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/).

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.

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.

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.

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.

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.