A simeprevir-inducible molecular switch for the control of cell and gene therapies.

Chemical inducer of dimerization (CID) modules can be used effectively as molecular switches to control biological processes, and thus there is significant interest within the synthetic biology community in identifying novel CID systems. To date, CID modules have been used primarily in engineering cells for in vitro applications. To broaden their utility to the clinical setting, including the potential to control cell and gene therapies, the identification of novel CID modules should consider factors such as the safety and pharmacokinetic profile of the small molecule inducer, and the orthogonality and immunogenicity of the protein components. Here we describe a CID module based on the orally available, approved, small molecule simeprevir and its target, the NS3/4A protease from hepatitis C virus. We demonstrate the utility of this CID module as a molecular switch to control biological processes such as gene expression and apoptosis in vitro, and show that the CID system can be used to rapidly induce apoptosis in tumor cells in a xenograft mouse model, leading to complete tumor regression.

High-Throughput Screening and Proteomic Characterization of Compounds Targeting Myeloid-Derived Suppressor Cells.

Myeloid-derived suppressor cells (MDSC) are a heterogeneous cell population of incompletely differentiated immune cells. They are known to suppress T cell activity and are implicated in multiple chronic diseases, which make them an attractive cell population for drug discovery. Here, we characterized the baseline proteomes and phospho-proteomes of mouse MDSC differentiated from a progenitor cell line to a depth of 7000 proteins and phosphorylation sites. We also validated the cellular system for drug discovery by recapitulating and identifying known and novel molecular responses to the well-studied MDSC drugs entinostat and mocetinostat. We established a high-throughput drug screening platform using a MDSC/T cell coculture system and assessed the effects of ∼21,000 small molecule compounds on T cell proliferation and IFN-γ secretion to identify novel MDSC modulator. The most promising candidates were validated in a human MDSC system, and subsequent proteomic experiments showed significant upregulation of several proteins associated with the reduction of reactive oxygen species (ROS). Proteome-wide solvent-induced protein stability assays identified Acyp1 and Cd74 as potential targets, and the ROS-reducing drug phenotype was validated by measuring ROS levels in cells in response to compound, suggesting a potential mode of action. We anticipate that the data and chemical tools developed in this study will be valuable for further research on MDSC and related drug discovery.

Discovery of Cycloalkyl[c]thiophenes as Novel Scaffolds for Hypoxia-Inducible Factor-2α Inhibitors.

Hypoxia-inducible factors (HIFs) are heterodimeric transcription factors induced in diverse pathophysiological settings. Inhibition of HIF-2α has become a strategy for cancer treatment since the discovery that small molecules, upon binding into a small cavity of the HIF-2α PAS B domain, can alter its conformation and disturb the activity of the HIF dimer complex. Herein, the design, synthesis, and systematic SAR exploration of cycloalkyl[c]thiophenes as novel HIF-2α inhibitors are described, providing the first chemotype featuring an alkoxy-aryl scaffold. X-ray data confirmed the ability of these inhibitors to induce perturbation of key amino acids by appropriately presenting key pharmacophoric elements in the hydrophobic cavity. Selected compounds showed inhibition of VEGF-A secretion in cancer cells and prevention of Arg1 expression and activity in IL4-stimulated macrophages. Moreover, in vivo target gene modulation was demonstrated with compound 35r. Thus, the disclosed HIF-2α inhibitors represent valuable tools for investigating selective HIF-2α inhibition and its effect on tumor biology.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1].

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.

Collaborative Assessment of Molecular Geometries and Energies from the Open Force Field.

Force fields form the basis for classical molecular simulations, and their accuracy is crucial for the quality of, for instance, protein-ligand binding simulations in drug discovery. The huge diversity of small-molecule chemistry makes it a challenge to build and parameterize a suitable force field. The Open Force Field Initiative is a combined industry and academic consortium developing a state-of-the-art small-molecule force field. In this report, industry members of the consortium worked together to objectively evaluate the performance of the force fields (referred to here as OpenFF) produced by the initiative on a combined public and proprietary dataset of 19,653 relevant molecules selected from their internal research and compound collections. This evaluation was important because it was completely blind; at most partners, none of the molecules or data were used in force field development or testing prior to this work. We compare the Open Force Field "Sage" version 2.0.0 and "Parsley" version 1.3.0 with GAFF-2.11-AM1BCC, OPLS4, and SMIRNOFF99Frosst. We analyzed force-field-optimized geometries and conformer energies compared to reference quantum mechanical data. We show that OPLS4 performs best, and the latest Open Force Field release shows a clear improvement compared to its predecessors. The performance of established force fields such as GAFF-2.11 was generally worse. While OpenFF researchers were involved in building the benchmarking infrastructure used in this work, benchmarking was done entirely in-house within industrial organizations and the resulting assessment is reported here. This work assesses the force field performance using separate benchmarking steps, external datasets, and involving external research groups. This effort may also be unique in terms of the number of different industrial partners involved, with 10 different companies participating in the benchmark efforts.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects.

