Engineering bio-inspired peptide–polyurea hybrids with thermo-responsive shape memory behaviour

This paper highlights the influence of peptide secondary structure on the shape memory behaviour of peptidic polyureas, driven by hydrogen bonding arrangement and microphase-separated morphology.

Effect of monovalent salt concentration and peptide secondary structure in peptide-micelle binding

We report computational (∼14.2 μs of MD) and experimental (CD, fluorescence) investigations to examine the salt-sensitivity and the role of the peptide secondary structure on LL-14 binding to simple membrane mimetic systems.

Chiroptical Properties and Conformation of Four Lasiocepsin-Related Antimicrobial Peptides: Structural Role of Disulfide Bridges

We report an investigation of the role of disulfide bridges in the 27-residue antimicrobial peptide lasiocepsin (I) containing two disulfide groups (Cys8–Cys25, Cys17–Cys27) and three its analogs lacking one (II, III) or both (IV) native disulfides. Selective alternate incorporation of one or both disulfide bridges influences symmetry, conformation and biological properties of these peptides as demonstrated in their chiroptical (particularly Raman) properties. The effect of modifying the disulfide bridge pattern on the peptide secondary structure is investigated in water and in the presence of 2,2,2-trifluoroethanol and sodium dodecyl sulphate. A combination of experimental electronic and vibrational chiroptical data shows that both disulfide groups are necessary for stabilizing lasiocepsin secondary structure. While the Cys8–Cys25 disulfide group is important for sustaining lasiocepsin tertiary structure and maintaining its biological activity, the Cys17–Cys27 disulfide bridge has a supporting function consisting in reducing peptide flexibility.

Peptide Secondary Structure Prediction using Evolutionary Information

ABSTRACTBACKGROUNDIn the past, large numbers of methods have been developed for predicting secondary structure of proteins. Best of author’s knowledge no method has been specifically developed for predicting secondary structure of peptides. We analyzed secondary structure of peptides and proteins; it was observed that same peptide in protein adopt different secondary structures. Considering the wide application of peptides in therapeutic market, we made attempt to develop a method called PEP2D for predicting secondary structure of peptides.RESULTSIn this study, 3107 unique peptides have been used to train, test and evaluate peptide secondary structure prediction models. It was observed that regular secondary structure content (e.g., helix, beta-sheet) increased with length of peptides. Firstly, models based on various machine-learning techniques have been developed using binary profile of peptides and achieved maximum overall accuracy (Q3) 79.5%. The performance of models further improved from 79.5% to 83.5% using evolutionary information in the form of PSSM profile. We also evaluate performance of protein secondary structure prediction method PSIPRED on our dataset and achieved maximum accuracy 76.9%; particularly poor (Q3 71.4%) for small peptides having length less than 10 residues. Overall, PEP2D has better prediction of beta-sheets (Q3 74%) and coil region (Q3 87%) of peptides as compare to PSIPRED (Q3 54.4% for beta-sheet and Q3 77.9% for coil). We also measure performance of PSIPRED and PEP2D in terms of segment overlap (SOV); achieved 69.3 and 76.7 respectively.CONCLUSIONOur observations indicate that there is a need of developing separate method for predicting secondary structure of peptides. It was also observed that models based on PSSM profile perform poor on small peptides in comparison to long peptides. Based on our study, we developed method for predicting secondary structure of peptides. In order to provide service to user, a webserver/standalone has been developed (https://webs.iiitd.edu.in/raghava/pep2d/).