By +Yasset Perez-Riverol and +Enrique Audain :
Isoelectric point (pI) can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge is equal to zero. Currently, there are available different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Peptide/Protein fractionation according to their pI is widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. The experimental pI records generated by pI-based fractionation procedures are a valuable information to validate the confidence of the identifications, to remove false positive and and could be used to re-compute peptide/protein posterior error probabilities in MS-based proteomics experiments.
Isoelectric point (pI) can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge is equal to zero. Currently, there are available different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Peptide/Protein fractionation according to their pI is widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. The experimental pI records generated by pI-based fractionation procedures are a valuable information to validate the confidence of the identifications, to remove false positive and and could be used to re-compute peptide/protein posterior error probabilities in MS-based proteomics experiments.
Theses approaches require an accurate theoretical prediction of pI. Even thought
there are several tools/methods to predict the isoelectric point, it remains
hard to define beforehand what methods perform well on a specific dataset.
We believe that the best way to compute the isoelectric point (pI) is to have a complete package with most of the algorithms and methods in the state of the art that can do the job for you [2]. We recently developed an R package (pIR) to
compute isoelectric point using long-standing and novels pI methods that can be
grouped in three main categories : a) iterative, b) Bjellvist-based methods and c) machine learning methods. In addition, pIR also offers a statistical
and graphical framework to evaluate the performance of each method and its
capability to “detect” outliers (those peptides/protein with theoretical pI
biased from experimental value) in a high-throughput environment.
First lets install the package:
First, we need to install
devtools:install.packages("devtools")
library(devtools)
Then we just call:
install_github("ypriverol/pIR")
library(pIR)
