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Spatially proximate amino acids in a protein tend to coevolve. A protein'sthree-dimensional (3D) structure hence leaves an echo of correlations in theevolutionary record. Reverse engineering 3D structures from such correlationsis an open problem in structural biology, pursued with increasing vigor as moreand more protein sequences continue to fill the data banks. Within this tasklies a statistical inference problem, rooted in the following: correlationbetween two sites in a protein sequence can arise from firsthand interactionbut can also be network-propagated via intermediate sites; observed correlationis not enough to guarantee proximity. To separate direct from indirectinteractions is an instance of the general problem of inverse statisticalmechanics, where the task is to learn model parameters (fields, couplings) fromobservables (magnetizations, correlations, samples) in large systems. In thecontext of protein sequences, the approach has been referred to asdirect-coupling analysis. Here we show that the pseudolikelihood method,applied to 21-state Potts models describing the statistical properties offamilies of evolutionarily related proteins, significantly outperforms existingapproaches to the direct-coupling analysis, the latter being based on standardmean-field techniques. This improved performance also relies on a modifiedscore for the coupling strength. The results are verified using known crystalstructures of specific sequence instances of various protein families. Codeimplementing the new method can be found at http://plmdca.csc.kth.se/.

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