Targeting DCAF5 suppresses SMARCB1-mutant cancer by stabilizing SWI/SNF

Whereas oncogenes can potentially be inhibited with small molecules, the loss of tumour suppressors is more common and is problematic because the tumour-suppressor proteins are no longer present to be targeted. Notable examples include SMARCB1-mutant cancers, which are highly lethal malignancies driven by the inactivation of a subunit of SWI/SNF (also known as BAF) chromatin-remodelling complexes. Here, to generate mechanistic insights into the consequences of SMARCB1 mutation and to identify vulnerabilities, we contributed 14 SMARCB1-mutant cell lines to a near genome-wide CRISPR screen as part of the Cancer Dependency Map Project1-3. We report that the little-studied gene DDB1-CUL4-associated factor 5 (DCAF5) is required for the survival of SMARCB1-mutant cancers. We show that DCAF5 has a quality-control function for SWI/SNF complexes and promotes the degradation of incompletely assembled SWI/SNF complexes in the absence of SMARCB1. After depletion of DCAF5, SMARCB1-deficient SWI/SNF complexes reaccumulate, bind to target loci and restore SWI/SNF-mediated gene expression to levels that are sufficient to reverse the cancer state, including in vivo. Consequently, cancer results not from the loss of SMARCB1 function per se, but rather from DCAF5-mediated degradation of SWI/SNF complexes. These data indicate that therapeutic targeting of ubiquitin-mediated quality-control factors may effectively reverse the malignant state of some cancers driven by disruption of tumour suppressor complexes.

Continuous evolution of compact protein degradation tags regulated by selective molecular glues

Conditional protein degradation tags (degrons) are usually >100 amino acids long or are triggered by small molecules with substantial off-target effects, thwarting their use as specific modulators of endogenous protein levels. We developed a phage-assisted continuous evolution platform for molecular glue complexes (MG-PACE) and evolved a 36-amino acid zinc finger (ZF) degron (SD40) that binds the ubiquitin ligase substrate receptor cereblon in complex with PT-179, an orthogonal thalidomide derivative. Endogenous proteins tagged in-frame with SD40 using prime editing are degraded by otherwise inert PT-179. Cryo-electron microscopy structures of SD40 in complex with ligand-bound cereblon revealed mechanistic insights into the molecular basis of SD40's activity and specificity. Our efforts establish a system for continuous evolution of molecular glue complexes and provide ZF tags that overcome shortcomings associated with existing degrons.

The human E3 ligase RNF185 is a regulator of the SARS-CoV-2 envelope protein

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hijacks multiple human proteins during infection and viral replication. To examine whether any viral proteins employ human E3 ubiquitin ligases, we evaluated the stability of SARS-CoV-2 proteins with inhibition of the ubiquitin proteasome pathway. Using genetic screens to dissect the molecular machinery involved in the degradation of candidate viral proteins, we identified human E3 ligase RNF185 as a regulator of protein stability for the SARS-CoV-2 envelope protein. We found that RNF185 and the SARS-CoV-2 envelope co-localize to the endoplasmic reticulum (ER). Finally, we demonstrate that the depletion of RNF185 significantly increases SARS-CoV-2 viral titer in a cellular model. Modulation of this interaction could provide opportunities for novel antiviral therapies.

Structural basis of regulated m7G tRNA modification by METTL1-WDR4

Chemical modifications of RNA have key roles in many biological processes1-3. N7-methylguanosine (m7G) is required for integrity and stability of a large subset of tRNAs4-7. The methyltransferase 1-WD repeat-containing protein 4 (METTL1-WDR4) complex is the methyltransferase that modifies G46 in the variable loop of certain tRNAs, and its dysregulation drives tumorigenesis in numerous cancer types8-14. Mutations in WDR4 cause human developmental phenotypes including microcephaly15-17. How METTL1-WDR4 modifies tRNA substrates and is regulated remains elusive18. Here we show,  through structural, biochemical and cellular studies of human METTL1-WDR4, that WDR4 serves as a scaffold for METTL1 and the tRNA T-arm. Upon tRNA binding, the αC region of METTL1 transforms into a helix, which together with the α6 helix secures both ends of the tRNA variable loop. Unexpectedly, we find that the predicted disordered N-terminal region of METTL1 is part of the catalytic pocket and essential for methyltransferase activity. Furthermore, we reveal that S27 phosphorylation in the METTL1 N-terminal region inhibits methyltransferase activity by locally disrupting the catalytic centre. Our results provide a molecular understanding of tRNA substrate recognition and phosphorylation-mediated regulation of METTL1-WDR4, and reveal the presumed disordered N-terminal region of METTL1 as a nexus of methyltransferase activity.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed "degradability", is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision-recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.

Serological testing for SARS-CoV-2 antibodies of employees shows low transmission working in a cancer center

BACKGROUND

The COVID-19 pandemic led to emergency measures to continue patient care and research at a comprehensive cancer center while protecting both employees and patients. Determining exposure and infection rates with SARS-CoV-2 were important to adjust workplace policies over time.

METHODS

Dana-Farber Cancer Institute (DFCI) has over 7,000 employees. Participation was voluntary. After consent, participants completed questionnaire of demographics, exposures and risk factors for COVID-19 illness at each time point (baseline, 3, 6, and 12 months) along with blood draws for SARS-CoV-2 antibody testing. Primary measure was determination of titers of SARS-CoV-2 spike protein IgG over time.

