2N9A image
Deposition Date 2015-11-12
Release Date 2016-11-16
Last Version Date 2024-11-20
Entry Detail
PDB ID:
2N9A
Title:
3D Structure of Decoralin-NH2 by Solution NMR
Biological Source:
Source Organism:
Method Details:
Experimental Method:
Conformers Calculated:
40
Conformers Submitted:
20
Selection Criteria:
target function
Macromolecular Entities
Polymer Type:polypeptide(L)
Molecule:Decoralin
Chain IDs:A
Chain Length:12
Number of Molecules:1
Biological Source:Oreumenes decoratus
Ligand Molecules
Primary Citation
MD simulations and multivariate studies for modeling the antileishmanial activity of peptides.
Chem Biol Drug Des 90 501 510 (2017)
PMID: 28267894 DOI: 10.1111/cbdd.12970

Abstact

Leishmaniasis, a protozoan-caused disease, requires alternative treatments with minimized side-effects and less prone to resistance development. Antimicrobial peptides represent a possible choice to be developed. We report on the prospection of structural parameters of 23 helical antimicrobial and leishmanicidal peptides as a tool for modeling and predicting the activity of new peptides. This investigation is based on molecular dynamic simulations (MD) in mimetic membrane environment, as most of these peptides share the feature of interacting with phospholipid bilayers. To overcome the lack of experimental data on peptides' structures, we started simulations from designed 100% α-helices. This procedure was validated through comparisons with NMR data and the determination of the structure of Decoralin-amide. From physicochemical features and MD results, descriptors were raised and statistically related to the minimum inhibitory concentration against Leishmania by the multivariate data analysis technique. This statistical procedure confirmed five descriptors combined by different loadings in five principal components. The leishmanicidal activity depends on peptides' charge, backbone solvation, volume, and solvent-accessible surface area. The generated model possesses good predictability (q2  = 0.715, r2  = 0.898) and is indicative for the most and the least active peptides. This is a novel theoretical path for structure-activity studies combining computational methods that identify and prioritize the promising peptide candidates.

Legend

Protein

Chemical

Disease

Primary Citation of related structures