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Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models
 
Creator Pande, Akshara
Patiyal, Sumeet
Lathwal, Anjali
Arora, Chakit
Kaur, Dilraj
Dhall, Anjali
Mishra, Gaurav
Kaur, Harpreet
Sharma, Neelam
Jain, Shipra
Usmani, Salman Sadullah
Agrawal, Piyush
Kumar, Rajesh
Kumar, Vinod
Raghava, Gajendra P. S.
 
Subject QR Microbiology
 
Description 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.
 
Publisher Mary Ann Libert, Inc, Publishers
 
Date 2022-10-13
 
Type Article
PeerReviewed
 
Relation https://www.liebertpub.com/doi/full/10.1089/cmb.2022.0241
http://crdd.osdd.net/open/3033/
 
Identifier 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.