An Activity-Guided Map of Electrophile-Cysteine Interactions in Primary Human T Cells.

Electrophilic compounds originating from nature or chemical synthesis have profound effects on immune cells. These compounds are thought to act by cysteine modification to alter the functions of immune-relevant proteins; however, our understanding of electrophile-sensitive cysteines in the human immune proteome remains limited. Here, we present a global map of cysteines in primary human T cells that are susceptible to covalent modification by electrophilic small molecules. More than 3,000 covalently liganded cysteines were found on functionally and structurally diverse proteins, including many that play fundamental roles in immunology. We further show that electrophilic compounds can impair T cell activation by distinct mechanisms involving the direct functional perturbation and/or degradation of proteins. Our findings reveal a rich content of ligandable cysteines in human T cells and point to electrophilic small molecules as a fertile source for chemical probes and ultimately therapeutics that modulate immunological processes and their associated disorders.

Enhanced mapping of small-molecule binding sites in cells.

Photoaffinity probes are routinely utilized to identify proteins that interact with small molecules. However, despite this common usage, resolving the specific sites of these interactions remains a challenge. Here we developed a chemoproteomic workflow to determine precise protein binding sites of photoaffinity probes in cells. Deconvolution of features unique to probe-modified peptides, such as their tendency to produce chimeric spectra, facilitated the development of predictive models to confidently determine labeled sites. This yielded an expansive map of small-molecule binding sites on endogenous proteins and enabled the integration with multiplexed quantitation, increasing the throughput and dimensionality of experiments. Finally, using structural information, we characterized diverse binding sites across the proteome, providing direct evidence of their tractability to small molecules. Together, our findings reveal new knowledge for the analysis of photoaffinity probes and provide a robust method for high-resolution mapping of reversible small-molecule interactions en masse in native systems.

Diversity oriented clicking delivers ß-substituted alkenyl sulfonyl fluorides as covalent human neutrophil elastase inhibitors.

Diversity Oriented Clicking (DOC) is a discovery method geared toward the rapid synthesis of functional libraries. It combines the best attributes of both classical and modern click chemistries. DOC strategies center upon the chemical diversification of core "SuFExable" hubs-exemplified by 2-Substituted-Alkynyl-1-Sulfonyl Fluorides (SASFs)-enabling the modular assembly of compounds through multiple reaction pathways. We report here a range of stereoselective Michael-type addition pathways from SASF hubs including reactions with secondary amines, carboxylates, 1H-1,2,3-triazole, and halides. These high yielding conjugate addition pathways deliver unprecedented ß-substituted alkenyl sulfonyl fluorides as single isomers with minimal purification, greatly enriching the repertoire of DOC and holding true to the fundamentals of modular click chemistry. Further, we demonstrate the potential for biological function - a key objective of click chemistry - of this family of SASF-derived molecules as covalent inhibitors of human neutrophil elastase.

Art and Science of the Cellular Mesoscale.

Experimental information from microscopy, structural biology, and bioinformatics may be integrated to build structural models of entire cells with molecular detail. This integrative modeling is challenging in several ways: the intrinsic complexity of biology results in models with many closely packed and heterogeneous components; the wealth of available experimental data is scattered among multiple resources and must be gathered, reconciled, and curated; and computational infrastructure is only now gaining the capability of modeling and visualizing systems of this complexity. We present recent efforts to address these challenges, both with artistic approaches to depicting the cellular mesoscale, and development and application of methods to build quantitative models.

Synergy of machine learning and density functional theory calculations for predicting experimental Lewis base affinity and Lewis polybase binding atoms.

Investigation of Lewis acid-base interactions has been conducted by ab initio calculations and machine learning (ML) models. This study aims to resolve two critical tasks that have not been quantitatively investigated. First, ML models developed from density functional theory (DFT) calculations predict experimental BF3 affinity with Pearson correlation coefficients around 0.9 and mean absolute errors around 10 kJ mol-1. The ML models are trained by DFT-calculated BF3 affinity of more than 3000 adducts, with input features readily obtained by rdkit. Second, the ML models have the capability of predicting the relative strength of Lewis base binding atoms in Lewis polybases, which is either an extremely challenging task to conduct experimentally or a computationally expensive task for ab initio methods. The study demonstrates and solidifies the potential of combining DFT calculations and ML models to predict experimental properties, especially those that are scarce and impractical to empirically acquire.

Reactive Docking: A Computational Method for High-Throughput Virtual Screenings of Reactive Species.

