Jonathan Fiorentino
Papers
2
Total Citations
31
H-Index
2
About
Jonathan Fiorentino is a rising computational biologist whose work is reshaping our understanding of liquid-liquid phase separation (LLPS)—the biophysical process that drives the formation of membraneless organelles within cells. His research centers on developing predictive algorithms that decode how specific protein sequences and structures drive phase separation, a phenomenon increasingly linked to both normal cellular organization and devastating neurodegenerative diseases. Fiorentino’s landmark contribution is the catGRANULE 2.0 ROBOT algorithm, a cutting-edge tool that integrates physicochemical properties with AlphaFold-derived structural features to predict LLPS propensity at single amino acid resolution. This work, published in 2025, has already garnered 27 citations, reflecting its immediate impact on the field. A precursor study from 2024 laid the groundwork for these advances, demonstrating Fiorentino’s ability to push the boundaries of computational precision. By enabling researchers to pinpoint the exact protein regions driving phase separation, his tools are accelerating discoveries in cell biology and opening new avenues for therapeutic intervention in diseases where LLPS goes awry. Fiorentino’s work stands at the forefront of a rapidly evolving field, merging machine learning with molecular biophysics to solve fundamental biological puzzles.
Research Focus
Key Achievements
Top Papers
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