PHACT: Phylogeny-Aware Computing of Tolerance for Missense Mutations

Abstract

Evolutionary conservation is a fundamental resource for predicting the substitutability of amino acids and loss of function in proteins. The use of multiple sequence alignment alone—without considering the evolutionary relationships among sequences—results in the redundant counting of evolutionarily related alteration events as if they were independent. Here we propose a new method, PHACT that predicts the pathogenicity of missense mutations directly from the phylogenetic tree of proteins. PHACT travels through the nodes of the phylogenetic tree and evaluates the deleteriousness of a substitution based on the probability differences of ancestral amino acids between neighboring nodes in the tree. Moreover, PHACT assigns weights to each node in the tree based on their distance to the query organism. For each potential amino acid substitution, the algorithm generates a score that is used to calculate the effect of substitution on protein function. To analyze the predictive performance of PHACT, we performed various experiments over the subsets of two datasets that include 3023 proteins and 61662 variants in total. The experiments demonstrated that our method outperformed the widely used pathogenicity prediction tools (i.e., SIFT and PolyPhen-2) and achieved better predictive performance than did other conventional statistical approaches presented in dbNSFP. The PHACT source code is available at https://github.com/CompGenomeLab/PHACT.

Evolutionary History of Calcium-Sensing Receptors Unveils Hyper/Hypocalcemia-Causing Mutations

“…To consider these two important factors, we designed an approach to identify and prioritize residues by specificity, which differentiate a subfamily from others in the CaSR group (CaSR, GPRC6A and TAS1Rs). Our approach is based on the idea presented in the functionally divergent residues method (Bradley & Beltrao, 2019) along with adaption of the PHACT method (Kuru et al ., 2022). We calculated the probability of each amino acid at each node of the CaSR-group phylogenetic tree by ancestral sequence reconstruction (Fig 4A).…”

“…To obtain members of a subfamily without requiring computationally expensive phylogenetic tree building and manual curation steps, we generated highly sensitive subfamily-specific profile hidden Markov models (HMMs) by using the functionally equivalent orthologs we determined using phylogenetic tree analysis. We calculated a specificity score for each residue in a subfamily by calculating scores based on a modified version of the PHACT algorithm (Kuru et al , 2022) scores which considers independent evolutionary events on the phylogenetic tree while scoring the acceptability of an amino acid substitution. We predicted the functional consequence of every potential substitution in CaSR by using the gradient boosting trees machine learning approach.…”

Uncovering the Dual Role of Mitochondrial and Nuclear DNA Variants in Pediatric Cardiomyopathies

“…General data handling was performed with and "R" base functions and (version 4.2.1, [R Core Team 2022]), and R packages; ggplot2 and dplyr for plotting(Dplyr: A Grammar of Data Manipulation, 2021;Wickham, 2016). After filters were established for nDNA variants, in silico evaluations of the variants were carried out using VEP through different prediction tools and PHACT, which was recently developed (Kuru et al, 2022;McLaren et al, 2016). For mtDNA variants, 'bam' files were converted to 'fasta' files through MitoSuite, which would give the consensus sequence of the reading.…”

PHACTboost: A Phylogeny-aware Boosting Algorithm to Compute the Pathogenicity of Missense Mutations

“…1A, observing the alternative amino acid in a close species to human on the corresponding phylogenetic tree, provides more evidence on the neutrality of the change. We recently introduced PHACT, a phylogeny-based algorithm to predict the effects of variants (Kuru et al, 2022), to overcome the limitations of prevalent tools that ignore independent and dependent alterations. PHACT scores the amino acid substitutions based on phylogenetic trees and the probabilities of the ancestral reconstruction of amino acids.…”

“…By building on the foundation of incorporating the evolutionary context into predictions of the effects of variants (Kuru et al, 2022), we propose PHACTboost, an improved version of PHACT, in this study. Because PHACT is score based, it does not learn from the large set of available data on pathogenic and neutral variants.…”