Phosphocholine-Decorated PPI-Dendrimers Mimic Cell Membrane Phosphocholine Clusters and Tune the Innate Immune Activity of C-Reactive Protein.

C-reactive protein (CRP) is widely used as biomarkers of infection and inflammation. It has a well-described ability to bind phosphocholine (PC), as well as PC-clusters from compromised and inflamed cell membranes and tissues. The binding of PC-clusters to CRP is of interest as this binding determines subsequent innate immune activity. We investigated PC-decorated dendrimers as mimics for PC-clusters. Five generations of poly(propylene imine) (PPI) dendrimers were modified with PC surface groups via a three-step synthetic sequence obtaining the PC-decorated dendrimers in high purity. The dendrimers were analyzed by NMR and infrared spectroscopy as well as HPLC. We developed immunoassays to show that dendrimer-PC binding to CRP was Ca2+-dependent with an apparent overall Kd of 11.9 nM for first generation (G1) PPI-PC, while G2-PPI-PC and G3-PPI-PC had slightly higher affinities, and G4-PPI-PC and G5-PPI-PC had slightly lower affinities. For all PC-dendrimers, the affinity was orders of magnitude higher than the affinity of free phosphocholine (PC), indicating a PC-cluster effect. Next, we investigated the binding of CRP:PPI-PC complexes to complement component C1q. C1q binding to CRP was dependent on the generation of PPI-PC bound to CRP, with second and third generation PPI-PCs leading to the highest affinity. The dendrimer-based approach to PC-cluster mimics and the simple binding assays presented here hold promise as tools to screen PC-compounds for their abilities to tune the innate immune activity of CRP.

AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides.

Dietary antioxidants are an important preservative in food and have been suggested to help in disease prevention. With consumer demands for less synthetic and safer additives in food products, the food industry is searching for antioxidants that can be marketed as natural. Peptides derived from natural proteins show promise, as they are generally regarded as safe and potentially contain other beneficial bioactivities. Antioxidative peptides are usually obtained by testing various peptides derived from hydrolysis of proteins by a selection of proteases. This slow and cumbersome trial-and-error approach to identify antioxidative peptides has increased interest in developing computational approaches for prediction of antioxidant activity and thereby reduce laboratory work. A few antioxidant predictors exist, however, no tool predicting the antioxidative properties of peptides is, to the best of our knowledge, currently available as a web-server. We here present the AnOxPePred tool and web-server ( http://services.bioinformatics.dtu.dk/service.php?AnOxPePred-1.0 ) that uses deep learning to predict the antioxidant properties of peptides. Our model was trained on a curated dataset consisting of experimentally-tested antioxidant and non-antioxidant peptides. For a variety of metrics our method displays a prediction performance better than a k-NN sequence identity-based approach. Furthermore, the developed tool will be a good benchmark for future predictors of antioxidant peptides.

proABC-2: PRediction of AntiBody contacts v2 and its application to information-driven docking.

MOTIVATION: Monoclonal antibodies are essential tools in the contemporary therapeutic armory. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalyzing the development of computational approaches able to offer valuable, faster and cheaper alternatives to classical experimental methodologies used for the study of antibody-antigen complexes. RESULTS: Here, we present proABC-2, an update of the original random-forest antibody paratope predictor, based on a convolutional neural network algorithm. We also demonstrate how the predictions can be fruitfully used to drive the docking in HADDOCK. AVAILABILITY AND IMPLEMENTATION: The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes.

The interaction between the class I major histocompatibility complex (MHC), the peptide presented by the MHC and the T-cell receptor (TCR) is a key determinant of the cellular immune response. Here, we present TCRpMHCmodels, a method for accurate structural modelling of the TCR-peptide-MHC (TCR-pMHC) complex. This TCR-pMHC modelling pipeline takes as input the amino acid sequence and generates models of the TCR-pMHC complex, with a median Cα RMSD of 2.31 Å. TCRpMHCmodels significantly outperforms TCRFlexDock, a specialised method for docking pMHC and TCR structures. TCRpMHCmodels is simple to use and the modelling pipeline takes, on average, only two minutes. Thanks to its ease of use and high modelling accuracy, we expect TCRpMHCmodels to provide insights into the underlying mechanisms of TCR and pMHC interactions and aid in the development of advanced T-cell-based immunotherapies and rational design of vaccines. The TCRpMHCmodels tool is available at http://www.cbs.dtu.dk/services/TCRpMHCmodels/ .