Journal Club du LPTMS: Andrey Lokhov


12:00 - 12:45

LPTMS, salle 201, 2ème étage, Bât 100, Campus d'Orsay
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Andrey Lokhov: Ph.D. Student LPTMS

Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models

M. Ekeberg, C. Lovkvist, Y. Lan, M. Weigt, E. Aurell,  Phys. Rev. E 87, 012707 (2013)
In this paper inference methods of inverse statistical mechanics are applied to the 21-state Potts model for predicting amino-acid contacts in proteins. This problem is important in biology since a successful estimation of contacts is essential for a prediction of proteins’ 3D structure and, therefore, of its biological function.
See also 
PNAS December 6, 2011 vol. 108 no. 49 E1293-E1301
PNAS 106(1), 67-72 (2009)
Abstract of the paper:

Spatially proximate amino acids in a protein tend to coevolve. A protein’s 3D structure hence leaves an echo of correlations in the evolutionary record. Reverse engineering 3D structures from such correlations is an open problem in structural biology, pursued with increasing vigor as more and more protein sequences continue to fill the data banks. Within this task lies a statistical inference problem, rooted in the following: correlation between two sites in a protein sequence can arise from firsthand interaction, 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 statistical 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 pseudolikelihood method, applied to 21-state Potts models describing the statistical properties of families of evolutionarily related proteins, significantly outperforms existing approaches to the direct-coupling analysis, the latter being based on standard mean-field techniques. This improved performance also relies on a modified score for the coupling strength. The results are verified using known crystal structures of specific sequence instances of various protein families. Code implementing the new method can be found at

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