Computing wide range of protein/peptide features from their sequence and structure

Pande, A.; Patiyal, S.; Lathwal, A.; Arora, C.; Kaur, D.; Dhall, A.; Mishra, G.; Kaur, H.; Sharma, N.; Jain, S.; Usmani, S. S.; Agrawal, P.; Kumar, R.; Kumar, V.; Raghava, G. P. S.

doi:10.1101/599126

bioRxiv · 10.1101/599126

Computing wide range of protein/peptide features from their sequence and structure

Pande, A.·Patiyal, S.·Lathwal, A.·Arora, C.·Kaur, D.·Dhall, A.·Mishra, G.·Kaur, H.·Sharma, N.·Jain, S.·Usmani, S. S.·Agrawal, P.·Kumar, R.·Kumar, V.·Raghava, G. P. S.

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Abstract

MotivationIn last three decades, a wide range of protein descriptors/features have been discovered to annotate a protein with high precision. A wide range of features have been integrated in numerous software packages (e.g., PROFEAT, PyBioMed, iFeature, protr, Rcpi, propy) to predict function of a protein. These features are not suitable to predict function of a protein at residue level such as prediction of ligand binding residues, DNA interacting residues, post translational modification etc.\n\nResultsIn order to facilitate scientific community, we have developed a software package that computes more than 50,000 features, important for predicting function of a protein and its residues. It has five major modules for computing; composition-based features, binary profiles, evolutionary information, structure-based features and patterns. The composition-based module allows user to compute; i) simple compositions like amino acid, dipeptide, tripeptide; ii) Properties based compositions; iii) Repeats and distribution of amino acids; iv) Shannon entropy to measure the low complexity regions; iv) Miscellaneous compositions like pseudo amino acid, autocorrelation, conjoint triad, quasi-sequence order. Binary profile of amino acid sequences provides complete information including order of residues or type of residues; specifically, suitable to predict function of a protein at residue level. Pfeature allows one to compute evolutionary information-based features in form of PSSM profile generated using PSIBLAST. Structure based module allows computing structure-based features, specifically suitable to annotate chemically modified peptides/proteins. Pfeature also allows generating overlapping patterns and feature from whole protein or its parts (e.g., N-terminal, C-terminal). In summary, Pfeature comprises of almost all features used till now, for predicting function of a protein/peptide including its residues.\n\nAvailabilityIt is available in form of a web server, named as Pfeature (https://webs.iiitd.edu.in/raghava/pfeature/), as well as python library and standalone package (https://github.com/raghavagps/Pfeature) suitable for Windows, Ubuntu, Fedora, MacOS and Centos based operating system.

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Pande, A., Patiyal, S., Lathwal, A., Arora, C., Kaur, D., Dhall, A., Mishra, G., Kaur, H., Sharma, N., Jain, S., Usmani, S. S., Agrawal, P., Kumar, R., Kumar, V., Raghava, G. P. S.. 2019-04-04. Computing wide range of protein/peptide features from their sequence and structure. https://doi.org/10.1101/599126

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