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UID:1-141@lptms.universite-paris-saclay.fr
DTSTART:20130408T120000Z
DTEND:20130408T124500Z
DTSTAMP:20130418T123956Z
URL:https://www.lptms.universite-paris-saclay.fr/seminars/journal-club-du-
 lptms-andrey-lokhov/
SUMMARY:Journal Club du LPTMS: Andrey Lokhov - LPTMS\, salle 201\, 2ème é
 tage\, Bât 100\, Campus d'Orsay - 8 Avr 13 12:00
DESCRIPTION:Andrey Lokhov: Ph.D. Student LPTMS\nImproved contact prediction
  in proteins: Using pseudolikelihoods to infer Potts models\nM. Ekeberg\, 
 C. Lovkvist\, Y. Lan\, M. Weigt\, E. Aurell\,  Phys. Rev. E 87\, 012707 (
 2013)\n \nIn this paper inference methods of inverse statistical mechanic
 s are applied to the 21-state Potts model for predicting amino-acid cont
 acts in proteins. This problem is important in biology since a successful 
 estimation of contacts is essential for a prediction of proteins' 3D struc
 ture and\, therefore\, of its biological function.\n \nSee also \nPNAS D
 ecember 6\, 2011 vol. 108 no. 49 E1293-E1301\nPNAS 106(1)\, 67-72 (2009)\n
 http://arxiv.org/abs/1212.3281\n \n \nAbstract of the paper:\n\n\n\n\nSp
 atially proximate amino acids in a protein tend to coevolve. A protein’s
  3D structure hence leaves an echo of correlations in the evolutionary rec
 ord. Reverse engineering 3D structures from such correlations is an open p
 roblem in structural biology\, pursued with increasing vigor as more and m
 ore protein sequences continue to fill the data banks. Within this task li
 es a statistical inference problem\, rooted in the following: correlation 
 between two sites in a protein sequence can arise from firsthand interacti
 on\, but can also be network-propagated via intermediate sites\; observed 
 correlation is not enough to guarantee proximity. To separate direct from 
 indirect interactions is an instance of the general problem of inverse sta
 tistical mechanics\, where the task is to learn model parameters (fields\,
  couplings) from observables (magnetizations\, correlations\, samples) in 
 large systems. In the context of protein sequences\, the approach has been
  referred to as direct-coupling analysis. Here we show that the pseudolike
 lihood method\, applied to 21-state Potts models describing the statistica
 l properties of families of evolutionarily related proteins\, significantl
 y outperforms existing approaches to the direct-coupling analysis\, the la
 tter being based on standard mean-field techniques. This improved performa
 nce also relies on a modified score for the coupling strength. The results
  are verified using known crystal structures of specific sequence instance
 s of various protein families. Code implementing the new method can be fou
 nd at http://plmdca.csc.kth.se/.\n\n\n\n\n 
LOCATION:LPTMS\, salle 201\, 2ème étage\, Bât 100\, Campus d'Orsay\, 15 
 Rue Georges Clemenceau\, Orsay\, 91405\, France
GEO:48.698185;2.181768
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  Orsay\, 91405\, France;X-APPLE-RADIUS=100;X-TITLE=LPTMS\, salle 201\, 2è
 me étage\, Bât 100\, Campus d'Orsay:geo:48.698185,2.181768
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