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SUMMARY:Protein Evolution in Sequence Landscapes - From Data to Models and
  Back - Professor Martin Weigt\, Institute of Biology\, Paris
DTSTART:20241125T123000Z
DTEND:20241125T133000Z
UID:TALK217141@talks.cam.ac.uk
CONTACT:Kate Davenport
DESCRIPTION:In the course of evolution\, proteins diversify their sequence
 s via a complex interplay between random mutations and neutral selection. 
 As a consequence\, we can today observe protein sequences of common evolut
 ionary origin\, with almost identical three-dimensional folds and biologic
 al functions\, which however differ by as much as 70-80% of their amino ac
 ids. In my presentation\, I will review our efforts to model protein evolu
 tion across multiple timescales\, from the emergence of single mutations i
 n a protein up to deep evolutionary time scales. To this aim\, we first mo
 del protein fitness landscapes via generative probabilistic models trained
  on genomic data\, and we show that these models are able to predict the e
 ffect of individual mutations\, and to generate non-natural but biological
 ly functional proteins. Second\, we describe evolution as a stochastic pro
 cess in these landscapes. The proposed framework accurately reproduces the
  sequence statistics of both short-time (experimental) and long-time (natu
 ral) protein evolution\, suggesting applicability also to relatively data-
 poor intermediate evolutionary time scales\, which are currently inaccessi
 ble to evolution experiments. Our model uncovers a highly collective natur
 e of epistasis\, gradually changing the fitness effect of mutations in a d
 iverging sequence context\, rather than acting via strong interactions bet
 ween individual mutations. This collective nature triggers the emergence o
 f a long evolutionary time scale\, separating fast mutational processes in
 side a given sequence context\, from the slow evolution of the context its
 elf. \n\n\n
LOCATION:CRUK CI Lecture Theatre
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