Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy

Abstract

Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, whereas phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein sequence alignment can more strongly affect inferred ancestral sequences. Here, we show that errors in sequence alignment can produce errors in ASR across a range of realistic and simplified evolutionary scenarios. Importantly, sequence reconstruction errors can lead to errors in estimates of structural and functional properties of ancestral proteins, potentially undermining the reliability of analyses relying on ASR. We introduce an alignment-integrated ASR approach that combines information from many different sequence alignments. We show that integrating alignment uncertainty improves ASR accuracy and the accuracy of downstream structural and functional inferences, often performing as well as highly accurate structure-guided alignment. Given the growing evidence that sequence alignment errors can impact the reliability of ASR studies, we recommend that future studies incorporate approaches to mitigate the impact of alignment uncertainty. Probabilistic modeling of insertion and deletion events has the potential to radically improve ASR accuracy when the model reflects the true underlying evolutionary history, but further studies are required to thoroughly evaluate the reliability of these approaches under realistic conditions.

Direct Molecular Evidence for an Ancient, Conserved Developmental Toolkit Controlling Posttranscriptional Gene Regulation in Land Plants

“…Plant HYL1 has been shown to mediate interactions with double-stranded RNA (dsRNA) and with the twin C-terminal double-stranded RNA-binding motifs (DSRMs) of DCL1 to facilitate RNAi ( Hiraguri et al 2005 ; Kurihara et al 2006 ; Yang et al 2010 , 2014 ). To determine when these interactions evolved, we used an alignment-integrated approach ( Aadland and Kolaczkowski 2020 ) to reconstruct maximum-likelihood ancestral DRB protein sequences at key nodes on the HYL1 phylogeny and measured their affinity for dsRNA and for the C-terminal DSRM+DSRM domains of A. thaliana DCL1 in vitro (see Materials and Methods). Ancestral protein sequences were reconstructed with high statistical support and low ambiguity ( supplementary figs.…”

Direct molecular evidence for an ancient, conserved developmental toolkit controlling post-transcriptional gene regulation in land plants

“…Plant HYL1 has been shown to mediate interactions with double-stranded RNA (dsRNA) and with the twin C-terminal double-stranded RNA-binding motifs (DSRMs) of DCL1 to facilitate RNA interference (Hiraguri et al 2005; Kurihara et al 2006; Yang et al 2010; Yang et al 2014). To determine when these interactions evolved, we used an alignment-integrated approach (Aadland and Kolaczkowski 2020) to reconstruct maximum-likelihood ancestral DRB protein sequences at key nodes on the HYL1 phylogeny and measured their affinity for dsRNA and for the C-terminal DSRM+DSRM domains of Arabidopsis thaliana DCL1 in vitro (see Methods). Ancestral protein sequences were reconstructed with high statistical support and low ambiguity (Supplementary Information Figure S1).…”

“…Sequence alignments were mapped to one another using the mafft --merge option. Alignment uncertainty was incorporated by combining ancestral sequence reconstructions from each alignment using a flat prior over alignments (Aadland and Kolaczkowski 2020).…”

Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond

“…Without integrated models for insertions and deletions together with substitutions, there exists bias in current methods that has been shown to lead to too long ancestral proteins (Vialle et al 2018 ). To take a different step towards reducing this bias, one method for dealing with alignment error integrates over alignments (Aadland and Kolaczkowski ( 2020 ) and this reduces the number of gapped positions. Towards the future, it is also the case that many of the most sophisticated models do not have software implementations and filling this gap will also be important in the future.…”