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.

The ubiquitin-conjugating enzyme UBE2K determines neurogenic potential through histone H3 in human embryonic stem cells.

Histones modulate gene expression by chromatin compaction, regulating numerous processes such as differentiation. However, the mechanisms underlying histone degradation remain elusive. Human embryonic stem cells (hESCs) have a unique chromatin architecture characterized by low levels of trimethylated histone H3 at lysine 9 (H3K9me3), a heterochromatin-associated modification. Here we assess the link between the intrinsic epigenetic landscape and ubiquitin-proteasome system of hESCs. We find that hESCs exhibit high expression of the ubiquitin-conjugating enzyme UBE2K. Loss of UBE2K upregulates the trimethyltransferase SETDB1, resulting in H3K9 trimethylation and repression of neurogenic genes during differentiation. Besides H3K9 trimethylation, UBE2K binds histone H3 to induce its polyubiquitination and degradation by the proteasome. Notably, ubc-20, the worm orthologue of UBE2K, also regulates histone H3 levels and H3K9 trimethylation in Caenorhabditis elegans germ cells. Thus, our results indicate that UBE2K crosses evolutionary boundaries to promote histone H3 degradation and reduce H3K9me3 repressive marks in immortal cells.

Less than five is less than ideal: replacing the "less than 5 cell size" rule with a risk-based data disclosure protocol in a public health setting.

SETTING: The Winnipeg Regional Health Authority (WRHA) is one of the largest and most diverse health regions in Canada. Within the WRHA, the Population and Public Health (PPH) Surveillance Team provides epidemiological support across a variety of public health service areas. INTERVENTION: We developed and deployed a risk-based data disclosure protocol that balances the need to share public health surveillance data with the need to protect personal health information. OUTCOMES: Unlike the conventional data disclosure standard adopted in Manitoba (suppress cell sizes < 5), the new protocol is based upon a risk-based re-identification approach that focuses on the size of the denominator instead of the numerator. This approach has allowed for innovation in data dissemination infrastructure within the unit that would not have been possible previously, including the deployment of public-facing cloud-based interactive maps and dashboards. It has also resulted in strengthened protection of personal health information as the risk of re-identification can now be precisely calculated across all data release situations. IMPLICATIONS: In challenging the "cell size less than five" rule, this project is an example of how a scientifically based data disclosure protocol can support a public health organization in meaningful sharing of population health data with community partners and the public. This helps ensure that program and policy responses are empirically based, strategically focused, and cross-jurisdictionally coordinated.

Endosomal escape enhancing compounds facilitate functional delivery of extracellular vesicle cargo.

Aim: Extracellular vesicles (EVs) are desirable delivery vehicles for therapeutic cargoes. We aimed to load EVs with Cre recombinase protein and determine whether functional delivery to cells could be improved by using endosomal escape enhancing compounds. Materials & methods: Overexpressed CreFRB protein was actively loaded into EVs by rapalog-induced dimerization to CD81FKBP, or passively loaded by overexpression in the absence of rapalog. Functional delivery of CreFRB was analysed using a HEK293 Cre reporter cell line in the absence and presence of endosomal escape enhancing compounds. Results: The EVs loaded with CreFRB by both active and passive mechanisms were able to deliver functional CreFRB to recipient cells only in the presence of endosomal escape enhancing compounds chloroquine and UNC10217938A. Conclusion: The use of endosomal escape enhancing compounds in conjunction with EVs loaded with therapeutic cargoes may improve efficacy of future EV based therapeutics.

Free energy calculations elucidate substrate binding, gating mechanism, and tolerance-promoting mutations in herbicide target 4-hydroxyphenylpyruvate dioxygenase.

