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SUMMARY:Global Landscapes of Protein-RNA Recognition Provide Quantitative 
 Tools to Predict and Engineer Specificity in RNA Structured Elements
DTSTART:20180130T140000
DTEND:20180130T150000
DTSTAMP:20260916T065516Z
UID:0a319390d91eb9a5f90ad1475d5488974c259904131e65c92eba0640
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Faruck Morcos\, University of Texas at Dallas\, TX (USA)
 \nSEMINAR IN BIOLOGICAL AND STATISTICAL PHYSICS\n\nAbstract:\nThe role of 
 RNA structured elements is fundamental in biology.  They help build a net
 work of regulatory interactions that has effects on the most important pro
 cesses in the cell such as transcription\, translation and mRNA decay.  A
  relevant biological phenomenon is the molecular interaction occurring bet
 ween proteins and these RNA elements. Although such interactions have been
  extensively studied\, the question of how proteins preferentially interac
 t with different sequences but similar structures is still unresolved.  C
 ollections of known binding elements are insufficient to characterize the 
 spectrum of potential mutations that contribute to functional RNA molecule
 s. In this work\, we developed an integrated framework based on in vitro s
 election\, high-throughput sequencing and global probabilistic modeling to
  quantify the landscapes of protein-RNA recognition.  This approach allow
 ed us to characterize the way that sequence and structural elements confer
  RNA binding recognition to proteins P22N\, 1N and BIV TAT. \n\nThe param
 eters of our global model allow us to discern the most important nucleotid
 e sequence interactions that contribute to recognition. By creating a quan
 titative metric based on such parameter inference\, we are able to discrim
 inate between regulated and non-regulated elements in their genomic contex
 t as well as the design of functional variants that preserve or enhance sp
 ecificity. We are able to verify such predictions experimentally and use t
 his framework to quantify pathways that reveal permissive/disruptive evolu
 tionary trajectories.  Although we show our results for a finite number o
 f protein-RNA systems\, our approach is broadly applicable and easily tran
 sferable. Our framework provides a detailed characterization of protein-RN
 A recognition landscapes with potential applications in unexplored systems
 .\n\nBio:\n2012-2015   Postdoctoral Scholar. Center for Theoretical Biolo
 gical Physics. Rice University\n2010   Postdoctoral Scholar. Center for T
 heoretical Biological Physics. UCSD\n2010   Ph.D. in Computer Science & 
 Engineering University of Notre Dame\n2010   M.S. in Applied Mathematics
 . Department of Mathematics. University of Notre Dame\n2004   M.S. in Co
 mmunications Engineering. Technische Universitat Munchen\n2001   B.S. El
 ectronics and Communications Engineering. ITESM Campus Monterrey\n\nResear
 ch Interests: Statistical Learning\, Information Theory\, Network Science\
 , Computational Biology and Bioinformatics and Systems Biology. Particular
 ly\, in the field of protein networks\, including prediction of protein an
 d domain interactions for diverse organisms.
LOCATION:BSP 727 https://plan.epfl.ch/?room==BSP%20727
STATUS:CONFIRMED
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