A phylogenetic tree commonly represents evolutionary relationships within a set of protein sequences. Various methods and strategies have been used to improve the accuracy of phylogenetic trees, but their capacity to derive a biologically credible relationship appears to be overestimated. Although the quality of the protein sequence alignment and the choice of substitution matrix are preliminary constraints to define the biological accuracy of the overlapped residues, the alignment is not iteratively optimized through the statistical testing of residue-substitution models. The exact alignment protocol and substitution model information are by default used for every sequence set by a server to construct an often-irrelevant phylogenetic tree, and no sequence-based tailoring of phylogenetic strategy is implemented by any server. Rigorously constructing 270 evolutionary trees, constructed using IQ-TREE based on 13 different alignments (Clustal-Omega, Kalign, MAFFT, MUSCLE, TCoffee, and Promals3D, as well as their HHPred-based hidden Markov model [HMM] alignments using HHPred) and nine substitution models (Dayhoff, JJT, block substitution matrix62, WAG, probability matrix from blocks [PMB], direct computation with mutability [DCMUT], JTTDCmut, LG, and variable time), the present study highlights the failure of the current methods and emphasizes the need for a more accurate scrutiny of the entire phylogenetic methodology. MUSCLE alignment and the LG and Dayhoff matrices yield more accurate phylogenetic results for sequences shorter than 500 residues for the log-likelihood measure. Moreover, Kalign 1 HMM alignment yields the top-ranked tree with the lowest tree length score with only the PMB matrix, making this substitution model more accurate in terms of total tree length score. The suggested strategy would be beneficial for understanding the potential pitfalls of phylogenetic inference and would aid us in deriving a more accurate evolutionary relationship for a sequence dataset.
Key words: Phylogenetic tree, substitution model, IQ-TREE, bootstrap, log-likelihood, branch length
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