanced knowledge to use it. The software relies on a non-parametric test for the difference of the AUCs that accounts for the correlation of the ROC curves. The results are displayed graphically and can be easily customized by the user. A human-readable report is generated and the complete data resulting from the analysis are also available for download, which can be used for further analysis with other software. The software is released as a web server that can be used in any client platform and also as a standalone application for the Linux operating system. Conclusion: A new software for the statistical comparison of ROC curves is released here as a web server and also as standalone software for the LINUX operating system.

Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.

Solute carriers are an operationally defined diverse family of membrane proteins involved in the transport of nutrients, metabolites, xenobiotics, and drugs. Here, we provide an integrative classification of solute carriers by combining evolutionary information with proteome-wide structure models recently made available through the AlphaFold resource. Analyses of orthologous relations among 455 protein-coding genes currently classified as human solute carriers, over the fully sequenced genomes of 2,100 species, suggest no more than approximately 180 independent evolutionary origins. Structural comparative analyses provided further insight revealing a total of 24 structurally distinct transmembrane folds, increasing by approximately 40% the number of previously described SLC structural folds. In addition, a structural comparative analysis identified a new human solute carrier member and revealed details of noncanonical ones. Our analyses uncover new ancestral relations between solute carrier genes, provide insights into the evolution of remote homologs and a platform to test hypotheses of functional deorphanization.

Zinc is the second most prevalent metal element present in living organisms, and control of its concentration is pivotal to physiology. The amount of zinc available to the cell cytoplasm is regulated by the activity of members of the SLC39 family, the ZIP proteins. Selectivity of ZIP transporters has been the focus of earlier studies which provided a biochemical and structural basis for the selectivity for zinc over other metals such as copper, iron, and manganese. However, several previous studies have shown how certain ZIP proteins exhibit higher selectivity for metal elements other than zinc. Sequence similarities suggest an evolutionary basis for the elemental selectivity within the ZIP family. Here, by engineering HEK293 cells to overexpress ZIP proteins, we have studied the selectivity of two phylogenetic clades of ZIP proteins, that is ZIP8/ZIP14 (previously known to be iron and manganese transporters) and ZIP5/ZIP10. By incubating ZIP over-expressing cells in presence of several divalent metals, we found that ZIP5 and ZIP10 are high affinity copper transporters with greater selectivity over other elements, revealing a novel substrate signature for the ZIP5/ZIP10 clade.

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.

Creatine is an essential metabolite for the storage and rapid supply of energy in muscle and nerve cells. In humans, impaired metabolism, transport, and distribution of creatine throughout tissues can cause varying forms of mental disability, also known as creatine deficiency syndrome (CDS). So far, 80 mutations in the creatine transporter (SLC6A8) have been associated to CDS. To better understand the effect of human genetic variants on the physiology of SLC6A8 and their possible impact on CDS, we studied 30 missense variants including 15 variants of unknown significance, two of which are reported here for the first time. We expressed these variants in HEK293 cells and explored their subcellular localization and transport activity. We also applied computational methods to predict variant effect and estimate site-specific changes in thermodynamic stability. To explore variants that might have a differential effect on the transporter's conformers along the transport cycle, we constructed homology models of the inward facing, and outward facing conformations. In addition, we used mass-spectrometry to study proteins that interact with wild type SLC6A8 and five selected variants in HEK293 cells. In silico models of the protein complexes revealed how two variants impact the interaction interface of SLC6A8 with other proteins and how pathogenic variants lead to an enrichment of ER protein partners. Overall, our integrated analysis disambiguates the pathogenicity of 15 variants of unknown significance revealing diverse mechanisms of pathogenicity, including two previously unreported variants obtained from patients suffering from the creatine deficiency syndrome.