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SUMMARY:Probabilistic methods for biomolecular structure simulations - Jes
  Frellsen (University of Copenhagen)
DTSTART:20121022T100000Z
DTEND:20121022T110000Z
UID:TALK41145@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:Biomolecules\, such as RNAs and proteins\, play fundamental ro
 les in all living cells. Understanding the exact function of these molecul
 es is of great importance in biology and medicine\, and this understand of
 ten requires knowledge of the molecules’ structure in atomic detail. As 
 experimental structure determination can be difficult and laborious\, ther
 e is a great interest in computational methods for biomolecular structure 
 simulation. In this talk I will present a number of probabilistic methods 
 that address challenges in Markov chain Monte Carlo (MCMC) based structure
  simulations.\n\nIn the first part of the talk\, I will present a probabil
 istic model of RNA conformational space. The current state-of-the-art in c
 onformational sampling is based on fragment assembly. However\, the discre
 te nature of fragments necessitates the use of carefully tuned\, unphysica
 l energy functions\, and their non-probabilistic nature impairs unbiased s
 ampling. I will present a solution to the sampling problem without these l
 imitations: a probabilistic model of RNA structure that allows for efficie
 nt sampling of RNA conformations in continuous space\, and with associated
  probabilities. The method provides both a theoretical and practical solut
 ion for a major bottleneck on the way to routine RNA structure prediction 
 in atomic detail. \n\nIn the second part of the talk\, I will present a ge
 neralized ensemble MCMC algorithm. The standard Metropolis-Hastings algori
 thm can suffer from the generic deficiency of slow convergence and poor mi
 xing. In biomolecular simulations this problem is normally address by usin
 g generalized ensembles. I will present an automated histogram based metho
 d for estimating generalized ensemble weights. In our method\, the density
  of states is estimated iteratively using a maximum likelihood approach. T
 he method scales to high dimensional systems due to the introduction of an
  automated binning procedure. The strength of the method is demonstrated\,
  by applying the method to simulate the folding of a small polypeptide.
LOCATION:Engineering Department\, CBL Room BE-438
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