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

Overview of attention for article published in Biology Direct, December 2015
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  • Good Attention Score compared to outputs of the same age and source (70th percentile)

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Title
PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues
Published in
Biology Direct, December 2015
DOI 10.1186/s13062-015-0103-4
Pubmed ID
Authors

Sandeep Singh, Harinder Singh, Abhishek Tuknait, Kumardeep Chaudhary, Balvinder Singh, S. Kumaran, Gajendra P. S. Raghava

Abstract

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. 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. 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/ . This article was reviewed by Prof Michael Gromiha, Dr. Bojan Zagrovic and Dr. Zoltan Gaspari.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 176 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 <1%
Uruguay 1 <1%
Unknown 174 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 24%
Researcher 25 14%
Student > Master 17 10%
Student > Bachelor 13 7%
Student > Doctoral Student 8 5%
Other 23 13%
Unknown 48 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 41 23%
Agricultural and Biological Sciences 27 15%
Chemistry 22 13%
Pharmacology, Toxicology and Pharmaceutical Science 5 3%
Computer Science 4 2%
Other 26 15%
Unknown 51 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 September 2016.
All research outputs
#5,948,565
of 23,005,189 outputs
Outputs from Biology Direct
#216
of 487 outputs
Outputs of similar age
#92,724
of 390,429 outputs
Outputs of similar age from Biology Direct
#4
of 17 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 487 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has gotten more attention than average, scoring higher than 54% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 390,429 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.