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Direct-Coupling Analysis is a group of methods to harvest information aboutcoevolving residues in a protein family by learning a generative model in anexponential family from data. In protein families of realistic size, thislearning can only be done approximately, and there is a trade-off betweeninference precision and computational speed. We here show that an earlierintroduced $l_2$-regularized pseudolikelihood maximization method called plmDCAcan be modified as to be easily parallelizable, as well as inherently faster ona single processor, at negligible difference in accuracy. We test the newincarnation of the method on 148 protein families from the Protein Familiesdatabase (PFAM), one of the largest tests of this class of algorithms to date.

Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences