Pathogenic variants in the autoimmune regulator (AIRE) cause autoimmune polyendocrine syndrome type 1 (APS-1), a rare primary immunodeficiency disease…

Proteome-wide prediction and structural modeling of disordered protein interaction interfaces advance characterization of disease-associated variants in disordered protein regions.

Background With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a powerful source of evidence which can be used in clinical variant classification. Increasingly, multiple published MAVEs are available for the same gene, sometimes measuring different aspects of variant impact. When multiple functional roles of a gene need to be considered, combining data from multiple MAVEs may provide a more comprehensive measure of the consequence of a genetic variant, which could impact variant classifications. Methods We curated published datasets from five MAVEs for the gene TP53, two MAVEs for LDLR and two MAVEs for PTEN. Statistical methods (principal component analysis), unsupervised learning (k-means clustering), and supervised learning (Naïve Bayes and random forest classifiers) were used to integrate multiple MAVE datasets. The utility of MAVE integration methods were assessed using standard metrics (sensitivity, specificity, etc) as well as evidence strength in a putative variant classification framework. Results Here, we provide guidance for combining such multiplexed functional data, incorporating a stepwise process from data curation and collection to model generation and validation. We also present a web applet that allows users to test various methods for combining score sets from multiple assays, calculate integrated functional scores for all variants, and assess whether combining data enables the application of stronger evidence for pathogenicity or benignity. In general, supervised learning methods such as random forest led to improved variant classification as compared to any individual MAVE dataset. Conclusions By following the steps outlined herein with appropriate guardrails, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes.

AlphaFold’s success in protein structure predictions has led to similar attempts to predict interactomes. Here, the authors demonstrate that AI-based screens are very limited in discovering truly nove...

AlphaFold’s success in protein structure predictions has led to similar attempts to predict interactomes. Here, the authors demonstrate that AI-based screens are very limited in discovering truly nove...

AlphaFold’s success in protein structure predictions has led to similar attempts to predict interactomes. Here, the authors demonstrate that AI-based screens are very limited in discovering truly nove...

Nature Microbiology, Published online: 26 January 2026; doi:10.1038/s41564-025-02241-yLarge-scale computational and in vitro analyses identify commensal type III secretion systems and substrates in the human gut microbiome that can interact with human proteins to modulate immune pathways.

Large-scale computational and in vitro analyses identify commensal type III secretion systems and substrates in the human gut microbiome that can interact with human proteins to modulate immune pathwa...

The ESPOD fellowship builds on the collaborative relationship between EMBL-EBI and the Wellcome Sanger Institute, offering projects that combine experimental (wet-lab) and computational (dry-lab) appr...

Variant effect prediction remains a key challenge to resolve in precision medicine. Sophisticated computational models that exploit sequence conservation and structure are increasingly successful in t...

Lambourne, Mattioli, Santoso, et al. compare protein isoforms of the same transcription factor genes through high-throughput profiling of DNA-binding, protein-binding, activation, localization, and condensate formation. Differences between isoforms are widespread, often unpredictable from sequence differences, and inform a disease-associated categorization of alternative isoforms into rewirers or negative regulators.

Lambourne, Mattioli, Santoso, et al. compare protein isoforms of the same transcription factor genes through high-throughput profiling of DNA-binding, protein-binding, activation, localization, and condensate formation. Differences between isoforms are widespread, often unpredictable from sequence differences, and inform a disease-associated categorization of alternative isoforms into rewirers or negative regulators.