Identifying recurrent changes in biological sequences is important to multiple aspects of biological research - from understanding the molecular basis of convergent phenotypes, to pinpointing the causative sequence changes that give rise to antibiotic resistance and disease. Here, we present RECUR, a method for identifying recurrent amino acid substitutions from multiple sequence alignments that is fast, easy to use, and scalable to thousands of sequences. We demonstrate that RECUR's recurrence detection achieves 100% accuracy on simulated data with known evolutionary histories. We further show that RECUR is robust to realistic levels of tree inference error. Finally, we apply RECUR to a large set of surface glycoprotein (S) protein sequences from SARS-CoV-2. This analysis identified widespread recurrent evolution throughout the protein with significant enrichment in the exposed receptor-binding S1 subunit and at the interface with the human angiotensin-converting enzyme 2 (hACE2). In contrast, recurrent substitutions were depleted at the trimeric interface of the S protein. In silico modelling showed that recurrent substitutions had no directional effect on stability at either interface, but effects at the hACE2 interface were significantly more variable. Multiple substitutions with large destabilising effects on hACE2 binding have been linked to immune escape, while others represented reversions back to the reference sequence, suggesting that recurrent evolution at this interface reflects opposing selective pressures balancing receptor binding with immune evasion. A standalone implementation of the algorithm is available under the GPLv3 licence at https://github.com/OrthoFinder/RECUR.
Robbins et al. (Tue,) studied this question.