Accurate protein-peptide titration experiments by nuclear magnetic resonance using low-volume samples

Fluorine NMR study of proline-rich sequences using fluoroprolines

Proline homopolymer motifs are found in many proteins; their peculiar conformational and dynamic properties are often directly involved in those proteins' functions. However, the dynamics of proline homopolymers is hard to study by NMR due to a lack of amide protons and small chemical shift dispersion. Exploiting the spectroscopic properties of fluorinated prolines opens interesting perspectives to address these issues. Fluorinated prolines are already widely used in protein structure engineering - they introduce conformational and dynamical biases - but their use as 19 F NMR reporters of proline conformation has not yet been explored. In this work, we look at model peptides where Cγ -fluorinated prolines with opposite configurations of the chiral Cγ centre have been introduced at two positions in distinct polyproline segments. By looking at the effects of swapping these (4R )-fluoroproline and (4S )-fluoroproline within the polyproline segments, we were able to separate the intrinsic conformational properties of the polyproline sequence from the conformational alterations instilled by fluorination. We assess the fluoroproline 19 F relaxation properties, and we exploit the latter in elucidating binding kinetics to the SH3 (Src homology 3) domain.

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

Background:

Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.

Methods:

We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.

Results:

Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.

Conclusion:

There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

NMR WaterLOGSY Reveals Weak Binding of Bisphenol A with Amyloid Fibers of a Conserved 11 Residue Peptide from Androgen Receptor

There is growing evidence that bisphenol A (BPA), a molecule largely released in the environment, has detrimental effects on ecosystems and on human health. It acts as an endocrine disruptor targeting steroid hormone receptors, such as the estrogen receptor (ER), estrogen-related receptor (ERR) and androgen receptor (AR). BPA-derived molecules have recently been shown to interact with the AR N-terminal domain (AR-NTD), which is known to be largely intrinsically disordered. This N-terminal domain contains an 11 residue conserved domain that forms amyloid fibers upon oxidative dimerisation through its strictly conserved Cys240 residue. We investigate here the interaction of BPA, and other potential endocrine disruptors, with AR-NTD amyloid fibers using the WaterLOGSY NMR experiment. We observed a selective binding of these compounds to the amyloid fibers formed by the AR-NTD conserved region and glutamine homopolymers. This observation suggests that the high potency of endocrine disruptors may result, in part, from their ability to bind amyloid forms of nuclear receptors in addition to their cognate binding sites. This property may be exploited to design future therapeutic strategies targeting AR related diseases such as the spinal bulbar muscular atrophy or prostate cancer. The ability of NMR WaterLOGSY experiments to detect weak interactions between small ligands and amyloid fibers may prove to be of particular interest for identifying promising hit molecules.