David Benjamin Ascher

The paper identifies and characterizes a broad-spectrum α-glucosidase from Marinovum sp., revealing its unexpected activity on various disaccharides rather than sulfosugars.

epitope1D predicts linear B-cell epitopes using explainable machine learning with graph-based descriptors and organism ontology, achieving high accuracy on cross-validation and blind tests.

The paper identifies a recurrent de novo splice site variant in DNM1 exon 10a causing severe DEE through a dominant-negative mechanism, highlighting the importance of considering alternative isoforms in disease causation.

VIVID is a web tool that visualizes and interprets DNA variants in 3D on protein models to study genetic variation and its association with phenotypes.

HGDiscovery provides functional and phenotypic information on novel HGD missense mutations using machine learning and structural analysis.

The paper shows that using structure-based predictors of intolerance improves the identification of pathogenic variants in whole exome sequences of patients with developmental and epileptic encephalopathies.

DHS-Crystallize predicts protein crystallization using a hybrid sequence-based method combining deep learning and physicochemical features, outperforming existing approaches.

The paper develops an online tool, cropCSM, using predictive models to identify safe and potent herbicides.

The paper presents a web resource, COVID-3D, that visualizes SARS-CoV-2 genetic variation in 3D structures to aid in understanding its impact on infection, immunity, and drug development.

This study identifies a missense mutation in MLKL causing lethal neonatal inflammation in mice and links it to increased frequency in human autoinflammatory disease.

This study tracks the evolution of carbapenem resistance in Acinetobacter baumannii during a 45-day infection in a burn patient, identifying novel mutations in resistance genes and showing collateral resistance from exposure to other antibiotics.