The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures.

Antibodies are key proteins of the adaptive immune system, and there exists a large body of academic literature and patents dedicated to their study and concomitant conversion into therapeutics, diagnostics, or reagents. These documents often contain extensive functional characterisations of the sets of antibodies they describe. However, leveraging these heterogeneous reports, for example to offer insights into the properties of query antibodies of interest, is currently challenging as there is no central repository through which this wide corpus can be mined by sequence or structure. Here, we present PLAbDab (the Patent and Literature Antibody Database), a self-updating repository containing over 150,000 paired antibody sequences and 3D structural models, of which over 65 000 are unique. We describe the methods used to extract, filter, pair, and model the antibodies in PLAbDab, and showcase how PLAbDab can be searched by sequence, structure, or keyword. PLAbDab uses include annotating query antibodies with potential antigen information from similar entries, analysing structural models of existing antibodies to identify modifications that could improve their properties, and facilitating the compilation of bespoke datasets of antibody sequences/structures that bind to a specific antigen. PLAbDab is freely available via Github (https://github.com/oxpig/PLAbDab) and as a searchable webserver (https://opig.stats.ox.ac.uk/webapps/plabdab/).

Tumor-associated macrophages in canine visceral hemangiosarcoma.

Canine hemangiosarcoma (HSA) is a highly malignant tumor derived from hematopoietic stem cells and commonly occurs in visceral organs or skin. Visceral HSAs are particularly aggressive and progress rapidly despite multimodal treatment. Tumor-associated macrophages (TAMs) play a central role in carcinogenesis, tumor progression, and metastasis in humans and murine models. In this retrospective study, we investigated the prevalence and phenotype of TAMs in privately owned, treatment-naïve dogs with naturally occurring HSA. We used CD204 as a general macrophage marker and CD206 as a marker for M2-polarized macrophages. Formalin-fixed paraffin-embedded tissues from HSAs in the spleen (n = 9), heart (n = 6), and other locations (n = 12) from 17 dogs were sectioned and immunohistochemically labeled with CD204 and CD206 antibodies. The mean number of log(CD204)- and log(CD206)-positive cells and the ratio of log(CD206/CD204)-positive cells were compared with normal surrounding tissues and between tumor locations. There were significantly more macrophages and M2 macrophages, and a higher ratio of M2 macrophages to total macrophages in tumor hot spots (P = .0002, P < .0001, and P = .0002, respectively) and in tumor tissues outside of hot spots (P = .009, P = .002, and P = .007, respectively) than in normal surrounding tissues. There were no significant differences between tumor locations, but there was a trend toward higher numbers of CD204-positive macrophages within the splenic tumors. There was no association between histological parameters or clinical stage and TAM numbers or phenotype. As in humans, TAMs in dogs with HSA have a predominantly M2-skewed phenotype. Dogs with HSA could serve as excellent models to evaluate new TAM-reprogramming therapies.

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.

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.

Biofunctionality of Enzymatically Derived Peptides from Codfish (Gadus morhua) Frame: Bulk In Vitro Properties, Quantitative Proteomics, and Bioinformatic Prediction.

Protein hydrolysates show great promise as bioactive food and feed ingredients and for valorization of side-streams from e.g., the fish processing industry. We present a novel approach for hydrolysate characterization that utilizes proteomics data for calculation of weighted mean peptide properties (length, molecular weight, and charge) and peptide-level abundance estimation. Using a novel bioinformatic approach for subsequent prediction of biofunctional properties of identified peptides, we are able to provide an unprecedented, in-depth characterization. The study further characterizes bulk emulsifying, foaming, and in vitro antioxidative properties of enzymatic hydrolysates derived from cod frame by application of Alcalase and Neutrase, individually and sequentially, as well as the influence of heat pre-treatment. All hydrolysates displayed comparable or higher emulsifying activity and stability than sodium caseinate. Heat-treatment significantly increased stability but showed a negative effect on the activity and degree of hydrolysis. Lower degrees of hydrolysis resulted in significantly higher chelating activity, while the opposite was observed for radical scavenging activity. Combining peptide abundance with bioinformatic prediction, we identified several peptides that are likely linked to the observed differences in bulk emulsifying properties. The study highlights the prospects of applying proteomics and bioinformatics for hydrolysate characterization and in food protein science.

KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies.

Antibodies with similar amino acid sequences, especially across their complementarity-determining regions, often share properties. Finding that an antibody of interest has a similar sequence to naturally expressed antibodies in healthy or diseased repertoires is a powerful approach for the prediction of antibody properties, such as immunogenicity or antigen specificity. However, as the number of available antibody sequences is now in the billions and continuing to grow, repertoire mining for similar sequences has become increasingly computationally expensive. Existing approaches are limited by either being low-throughput, non-exhaustive, not antibody specific, or only searching against entire chain sequences. Therefore, there is a need for a specialized tool, optimized for a rapid and exhaustive search of any antibody region against all known antibodies, to better utilize the full breadth of available repertoire sequences. We introduce Known Antibody Search (KA-Search), a tool that allows for the rapid search of billions of antibody variable domains by amino acid sequence identity across either the variable domain, the complementarity-determining regions, or a user defined antibody region. We show KA-Search in operation on the [Formula: see text]2.4 billion antibody sequences available in the OAS database. KA-Search can be used to find the most similar sequences from OAS within 30 minutes and a representative subset of 10 million sequences in less than 9 seconds. We give examples of how KA-Search can be used to obtain new insights about an antibody of interest. KA-Search is freely available at https://github.com/oxpig/kasearch .

Antioxidant peptides from alternative sources reduce lipid oxidation in 5% fish oil-in-water emulsions (pH 4) and fish oil-enriched mayonnaise.

Bioinformatics tools were used to predict radical scavenging and metal chelating activities of peptides derived from abundant potato, seaweed, microbial, and spinach proteins. The antioxidant activity was evaluated in 5% oil-in-water emulsions (pH4) and best-performing peptides were tested in mayonnaise and compared with EDTA. Emulsion physical stability was intact. The peptide DDDNLVLPEVYDQD showed the highest protection against oxidation in both emulsions by retarding the formation of oxidation products and depletion of tocopherols during storage, but it was less efficient than EDTA when evaluated in mayonnaise. In low-fat emulsions, formation of hydroperoxides was reduced 4-folds after 5 days compared to control. The concentration effect of the peptide was confirmed in mayonnaise at the EDTA equimolar concentration. The second-best performing peptides were NNKWVPCLEFETEHGFVYREHH in emulsion and AGDWLIGDR in mayonnaise. In general, the peptide efficacy was higher in low-fat emulsions. Results demonstrated that peptide negative net charge was important for chelating activity.

Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences.

The antibody repertoires of individuals and groups have been used to explore disease states, understand vaccine responses, and drive therapeutic development. The arrival of B-cell receptor repertoire sequencing has enabled researchers to get a snapshot of these antibody repertoires, and as more data are generated, increasingly in-depth studies are possible. However, most publicly available data only exist as raw FASTQ files, making the data hard to access, process, and compare. The Observed Antibody Space (OAS) database was created in 2018 to offer clean, annotated, and translated repertoire data. In this paper, we describe an update to OAS that has been driven by the increasing volume of data and the appearance of paired (VH/VL) sequence data. OAS is now accessible via a new web server, with standardized search parameters and a new sequence-based search option. The new database provides both nucleotides and amino acids for every sequence, with additional sequence annotations to make the data Minimal Information about Adaptive Immune Receptor Repertoire compliant, and comments on potential problems with the sequence. OAS now contains 25 new studies, including severe acute respiratory syndrome coronavirus 2 data and paired sequencing data. The new database is accessible at http://opig.stats.ox.ac.uk/webapps/oas/, and all data are freely available for download.

AbLang: an antibody language model for completing antibody sequences.

