Secondary structure specific simpler prediction models for protein backbone angles

Motivation Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories of secondary structures they belong to. In this paper, we propose to train separate deep learning models for each category of secondary structures. Machine learning methods strive to achieve generality over the training examples and consequently loose accuracy. In this work, we explicitly exploit classification knowledge to restrict generalisation within the specific class of training examples. This is to compensate the loss of generalisation by exploiting specialisation knowledge in an informed way. Results The new method named SAP4SS obtains mean absolute error (MAE) values of 15.59, 18.87, 6.03, and 21.71 respectively for four types of backbone angles $$\phi$$ ϕ , $$\psi$$ ψ , $$\theta$$ θ , and $$\tau$$ τ . Consequently, SAP4SS significantly outperforms existing state-of-the-art methods SAP, OPUS-TASS, and SPOT-1D: the differences in MAE for all four types of angles are from 1.5 to 4.1% compared to the best known results. Availability SAP4SS along with its data is available from https://gitlab.com/mahnewton/sap4ss.

Metabolic “footprints” of the circulating cancer mucins: CA125 in the high-grade ovarian cancer

Mucins are large glycoproteins characterized by the abundant O-linked oligosaccharides (O-glycans) clustered on a protein backbone. Most of the circulating mucins are rapidly cleared by glycan-recognizing hepatic clearance receptors in the liver. Those mucins that remain in the bloodstream are most commonly used as markers in clinical diagnostics. One of such circulating mucins is MUC16; a peptide epitope of which is known as CA125 antigen — a marker for ovarian cancer. Here, using a targeted 1H-NMR profiling of plasma we are exploring a link between the measured CA125 values and the systemic metabolism of the patients within a group with confirmed high-grade ovarian cancer. The study allowed identifying statistically significant associations between the measured values of CA125 epitope and the plasma concentrations of glucose, glutamine, alanine, betaine and serine. The significance of the identified associations for the listed compounds is below 0.01. This, in turn, enables us to hypothesize about a possibility of including the metabolic measures into a composite score of the ovarian cancer based on the CA125 epitope of MUC16.

RadA, a MSCRAMM Adhesin of the Dominant Symbiote Ruminococcus gnavus E1, Binds Human Immunoglobulins and Intestinal Mucins

Adhesion to the digestive mucosa is considered a key factor for bacterial persistence within the gut. In this study, we show that Ruminococcus gnavus E1 can express the radA gene, which encodes an adhesin of the MSCRAMMs family, only when it colonizes the gut. The RadA N-terminal region contains an all-β bacterial Ig-like domain known to interact with collagens. We observed that it preferentially binds human immunoglobulins (IgA and IgG) and intestinal mucins. Using deglycosylated substrates, we also showed that the RadA N-terminal region recognizes two different types of motifs, the protein backbone of human IgG and the glycan structure of mucins. Finally, competition assays with lectins and free monosaccharides identified Galactose and N-Acetyl-Galactosamine motifs as specific targets for the binding of RadA to mucins and the surface of human epithelial cells.

Local topology and bifurcation hot-spots in proteins with SARS-CoV-2 spike protein as an example

Novel topological methods are introduced to protein research. The aim is to identify hot-spot sites where a bifurcation can alter the local topology of the protein backbone. Since the shape of a protein is intimately related to its biological function, a substitution that causes a bifurcation should have an enhanced capacity to change the protein’s function. The methodology applies to any protein but it is developed with the SARS-CoV-2 spike protein as a timely example. First, topological criteria are introduced to identify and classify potential bifurcation hot-spot sites along the protein backbone. Then, the expected outcome of asubstitution, if it occurs, is estimated for a general class of hot-spots, using a comparative analysis of the surrounding backbone segments. The analysis combines the statistics of structurally commensurate amino acid fragments in the Protein Data Bank with general stereochemical considerations. It is observed that the notorious D614G substitution of the spike protein is a good example of a bifurcation hot-spot. A number of topologically similar examples are then analyzed in detail, some of them are even better candidates for a bifurcation hot-spot than D614G. The local topology of the more recently observed N501Y substitution is also inspected, and it is found that this site is proximal to a different kind of local topology changing bifurcation.

GlyConnect: a glycan-based conjugation extension of the GlycoDelete technology

Recently, our lab developed GlycoDelete, a technology suite that allows a radical simplification of eukaryotic N-glycosylation. The technology allows to produce glycoproteins that carry single GlcNAc, LacNAc, or LacNAc-Sia type glycans on their N-linked glycosylation sequons. GlycoDelete-type N-glycans are uniquely suited for glycan-based conjugation purposes, as these provide a short, homogeneous and hydrophilic link to the protein backbone. Targeting GlycoDelete-glycans allows for highly site-specific conjugation at sites in the protein which are normally occupied by bulky glycans, thus ensuring minimal interference with protein structure and function. The current manuscript describes the evaluation and optimization of both chemical and chemo-enzymatic conjugation of molecules onto the GlycoDelete-type glycans of a limited set of benchmark proteins