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DTSTART:20260329T030000
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DTSTART:20261025T020000
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DTSTAMP:20261014T135251Z
UID:1792670400@ist.ac.at
DTSTART:20261022T140000
DTEND:20261022T160000
DESCRIPTION:Speaker: Alisia Fadini\nhosted by Alexander Bronstein\nAbstract
 : Proteins are dynamic machines whose function arises from coordinated ato
 mic motions\, yet breakthroughs like AlphaFold remain limited in capturing
  biomolecular interactions and conformational flexibility due to scarce dy
 namic structural data. Meanwhile\, time-resolved experiments\, cryo-electr
 on microscopy and tomography\, and high-throughput crystallography generat
 e large datasets of protein motion that remain underused without scalable 
 analysis of structural heterogeneity – limiting efforts to understand mu
 tation effects or design allosteric drugs.Unlocking these questions requir
 es treating experimental datasets not as sources of single structures\, bu
 t as samples from conformational distributions shaped by time\, ligand bin
 ding\, mutation\, and cellular context. To do this\, I will detail how we 
 can leverage pre-learned structural understanding from deep learning model
 s to decode conformational dynamics directly from experiment. This vision 
 builds on lessons from ROCKET\, an experimental augmentation of AlphaFold 
 that captures biologically meaningful structural variation inaccessible to
  prediction alone or conventional automated modeling approaches\, particul
 arly in low signal-to-noise regimes. I will then explore the hypothesis th
 at\, when purposefully engineered\, such methods provide a general and sca
 lable framework for integrating experimental observables with biomolecular
  machine learning\, yielding low-dimensional\, interpretable representatio
 ns that reveal mechanistic drivers of protein dynamics.
LOCATION:Central Bldg / Ballroom\, ISTA
ORGANIZER:
SUMMARY:Alisia Fadini: Protein Motion Beyond Snapshots: Learning from Many 
 Structures to Decode Dynamics
URL:https://talks-calendar.ista.ac.at/events/6694
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