We describe the formalization of the reactive docking protocol, a method developed to model and predict reactions between small molecules and biological macromolecules. The method has been successfully used in a number of applications already, including recapitulating large proteomics data sets, performing structure-reactivity target optimizations, and prospective virtual screenings. By modeling a near-attack conformation-like state, no QM calculations are required to model the ligand and receptor geometries. Here, we present its generalization using a large data set containing more than 400 ligand-target complexes, 8 nucleophilic modifiable residue types, and more than 30 warheads. The method correctly predicts the modified residue in ∼85% of complexes and shows enrichments comparable to standard focused virtual screenings in ranking ligands. This performance supports this approach for the docking and screening of reactive ligands in virtual chemoproteomics and drug design campaigns.

Ringtail: A Python Tool for Efficient Management and Storage of Virtual Screening Results.

Virtual screening using molecular docking is now routinely used for the rapid evaluation of very large ligand libraries in early stage drug discovery. As the size of compound libraries which can feasibly be screened grows, so do the challenges in result management and storage. Here we introduce Ringtail, a new Python tool in the AutoDock Suite for efficient storage and analysis of virtual screening data based on portable SQLite databases. Ringtail is designed to work with AutoDock-GPU and AutoDock Vina out-of-the-box. Its modular design also allows for easy extension to support input file types from other docking software, different storage solutions, and incorporation into other applications. Ringtail's SQLite database output can dramatically reduce the required disk storage (36-46 fold) by selecting individual poses to store and by taking advantage of the relational database format. Filtering times are also dramatically reduced, requiring minutes to filter millions of ligands. Thus, Ringtail is a tool that can immediately integrate into existing virtual screening pipelines using AutoDock-GPU and Vina, and is scriptable and modifiable to fit specific user needs.

Discovery of small-molecule enzyme activators by activity-based protein profiling.

Activity-based protein profiling (ABPP) has been used extensively to discover and optimize selective inhibitors of enzymes. Here, we show that ABPP can also be implemented to identify the converse-small-molecule enzyme activators. Using a kinetically controlled, fluorescence polarization-ABPP assay, we identify compounds that stimulate the activity of LYPLAL1-a poorly characterized serine hydrolase with complex genetic links to human metabolic traits. We apply ABPP-guided medicinal chemistry to advance a lead into a selective LYPLAL1 activator suitable for use in vivo. Structural simulations coupled to mutational, biochemical and biophysical analyses indicate that this compound increases LYPLAL1's catalytic activity likely by enhancing the efficiency of the catalytic triad charge-relay system. Treatment with this LYPLAL1 activator confers beneficial effects in a mouse model of diet-induced obesity. These findings reveal a new mode of pharmacological regulation for this large enzyme family and suggest that ABPP may aid discovery of activators for additional enzyme classes.

Structure-based virtual screening workflow to identify antivirals targeting HIV-1 capsid.

We have identified novel HIV-1 capsid inhibitors targeting the PF74 binding site. Acting as the building block of the HIV-1 capsid core, the HIV-1 capsid protein plays an important role in the viral life cycle and is an attractive target for antiviral development. A structure-based virtual screening workflow for hit identification was employed, which includes docking 1.6 million commercially-available drug-like compounds from the ZINC database to the capsid dimer, followed by applying two absolute binding free energy (ABFE) filters on the 500 top-ranked molecules from docking. The first employs the Binding Energy Distribution Analysis Method (BEDAM) in implicit solvent. The top-ranked compounds are then refined using the Double Decoupling method in explicit solvent. Both docking and BEDAM refinement were carried out on the IBM World Community Grid as part of the FightAIDS@Home project. Using this virtual screening workflow, we identified 24 molecules with calculated binding free energies between - 6 and - 12 kcal/mol. We performed thermal shift assays on these molecules to examine their potential effects on the stability of HIV-1 capsid hexamer and found that two compounds, ZINC520357473 and ZINC4119064 increased the melting point of the latter by 14.8 °C and 33 °C, respectively. These results support the conclusion that the two ZINC compounds are primary hits targeting the capsid dimer interface. Our simulations also suggest that the two hit molecules may bind at the capsid dimer interface by occupying a new sub-pocket that has not been exploited by existing CA inhibitors. The possible causes for why other top-scored compounds suggested by ABFE filters failed to show measurable activity are discussed.

Proteome-wide covalent ligand discovery in native biological systems.

Small molecules are powerful tools for investigating protein function and can serve as leads for new therapeutics. Most human proteins, however, lack small-molecule ligands, and entire protein classes are considered 'undruggable'. Fragment-based ligand discovery can identify small-molecule probes for proteins that have proven difficult to target using high-throughput screening of complex compound libraries. Although reversibly binding ligands are commonly pursued, covalent fragments provide an alternative route to small-molecule probes, including those that can access regions of proteins that are difficult to target through binding affinity alone. Here we report a quantitative analysis of cysteine-reactive small-molecule fragments screened against thousands of proteins in human proteomes and cells. Covalent ligands were identified for >700 cysteines found in both druggable proteins and proteins deficient in chemical probes, including transcription factors, adaptor/scaffolding proteins, and uncharacterized proteins. Among the atypical ligand-protein interactions discovered were compounds that react preferentially with pro- (inactive) caspases. We used these ligands to distinguish extrinsic apoptosis pathways in human cell lines versus primary human T cells, showing that the former is largely mediated by caspase-8 while the latter depends on both caspase-8 and -10. Fragment-based covalent ligand discovery provides a greatly expanded portrait of the ligandable proteome and furnishes compounds that can illuminate protein functions in native biological systems.

