creators_name: Pande, Akshara
creators_name: Patiyal, Sumeet
creators_name: Lathwal, Anjali
creators_name: Arora, Chakit
creators_name: Kaur, Dilraj
creators_name: Dhall, Anjali
creators_name: Mishra, Gaurav
creators_name: Kaur, Harpreet
creators_name: Sharma, Neelam
creators_name: Jain, Shipra
creators_name: Usmani, Salman Sadullah
creators_name: Agrawal, Piyush
creators_name: Kumar, Rajesh
creators_name: Kumar, Vinod
creators_name: Raghava, Gajendra P. S.
type: article
datestamp: 2022-12-15 05:55:58
lastmod: 2022-12-15 05:55:58
metadata_visibility: show
title: Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models
ispublished: pub
subjects: QR
keywords: PSSM; Shannon entropy; binary profile; feature selection; machine learning techniques; protein composition
note: The copyright of this article belongs to Mary Ann Libert, Inc, Publishers
abstract: In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.
date: 2022-10-13
date_type: published
publication: JOURNAL OF COMPUTATIONAL BIOLOGY
publisher: Mary Ann Libert, Inc, Publishers
refereed: TRUE
official_url: https://www.liebertpub.com/doi/full/10.1089/cmb.2022.0241
citation:   Pande, Akshara and Patiyal, Sumeet and Lathwal, Anjali and Arora, Chakit and Kaur, Dilraj and Dhall, Anjali and Mishra, Gaurav and Kaur, Harpreet and Sharma, Neelam and Jain, Shipra and Usmani, Salman Sadullah and Agrawal, Piyush and Kumar, Rajesh and Kumar, Vinod and Raghava, Gajendra P. S.  (2022) Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models.  JOURNAL OF COMPUTATIONAL BIOLOGY.