The selectivity filter is involved in the U-type inactivation process of Kv2.1 and Kv3.1 channels

Voltage-gated potassium (Kv) channels display several types of inactivation processes, including N-, C-, and U-types. C-type inactivation is attributed to a nonconductive conformation of the selectivity filter (SF). It has been proposed that the activation gate and the channel's SF are allosterically coupled because the conformational changes of the former affect the structure of the latter and vice versa. The second threonine of the SF signature sequence (e.g., TTVGYG) has been proven to be essential for this allosteric coupling. To further study the role of the SF in U-type inactivation, we substituted the second threonine of the TTVGYG sequence by an alanine in the hKv2.1 and hKv3.1 channels, which are known to display U-type inactivation. Both hKv2.1-T377A and hKv3.1-T400A yielded channels that were resistant to inactivation, and as a result, they displayed noninactivating currents upon channel opening; i.e., hKv2.1-T377A and hKv3.1-T400A remained fully conductive upon prolonged moderate depolarizations, whereas in wild-type hKv2.1 and hKv3.1, the current amplitude typically reduces because of U-type inactivation. Interestingly, increasing the extracellular K+ concentration increased the macroscopic current amplitude of both hKv2.1-T377A and hKv3.1-T400A, which is similar to the response of the homologous T to A mutation in Shaker and hKv1.5 channels that display C-type inactivation. Our data support an important role for the second threonine of the SF signature sequence in the U-type inactivation gating of hKv2.1 and hKv3.1. SIGNIFICANCE Voltage-dependent K+ (Kv) channels generate cells' repolarizing power, which is consequently regulated by the channel's conductance. Aside from the opening or closure, Kv channels undergo inactivation that drives them into a lower or nonconductive state. Among the different inactivation processes described in Kv channels, the U-type process develops from a preopen but activated state. The molecular determinants of this process are, in contrast to the Ctype mechanism, not well characterized. Our data show that the intracellular part of the K+ selectivity filter within the pore domain is involved. An alanine for threonine substitution results in channels that do not inactivate upon opening, suggesting that an allosteric coupling between the activation gate and selectivity filter exists in U-type inactivation

Covalent Cysteine Targeting of Bruton's Tyrosine Kinase (BTK) Family by Withaferin-A Reduces Survival of Glucocorticoid-Resistant Multiple Myeloma MM1 Cells

Simple Summary Glucocorticoid therapy resistance in B-cell malignancies is often associated with constitutive activation of tyrosine kinases. Novel anticancer drugs targeting hyperactivated tyrosine kinases, such as Bruton's tyrosine kinase (BTK), have, therefore, gained much interest over the past few decades and have already been approved for clinical use. In this study, we compared the therapeutic efficacy of the phytochemical kinase inhibitor withaferin A with the clinically approved BTK inhibitor ibrutinib to target hyperactivated tyrosine kinase signaling in glucocorticoid-resistant multiple myeloma cells. Our results demonstrate that withaferin A-induced cell death of glucocorticoid-resistant MM1R cells involves covalent cysteine targeting of multiple Hinge-6 domain type tyrosine kinases of the kinase cysteinome classification, including BTK. Multiple myeloma (MM) is a hematological malignancy characterized by plasma cells' uncontrolled growth. The major barrier in treating MM is the occurrence of primary and acquired therapy resistance to anticancer drugs. Often, this therapy resistance is associated with constitutive hyperactivation of tyrosine kinase signaling. Novel covalent kinase inhibitors, such as the clinically approved BTK inhibitor ibrutinib (IBR) and the preclinical phytochemical withaferin A (WA), have, therefore, gained pharmaceutical interest. Remarkably, WA is more effective than IBR in killing BTK-overexpressing glucocorticoid (GC)-resistant MM1R cells. To further characterize the kinase inhibitor profiles of WA and IBR in GC-resistant MM cells, we applied phosphopeptidome- and transcriptome-specific tyrosine kinome profiling. In contrast to IBR, WA was found to reverse BTK overexpression in GC-resistant MM1R cells. Furthermore, WA-induced cell death involves covalent cysteine targeting of Hinge-6 domain type tyrosine kinases of the kinase cysteinome classification, including inhibition of the hyperactivated BTK. Covalent interaction between WA and BTK could further be confirmed by biotin-based affinity purification and confocal microscopy. Similarly, molecular modeling suggests WA preferably targets conserved cysteines in the Hinge-6 region of the kinase cysteinome classification, favoring inhibition of multiple B-cell receptors (BCR) family kinases. Altogether, we show that WA's promiscuous inhibition of multiple BTK family tyrosine kinases represents a highly effective strategy to overcome GC-therapy resistance in MM

Detection of enriched T cell epitope specificity in full T cell receptor sequence repertoires

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be

Data for "Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification" (ImRex)

