Proteomic analysis identifies novel binding partners of BAP1

BRCA1-associated protein 1 (BAP1) is a tumor suppressor and its loss can result in mesothelioma, uveal and cutaneous melanoma, clear cell renal cell carcinoma and bladder cancer. BAP1 is a deubiquitinating enzyme of the UCH class that has been implicated in various cellular processes like cell growth, cell cycle progression, ferroptosis, DNA damage response and ER metabolic stress response. ASXL proteins activate BAP1 by forming the polycomb repressive deubiquitinase (PR-DUB) complex which acts on H2AK119ub1. Besides the ASXL proteins, BAP1 is known to interact with an established set of additional proteins. Here, we identify novel BAP1 interacting proteins in the cytoplasm by expressing GFP-tagged BAP1 in an endogenous BAP1 deficient cell line using affinity purification followed by mass spectrometry (AP-MS) analysis. Among these novel interacting proteins are Histone acetyltransferase 1 (HAT1) and all subunits of the heptameric coat protein complex I (COPI) that is involved in vesicle formation and protein cargo binding and sorting. We validate that the HAT1 and COPI interactions occur at endogenous levels but find that this interaction with COPI is not mediated through the C-terminal KxKxx cargo sorting signals of the COPI complex.

Robustness and innovation along the endocytic route: Lessons from darkness

What mechanisms ensure the loading of a SNARE into a nascent carrier? In this issue, Bowman et al. (2021. J. Cell Biol.https://doi.org/10.1083/jcb.202005173) describe an unprecedented mechanism where two sorting complexes, AP-3 and BLOC-1, the latter bound to syntaxin 13, work as a fail-safe to recognize sorting signals in VAMP7, a membrane protein required for fusion to melanosomes. Their observations define one of the first examples of distributed robustness in membrane traffic mechanisms.

Proteomic analysis identifies novel binding partners of BAP1

BRCA1-associated protein 1 (BAP1) is a tumor suppressor and its loss can result in mesothelioma, uveal and cutaneous melanoma, clear cell renal cell carcinoma and bladder cancer. BAP1 is a deubiquitinating enzyme of the UCH class that has been implicated in various cellular processes like cell growth, cell cycle progression, ferroptosis and ER metabolic stress response. ASXL proteins activate BAP1 by forming the polycomb repressive deubiquitinase (PR-DUB) complex which acts on H2AK119ub1. Besides the ASXL proteins, BAP1 is known to interact with an established set of additional proteins.Here, we identify novel BAP1 interacting proteins in the cytoplasm by expressing GFP-tagged BAP1 in an endogenous BAP1 deficient cell line using affinity purification followed by mass spectrometry (AP-MS) analysis. Among these novel interacting proteins are Histone acetyltransferase 1 (HAT1) and all subunits of the heptameric coat protein complex I (COPI) that is involved in vesicle formation and protein cargo binding and sorting. We validate that the HAT1 and COPI interactions occur at endogenous levels but find that this interaction with COPI is not mediated through the C-terminal KxKxx cargo sorting signals of the COPI complex.

Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.

GP4: an integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting

Subcellular localization is a critical aspect of protein function and the potential application of proteins either as drugs or drug targets, or in industrial and domestic applications. However, the experimental determination of protein localization is time consuming and expensive. Therefore, various localization predictors have been developed for particular groups of species. Intriguingly, despite their major representation amongst biotechnological cell factories and pathogens, a meta-predictor based on sorting signals and specific for Gram-positive bacteria was still lacking. Here we present GP4, a protein subcellular localization meta-predictor mainly for Firmicutes, but also Actinobacteria, based on the combination of multiple tools, each specific for different sorting signals and compartments. Novelty elements include improved cell-wall protein prediction, including differentiation of the type of interaction, prediction of non-canonical secretion pathway target proteins, separate prediction of lipoproteins and better user experience in terms of parsability and interpretability of the results. GP4 aims at mimicking protein sorting as it would happen in a bacterial cell. As GP4 is not homology based, it has a broad applicability and does not depend on annotated databases with homologous proteins. Non-canonical usage may include little studied or novel species, synthetic and engineered organisms, and even re-use of the prediction data to develop custom prediction algorithms. Our benchmark analysis highlights the improved performance of GP4 compared to other widely used subcellular protein localization predictors. A webserver running GP4 is available at http://gp4.hpc.rug.nl/