4-Hydroxyphenylpyruvate dioxygenase (HPPD) catalyzes the second reaction in the tyrosine catabolism and is linked to the production of cofactors plastoquinone and tocopherol in plants. This important biological role has put HPPD in the focus of current herbicide design efforts including the development of herbicide-tolerant mutants. However, the molecular mechanisms of substrate binding and herbicide tolerance have yet to be elucidated. In this work, we performed molecular dynamics simulations and free energy calculations to characterize active site gating by the C-terminal helix H11 in HPPD. We compared gating equilibria in Arabidopsis thaliana (At) and Zea mays (Zm) wild-type proteins retrieving the experimentally observed preferred orientations from the simulations. We investigated the influence of substrate and product binding on the open-closed transition and discovered a ligand-mediated conformational switch in H11 that mediates rapid substrate access followed by active site closing and efficient product release through H11 opening. We further studied H11 gating in At mutant HPPD, and found large differences with correlation to experimentally measured herbicide tolerance. The computational findings were then used to design a new At mutant HPPD protein that showed increased tolerance to six commercially available HPPD inhibitors in biochemical in vitro experiments. Our results underline the importance of protein flexibility and conformational transitions in substrate recognition and enzyme inhibition by herbicides.

Exosomal delivery of doxorubicin enables rapid cell entry and enhanced in vitro potency.

Doxorubicin is a chemotherapeutic agent that is commonly used to treat a broad range of cancers. However, significant cardiotoxicity, associated with prolonged exposure to doxorubicin, limits its continued therapeutic use. One strategy to prevent the uptake of doxorubicin into cardiac cells is the encapsulation of the drug to prevent non-specific uptake and also to improve the drugs' pharmacokinetic properties. Although encapsulated forms of doxorubicin limit the cardiotoxicity observed, they are not without their own liabilities as an increased amount of drug is deposited in the skin where liposomal doxorubicin can cause palmar-plantar erythrodysesthesia. Exosomes are small endogenous extracellular vesicles, that transfer bioactive material from one cell to another, and are considered attractive drug delivery vehicles due to their natural origin. In this study, we generated doxorubicin-loaded exosomes and demonstrate their rapid cellular uptake and re-distribution of doxorubicin from endosomes to the cytoplasm and nucleus resulting in enhanced potency in a number of cultured and primary cell lines when compared to free doxorubicin and liposomal formulations of doxorubicin. In contrast to other delivery methods for doxorubicin, exosomes do not accumulate in the heart, thereby providing potential for limiting the cardiac side effects and improved therapeutic index.

High affinity single-chain variable fragments are specific and versatile targeting motifs for extracellular vesicles.

Exosomes are extracellular vesicles that mediate cell-to-cell communication by transferring biological cargo, such as DNA, RNA and proteins. Through genetic engineering of exosome-producing cells or manipulation of purified exosomes, it is possible to load exosomes with therapeutic molecules and target them to specific cells via the display of targeting moieties on their surface. This provides an opportunity to exploit a naturally-occurring biological process for therapeutic purposes. In this study, we explored the potential of single chain variable fragments (scFv) as targeting domains to achieve delivery of exosomes to cells expressing a cognate antigen. We generated exosomes targeting the Her2 receptor and, by varying the affinity of the scFvs and the Her2 expression level on recipient cells, we determined that both a high-affinity anti-Her2-scFv (KD≤ 1 nM) and cells expressing a high level (≥106 copies per cell) of Her2 were optimally required to enable selective uptake. We also demonstrate that targeting exosomes to cells via a specific cell surface receptor can alter their intracellular trafficking route, providing opportunities to influence the efficiency of delivery and fate of intracellular cargo. These experiments provide solid data to support the wider application of exosomes displaying antibody fragments as vehicles for the targeted delivery of therapeutic molecules.

Structural Architecture of the Nucleosome Remodeler ISWI Determined from Cross-Linking, Mass Spectrometry, SAXS, and Modeling.

Chromatin remodeling factors assume critical roles by regulating access to nucleosomal DNA. To determine the architecture of the Drosophila ISWI remodeling enzyme, we developed an integrative structural approach that combines protein cross-linking, mass spectrometry, small-angle X-ray scattering, and computational modeling. The resulting structural model shows the ATPase module in a resting state with both ATPase lobes twisted against each other, providing support for a conformation that was recently trapped by crystallography. The autoinhibiting NegC region does not protrude from the ATPase module as suggested previously. The regulatory NTR domain is located near both ATPase lobes. The full-length enzyme is flexible and can adopt a compact structure in solution with the C-terminal HSS domain packing against the ATPase module. Our data imply a series of conformational changes upon activation of the enzyme and illustrate how the NTR, NegC, and HSS domains contribute to regulation of the ATPase module.

Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP.

Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

A post-transcriptional program coordinated by CSDE1 prevents intrinsic neural differentiation of human embryonic stem cells.