Motivation: General protein language models have been shown to summarize the semantics of protein sequences into representations that are useful for state-of-the-art predictive methods. However, for antibody specific problems, such as restoring residues lost due to sequencing errors, a model trained solely on antibodies may be more powerful. Antibodies are one of the few protein types where the volume of sequence data needed for such language models is available, e.g. in the Observed Antibody Space (OAS) database. Results: Here, we introduce AbLang, a language model trained on the antibody sequences in the OAS database. We demonstrate the power of AbLang by using it to restore missing residues in antibody sequence data, a key issue with B-cell receptor repertoire sequencing, e.g. over 40% of OAS sequences are missing the first 15 amino acids. AbLang restores the missing residues of antibody sequences better than using IMGT germlines or the general protein language model ESM-1b. Further, AbLang does not require knowledge of the germline of the antibody and is seven times faster than ESM-1b. Availability and implementation: AbLang is a python package available at https://github.com/oxpig/AbLang. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Antioxidant peptides derived from potato, seaweed, microbial and spinach proteins: Oxidative stability of 5% fish oil-in-water emulsions.

In this study, we used a combination of quantitative proteomics and bioinformatic prediction for identifying novel antioxidant peptides. Thirty-five peptides from potato, seaweed, microbial, and spinach proteins were investigated. Based on high DPPH radical scavenging activity (IC50 ≤ 16 mg/mL), metal chelation activity, isoelectric point, and high relative abundance in the parent protein sources, 11 peptides were selected. Lipid oxidation retardation was evaluated in 5% fish oil-in-water emulsions stabilized with Tween 20, where emulsion physical stability was unaffected by peptide addition. The secondary structure of selected peptides was similar in the aqueous solution and emulsions, as confirmed by synchrotron radiation circular dichroism spectroscopy. The emulsions containing the selected peptides had lower levels of hydroperoxides and volatile compounds during storage compared to the control (without peptide). This study contributes to elucidating the effect of antioxidant peptides in emulsions and demonstrates the ability of quantitative proteomics and bioinformatics prediction to identify peptides with strong antioxidant properties.

Emulsifier peptides derived from seaweed, methanotrophic bacteria, and potato proteins identified by quantitative proteomics and bioinformatics.

Global focus on sustainability has accelerated research into alternative non-animal sources of food protein and functional food ingredients. Amphiphilic peptides represent a class of promising biomolecules to replace chemical emulsifiers in food emulsions. In contrast to traditional trial-and-error enzymatic hydrolysis, this study utilizes a bottom-up approach combining quantitative proteomics, bioinformatics prediction, and functional validation to identify novel emulsifier peptides from seaweed, methanotrophic bacteria, and potatoes. In vitro functional validation reveal that all protein sources contained embedded novel emulsifier peptides comparable to or better than sodium caseinate (CAS). Thus, peptides efficiently reduced oil-water interfacial tension and generated physically stable emulsions with higher net zeta potential and smaller droplet sizes than CAS. In silico structure modelling provided further insight on peptide structure and the link to emulsifying potential. This study clearly demonstrates the potential and broad applicability of the bottom-up approach for identification of abundant and potent emulsifier peptides.

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.

Identification of emulsifier potato peptides by bioinformatics: application to omega-3 delivery emulsions and release from potato industry side streams.

In this work, we developed a novel approach combining bioinformatics, testing of functionality and bottom-up proteomics to obtain peptide emulsifiers from potato side-streams. This is a significant advancement in the process to obtain emulsifier peptides and it is applicable to any type of protein. Our results indicated that structure at the interface is the major determining factor of the emulsifying activity of peptide emulsifiers. Fish oil-in-water emulsions with high physical stability were stabilized with peptides to be predicted to have facial amphiphilicity: (i) peptides with predominantly α-helix conformation at the interface and having 18-29 amino acids, and (ii) peptides with predominantly ß-strand conformation at the interface and having 13-15 amino acids. In addition, high physically stable emulsions were obtained with peptides that were predicted to have axial hydrophobic/hydrophilic regions. Peptides containing the sequence FCLKVGV showed high in vitro antioxidant activity and led to emulsions with high oxidative stability. Peptide-level proteomics data and sequence analysis revealed the feasibility to obtain the potent emulsifier peptides found in this study (e.g. γ-1) by trypsin-based hydrolysis of different side streams in the potato industry.

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/ .