Substrate recognition principles for the PP2A-B55 protein phosphatase.

The PP2A-B55 phosphatase regulates a plethora of signaling pathways throughout eukaryotes. How PP2A-B55 selects its substrates presents a severe knowledge gap. By integrating AlphaFold modelling with comprehensive high resolution mutational scanning, we show that α-helices in substrates bind B55 through an evolutionary conserved mechanism. Despite a large diversity in sequence and composition, these α-helices share key amino acid determinants that engage discrete hydrophobic and electrostatic patches. Using deep learning protein design, we generate a specific and potent competitive peptide inhibitor of PP2A-B55 substrate interactions. With this inhibitor, we uncover that PP2A-B55 regulates the nuclear exosome targeting complex by binding to an α-helical recruitment module in RBM7. Collectively, our findings provide a framework for the understanding and interrogation of PP2A-B55 in health and disease.

Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield.

PURPOSE: Widespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching. METHODS: Rare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim. RESULTS: 33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3. CONCLUSION: Semi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.

Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation.

Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.

Structural study of UFL1-UFC1 interaction uncovers the role of UFL1 N-terminal helix in ufmylation.

Ufmylation plays a crucial role in various cellular processes including DNA damage response, protein translation, and ER homeostasis. To date, little is known about how the enzymes responsible for ufmylation coordinate their action. Here, we study the details of UFL1 (E3) activity, its binding to UFC1 (E2), and its relation to UBA5 (E1), using a combination of structural modeling, X-ray crystallography, NMR, and biochemical assays. Guided by Alphafold2 models, we generate an active UFL1 fusion construct that includes its partner DDRGK1 and solve the crystal structure of this critical interaction. This fusion construct also unveiled the importance of the UFL1 N-terminal helix for binding to UFC1. The binding site suggested by our UFL1-UFC1 model reveals a conserved interface, and competition between UFL1 and UBA5 for binding to UFC1. This competition changes in the favor of UFL1 following UFM1 charging of UFC1. Altogether, our study reveals a novel, terminal helix-mediated regulatory mechanism, which coordinates the cascade of E1-E2-E3-mediated transfer of UFM1 to its substrate and provides new leads to target this modification.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Who Binds Better? Let Alphafold2 Decide!

Deep learning is revolutionizing structural biology to an unprecedented extent. Spearheaded by DeepMind's Alphafold2, structural models of high quality can be generated, and are now available for most known proteins and many protein interactions. The next challenge will be to leverage this rich structural corpus to learn about binding: which protein can contact which partner(s), and at what affinity? In a recent study, Chang and Perez have presented an elegant approach towards this challenging goal for interactions that involve a short peptide binding to its receptor. The basic idea is straightforward: given a receptor that binds to two peptides, if the receptor sequence is presented with both peptides together at the same time, AlphaFold2 should model the tighter binding peptide into the binding site, while excluding the second. A simple idea that works!

Colon tumour cell death causes mTOR dependence by paracrine P2X4 stimulation.

Solid cancers exhibit a dynamic balance between cell death and proliferation ensuring continuous tumour maintenance and growth1,2. Increasing evidence links enhanced cancer cell apoptosis to paracrine activation of cells in the tumour microenvironment initiating tissue repair programs that support tumour growth3,4, yet the direct effects of dying cancer cells on neighbouring tumour epithelia and how this paracrine effect potentially contributes to therapy resistance are unclear. Here we demonstrate that chemotherapy-induced tumour cell death in patient-derived colorectal tumour organoids causes ATP release triggering P2X4 (also known as P2RX4) to mediate an mTOR-dependent pro-survival program in neighbouring cancer cells, which renders surviving tumour epithelia sensitive to mTOR inhibition. The induced mTOR addiction in persisting epithelial cells is due to elevated production of reactive oxygen species and subsequent increased DNA damage in response to the death of neighbouring cells. Accordingly, inhibition of the P2X4 receptor or direct mTOR blockade prevents induction of S6 phosphorylation and synergizes with chemotherapy to cause massive cell death induced by reactive oxygen species and marked tumour regression that is not seen when individually applied. Conversely, scavenging of reactive oxygen species prevents cancer cells from becoming reliant on mTOR activation. Collectively, our findings show that dying cancer cells establish a new dependency on anti-apoptotic programs in their surviving neighbours, thereby creating an opportunity for combination therapy in P2X4-expressing epithelial tumours.

Matching protein surface structural patches for high-resolution blind peptide docking.

Peptide docking can be perceived as a subproblem of protein­protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein­peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide­protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide­protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.

Structure-based prediction of HDAC6 substrates validated by enzymatic assay reveals determinants of promiscuity and detects new potential substrates.

