Ancestral sequence reconstruction: accounting for structural information by averaging over replacement matrices

Abstract

Motivation Ancestral sequence reconstruction (ASR) is widely used to understand protein evolution, structure and function. Current ASR methodologies do not fully consider differences in evolutionary constraints among positions imposed by the three-dimensional (3D) structure of the protein. Here, we developed an ASR algorithm that allows different protein sites to evolve according to different mixtures of replacement matrices. We show that assigning replacement matrices to protein positions based on their solvent accessibility leads to ASR with higher log-likelihoods compared to naïve models that assume a single replacement matrix for all sites. Improved ASR log-likelihoods are also demonstrated when solvent accessibility is predicted from protein sequences rather than inferred from a known 3D structure. Finally, we show that using such structure-aware mixture models results in substantial differences in the inferred ancestral sequences. Availability and implementation http://fastml.tau.ac.il. Supplementary information Supplementary data are available at Bioinformatics online.

Consequences of Substitution Model Selection on Protein Ancestral Sequence Reconstruction

“…However, a few recent works suggested that substitution model selection has little effect on phylogenetic tree reconstruction ( Abadi et al 2019 ; Spielman and Shapiro 2020 ; Tao et al 2020 ) leading to a debate topic in the field. With regard to ASR, a study suggested that the selection among substitution models of DNA evolution does not influence nucleotide ASR ( Abadi et al 2019 ) and others investigated the influence of substitution rate variation among sites ( Yang 1994 ; but under the same exchangeability matrix) on protein ASR ( Pupko et al 2002 ; Williams et al 2006 ) or did not quantify the protein ASR error with computer simulations ( Moshe and Pupko 2019 ). At the DNA level, we believe that the effect of substitution model selection on the accuracy of phylogenetic reconstructions could be reduced due to its lower number of character states compared with amino acids.…”

Statistical modeling of SARS-CoV-2 substitution processes: predicting the next variant

“…We now describe our heuristic, inspired by Fitch’s algorithm 61 , used to reconstruct the sequences in the internal nodes. Model-based approaches for ancestral sequence reconstruction (such as FastML 62 ) cannot be applied here due to a large number of sequences.…”

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

“…Variants of the mutation-selection framework remain at the cutting edge of amino acid substitution models, but have not been widely used for ancestral sequence reconstruction yet. The strategy utilizing mixtures of substitution matrices, including while explicitly considering an attribute of protein structure (position solvent accessibility), for ancestral sequence reconstruction has recently been revisited with promising results (Moshe and Pupko 2019). Specifically, an improvement in the log-likelihood describing fit to empirical datasets was found together with the observation that the mixture of matrices resulted in major differences in inferred ancestral sequences in those datasets.…”