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Peptide Secondary Structure Prediction using Evolutionary Information

View ORCID ProfileHarinder Singh, Sandeep Singh, Gajendra Pal Singh Raghava
doi: https://doi.org/10.1101/558791
Harinder Singh
1J. Craig Venter Institute, 9605 Medical Center Drive, Rockville, MD, USA
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Sandeep Singh
2Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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Gajendra Pal Singh Raghava
3Department of Compuational Biology, Indraprastha Institute of Information Technology (IIIT), Okhla Phase III, New Delhi, India
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  • For correspondence: raghava@iiitd.ac.in
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ABSTRACT

BACKGROUND In the past, large numbers of methods have been developed for predicting secondary structure of proteins. Best of author’s knowledge no method has been specifically developed for predicting secondary structure of peptides. We analyzed secondary structure of peptides and proteins; it was observed that same peptide in protein adopt different secondary structures. Considering the wide application of peptides in therapeutic market, we made attempt to develop a method called PEP2D for predicting secondary structure of peptides.

RESULTS In this study, 3107 unique peptides have been used to train, test and evaluate peptide secondary structure prediction models. It was observed that regular secondary structure content (e.g., helix, beta-sheet) increased with length of peptides. Firstly, models based on various machine-learning techniques have been developed using binary profile of peptides and achieved maximum overall accuracy (Q3) 79.5%. The performance of models further improved from 79.5% to 83.5% using evolutionary information in the form of PSSM profile. We also evaluate performance of protein secondary structure prediction method PSIPRED on our dataset and achieved maximum accuracy 76.9%; particularly poor (Q3 71.4%) for small peptides having length less than 10 residues. Overall, PEP2D has better prediction of beta-sheets (Q3 74%) and coil region (Q3 87%) of peptides as compare to PSIPRED (Q3 54.4% for beta-sheet and Q3 77.9% for coil). We also measure performance of PSIPRED and PEP2D in terms of segment overlap (SOV); achieved 69.3 and 76.7 respectively.

CONCLUSION Our observations indicate that there is a need of developing separate method for predicting secondary structure of peptides. It was also observed that models based on PSSM profile perform poor on small peptides in comparison to long peptides. Based on our study, we developed method for predicting secondary structure of peptides. In order to provide service to user, a webserver/standalone has been developed (https://webs.iiitd.edu.in/raghava/pep2d/).

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 25, 2019.
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Peptide Secondary Structure Prediction using Evolutionary Information
Harinder Singh, Sandeep Singh, Gajendra Pal Singh Raghava
bioRxiv 558791; doi: https://doi.org/10.1101/558791
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Peptide Secondary Structure Prediction using Evolutionary Information
Harinder Singh, Sandeep Singh, Gajendra Pal Singh Raghava
bioRxiv 558791; doi: https://doi.org/10.1101/558791

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