Histone deacetylases play important biological roles well beyond the deacetylation of histone tails. In particular, HDAC6 is involved in multiple cellular processes such as apoptosis, cytoskeleton reorganization, and protein folding, affecting substrates such as ɑ-tubulin, Hsp90 and cortactin proteins. We have applied a biochemical enzymatic assay to measure the activity of HDAC6 on a set of candidate unlabeled peptides. These served for the calibration of a structure-based substrate prediction protocol, Rosetta FlexPepBind, previously used for the successful substrate prediction of HDAC8 and other enzymes. A proteome-wide screen of reported acetylation sites using our calibrated protocol together with the enzymatic assay provide new peptide substrates and avenues to novel potential functional regulatory roles of this promiscuous, multi-faceted enzyme. In particular, we propose novel regulatory roles of HDAC6 in tumorigenesis and cancer cell survival via the regulation of EGFR/Akt pathway activation. The calibration process and comparison of the results between HDAC6 and HDAC8 highlight structural differences that explain the established promiscuity of HDAC6.

Harnessing protein folding neural networks for peptide-protein docking.

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

The BAF chromatin remodeling complexes: structure, function, and synthetic lethalities.

BAF complexes are multi-subunit chromatin remodelers, which have a fundamental role in genomic regulation. Large-scale sequencing efforts have revealed frequent BAF complex mutations in many human diseases, particularly in cancer and neurological disorders. These findings not only underscore the importance of the BAF chromatin remodelers in cellular physiological processes, but urge a more detailed understanding of their structure and molecular action to enable the development of targeted therapeutic approaches for diseases with BAF complex alterations. Here, we review recent progress in understanding the composition, assembly, structure, and function of BAF complexes, and the consequences of their disease-associated mutations. Furthermore, we highlight intra-complex subunit dependencies and synthetic lethal interactions, which have emerged as promising treatment modalities for BAF-related diseases.

A method for characterizing Cas9 variants via a one-million target sequence library of self-targeting sgRNAs.

Detailed target-selectivity information and experiment-based efficacy prediction tools are primarily available for Streptococcus pyogenes Cas9 (SpCas9). One obstacle to develop such tools is the rarity of accurate data. Here, we report a method termed 'Self-targeting sgRNA Library Screen' (SLS) for assaying the activity of Cas9 nucleases in bacteria using random target/sgRNA libraries of self-targeting sgRNAs. Exploiting more than a million different sequences, we demonstrate the use of the method with the SpCas9-HF1 variant to analyse its activity and reveal motifs that influence its target-selectivity. We have also developed an algorithm for predicting the activity of SpCas9-HF1 with an accuracy matching those of existing tools. SLS is a facile alternative to the much more expensive and laborious approaches used currently and has the capability of delivering sufficient amount of data for most of the orthologs and variants of SpCas9.

[Irony comprehension in first degree relatives of patients with bipolar disorder - a pilot fMRI study].

No abstract avalilable.

AKT-dependent NOTCH3 activation drives tumor progression in a model of mesenchymal colorectal cancer.

Recently, a transcriptome-based consensus molecular subtype (CMS) classification of colorectal cancer (CRC) has been established, which may ultimately help to individualize CRC therapy. However, the lack of animal models that faithfully recapitulate the different molecular subtypes impedes adequate preclinical testing of stratified therapeutic concepts. Here, we demonstrate that constitutive AKT activation in intestinal epithelial cells markedly enhances tumor invasion and metastasis in Trp53ΔIEC mice (Trp53ΔIECAktE17K) upon challenge with the carcinogen azoxymethane. Gene-expression profiling indicates that Trp53ΔIECAktE17K tumors resemble the human mesenchymal colorectal cancer subtype (CMS4), which is characterized by the poorest survival rate among the four CMSs. Trp53ΔIECAktE17K tumor cells are characterized by Notch3 up-regulation, and treatment of Trp53ΔIECAktE17K mice with a NOTCH3-inhibiting antibody reduces invasion and metastasis. In CRC patients, NOTCH3 expression correlates positively with tumor grading and the presence of lymph node as well as distant metastases and is specifically up-regulated in CMS4 tumors. Therefore, we suggest NOTCH3 as a putative target for advanced CMS4 CRC patients.

Partial proteolysis improves the identification of the extracellular segments of transmembrane proteins by surface biotinylation.

Transmembrane proteins (TMP) play a crucial role in several physiological processes. Despite their importance and diversity, only a few TMP structures have been determined by high-resolution protein structure characterization methods so far. Due to the low number of determined TMP structures, the parallel development of various bioinformatics and experimental methods was necessary for their topological characterization. The combination of these methods is a powerful approach in the determination of TMP topology as in the Constrained Consensus TOPology prediction. To support the prediction, we previously developed a high-throughput topology characterization method based on primary amino group-labelling that is still limited in identifying all TMPs and their extracellular segments on the surface of a particular cell type. In order to generate more topology information, a new step, a partial proteolysis of the cell surface has been introduced to our method. This step results in new primary amino groups in the proteins that can be biotinylated with a membrane-impermeable agent while the cells still remain intact. Pre-digestion also promotes the emergence of modified peptides that are more suitable for MS/MS analysis. The modified sites can be utilized as extracellular constraints in topology predictions and may contribute to the refined topology of these proteins.

