Abstract
BackgroundIn 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.ResultsPEPstrMOD 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.ConclusionsIn 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/.ReviewersThis article was reviewed by Prof Michael Gromiha, Dr. Bojan Zagrovic and Dr. Zoltan Gaspari.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-015-0103-4) contains supplementary material, which is available to authorized users.
Highlights
In the past, many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids
In order to provide a direct comparison with the existing method PEP-FOLD, we used PEPFOLD server for computing structure of peptides
In the rigid core (RC) region, the same trend was observed i.e. the overall performance of PEPstrMOD and PEP-FOLD was comparable with average C-alpha Root Mean Square Deviation (RMSD) (CA-RMSD) of 3.69 Å and 3.74 Å respectively (Additional file 2: Table S5)
Summary
Many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids. In 1999, Ishikawa et al [11] developed an ab initio method (Geocore) for finding the native-like structures within a small ensemble of conformations. It was devised as a filtering algorithm instead of a folding algorithm, exploring a large conformational space (~billion conformations) and thereby limiting its use for very small peptides. In 2007, Kaur et al [12] developed PEPstr algorithm to predict the tertiary structure of small bioactive peptides. Shen et al (2014) developed PEP-FOLD2 (improved version of PEP-FOLD) [20] and compared it with PEP-FOLD and Rosetta on a dataset comprising 56 structurally diverse peptides
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