RESULTS

In total, 745 employees enrolled from May 2020 to February 2021 (mean [SD] age, 40[14] years; 572[80%] women). From May to July 2020, 47 of 519 employees (9.2%, 95% confidence interval [CI] 6.7-12.0%) tested positive for SARS-CoV-2 spike protein IgG antibodies. Three months later, 40 of 428 employees had positive antibodies (8.5%, 95% CI 6.0-11.0%) with 17 newly positive. At month 6, 78.5% of participants reported having received at least one dose of vaccine and the positivity rate for those vaccinated was 98% (95% CI, 95-100%). Spike protein IgG titers for those vaccinated were 7.9 times higher than participants not vaccinated (median IgG titer = 0.28 for positive antibody but not vaccinated versus 2.2 for vaccinated) but demonstrate evidence of waning over time.

CONCLUSIONS

SARS-CoV-2 antibody positivity remained less than 10% at a single comprehensive cancer center prior to vaccination and there is evidence of waning IgG titers over time after vaccination.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI rounds 37-45

Critical Assessment of PRediction of Interactions (CAPRI) rounds 37 through 45 introduced larger complexes, new macromolecules, and multistage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pregenerated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in round 41. Although the results were broadly encouraging, they also highlighted the pressing need to invest in (a) flexible docking algorithms with the ability to model loop and linker motions and in (b) new sampling and scoring methods for oligosaccharide-protein interactions.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Efficient flexible backbone protein-protein docking for challenging targets

Motivation

Binding-induced conformational changes challenge current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased flexibility, these methods struggle with efficiency and high false positive rates.

Results

Here, we develop and benchmark RosettaDock 4.0, which efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of near-native candidate structures in the coarse-grained phase compared to RosettaDock 3.2. It adaptively samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20-80%. In local docking of a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate (defined as cases with ≥50% chance of achieving 3 near-native structures in the 5 top-ranked ones) for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, the method successfully docks the complex.

Availability and implementation

As a part of the Rosetta software suite, RosettaDock 4.0 is available at https://www.rosettacommons.org to all non-commercial users for free and to commercial users for a fee.

Supplementary information

Supplementary data are available at Bioinformatics online.

Flexible Backbone Assembly and Refinement of Symmetrical Homomeric Complexes

Symmetrical homomeric proteins are ubiquitous in every domain of life, and information about their structure is essential to decipher function. The size of these complexes often makes them intractable to high-resolution structure determination experiments. Computational docking algorithms offer a promising alternative for modeling large complexes with arbitrary symmetry. Accuracy of existing algorithms, however, is limited by backbone inaccuracies when using homology-modeled monomers. Here, we present Rosetta SymDock2 with a broad search of symmetrical conformational space using a six-dimensional coarse-grained score function followed by an all-atom flexible-backbone refinement, which we demonstrate to be essential for physically realistic modeling of tightly packed complexes. In global docking of a benchmark set of complexes of different point symmetries-starting from homology-modeled monomers-we successfully dock (defined as predicting three near-native structures in the five top-scoring models) 17 out of 31 cyclic complexes and 3 out of 12 dihedral complexes.

Targeting the CoREST complex with dual histone deacetylase and demethylase inhibitors

Here we report corin, a synthetic hybrid agent derived from the class I HDAC inhibitor (entinostat) and an LSD1 inhibitor (tranylcypromine analog). Enzymologic analysis reveals that corin potently targets the CoREST complex and shows more sustained inhibition of CoREST complex HDAC activity compared with entinostat. Cell-based experiments demonstrate that corin exhibits a superior anti-proliferative profile against several melanoma lines and cutaneous squamous cell carcinoma lines compared to its parent monofunctional inhibitors but is less toxic to melanocytes and keratinocytes. CoREST knockdown, gene expression, and ChIP studies suggest that corin's favorable pharmacologic effects may rely on an intact CoREST complex. Corin was also effective in slowing tumor growth in a melanoma mouse xenograft model. These studies highlight the promise of a new class of two-pronged hybrid agents that may show preferential targeting of particular epigenetic regulatory complexes and offer unique therapeutic opportunities.

Modeling oblong proteins and water-mediated interfaces with RosettaDock in CAPRI rounds 28-35

The 28th-35th rounds of the Critical Assessment of PRotein Interactions (CAPRI) served as a practical benchmark for our RosettaDock protein-protein docking protocols, highlighting strengths and weaknesses of the approach. We achieved acceptable or better quality models in three out of 11 targets. For the two α-repeat protein-green fluorescent protein (αrep-GFP) complexes, we used a novel ellipsoidal partial-global docking method (Ellipsoidal Dock) to generate models with 2.2 Å/1.5 Å interface RMSD, capturing 49%/42% of the native contacts, for the 7-/5-repeat αrep complexes. For the DNase-immunity protein complex, we used a new predictor of hydrogen-bonding networks, HBNet with Bridging Waters, to place individual water models at the complex interface; models were generated with 1.8 Å interface RMSD and 12% native water contacts recovered. The targets for which RosettaDock failed to create an acceptable model were typically difficult in general, as six had no acceptable models submitted by any CAPRI predictor. The UCH-L5-RPN13 and UCH-L5-INO80G de-ubiquitinating enzyme-inhibitor complexes comprised inhibitors undergoing significant structural changes upon binding, with the partners being highly interwoven in the docked complexes. Our failure to predict the nucleosome-enzyme complex in Target 95 was largely due to tight constraints we placed on our model based on sparse biochemical data suggesting two specific cross-interface interactions, preventing the correct structure from being sampled. While RosettaDock's three successes show that it is a state-of-the-art docking method, the difficulties with highly flexible and multi-domain complexes highlight the need for better flexible docking and domain-assembly methods. Proteins 2017; 85:479-486. © 2016 Wiley Periodicals, Inc.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.