Green tea infusion alleviates neurodegeneration induced by mutant Huntingtin in Drosophila.

Objectives: Green tea infusion contains a complex mixture of polyphenolic compounds that were shown to provide health benefits. It was previously demonstrated that (-)-epigallocatechin-3-gallate, one of the major polyphenols present in green tea, has a suppressing effect on various aspects of pathogenesis in models of Huntington's disease (HD), an inherited neurodegenerative disorder. In this study, we aimed to investigate, whether green tea infusion prepared as for human consumption has similar positive effects.Methods: We used a transgenic Drosophila model of HD to study the effects of green tea on mutant Huntingtin induced phenotypes. We tested the effects of green tea infusion on mutant Huntingtin induced neurodegeneration, impaired motor performance, reduced viability and lifespan by pseudopupil assay, climbing assay, eclosion and survival tests, respectively. We used immunoblots to measure Huntingtin protein levels and tested generic health benefits of green tea by longevity analysis.Results: We found that green tea supplementation reduced mutant Huntingtin induced neurodegeneration in Drosophila and positively impacted the longevity of mutant Huntingtin expressing flies. However, green tea did not rescue reduced viability of Drosophila expressing mutant Huntingtin or increased longevity of wild-type fruit flies.Discussion: Our results indicate that green tea consumption might have a modest positive effect on symptoms of HD.

Hat1 acetylates histone H4 and modulates the transcriptional program in Drosophila embryogenesis.

Post-translational modifications of histone proteins play a pivotal role in DNA packaging and regulation of genome functions. Histone acetyltransferase 1 (Hat1) proteins are conserved enzymes that modify histones by acetylating lysine residues. Hat1 is implicated in chromatin assembly and DNA repair but its role in cell functions is not clearly elucidated. We report the generation and characterization of a Hat1 loss-of-function mutant in Drosophila. Hat1 mutants are viable and fertile with a mild sub-lethal phenotype showing that Hat1 is not essential in fruit flies. Lack of Hat1 results in the near complete loss of histone H4 lysine (K) 5 and K12 acetylation in embryos, indicating that Hat1 is the main acetyltransferase specific for these marks in this developmental stage. We found that Hat1 function and the presence of these acetyl marks are not required for the nuclear transport of histone H4 as histone variant His4r retained its nuclear localization both in Hat1 mutants and in His4r-K5R-K12R double point mutants. RNA-seq analysis of embryos indicate that in Hat1 mutants over 2000 genes are dysregulated and the observed transcriptional changes imply a delay in the developmental program of gene expression.

The TMCrys server for supporting crystallization of transmembrane proteins.

MOTIVATION: Due to their special properties, the structures of transmembrane proteins are extremely hard to determine. Several methods exist to predict the propensity of successful completion of the structure determination process. However, available predictors incorporate data of any kind of proteins, hence they can hardly differentiate between crystallizable and non-crystallizable membrane proteins. RESULTS: We implemented a web server to simplify running TMCrys prediction method that was developed specifically to separate crystallizable and non-crystallizable membrane proteins. AVAILABILITY AND IMPLEMENTATION: http://tmcrys.enzim.ttk.mta.hu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TMCrys: predict propensity of success for transmembrane protein crystallization.

Motivation: Transmembrane proteins (TMPs) are crucial in the life of the cells. As they have special properties, their structure is hard to determine--the PDB database consists of 2% TMPs, despite the fact that they are predicted to make up to 25% of the human proteome. Crystallization prediction methods were developed to aid the target selection for structure determination, however, there is a need for a TMP specific service. Results: Here, we present TMCrys, a crystallization prediction method that surpasses existing prediction methods in performance thanks to its specialization for TMPs. We expect TMCrys to improve target selection of TMPs. Availability and implementation: https://github.com/brgenzim/tmcrys. Supplementary information: Supplementary data are available at Bioinformatics online.

Cell plasticity in epithelial homeostasis and tumorigenesis.

The adult organism is characterized by remarkable plasticity, which enables efficient regeneration and restoration of homeostasis after damage. When aberrantly activated, this plasticity contributes to tumour initiation and progression. Here we review recent advances in this field with a focus on cell fate changes and the epithelial-mesenchymal transition-two distinct, yet closely related, forms of plasticity with fundamental roles in homeostasis and cancer.