DegronMD

DegronMD characterizes degrons and quantifies how somatic mutations and molecular features affect their recognition by E3 ubiquitin ligases within the ubiquitin-proteasome system to inform degradation-based therapeutic strategies.

Key Features:

• Degron knowledgebase: Comprehensive dataset of degrons across the human proteome with annotations of evolutionary conservation and association with protein translational modifications, particularly in disordered regions with high solvent accessibility.
• Degrome landscape construction: Pattern recognition and machine learning techniques were used to identify a comprehensive degrome, reporting over 18,000 new degrons.
• Mutation impact analysis: Systematic quantification of the impact of somatic mutations on degron function across cancers, producing a global mutational map that highlights 89,318 actionable mutations.
• Drug resistance insights: Multiomics integrative analysis linking mutations in functional degrons to over 400 drug resistance events.

Scientific Applications:

• Mechanistic studies: Exploring biological mechanisms underlying ubiquitin-proteasome-mediated protein degradation and E3 ubiquitin ligase–degron interactions.
• Target identification: Inferring potential protein targets for degradation-based therapies based on degron presence and mutation impact.
• Drug discovery and design: Prioritizing degron-associated mutations and events that influence therapeutic response and resistance.
• Cancer research: Investigating how degron dysfunction and somatic mutations contribute to abnormal protein accumulation and disease progression in cancer.

Methodology:

Pattern recognition and machine learning were applied to construct the degrome landscape (>18,000 degrons); evolutionary conservation and associations with protein translational modifications and disordered, highly solvent-accessible regions were analyzed; somatic mutation impacts were systematically quantified to produce a global mutational map (89,318 actionable mutations); and multiomics integrative analysis was used to identify >400 drug resistance events.