ACES: A co-evolution simulator generates co-varying protein and nucleic acid sequences

Abstract

Sequence-specific and consequential interactions within or between proteins and/or RNAs can be predicted by identifying co-evolution of residues in these molecules. Different algorithms have been used to detect co-evolution, often using biological data to benchmark a methods ability to discriminate against indirect co-evolution. Such a benchmark is problematic, because not all the interactions and evolutionary constraints underlying real data can be known a priori. Instead, sequences generated in silico to simulate co-evolution would be preferable, and can be obtained using aCES, the software tool presented here. Conservation and co-evolution constraints can be specified for any residue across a number of molecules, allowing the user to capture a complex, realistic set of interactions. Resulting alignments were used to benchmark several co-evolution detection tools for their ability to separate signal from background as well as discriminating direct from indirect signals. This approach can aid in refinement of these algorithms. In addition, systematic tuning of these constraints sheds new light on how they drive co-evolution between residues. Better understanding how to detect co-evolution and the residue interactions they predict can lead to a wide range of insights important for synthetic biologists interested in engineering new, orthogonal interactions between two macromolecules.