DeepLoc 2.1: multi-label membrane protein type prediction using protein language models.

DeepLoc 2.0 is a popular web server for the prediction of protein subcellular localization and sorting signals. Here, we introduce DeepLoc 2.1, which additionally classifies the input proteins into the membrane protein types Transmembrane, Peripheral, Lipid-anchored and Soluble. Leveraging pre-trained transformer-based protein language models, the server utilizes a three-stage architecture for sequence-based, multi-label predictions. Comparative evaluations with other established tools on a test set of 4933 eukaryotic protein sequences, constructed following stringent homology partitioning, demonstrate state-of-the-art performance. Notably, DeepLoc 2.1 outperforms existing models, with the larger ProtT5 model exhibiting a marginal advantage over the ESM-1B model. The web server is available at https://services.healthtech.dtu.dk/services/DeepLoc-2.1.

SignalP: The Evolution of a Web Server.

SignalP ( https://services.healthtech.dtu.dk/services/SignalP-6.0/ ) is a very popular prediction method for signal peptides, the intrinsic signals that make proteins secretory. The SignalP web server has existed since 1995 and is now in its sixth major version. In this historical account, we (three authors who have taken part in the entire journey plus the first author of the latest version) describe the differences between the versions and discuss the various decisions taken along the way.

The Association Between Depressive Symptoms, Access to Diabetes Care, and Glycemic Control in Five Middle-Income Countries.

OBJECTIVE: The relationship between depression, diabetes, and access to diabetes care is established in high-income countries (HICs) but not in middle-income countries (MICs), where contexts and health systems differ and may impact this relationship. In this study, we investigate access to diabetes care for individuals with and without depressive symptoms in MICs. RESEARCH DESIGN AND METHODS: We analyzed pooled data from nationally representative household surveys across Brazil, Chile, China, Indonesia, and Mexico. Validated survey tools Center for Epidemiologic Studies Depression Scale Revised, Composite International Diagnostic Interview, Short Form, and Patient Health Questionnaire identified participants with depressive symptoms. Diabetes, defined per World Health Organization Package of Essential Noncommunicable Disease Interventions guidelines, included self-reported medication use and biochemical data. The primary focus was on tracking diabetes care progression through the stages of diagnosis, treatment, and glycemic control. Descriptive and multivariable logistic regression analyses, accounting for gender, age, education, and BMI, examined diabetes prevalence and care continuum progression. RESULTS: The pooled sample included 18,301 individuals aged 50 years and above; 3,309 (18.1%) had diabetes, and 3,934 (21.5%) exhibited depressive symptoms. Diabetes prevalence was insignificantly higher among those with depressive symptoms (28.9%) compared with those without (23.8%, P = 0.071). Co-occurrence of diabetes and depression was associated with increased odds of diabetes detection (odds ratio [OR] 1.398, P < 0.001) and treatment (OR 1.344, P < 0.001), but not with higher odds of glycemic control (OR 0.913, P = 0.377). CONCLUSIONS: In MICs, individuals aged 50 years and older with diabetes and depression showed heightened diabetes identification and treatment probabilities, unlike patterns seen in HICs. This underscores the unique interplay of these conditions in different income settings.