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predatoR: Mutation Impact Prediction Based on Network Properties

Overview

predatoR is a tool for mutation impact prediction based on network properties.

predatoR() function is the wrapper function of predatoR package.

predatoR() works on each PDB respectively. For each PDB;

  • Downloads/Reads PDB file
  • Calculates distance between each atom in the structure
  • Creates a network from PDB
  • Calculates Degree Centrality Z-Score of each atom
  • Calculates Eigen Centrality Z-Score of each atom
  • Calculates Shortest Path Z-Score of each atom
  • Calculates Betweenness Z-Score of each atom
  • Calculates Clique Z-Score of each atom
  • Calculates PageRank Z-Score of each atom
  • Gets gnomAD Synonymous Z-Score, Missense Z-Score, and pLI Score
  • Gets BLOSUM62 score of the mutation
  • Finds the number of KEGG Pathways which contains the input gene
  • Gets Genic Intolerance Score
  • Finds the number of GO terms associated with the input gene
  • Finds the number of diseases associated with the input gene from DisGeNET
  • Gets the Gene Essentiality Score from Online Gene Essentiality (OGEE) Database
  • Calculates the median gene expression value of the input gene from GTEx
  • Calculates 6 different features from Accessible Surface Area and Hydrophobicity Scale of reference and mutant amino acids
  • Makes prediction

Installation

You can install the predatoR via devtools:

library(devtools)
install_github("berkgurdamar/predatoR")

Usage

Mutation impact prediction can be done via predatoR() function:

predatoR() uses data.frame structures as an input. data.frame should consist of ‘PDB_ID’, ‘Chain’, ‘Position’, ‘Orig_AA’, ‘Mut_AA’ and ‘Gene_Name’ (optional). Predictions can be made by using 2 different models, 5 Angstrom (Å)-all atoms model and 7Å-carbon alpha (Cα) atoms only model.

PDB_ID Chain Position Orig_AA Mut_AA Gene_Name
3SQJ A 196 GLN LEU ALB
3SQJ A 396 GLU LYS ALB

predatoR() can work with input which has partially included gene names.

library(predatoR)
# Gene name included
input_df <- as.data.frame(rbind(c("3SQJ", "A", 196, "GLN", "LEU", "ALB"),
                               c("3SQJ", "A", 396, "GLU", "LYS", "ALB")))
pred_res <- predatoR(info_df =  input_df, n_threads = 8, gene_name_info = TRUE)
# Gene name not included
input_df <- as.data.frame(rbind(c("3SQJ", "A", 196, "GLN", "LEU"),
                               c("3SQJ", "A", 396, "GLU", "LYS")))
pred_res <- predatoR(info_df =  input_df, n_threads = 8, gene_name_info = FALSE)
# Partially included gene names
input_df <- as.data.frame(rbind(c("3SQJ", "A", 196, "GLN", "LEU", "ALB"),
                               c("3SQJ", "A", 396, "GLU", "LYS", "")))
pred_res <- predatoR(info_df =  input_df, n_threads = 8, gene_name_info = TRUE)

predatoR() function returns a data.frame which contains additional two columns; ‘Prediction’ and ‘Probability’. ‘Prediction’ represents the result of the impact prediction and ‘Probability’ represents the probability that the mutation classified as Pathogenic or Neutral.

PDB_ID Chain Position Orig_AA Mut_AA Gene_Name Prediction Probability
3SQJ A 196 GLN LEU ALB Neutral 0.6521832
3SQJ A 396 GLU LYS ALB Neutral 0.6009792

Exploratory Usage

Network properties can be calculated by using different distance cutoffs. In this approach, predatoR() does not make any prediction about the mutation, but returns a data frame contains all 24 features annotated to the dataset. Both network formalisation approaches also can be used.

# networks build using all atoms and 7.6Å cutoff
prediction_result <- predatoR(input_df, distance_cutoff = 7.6, network_approach = "all") 

# networks build using only Cα atoms and 8Å cutoff
prediction_result <- predatoR(input_df, distance_cutoff = 8, network_approach = "ca")

Utility Functions

The wrapper function predatoR() uses the utility functions below;

Utility functions can be used alone, for more detail please see vignette via vignette("predatoR_Vignette").