Impact of Hapten Presentation on Antibody Binding at Lipid Membrane Interfaces

AbstractWe report the effects of ligand presentation on the binding of aqueous proteins to solid supported lipid bilayers. Specifically, we show that the equilibrium dissociation constant can be strongly affected by ligand lipophilicity and linker length/structure. The apparent equilibrium dissociation constants (KD) were compared for two model systems, biotin/anti-biotin and 2,4-dinitrophenyl (DNP)/anti-DNP, in bulk solution and at model membrane surfaces. The binding constants in solution were obtained from fluorescence anisotropy measurements. The surface binding constants were determined by microfluidic techniques in conjunction with total internal reflection fluorescence microscopy. The results showed that the bulk solution equilibrium dissociation constants for anti-biotin and anti-DNP were almost identical, KD(bulk)=1.7±0.2 nM vs. 2.9±0.1 nM. By contrast, the dissociation constant for anti-biotin antibody was three orders of magnitude tighter than for anti-DNP at a lipid membrane interface, KD=3.6±1.1 nM vs. 2.0±0.2 μM. We postulate that the pronounced difference in surface binding constants for these two similar antibodies is due to differences in the ligands’ relative lipophilicity, i.e., the more hydrophobic DNP molecules had a stronger interaction with the lipid bilayers, rendering them less available to incoming anti-DNP antibodies compared with the biotin/anti-biotin system. However, when membrane-bound biotin ligands were well screened by a poly(ethylene glycol) (PEG) polymer brush, the KD value for the anti-biotin antibody could also be weakened by three orders of magnitude, 2.4±1.1μM. On the other hand, the dissociation constant for anti-DNP antibodies at a lipid interface could be significantly enhanced when DNP haptens were tethered to the end of very long hydrophilic PEG lipopolymers (KD=21±10nM) rather than presented on short lipid-conjugated tethers. These results demonstrate that ligand presentation strongly influences protein interactions with membrane-bound ligands

Peptide Sequence and Conformation Strongly Influence Tryptophan Fluorescence

AbstractThis article probes the denatured state ensemble of ribonuclease Sa (RNase Sa) using fluorescence. To interpret the results obtained with RNase Sa, it is essential that we gain a better understanding of the fluorescence properties of tryptophan (Trp) in peptides. We describe studies of N-acetyl-L-tryptophanamide (NATA), a tripeptide: AWA, and six pentapeptides: AAWAA, WVSGT, GYWHE, HEWTV, EAWQE, and DYWTG. The latter five peptides have the same sequence as those surrounding the Trp residues studied in RNase Sa. The fluorescence emission spectra, the fluorescence lifetimes, and the fluorescence quenching by acrylamide and iodide were measured in concentrated solutions of urea and guanidine hydrochloride. Excited-state electron transfer from the indole ring of Trp to the carbonyl groups of peptide bonds is thought to be the most important mechanism for intramolecular quenching of Trp fluorescence. We find the maximum fluorescence intensities vary from 49,000 for NATA with two carbonyls, to 24,400 for AWA with four carbonyls, to 28,500 for AAWAA with six carbonyls. This suggests that the four carbonyls of AWA are better able to quench Trp fluorescence than the six carbonyls of AAWAA, and this must reflect a difference in the conformations of the peptides. For the pentapeptides, EAWQE has a fluorescence intensity that is more than 50% greater than DYWTG, showing that the amino acid sequence influences the fluorescence intensity either directly through side-chain quenching and/or indirectly through an influence on the conformational ensemble of the peptides. Our results show that peptides are generally better models for the Trp residues in proteins than NATA. Finally, our results emphasize that we have much to learn about Trp fluorescence even in simple compounds

Microbial ligand costimulation drives neutrophilic steroid-refractory asthma

Funding: The authors thank the Wellcome Trust (102705) and the Universities of Aberdeen and Cape Town for funding. This research was also supported, in part, by National Institutes of Health GM53522 and GM083016 to DLW. KF and BNL are funded by the Fonds Wetenschappelijk Onderzoek, BNL is the recipient of an European Research Commission consolidator grant and participates in the European Union FP7 programs EUBIOPRED and MedALL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Toxicity in mice expressing short hairpin RNAs gives new insight into RNAi

Short hairpin RNAs can provide stable gene silencing via RNA interference. Recent studies have shown toxicity in vivo that appears to be related to saturation of the endogenous microRNA pathway. Will these findings limit the therapeutic use of such hairpins

MTar: a computational microRNA target prediction architecture for human transcriptome

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.</p> <p>Results</p> <p>We present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.</p> <p>Conclusion</p> <p>MTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.</p