Proline not the only culprit?

Collagen analysis with mass spectrometry

Mass spectrometry-based techniques can be applied to investigate collagen with respect to identification, quantification, supramolecular organization, and various post-translational modifications. The continuous interest in collagen research has led to a shift from techniques to analyze the physical characteristics of collagen to methods to study collagen abundance and modifications. In this review, we illustrate the potential of mass spectrometry for in-depth analyses of collagen.

Structural Variants of a Liver Fluke Derived Granulin Peptide Potently Stimulate Wound Healing

Granulins are a family of growth factors involved in cell proliferation. The liver-fluke granulin, Ov-GRN-1, isolated from a carcinogenic liver fluke Opisthorchis viverrini, can significantly accelerate wound repair in vivo and in vitro. However, it is difficult to express Ov-GRN-1 in recombinant form at high yield, impeding its utility as a drug lead. Previously we reported that a truncated analogue ( Ov-GRN_{12-35_3s}) promotes healing of cutaneous wounds in mice. NMR analysis of this analogue indicates the presence of multiple conformations, most likely as a result of proline cis/ trans isomerization. To further investigate whether the proline residues are involved in adopting the multiple confirmations, we have synthesized analogues involving mutation of the proline residues. We have shown that the proline residues have a significant influence on the structure, activity, and folding of Ov-GRN_{12-35_3s}. These results provide insight into improving the oxidative folding yield and bioactivity of Ov-GRN_{12-35_3s} and might facilitate the development of a novel wound healing agent.

Hydrogen bonds are a primary driving force for de novo protein folding

The protein-folding mechanism remains a major puzzle in life science. Purified soluble activation-induced cytidine deaminase (AID) is one of the most difficult proteins to obtain. Starting from inclusion bodies containing a C-terminally truncated version of AID (residues 1-153; AID153), an optimized in vitro folding procedure was derived to obtain large amounts of AID153, which led to crystals with good quality and to final structural determination. Interestingly, it was found that the final refolding yield of the protein is proline residue-dependent. The difference in the distribution of cis and trans configurations of proline residues in the protein after complete denaturation is a major determining factor of the final yield. A point mutation of one of four proline residues to an asparagine led to a near-doubling of the yield of refolded protein after complete denaturation. It was concluded that the driving force behind protein folding could not overcome the cis-to-trans proline isomerization, or vice versa, during the protein-folding process. Furthermore, it was found that successful refolding of proteins optimally occurs at high pH values, which may mimic protein folding in vivo. It was found that high pH values could induce the polarization of peptide bonds, which may trigger the formation of protein secondary structures through hydrogen bonds. It is proposed that a hydrophobic environment coupled with negative charges is essential for protein folding. Combined with our earlier discoveries on protein-unfolding mechanisms, it is proposed that hydrogen bonds are a primary driving force for de novo protein folding.

Randomized Subspace Learning for Proline Cis-Trans Isomerization Prediction

Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide bonds is a widely recognized rate-limiting factor, which can not only induces additional slow phases in protein folding but also modifies the millisecond and sub-millisecond dynamics of the protein. An accurate computational prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. In our earlier work, we successfully developed a biophysically motivated proline CTI predictor utilizing a novel tree-based consensus model with a powerful metalearning technique and achieved 86.58 percent Q2 accuracy and 0.74 Mcc, which is a better result than the results (70-73 percent Q2 accuracies) reported in the literature on the well-referenced benchmark dataset. In this paper, we describe experiments with novel randomized subspace learning and bootstrap seeding techniques as an extension to our earlier work, the consensus models as well as entropy-based learning methods, to obtain better accuracy through a precise and robust learning scheme for proline CTI prediction.

A readily applicable strategy to convert peptides to peptoid-based therapeutics

Incorporation of unnatural amino acids and peptidomimetic residues into therapeutic peptides is highly efficacious and commonly employed, but generally requires laborious trial-and-error approaches. Previously, we demonstrated that C20 peptide has the potential to be a potential antiviral agent. Herein we report our attempt to improve the biological properties of this peptide by introducing peptidomimetics. Through combined alanine, proline, and sarcosine scans coupled with a competitive fluorescence polarization assay developed for identifying antiviral peptides, we enabled to pinpoint peptoid-tolerant peptide residues within C20 peptide. The synergistic benefits of combining these (and other) commonly employed methods could lead to a easily applicable strategy for designing and refining therapeutically-attractive peptidomimetics.