Recognition of carbohydrate by major histocompatibility complex class I-restricted, glycopeptide-specific cytotoxic T lymphocytes

6 pages, 5 figures.-- PMID: 8046349 [PubMed].-- PMCID: PMC2191607.Cytotoxic T cells (CTL) recognize short peptide epitopes presented by class I glycoproteins encoded by the major histocompatibility complex (MHC). It is not yet known whether peptides containing posttranslationally modified amino acids can also be recognized by CTL. To address this issue, we have studied the immunogenicity and recognition of a glycopeptide carrying an O-linked N-acetylglucosamine (GlcNAc) monosaccharide-substituted serine residue. This posttranslational modification is catalyzed by a recently described cytosolic glycosyltransferase. We show that glycosylation does not affect peptide binding to MHC class I and that glycopeptides can elicit a strong CTL response that is glycopeptide specific. Furthermore, glycopeptide recognition by cytotoxic T cells is dependent on the chemical structure of the glycan as well as its position within the peptide.We wish to thank Dr. Elena Sadovnikova and Dr. Hans J. Stanss (Imperial Cancer Research Foundation, London, UK) for their valuable help with raising antipeptide CTLs; and Professor Jens Chr. Jensenius (University of Aarbus, Denmark) for helpful discussions. J. S. Haurum is a Carlsberg-Wellcome Travelling Research Fellow, G. Asequell is an EC Fellow, and A. C. Lellouch is supported by a United States Public Health Service National Research Service Award F32 GM- 15811. This work was supported by the Carlsberg Foundation, the Wellcome trust, the Beckett Foundation, and Statens Sundhedsvidenskabelige Forskningsr~d, Denmark.Peer reviewe

Zero-shot Clustering of Embeddings with Self-Supervised Learnt Encoders

We explore whether self-supervised pretrained models can provide a useful representation space for datasets they were not trained on, and whether these representations can be used to group novel unlabelled data into meaningful clusters. To this end, we conduct experiments using image representation encoders pretrained on ImageNet using a variety of self-supervised training techniques. These encoders are deployed on image datasets that were not seen during training, without fine-tuning, and we investigate whether their embeddings can be clustered with conventional clustering algorithms. We find that it is possible to create well-defined clusters using self-supervised feature encoders, especially when using the Agglomerative Clustering method, and that it is possible to do so even for very fine-grained datasets such as NABirds. We also find indications that the Silhouette score is a good proxy of cluster quality when no ground-truth is available

A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset

In an effort to catalog insect biodiversity, we propose a new large dataset of hand-labelled insect images, the BIOSCAN-Insect Dataset. Each record is taxonomically classified by an expert, and also has associated genetic information including raw nucleotide barcode sequences and assigned barcode index numbers, which are genetically-based proxies for species classification. This paper presents a curated million-image dataset, primarily to train computer-vision models capable of providing image-based taxonomic assessment, however, the dataset also presents compelling characteristics, the study of which would be of interest to the broader machine learning community. Driven by the biological nature inherent to the dataset, a characteristic long-tailed class-imbalance distribution is exhibited. Furthermore, taxonomic labelling is a hierarchical classification scheme, presenting a highly fine-grained classification problem at lower levels. Beyond spurring interest in biodiversity research within the machine learning community, progress on creating an image-based taxonomic classifier will also further the ultimate goal of all BIOSCAN research: to lay the foundation for a comprehensive survey of global biodiversity. This paper introduces the dataset and explores the classification task through the implementation and analysis of a baseline classifier

