A molecular dynamics strategy for CSαβ peptides disulfide-assisted model refinement. Issue 12 (10th September 2017)

• Record Type: Journal Article
• Authors: Franzoi, Marco; Sturlese, Mattia; Bellanda, Massimo; Mammi, Stefano
• Is Part Of: Journal of biomolecular structure & dynamics. Volume 35:Issue 12(2017)
• Page Start: 2736
• Page End: 2744
• Publication Date: 2017-09-10
• Subjects: molecular dynamics -- disulfides -- antimicrobial peptides -- CSαβ -- structural prediction; Biomolecules -- Periodicals; Molecular structure -- Periodicals; Molecular Biology -- Periodicals; Biomechanics -- Periodicals; 572
• DOI: 10.1080/07391102.2016.1231081
• Languages: English
• ISSNs: 0739-1102

Abstract

Abstract : Many cysteine-stabilized antimicrobial peptides from a variety of living organisms could be good candidates for the development of anti-infective agents. In the absence of experimentally obtained structural data, peptide modeling is an essential tool for understanding structure–activity relationships and for optimizing the bioactive moieties. Focusing on cysteine-rich peptide structures, we reproduced the case of structure predictions in the so-called midnight zone. We developed our protocol on a training set derived by clustering the available cysteine-stabilized αβ (CSαβ) structures in nine different representative families and tested it on peptides randomly selected from each family. Starting from draft models, we tested a structure-based disulfide predictor and we used cysteine distances as constraints during molecular dynamics. Finally, we proposed an analysis for final structure selection. Accordingly, we obtained a mean root mean square deviation improvement of 21% for the test set. Our findings demonstrate that it is possible to predict the network of disulfide bridges in cysteine-stabilized peptides and to use this result to improve the accuracy of structural predictions. Finally, we applied the methods to predict the structure of royalisin, a cysteine-rich peptide with unknown structure.