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.

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 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.

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.

Protein-protein and peptide-protein docking and refinement using ATTRACT in CAPRI.

The ATTRACT coarse-grained docking approach in combination with various types of atomistic, flexible refinement methods has been applied to predict protein-protein and peptide-protein complexes in CAPRI rounds 28-36. For a large fraction of CAPRI targets (12 out of 18), at least one model of acceptable or better quality was generated, corresponding to a success rate of 67%. In particular, for several peptide-protein complexes excellent predictions were achieved. In several cases, a combination of template-based modeling and extensive molecular dynamics-based refinement yielded medium and even high quality solutions. In one particularly challenging case, the structure of an ubiquitylation enzyme bound to the nucleosome was correctly predicted as a set of acceptable quality solutions. Based on the experience with the CAPRI targets, new interface refinement approaches and methods for ab-initio peptide-protein docking have been developed. Failures and possible improvements of the docking method with respect to scoring and protein flexibility will also be discussed. Proteins 2017; 85:391-398. © 2016 Wiley Periodicals, Inc.

Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling.

Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.

A web interface for easy flexible protein-protein docking with ATTRACT.

Protein-protein docking programs can give valuable insights into the structure of protein complexes in the absence of an experimental complex structure. Web interfaces can facilitate the use of docking programs by structural biologists. Here, we present an easy web interface for protein-protein docking with the ATTRACT program. While aimed at nonexpert users, the web interface still covers a considerable range of docking applications. The web interface supports systematic rigid-body protein docking with the ATTRACT coarse-grained force field, as well as various kinds of protein flexibility. The execution of a docking protocol takes up to a few hours on a standard desktop computer.

iATTRACT: simultaneous global and local interface optimization for protein-protein docking refinement.

Protein-protein interactions are abundant in the cell but to date structural data for a large number of complexes is lacking. Computational docking methods can complement experiments by providing structural models of complexes based on structures of the individual partners. A major caveat for docking success is accounting for protein flexibility. Especially, interface residues undergo significant conformational changes upon binding. This limits the performance of docking methods that keep partner structures rigid or allow limited flexibility. A new docking refinement approach, iATTRACT, has been developed which combines simultaneous full interface flexibility and rigid body optimizations during docking energy minimization. It employs an atomistic molecular mechanics force field for intermolecular interface interactions and a structure-based force field for intramolecular contributions. The approach was systematically evaluated on a large protein-protein docking benchmark, starting from an enriched decoy set of rigidly docked protein-protein complexes deviating by up to 15 Å from the native structure at the interface. Large improvements in sampling and slight but significant improvements in scoring/discrimination of near native docking solutions were observed. Complexes with initial deviations at the interface of up to 5.5 Å were refined to significantly better agreement with the native structure. Improvements in the fraction of native contacts were especially favorable, yielding increases of up to 70%.

Structure based computational assessment of channel properties of assembled ORF-8a from SARS-CoV.

ORF 8a is a short 39 amino acid bitopic membrane protein encoded by severe acute respiratory syndrome causing corona virus (SARS-CoV). It has been identified to increase permeability of the lipid membrane for cations. Permeability is suggested to occur due to the assembly of helical bundles. Computational models of a pentameric assembly of 8a peptides are generated using the first 22 amino acids, which include the transmembrane domain. Low energy structures reveal a hydrophilic pore mantled by residues Thr-8, and -18, Ser-11, Cys-13, and Arg-22. Potential of mean force (PMF) profiles for mono (Na(+) , K(+) , Cl(-) ) and divalent (Ca(2+) ) ions along the pore are calculated. The data support experimental findings of a weak cation selectivity of the channel. Calculations on 8a are compared to data derived for a pentameric bundle consisting of the M2 helices of the bacterial pentameric ligand gated ion channel GLIC (3EHZ). PMF curves of both, bundles 8a and M2, show sigmoidal shaped profiles. In comparison to the data for the M2-GLIC model, data of the 8a bundle show lower amplitude of the PMF values between maximum and minimum and less discrimination amongst ions.

Assembly and architecture of biogenesis of lysosome-related organelles complex-1 (BLOC-1).