The INNs and outs of antibody nonproprietary names

An important step in drug development is the assignment of an International Nonproprietary Name (INN) by the World Health Organization (WHO) that provides healthcare professionals with a unique and universally available designated name to identify each pharmaceutical substance. Monoclonal antibody INNs comprise a –mab suffix preceded by a substem indicating the antibody type, e.g., chimeric (-xi-), humanized (-zu-), or human (-u-). The WHO publishes INN definitions that specify how new monoclonal antibody therapeutics are categorized and adapts the definitions to new technologies. However, rapid progress in antibody technologies has blurred the boundaries between existing antibody categories and created a burgeoning array of new antibody formats. Thus, revising the INN system for antibodies is akin to aiming for a rapidly moving target. The WHO recently revised INN definitions for antibodies now to be based on amino acid sequence identity. These new definitions, however, are critically flawed as they are ambiguous and go against decades of scientific literature. A key concern is the imposition of an arbitrary threshold for identity against human germline antibody variable region sequences. This leads to inconsistent classification of somatically mutated human antibodies, humanized antibodies as well as antibodies derived from semi-synthetic/synthetic libraries and transgenic animals. Such sequence-based classification implies clear functional distinction between categories (e.g., immunogenicity). However, there is no scientific evidence to support this. Dialog between the WHO INN Expert Group and key stakeholders is needed to develop a new INN system for antibodies and to avoid confusion and miscommunication between researchers and clinicians prescribing antibodies

SP-A binds alpha(1)-antitrypsin in vitro and reduces the association rate constant for neutrophil elastase

BACKGROUND: α1-antitrypsin and surfactant protein-A (SP-A) are major lung defense proteins. With the hypothesis that SP-A could bind α1-antitrypsin, we designed a series of in vitro experiments aimed at investigating the nature and consequences of such an interaction. METHODS AND RESULTS: At an α1-antitrypsin:SP-A molar ratio of 1:1, the interaction resulted in a calcium-dependent decrease of 84.6% in the association rate constant of α1-antitrypsin for neutrophil elastase. The findings were similar when SP-A was coupled with the Z variant of α1-antitrypsin. The carbohydrate recognition domain of SP-A appeared to be a major determinant of the interaction, by recognizing α1-antitrypsin carbohydrate chains. However, binding of SP-A carbohydrate chains to the α1-antitrypsin amino acid backbone and interaction between carbohydrates of both proteins are also possible. Gel filtration chromatography and turnover per inactivation experiments indicated that one part of SP-A binds several molar parts of α1-antitrypsin. CONCLUSION: We conclude that the binding of SP-A to α1-antitrypsin results in a decrease of the inhibition of neutrophil elastase. This interaction could have potential implications in the physiologic regulation of α1-antitrypsin activity, in the pathogenesis of pulmonary emphysema, and in the defense against infectious agents

Zero-shot Clustering of Embeddings with Pretrained and Self-Supervised Learnt Encoders

We explore whether large pretrained models can provide a useful representation space for datasets they were not trained on, and whether these representations can be used to group novel unlabelled data into meaningful clusters. To this end, we conduct experiments using image encoders pretrained on ImageNet using either supervised or self-supervised training techniques. These encoders are deployed on image datasets that were not seen during training, and we investigate whether their embeddings can be clustered with conventional clustering algorithms. We find that it is possible to create well-defined clusters using self-supervised feature encoders, especially when using the Agglomerative Clustering method, and that it is possible to do so even for very fine-grained datasets such as NABirds. We also find indications that the Silhouette score is a good proxy of cluster quality for self-supervised feature encoders when no ground-truth is available

BarcodeBERT: Transformers for Biodiversity Analysis

Understanding biodiversity is a global challenge, in which DNA barcodes—shortsnippets of DNA that cluster by species—play a pivotal role. In particular, invertebrates, a highly diverse and under-explored group, pose unique taxonomic complexities. We explore machine learning approaches, comparing supervised CNNs, fine-tuned foundation models, and a DNA barcode-specific masking strategy across datasets of varying complexity. While simpler datasets and tasks favor supervised CNNs or fine-tuned transformers, challenging species-level identification demands a paradigm shift towards self-supervised pretraining. We propose BarcodeBERT, the first self-supervised method for general biodiversity analysis, leveraging a 1.5 M invertebrate DNA barcode reference library. This work highlights how dataset specifics and coverage impact model selection, and underscores the role of self-supervised pretraining in achieving high-accuracy DNA barcode-based identification at the species and genus level. Indeed, without the fine-tuning step, BarcodeBERT pretrained on a large DNA barcode dataset outperforms DNABERT and DNABERT-2 on multiple downstream classification tasks. The code repository is available at https://github.com/Kari-Genomics-Lab/BarcodeBER