Miguel Andrade

Miguel Andrade is a Professor in the Faculty of Biology at the University of Mainz and serves as Adjunct Director at the Institute of Molecular Biology (IMB) since 2014. Previously, he was a Group Leader at the Max Delbrück Center for Molecular Medicine (2007-2014) and Assistant Professor at the University of Ottawa (2003-2007). His research focuses on computational analysis of protein sequences, particularly tandem repeats and low complexity regions, with applications in neurodegenerative diseases like Huntington's.

His educational background includes:

• PhD in Biochemistry from Universidad Complutense de Madrid (1994)

Professor Andrade's research spans bioinformatics and computational biology with emphasis on protein sequence evolution, structural implications of repetitive elements, and development of analytical tools. He investigates how tandem repeats and low complexity regions influence protein folding, aggregation, and interactions in diseases, while exploring evolutionary conservation across species. His work bridges computational methodologies with experimental validation in neurodegenerative contexts.

Recent publications (2023-2025) demonstrate consistent innovation in protein sequence analysis, featuring computational tools for homorepeat detection, machine learning applications in proteomics, and multi-omics integration. Key themes include neurodegenerative disease mechanisms, immune system regulation, and evolutionary adaptations, with significant contributions to databases like RepeatsDB and tools such as REP2 and seqQscorer.

No scientific awards were documented in the source materials.

While specific student names and grant details are unlisted, his leadership at IMB indicates supervision of graduate researchers and postdoctoral fellows. His collaborative network spans immunology, cancer research, and neuroscience, evidenced by co-authorship on diverse projects from lipid-disease associations to T-cell differentiation studies.

He directs a research group at IMB Mainz focused on computational genomics, developing algorithms for protein sequence analysis and maintaining community resources. The team actively investigates polyglutamine dynamics in neurodegeneration and applies machine learning to proteome-wide challenges, maintaining strong ties with experimental laboratories for biological validation.