An Improved General Amino Acid Replacement Matrix

Abstract

Amino acid replacement matrices are an essential basis of protein phylogenetics. They are used to compute substitution probabilities along phylogeny branches and thus the likelihood of the data. They are also essential in protein alignment. A number of replacement matrices and methods to estimate these matrices from protein alignments have been proposed since the seminal work of Dayhoff et al. (1972). An important advance was achieved by Whelan and Goldman (2001) and their WAG matrix, thanks to an efficient maximum likelihood estimation approach that accounts for the phylogenies of sequences within each training alignment. We further refine this method by incorporating the variability of evolutionary rates across sites in the matrix estimation and using a much larger and diverse database than BRKALN, which was used to estimate WAG. To estimate our new matrix (called LG after the authors), we use an adaptation of the XRATE software and 3,912 alignments from Pfam, comprising approximately 50,000 sequences and approximately 6.5 million residues overall. To evaluate the LG performance, we use an independent sample consisting of 59 alignments from TreeBase and randomly divide Pfam alignments into 3,412 training and 500 test alignments. The comparison with WAG and JTT shows a clear likelihood improvement. With TreeBase, we find that 1) the average Akaike information criterion gain per site is 0.25 and 0.42, when compared with WAG and JTT, respectively; 2) LG is significantly better than WAG for 38 alignments (among 59), and significantly worse with 2 alignments only; and 3) tree topologies inferred with LG, WAG, and JTT frequently differ, indicating that using LG impacts not only the likelihood value but also the output tree. Results with the test alignments from Pfam are analogous. LG and a PHYML implementation can be downloaded from http://atgc.lirmm.fr/LG.

Constraining the timing of the Great Oxidation Event within the Rubisco phylogenetic tree

“…Using 81 curated sequences, multiple sequence alignments were inferred using MSAProbs (Liu, Schmidt, & Maskell, 2010) and MUSCLE (Edgar, 2004) with the default settings. Both of these alignments were best‐fit by the PROTCATWAG model (Lartillot & Philippe, 2004; Le & Gascuel, 2008), with model fitness assessed using the Akaike information criterion (Abascal, Zardoya, & Posada, 2005). The 81 protein sequences used in this study are available to download from the following URL: http://www.phylobot.com/582058404/RuBisCO.noalign.fasta …”

Informational Gene Phylogenies Do Not Support a Fourth Domain of Life for Nucleocytoplasmic Large DNA Viruses

“…In each case, the LG matrix [44] was chosen over the JTT [36] and WAG [37] matrices used in the original analysis by both the AIC and BIC criteria (see Table S1). We nonetheless performed our analyses with all three matrices in order to compare our results to those of Boyer et al [18].…”

Genomic adaptation to polyphagy and insecticides in a major East Asian noctuid pest

“…When the alignment showed highly conservative and non-conservative regions, only the conservative regions were retained for further analysis. Model selection was conducted by MEGA version 6 75 and the LG+ Gamma+ I mode [76][77][78] . The maximum likelihood tree was inferred by RaxML version 8 79 using the LG+ Gamma+ I model.…”