Improving contact predictions by the combination of correlated mutations and other sources of sequence information

We have previously developed a method for predicting interresidue contacts using information about correlated mutations in multiple sequence alignments. The predictions generated with this method were clearly better than random but not enough for their use in de novo protein folding experiments, We assess the possibility of improving contact predictions combining information from the following variables: correlated mutations, sequence conservation, sequence separation along the chain, alignment stability, family size, residue-specific contact occupancy and formation of contact networks, The application of a protocol for combining these independent variables leads to contact predictions that are on average two times better than those obtained initially with correlated mutations, Correlated mutations can be effectively combined with other types of information derived from multiple sequence alignments, Among the different variables tried, sequence conservation and contact density are particularly relevant for the combination with correlated mutations.