Opposite Regulation of F508del-CFTR Biogenesis by Four Poly-Lysine Ubiquitin Chains

Background: As a misfolding protein, more than 99% of F508del-CFTR (F508del) is degraded by the ubiquitin-proteasome system before its maturation, which results in almost no membrane expression of CFTR and thereby no chloride secretion across epithelial cells of cystic fibrosis patients. The conjugation of ubiquitin chains to the protein substrates is necessary for ubiquitin-mediated proteasomal degradation. Ubiquitin contains seven lysine (K) residues (K6, K11, K27, K29, K33, K48, and K63), all of them can be conjugated one to another forming poly-ubiquitin chains on substrates by mixing together or by only one type of lysine that provides sorting signals for different pathways. Results: Here we report that four lysine linked poly-Ub chains (LLPUCs) were involved in F508del biogenesis: two LLPUCs linked by K11 or K48 of Ub facilitated F508del degradation; whereas the other two linked by K63 and K33 of Ub protected F508del from degradation. LLPUC K11 is more potent for F508del degradation than K48. Moreover, E3 ligase CHIP and RNF5 catalyzed LLPUCs K48 formation and RNF5 also catalyzed LLPUC K11 formation on F508del. Importantly, Those LLPUCs provide F508del with different affinities to the proteasomal shuttle protein (Rad23a) and the proteasomal receptors (Adrm1 and S5a), offering a mechanistic insight of differential regulation F508del different by different LLPUCs. Conclusions: F508del utilizes four specific lysine-linked poly Ub chains and during its biogenesis for opposite destiny through different identification by proteasomal shuttle protein or receptors. These findings provide new insights to understand the CF pathogenesis and are expected to facilitate the development of therapies for this devastating disease.

Invariant chain with an AP3 interacting sorting signal is sorted to late endosomal compartments and may improve MHC class I loading and presentation

ABSTRACTInvariant chain (Ii) is traditionally known as the dedicated MHCII chaperone. Recent reports have broadened our understanding about various tasks that Ii plays including its physiological role in MHCI cross-presentation. Ii bound MHCI via the MHCII scaffolding CLIP peptide may facilitate MHCI trafficking to the endosomal pathway. The sorting function of Ii depends on two leucine-based sorting signals present in the cytoplasmic tail that acts as binding sites for the adaptor proteins AP-1/AP-2. Here we increased the Ii cross-presentation potency by replacing these with an AP3 motif resulting an efficient transport of Ii from TGN to late endosomes. We also replaced the CLIP region of li with a therapeutically relevant peptide, MART-1. We found the Ii AP3mutant-MART1 construct was capable of loading MHCI and stimulate specific T-cell response more efficiently than the wild type counterpart. The results show that Ii with an AP3 binding sorting motif carrying peptide epitope(s) can promote efficient antigen presentation to cytotoxic T cells (CTLs) independent of the ER located classical MHCI peptide loading machinery.

Detecting sequence signals in targeting peptides using deep learning

In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state-of-the-art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria, and chloroplasts or other plastids. By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, that is, the one following the initial methionine, has a strong influence on the classification. We observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position 2, compared with 20% in other plant proteins. We also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide. The importance of this feature for predictions has not been highlighted before.

Detecting Novel Sequence Signals in Targeting Peptides Using Deep Learning

In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state of art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria and chloroplasts or other plastids.By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, i.e. the one following the initial methionine, has a strong influence on the classification. When subsequently examining all targeting peptides, we observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position two, but only 20% of other plant proteins. Further highlighting the importance of the second residue, we also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide.TargetP 2.0 is available at http://www.cbs.dtu.dk/services/TargetP-2.0/index.php