Structural Characterization of Monoclonal Antibodies and Epitope Mapping by FFAP Footprinting.

Covalent labeling in combination with mass spectrometry is a powerful approach used in structural biology to study protein structures, interactions, and dynamics. Recently, the toolbox of covalent labeling techniques has been expanded with fast fluoroalkylation of proteins (FFAP). FFAP is a novel radical labeling method that utilizes fluoroalkyl radicals generated from hypervalent Togni reagents for targeting aromatic residues. This report further demonstrates the benefits of FFAP as a new method for structural characterization of therapeutic antibodies and interaction interfaces of antigen-antibody complexes. The results obtained from human trastuzumab and its complex with human epidermal growth factor receptor 2 (HER2) correlate well with previously published structural data and demonstrate the potential of FFAP in structural biology.

A user DNS fingerprint dataset.

Using a user DNS fingerprint allows one to identify a specific network user regardless of the knowledge of his IP address. This method is proper, for example, when examining the behavior of a monitored network user in more depth. In contrast to other studies, this work introduces a dataset for possible user identification based only on the knowledge of its DNS fingerprint created from the previously sent DNS queries. We created a large dataset from the real network traffic of a metropolitan Internet service provider. The dataset was created from 2.3 billion DNS queries representing 6.2 million different domain names. The data collection took place over three months from 12/2023 to 02/2024. The dataset contains a detailed user activity description in the sense of overall daily activity statistics and detailed 24 h activity statistics. Each dataset record contains a list of 1137 classification attributes. The absolutely unique feature of this data set is the classification of user activity based on categories of content accessed by a user. The new dataset can be used for the creation of machine learning models, allowing the identification of a specific user without direct knowledge of their IP addresses or additional network location information. The dataset can also serve as a reference dataset for the creation of DNS fingerprints of users.

Dual-Probe Activity-Based Protein Profiling Reveals Site-Specific Differences in Protein Binding of EGFR-Directed Drugs.

Comparative, dose-dependent analysis of interactions between small molecule drugs and their targets, as well as off-target interactions, in complex proteomes is crucial for selecting optimal drug candidates. The affinity of small molecules for targeted proteins is largely dictated by interactions between amino acid side chains and these drugs. Thus, studying drug-protein interactions at an amino acid resolution provides a comprehensive understanding of the drug selectivity and efficacy. In this study, we further refined the site-specific activity-based protein profiling strategy (ABPP), PhosID-ABPP, on a timsTOF HT mass spectrometer. This refinement enables dual dose-dependent competition of inhibitors within a single cellular proteome. Here, a comparative analysis of two activity-based probes (ABPs), developed to selectively target the epidermal growth factor receptor (EGFR), namely, PF-06672131 (PF131) and PF-6422899 (PF899), facilitated the simultaneous identification of ABP-specific binding sites at a proteome-wide scale within a cellular proteome. Dose-dependent probe-binding preferences for proteinaceous cysteines, even at low nanomolar ABP concentrations, could be revealed. Notably, in addition to the intrinsic affinity of the electrophilic probes for specific sites in targeted proteins, the observed labeling intensity is influenced by several other factors. These include the efficiency of cellular uptake, the stability of the probes, and their intracellular distribution. While both ABPs showed comparable labeling efficiency for EGFR, PF131 had a broader off-target reactivity profile. In contrast, PF899 exhibited a higher labeling efficiency for the ERBB2 receptor and bound to catalytic cysteines in several other enzymes, which is likely to disrupt their catalytic activity. Notably, PF131 effectively labeled ADP/ATP translocase proteins at a concentration of just 1 nm, and we found this affected ATP transport. Analysis of the effect of PF131 and its parent inhibitor Afatinib on murine translocase SLC25A4 (ANT1)-mediated ATP transport strongly indicated that PF131 (10 µM) partially blocked ATP transport. Afatinib was less efficient at inhibiting ATP transport by SLC25A4 than PF131, and the reduction of ATP transport by Afatinib was not significant. Follow-up analysis is required to evaluate the affinity of these inhibitors for ADP/ATP translocase SLC25A4 in more detail. Additionally, the analysis of different binding sites within the EGF receptor and the voltage-dependent anion channel 2 revealed secondary binding sites of both probes and provided insights into the binding poses of inhibitors on these proteins. Insights from the PhosID-ABPP analysis of these two ABPs serve as a valuable resource for understanding drug on- and off-target engagement in a dose- and site-specific manner.

Data center TCP dataset.

In this paper, we would like to introduce a unique dataset that covers thousands of network flow measurements realized through TCP in a data center environment. The TCP protocol is widely used for reliable data transfers and has many different versions. The various versions of TCP are specific in how they deal with link congestion through the congestion control algorithm (CCA). Our dataset represents a unique, comprehensive comparison of the 17 currently used versions of TCP with different CCAs. Each TCP flow was measured precisely 50 times to eliminate the measurement instability. The comparison of the various TCP versions is based on the knowledge of 18 quantitative attributes representing the parameters of a TCP transmission. Our dataset is suitable for testing and comparing different versions of TCP, creating new CCAs based on machine learning models, or creating and testing machine learning models, allowing the identification and optimization of the currently existing versions of TCP.

Protein-Centric Analysis of Personalized Antibody Repertoires Using LC-MS-Based Fab-Profiling on a timsTOF.

Endogenous antibodies, or immunoglobulins (Igs), abundantly present in body fluids, represent some of the most challenging samples to analyze, largely due to the immense variability in their sequences and concentrations. It has been estimated that our body can produce billions of different Ig proteins with different isotypes, making their individual analysis seemingly impossible. However, recent advances in protein-centric proteomics using LC-MS coupled to Orbitrap mass analyzers to profile intact Fab fragments formed by selective cleavage at the IgG-hinge revealed that IgG repertoires may be less diverse, albeit unique for each donor. Serum repertoires seem to be dominated by a few hundred clones that cumulatively make up 50-95% of the total IgG content. Enabling such analyses required careful optimization of the chromatography and mass analysis, as all Fab analytes are highly alike in mass (46-51 kDa) and sequence. To extend the opportunities of this mass-spectrometry-based profiling of antibody repertoires, we here report the optimization and evaluation of an alternative MS platform, namely, the timsTOF, for antibody repertoire profiling. The timsTOF mass analyzer has gained traction in recent years for peptide-centric proteomics and found wide applicability in plasma proteomics, affinity proteomics, and HLA peptidomics, to name a few. However, for protein-centric analysis, this platform has been less explored. Here, we demonstrate that the timsTOF platform can be adapted to perform protein-centric LC-MS-based profiling of antibody repertoires. In a side-by-side comparison of the timsTOF and the Orbitrap we demonstrate that the extracted serum antibody repertoires are alike qualitatively and quantitatively, whereby in particular the sensitivity of the timsTOF platform excels. Future incorporation of advanced top-down capabilities on the timsTOF may make this platform a very valuable alternative for protein-centric proteomics and top-down proteomics and thus also for personalized antibody repertoire profiling.