BLOC-1 (biogenesis of lysosome-related organelles complex-1) is critical for melanosome biogenesis and has also been implicated in neurological function and disease. We show that BLOC-1 is an elongated complex that contains one copy each of the eight subunits pallidin, Cappuccino, dysbindin, Snapin, Muted, BLOS1, BLOS2, and BLOS3. The complex appears as a linear chain of eight globular domains, ∼300 Å long and ∼30 Å in diameter. The individual domains are flexibly connected such that the linear chain undergoes bending by as much as 45°. Two stable subcomplexes were defined, pallidin-Cappuccino-BLOS1 and dysbindin-Snapin-BLOS2. Both subcomplexes are 1:1:1 heterotrimers that form extended structures as indicated by their hydrodynamic properties. The two subcomplexes appear to constitute flexible units within the larger BLOC-1 chain, an arrangement conducive to simultaneous interactions with multiple BLOC-1 partners in the course of tubular endosome biogenesis and sorting.

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.

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.

The GAP domain and the SNARE, coatomer and cargo interaction region of the ArfGAP2/3 Glo3 are sufficient for Glo3 function.

The ArfGAP Glo3 is required for coat protein I vesicle generation in the Golgi-endoplasmic reticulum (ER) shuttle. The best-understood role of Glo3 is the stimulation of the GTPase activity of Arf1. In this study, we characterized functional domains of the ArfGAP Glo3 and identified an interaction interface for coatomer, SNAREs and cargo in the central region of Glo3 (BoCCS region). The GAP domain together with the BoCCS region is necessary and sufficient for all vital Glo3 functions. Expression of a truncated Glo3 lacking the GAP domain results in a dominant negative growth phenotype in glo3Delta cells at 37 degrees C. This phenotype was alleviated by mutating either the BoCCS region or the Glo3 regulatory motif (GRM), or by overexpression of ER-Golgi SNAREs or the ArfGAP Gcs1. The GRM is not essential for Glo3 function; it may act as an intrinsic sensor coupling GAP activity to SNARE binding to avoid dead-end complex formation at the Golgi membrane. Our data suggest that membrane-interaction modules and cargo-sensing regions have evolved independently in ArfGAP1s versus ArfGAP2/3s.

Interaction of SNAREs with ArfGAPs precedes recruitment of Sec18p/NSF.

Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins are key components of the fusion machinery in vesicular transport and in homotypic membrane fusion. We previously found that ADP-ribosylation factor GTPase activating proteins (ArfGAPs) promoted a conformational change on SNAREs that allowed recruitment of the small GTPase Arf1p in stoichiometric amounts. Here, we show that the ArfGAP Gcs1p accelerates vesicle (v)-target membrane (t)-SNARE complex formation in vitro, indicating that ArfGAPs may act as folding chaperones. These SNARE complexes were resolved in the presence of ATP by the yeast homologues of alpha-soluble N-ethylmaleimide-sensitive factor attachment protein and N-ethylmaleimide-sensitive factor, Sec17p and Sec18p, respectively. In addition, Sec18p and Sec17p also recognized the "activated" SNAREs even when they were not engaged in v-t-SNARE complexes. Here again, the induction of a conformational change by ArfGAPs was essential. Surprisingly, recruitment of Sec18p to SNAREs did not require Sec17p or ATP hydrolysis. Moreover, Sec18p displaced prebound Arf1p from SNAREs, indicating that Sec18p may have more than one function: first, to ensure that all vesicle coat proteins are removed from the SNAREs before the engagement in a trans-SNARE complex; and second, to resolve cis-SNARE complexes after fusion has occurred.

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.

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.

The Gcs1 Arf-GAP mediates Snc1,2 v-SNARE retrieval to the Golgi in yeast.

Gcs1 is an Arf GTPase-activating protein (Arf-GAP) that mediates Golgi-ER and post-Golgi vesicle transport in yeast. Here we show that the Snc1,2 v-SNAREs, which mediate endocytosis and exocytosis, interact physically and genetically with Gcs1. Moreover, Gcs1 and the Snc v-SNAREs colocalize to subcellular structures that correspond to the trans-Golgi and endosomal compartments. Studies performed in vitro demonstrate that the Snc-Gcs1 interaction results in the efficient binding of recombinant Arf1Delta17N-Q71L to the v-SNARE and the recruitment of purified coatomer. In contrast, the presence of Snc had no effect on Gcs1 Arf-GAP activity in vitro, suggesting that v-SNARE binding does not attenuate Arf1 function. Disruption of both the SNC and GCS1 genes results in synthetic lethality, whereas overexpression of either SNC gene inhibits the growth of a distinct subset of COPI mutants. We show that GFP-Snc1 recycling to the trans-Golgi is impaired in gcs1Delta cells and these COPI mutants. Together, these results suggest that Gcs1 facilitates the incorporation of the Snc v-SNAREs into COPI recycling vesicles and subsequent endosome-Golgi sorting in yeast.

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.