Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Dec 21:10:73.
doi: 10.1186/s13062-015-0103-4.

PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues

Affiliations

PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues

Sandeep Singh et al. Biol Direct. .

Abstract

Background: In the past, many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids. This study describes a method PEPstrMOD, which is an updated version of PEPstr, developed specifically for predicting the structure of peptides containing natural and non-natural/modified residues.

Results: PEPstrMOD integrates Forcefield_NCAA and Forcefield_PTM force field libraries to handle 147 non-natural residues and 32 types of post-translational modifications respectively by performing molecular dynamics using AMBER. AMBER was also used to handle other modifications like peptide cyclization, use of D-amino acids and capping of terminal residues. In addition, GROMACS was used to implement 210 non-natural side-chains in peptides using SwissSideChain force field library. We evaluated the performance of PEPstrMOD on three datasets generated from Protein Data Bank; i) ModPep dataset contains 501 non-natural peptides, ii) ModPep16, a subset of ModPep, and iii) CyclicPep contains 34 cyclic peptides. We achieved backbone Root Mean Square Deviation between the actual and predicted structure of peptides in the range of 3.81-4.05 Å.

Conclusions: In summary, the method PEPstrMOD has been developed that predicts the structure of modified peptide from the sequence/structure given as input. We validated the PEPstrMOD application using a dataset of peptides having non-natural/modified residues. PEPstrMOD offers unique advantages that allow the users to predict the structures of peptides having i) natural residues, ii) non-naturally modified residues, iii) terminal modifications, iv) post-translational modifications, v) D-amino acids, and also allows extended simulation of predicted peptides. This will help the researchers to have prior structural information of modified peptides to further design the peptides for desired therapeutic property. PEPstrMOD is freely available at http://osddlinux.osdd.net/raghava/pepstrmod/.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Graphical representation of algorithmic steps of PEPstrMOD showing its working
Fig. 2
Fig. 2
A case study of the comparison of PEPstrMOD and ab initio model of a short cyclic peptide (1n0c)
Fig. 3
Fig. 3
Graphical representation of the result page of PEPstrMOD with multiple tabs. a Visualization of the predicted structure using Jmol Viewer. b Links to download PDB file of predicted structure, topology, coordinate, trajectory files and representative structures from cluster analysis. c Energy graph of the simulation. d RMS graph of the simulation. e Visualization of the simulation in animated form. f Visualization of the alignment of predicted structure and representative structures obtained after cluster analysis

References

    1. Albericio F, Kruger HG. Therapeutic peptides. Future Med Chem. 2012;4(12):1527–31. doi: 10.4155/fmc.12.94. - DOI - PubMed
    1. Otvos L. Peptide-Based Drug Design Methods and Protocols. vol 494. Humana Press; 2008. doi:10.1007/978-1-59745-419-3. - PubMed
    1. Craik DJ, Fairlie DP, Liras S, Price D. The future of peptide-based drugs. Chem Biol Drug Des. 2013;81(1):136–47. doi: 10.1111/cbdd.12055. - DOI - PubMed
    1. Stalmach A, Johnsson H, McInnes IB, Husi H, Klein J, Dakna M, et al. Identification of urinary peptide biomarkers associated with rheumatoid arthritis. PLoS One. 2014;9(8):e104625. doi: 10.1371/journal.pone.0104625. - DOI - PMC - PubMed
    1. Gautam A, Kapoor P, Chaudhary K, Kumar R, Raghava GP. Tumor homing peptides as molecular probes for cancer therapeutics, diagnostics and theranostics. Curr Med Chem. 2014;21(21):2367–91. doi: 10.2174/0929867321666140217122100. - DOI - PubMed

Publication types

LinkOut - more resources