Effect of macrocyclization and tetramethylrhodamine labeling on chemokine binding peptides.

Receptor-derived peptides have played an important role in elucidating chemokine-receptor interactions. For the inflammatory chemokine CXC-class chemokine ligand 8 (CXCL8), a site II-mimetic peptide has been derived from parts of extracellular loops 2 and 3 and adjacent transmembrane helices of its receptor CXC-class chemokine receptor 1 (Helmer et al., RSC Adv., 2015, 5, 25657). The peptide sequence with a C-terminal glutamine did not bind to CXCL8, whereas one with a C-terminal glutamate did but with low micromolar affinity. We sought to improve the affinity and protease stability of the latter peptide through cyclization while also cyclizing the former for control purposes. To identify a cyclization strategy that permits a receptor-like interaction, we conducted a molecular dynamics simulation of CXCL8 in complex with full-length CXC-class chemokine receptor 1. We introduced a linker to provide an appropriate spacing between the termini and used an on-resin side-chain-to-tail cyclization strategy. Upon chemokine binding, the fluorescence intensity of the tetramethylrhodamine (TAMRA)-labeled cyclic peptides increased whereas the fluorescence anisotropy decreased. Additional molecular dynamics simulations indicated that the fluorophore interacts with the peptide macrocycle so that chemokine binding leads to its displacement and observed changes in fluorescence. Macrocyclization of both 18-amino acid-long peptides led to the same low micromolar affinity for CXCL8. Likewise, both TAMRA-labeled linear peptides interacted with CXCL8 with similar affinities. Interestingly, the linear TAMRA-labeled peptides were more resistant to tryptic digestion than the unlabeled counterparts, whereas the cyclized peptides were not degraded at all. We conclude that the TAMRA fluorophore tends to interact with peptides altering their protease stability and behavior in fluorescence-based assays.

StreAM-[Formula: see text]: algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs.

BACKGROUND: In this work, we present a new coarse grained representation of RNA dynamics. It is based on adjacency matrices and their interactions patterns obtained from molecular dynamics simulations. RNA molecules are well-suited for this representation due to their composition which is mainly modular and assessable by the secondary structure alone. These interactions can be represented as adjacency matrices of k nucleotides. Based on those, we define transitions between states as changes in the adjacency matrices which form Markovian dynamics. The intense computational demand for deriving the transition probability matrices prompted us to develop StreAM-[Formula: see text], a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA. RESULTS: We benchmark StreAM-[Formula: see text] (a) for random and RNA unit sphere dynamic graphs (b) for the robustness of our method against different parameters. Moreover, we address a riboswitch design problem by applying StreAM-[Formula: see text] on six long term molecular dynamics simulation of a synthetic tetracycline dependent riboswitch (500 ns) in combination with five different antibiotics. CONCLUSIONS: The proposed algorithm performs well on large simulated as well as real world dynamic graphs. Additionally, StreAM-[Formula: see text] provides insights into nucleotide based RNA dynamics in comparison to conventional metrics like the root-mean square fluctuation. In the light of experimental data our results show important design opportunities for the riboswitch.