Repository containing the different experiments described in the manuscript titled: "Current challenges for epitope-agnostic TCR interaction prediction and a new perspective derived from image classification". Publication DOI: TBA Originally appeared as a preprint on bioRxiv: https://doi.org/10.1101/2019.12.18.880146. Contains: Trained model files (.h5) Associated train and validation datasets for each model. Learning curves and evaluation metrics. Log files with training and data arguments (full training scripts are available in GitHub repository). Comparisons between different models. Complete raw and processed datasets (also available in the associated GitHub repository). Please refer to the associated GitHub repository (https://github.com/pmoris/ImRex) for more information on the directory structure and contents, as well as the scripts that generated these output files. Contents: data.zip: Contains raw and preprocessed datasets. READMEs in subdirectory describe the data sources and preprocessing steps. Please refer to the associated GitHub repository for the specific scripts that generated these files. Note that the full training and test sets (i.e. containing both positive and negative examples) are stored separately for each model/CV iteration in the models archives. models-main.zip: contains the trained models and evaluation metrics for the main different experiments described in the bash and pbs scripts in ./src/scripts/hpc_scripts. Log files for the experiments outlined here can be found in ./src/scripts/hpc_scripts. models-full.zip: contains models that were trained on the complete VDJdb dataset without cross-validation, filtered on human TRB data, no 10x data and restricted to 10-20 (CDR3) or 8-11 (epitope) amino acid residues, with negatives that were generated by shuffling (i.e. sampling an negative epitope for each positive CDR3 sequence). One set of models uses downsampling to reduce the most abundant epitopes down to 400 pairs each, the other one does not use any downsampling. These models were also used for evaluating on the external Adaptive dataset, as outlined in ./src/scripts/evaluate/evaluate_adaptive.sh, and the TRA subset of sequences (./src/scripts/evaluate/evaluate_tra.sh). models-decoyfit.zip: contains models that were trained on true data, but evaluated on data where epitopes were replaced by decoys. models-padded-epitoperatio.zip: contains a quick test of trained models (padded/interaction map) that use a different type of negative shuffling, see docstrings in ./src/processing/negative_sampler.py for more info. models-repeat-local.zip: contains a number of repeated runs from models-main, used to estimate variability in model performance for multiple identical runs. comparisons.zip: contains comparison directories, each consisting of two or more model output directories, that contrast the performance metrics of the models. These outputs were generated by using the ./src/scripts/evaluate/visualize.py script, or by using the oneliners in ./src/scripts/evaluate/visualise.sh, which can operate on the entire comparisons directory at once. Note that any file paths described here are in reference to the associated GitHub repository (https://github.com/pmoris/ImRex). Overview of different experiments: Two main architectures were compared: the interaction map (or padded) CNN and a dual input CNN based on NetTCR (nettcr). Two different cross-validation strategies were used: a 5x repeated 5-fold CV (repeated5fold) and an epitope-grouped CV (epitope_grouped). The different dataset subsets are labelled as follows. Check the Makefile's preprocess-vdjdb-aug-2019 command (and the underlying script ./src/scripts/preprocessing/preprocess_vdjdb.py) for a more thorough overview of the different filtering options. mhci: only MHCI class presented epitopes. trb: only TRB CDR3 sequences. tra: only TRA CDR3 sequences. tratrb: both types of CDR3 sequences. down: moderate downsampling of most abundant epitopes to 1000 pairs. down400: strong downsampling of most abundant epitopes to 400 pairs. decoy: decoy epitope data. reg001: regularization factor 0.01 (only for padded/interaction type models, fixed value) Two different methods of generating negative TCR-epitope pairs were used: shuffling of positive pairs, i.e. sampling a single epitope from the positive pairs for each CDR3 sequence (shuffle), and sampling CDR3s from a reference repertoire (negref). The batch size is labelled as b32 = a batch size of 32. The learning rate was always 0.0001 (lre4) or 0.001 (lre3)

Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification

The prediction of epitope recognition by T-cell receptors (TCRs) has seen many advancements in recent years, with several methods now available that can predict recognition for a specific set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of the interacting sequences and the limited amount of currently available training data. In this work, we provide an overview of the current state of this unsolved problem. First, we examine appropriate validation strategies to accurately assess the generalization performance of generic TCR-epitope recognition models when applied to both seen and unseen epitopes. In addition, we present a novel feature representation approach, which we call ImRex (interaction map recognition). This approach is based on the pairwise combination of physicochemical properties of the individual amino acids in the CDR3 and epitope sequences, which provides a convolutional neural network with the combined representation of both sequences. Lastly, we highlight various challenges that are specific to TCR-epitope data and that can adversely affect model performance. These include the issue of selecting negative data, the imbalanced epitope distribution of curated TCR-epitope datasets and the potential exchangeability of TCR alpha and beta chains. Our results indicate that while extrapolation to unseen epitopes remains a difficult challenge, ImRex makes this feasible for a subset of epitopes that are not too dissimilar from the training data. We show that appropriate feature engineering methods and rigorous benchmark standards are required to create and validate TCR-epitope predictive models