While the transcriptional network of human embryonic stem cells (hESCs) has been extensively studied, relatively little is known about how post-transcriptional modulations determine hESC function. RNA-binding proteins play central roles in RNA regulation, including translation and turnover. Here we show that the RNA-binding protein CSDE1 (cold shock domain containing E1) is highly expressed in hESCs to maintain their undifferentiated state and prevent default neural fate. Notably, loss of CSDE1 accelerates neural differentiation and potentiates neurogenesis. Conversely, ectopic expression of CSDE1 impairs neural differentiation. We find that CSDE1 post-transcriptionally modulates core components of multiple regulatory nodes of hESC identity, neuroectoderm commitment and neurogenesis. Among these key pro-neural/neuronal factors, CSDE1 binds fatty acid binding protein 7 (FABP7) and vimentin (VIM) mRNAs, as well as transcripts involved in neuron projection development regulating their stability and translation. Thus, our results uncover CSDE1 as a central post-transcriptional regulator of hESC identity and neurogenesis.

The pepATTRACT web server for blind, large-scale peptide-protein docking.

Peptide-protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. pepATTRACT is a novel docking protocol that is fully blind, i.e. it does not require any information about the binding site. In various stages of its development, pepATTRACT has participated in CAPRI, making successful predictions for five out of seven protein-peptide targets. Its performance is similar or better than state-of-the-art local docking protocols that do require binding site information. Here we present a novel web server that carries out the rigid-body stage of pepATTRACT. On the peptiDB benchmark, the web server generates a correct model in the top 50 in 34% of the cases. Compared to the full pepATTRACT protocol, this leads to some loss of performance, but the computation time is reduced from ∼18 h to ∼10 min. Combined with the fact that it is fully blind, this makes the web server well-suited for large-scale in silico protein-peptide docking experiments. The rigid-body pepATTRACT server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/pepATTRACT.

Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions.

Peptide-protein interactions are abundant in the cell and form an important part of the interactome. Large-scale modeling of peptide-protein complexes requires a fully blind approach; i.e., simultaneously predicting the peptide-binding site and the peptide conformation to high accuracy. Here, we present one of the first fully blind peptide-protein docking protocols, pepATTRACT. It combines a coarse-grained ensemble docking search of the entire protein surface with two stages of atomistic flexible refinement. pepATTRACT yields high-quality predictions for 70 % of the cases when tested on a large benchmark of peptide-protein complexes. This performance in fully blind mode is similar to state-of-the-art local docking approaches that use information on the location of the binding site. Limiting the search to the peptide-binding region, the resulting pepATTRACT-local approach further improves the performance. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html . Here, we explain how to set up a docking run with the pepATTRACT web interface and demonstrate its usage by an application on binding of disordered regions from tumor suppressor p53 to a partner protein.

Concerted regulation of ISWI by an autoinhibitory domain and the H4 N-terminal tail.

ISWI-family nucleosome remodeling enzymes need the histone H4 N-terminal tail to mobilize nucleosomes. Here we mapped the H4-tail binding pocket of ISWI. Surprisingly the binding site was adjacent to but not overlapping with the docking site of an auto-regulatory motif, AutoN, in the N-terminal region (NTR) of ISWI, indicating that AutoN does not act as a simple pseudosubstrate as suggested previously. Rather, AutoN cooperated with a hitherto uncharacterized motif, termed AcidicN, to confer H4-tail sensitivity and discriminate between DNA and nucleosomes. A third motif in the NTR, ppHSA, was functionally required in vivo and provided structural stability by clamping the NTR to Lobe 2 of the ATPase domain. This configuration is reminiscent of Chd1 even though Chd1 contains an unrelated NTR. Our results shed light on the intricate structural and functional regulation of ISWI by the NTR and uncover surprising parallels with Chd1.

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.

Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scoring step is one of the bottlenecks in the performance of many state-of-the-art protocols. The performance of scoring functions depends on the quality of the generated structures and its coupling to the sampling algorithm. A tool kit, GRADSCOPT (GRid Accelerated Directly SCoring OPTimizing), was designed to allow rapid development and optimization of different knowledge-based scoring potentials for specific objectives in protein-protein docking. Different atomistic and coarse-grained potentials can be created by a grid-accelerated directly scoring dependent Monte-Carlo annealing or by a linear regression optimization. We demonstrate that the scoring functions generated by our approach are similar to or even outperform state-of-the-art scoring functions for predicting near-native solutions. Of additional importance, we find that potentials specifically trained to identify the native bound complex perform rather poorly on identifying acceptable or medium quality (near-native) solutions. In contrast, atomistic long-range contact potentials can increase the average fraction of near-native poses by up to a factor 2.5 in the best scored 1% decoys (compared to existing scoring), emphasizing the need of specific docking potentials for different steps in the docking protocol.