Reject option models comprising out-of-distribution detection

The optimal prediction strategy for out-of-distribution (OOD) setups is a fundamental question in machine learning. In this paper, we address this question and present several contributions. We propose three reject option models for OOD setups: the Cost-based model, the Bounded TPR-FPR model, and the Bounded Precision-Recall model. These models extend the standard reject option models used in non-OOD setups and define the notion of an optimal OOD selective classifier. We establish that all the proposed models, despite their different formulations, share a common class of optimal strategies. Motivated by the optimal strategy, we introduce double-score OOD methods that leverage uncertainty scores from two chosen OOD detectors: one focused on OOD/ID discrimination and the other on misclassification detection. The experimental results consistently demonstrate the superior performance of this simple strategy compared to state-of-the-art methods. Additionally, we propose novel evaluation metrics derived from the definition of the optimal strategy under the proposed OOD rejection models. These new metrics provide a comprehensive and reliable assessment of OOD methods without the deficiencies observed in existing evaluation approaches

Charting the Proteoform Landscape of Serum Proteins in Individual Donors by High-Resolution Native Mass Spectrometry

Most proteins in serum are glycosylated, with several annotated as biomarkers and thus diagnostically important and of interest for their role in disease. Most methods for analyzing serum glycoproteins employ either glycan release or glycopeptide centric mass spectrometry-based approaches, which provide excellent tools for analyzing known glycans but neglect previously undefined or unknown glycosylation and/or other co-occurring modifications. High-resolution native mass spectrometry is a relatively new technique for the analysis of intact glycoproteins, providing a "what you see is what you get"mass profile of a protein, allowing the qualitative and quantitative observation of all modifications present. So far, a disadvantage of this approach has been that it centers mostly on just one specific serum glycoprotein at the time. To address this issue, we introduce an ion-exchange chromatography-based fractionation method capable of isolating and analyzing, in parallel, over 20 serum (glyco)proteins, covering a mass range between 30 and 190 kDa, from 150 μL of serum. Although generating data in parallel for all these 20 proteins, we focus the discussion on the very complex proteoform profiles of four selected proteins, i.e., α-1-antitrypsin, ceruloplasmin, hemopexin, and complement protein C3. Our analyses provide an insight into the extensive proteoform landscape of serum proteins in individual donors, caused by the occurrence of various N- and O-glycans, protein cysteinylation, and co-occurring genetic variants. Moreover, native mass intact mass profiling also provided an edge over alternative approaches revealing the presence of apo- and holo-forms of ceruloplasmin and the endogenous proteolytic processing in plasma of among others complement protein C3. We also applied our approach to a small cohort of serum samples from healthy and diseased individuals. In these, we qualitatively and quantitatively monitored the changes in proteoform profiles of ceruloplasmin and revealed a substantial increase in fucosylation and glycan occupancy in patients with late-stage hepatocellular carcinoma and pancreatic cancer as compared to healthy donor samples

Identifying glycation hot-spots in bovine milk proteins during production and storage of skim milk powder

We investigated protein glycation in a complex milk system under controlled conditions representative of real-life consumer products, analysing intermediate and final products from skim milk powder production, and aged powder samples. We combined protein-centric LC-MS(/MS) with peptide-centric multi-protease LC-MS/MS focusing on the six most abundant bovine milk proteins. This strategy resulted in the identification of glycated proteoforms and of the extent of glycation per protein, high protein sequence coverage, and identification and relative occupancy of the glycation sites. We identified new glycation hot-spots additionally to the ones already described in literature. Primary sequence motif analysis revealed that glycation hot-spots were preceded N-terminally by a stretch rich in basic amino acids, and followed C-terminally by a stretch enriched in aliphatic and hydrophobic amino acids. Our study considerably extends the current understanding of milk protein glycation, discussing glycation hot-spots and their localisation in relation to the primary sequences and higher-order protein structures

Characterization of high-molecular weight by-products in the production of a trivalent bispecific 2+1 heterodimeric antibody

The development of increasingly complex antibody formats, such as bispecifics, can lead to the formation of increasingly complex high- and low-molecular-weight by-products. Here, we focus on the characterization of high molecular weight species (HMWs) representing the highest complexity of size variants. Standard methods used for product release, such as size exclusion chromatography (SEC), can separate HMW by-products from the main product, but cannot distinguish smaller changes in mass. Here, for the identification of the diverse and complex HMW variants of a trivalent bispecific CrossMAb antibody, offline fractionation, as well as production of HMW by-products combined with comprehensive analytical testing, was applied. Furthermore, HMW variants were analyzed regarding their chemical binding nature and tested in functional assays regarding changes in potency of the variants. Changes in potency were explained by detailed characterization using mass photometry, SDS-PAGE analysis, native mass spectrometry (MS) coupled to SEC and bottom-up proteomics. We identified a major portion of the HMW by-products to be non-covalently linked, leading to dissociation and changes in activity. We also identified and localized high heterogeneity of a by-product of concern and applied a CD3 affinity column coupled to native MS to annotate unexpected by-products. We present here a multi-method approach for the characterization of complex HMW by-products. A better understanding of these by-products is beneficial to guide analytical method development and proper specification setting for therapeutic bispecific antibodies to ensure constant efficacy and patient safety of the product through the assessment of by-products

Soluble MAC is primarily released from MAC-resistant bacteria that potently convert complement component C5

The membrane attack complex (MAC or C5b-9) is an important effector of the immune system to kill invading microbes. MAC formation is initiated when complement enzymes on the bacterial surface convert complement component C5 into C5b. Although the MAC is a membrane-inserted complex, soluble forms of MAC (sMAC), or terminal complement complex (TCC), are often detected in sera of patients suffering from infections. Consequently, sMAC has been proposed as a biomarker, but it remains unclear when and how it is formed during infections. Here, we studied mechanisms of MAC formation on different Gram-negative and Gram-positive bacteria and found that sMAC is primarily formed in human serum by bacteria resistant to MAC-dependent killing. Surprisingly, C5 was converted into C5b more potently by MAC-resistant compared to MAC-sensitive Escherichia coli strains. In addition, we found that MAC precursors are released from the surface of MAC-resistant bacteria during MAC assembly. Although release of MAC precursors from bacteria induced lysis of bystander human erythrocytes, serum regulators vitronectin (Vn) and clusterin (Clu) can prevent this. Combining size exclusion chromatography with mass spectrom-etry profiling, we show that sMAC released from bacteria in serum is a heterogeneous mixture of complexes composed of C5b-8, up to three copies of C9 and multiple copies of Vn and Clu. Alto-gether, our data provide molecular insight into how sMAC is generated during bacterial infections. This fundamental knowledge could form the basis for exploring the use of sMAC as biomarker