SuFEx-enabled, agnostic discovery of covalent inhibitors of human neutrophil elastase.

Sulfur fluoride exchange (SuFEx) has emerged as the new generation of click chemistry. We report here a SuFEx-enabled, agnostic approach for the discovery and optimization of covalent inhibitors of human neutrophil elastase (hNE). Evaluation of our ever-growing collection of SuFExable compounds toward various biological assays unexpectedly revealed a selective and covalent hNE inhibitor: benzene-1,2-disulfonyl fluoride. Synthetic derivatization of the initial hit led to a more potent agent, 2-(fluorosulfonyl)phenyl fluorosulfate with IC50 0.24 µM and greater than 833-fold selectivity over the homologous neutrophil serine protease, cathepsin G. The optimized, yet simple benzenoid probe only modified active hNE and not its denatured form.

Benchmarking the Performance of Irregular Computations in AutoDock-GPU Molecular Docking.

Irregular applications can be found in different scientific fields. In computer-aided drug design, molecular docking simulations play an important role in finding promising drug candidates. AutoDock is a software application widely used for predicting molecular interactions at close distances. It is characterized by irregular computations and long execution runtimes. In recent years, a hardware-accelerated version of AutoDock, called AutoDock-GPU, has been under active development. This work benchmarks the recent code and algorithmic enhancements incorporated into AutoDock-GPU. Particularly, we analyze the impact on execution runtime of techniques based on early termination. These enable AutoDock-GPU to explore the molecular space as necessary, while safely avoiding redundant computations. Our results indicate that it is possible to achieve average runtime reductions of 50% by using these techniques. Furthermore, a comprehensive literature review is also provided, where our work is compared to relevant approaches leveraging hardware acceleration for molecular docking.

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.

AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina.

Improving Docking Power for Short Peptides Using Random Forest.

In recent years, therapeutic peptides have gained a lot interest as demonstrated by the 60 peptides approved as drugs in major markets and 150+ peptides currently in clinical trials. However, while small molecule docking is routinely used in rational drug design efforts, docking peptides has proven challenging partly because docking scoring functions, developed and calibrated for small molecules, perform poorly for these molecules. Here, we present random forest classifiers trained to discriminate correctly docked peptides. We show that, for a testing set of 47 protein-peptide complexes, structurally dissimilar from the training set and previously used to benchmark AutoDock Vina's ability to dock short peptides, these random forest classifiers improve docking power from ∼25% for AutoDock scoring functions to an average of ∼70%. These results pave the way for peptide-docking success rates comparable to those of small molecule docking. To develop these classifiers, we compiled the ProptPep37_2021 data set, a curated, high-quality set of 322 crystallographic protein-peptides complexes annotated with structural similarity information. The data set also provides a collection of high-quality putative poses with a range of deviations from the crystallographic pose, providing correct and incorrect poses (i.e., decoys) of the peptide for each entry. The ProptPep37_2021 data set as well as the classifiers presented here are freely available.

Synthetic, Mechanistic, and Biological Interrogation of Ginkgo biloba Chemical Space En Route to (-)-Bilobalide.

Here we interrogate the structurally dense (1.64 mcbits/Å3) GABAA receptor antagonist bilobalide, intermediates en route to its synthesis, and related mechanistic questions. 13C isotope labeling identifies an unexpected bromine migration en route to an α-selective, catalytic asymmetric Reformatsky reaction, ruling out an asymmetric allylation pathway. Experiment and computation converge on the driving forces behind two surprising observations. First, an oxetane acetal persists in concentrated mineral acid (1.5 M DCl in THF-d8/D2O); its longevity is correlated to destabilizing steric clash between substituents upon ring-opening. Second, a regioselective oxidation of des-hydroxybilobalide is found to rely on lactone acidification through lone-pair delocalization, which leads to extremely rapid intermolecular enolate equilibration. We also establish equivalent effects of (-)-bilobalide and the nonconvulsive sesquiterpene (-)-jiadifenolide on action potential-independent inhibitory currents at GABAergic synapses, using (+)-bilobalide as a negative control. The high information density of bilobalide distinguishes it from other scaffolds and may characterize natural product (NP) space more generally. Therefore, we also include a Python script to quickly (ca. 132 000 molecules/min) calculate information content (Böttcher scores), which may prove helpful to identify important features of NP space.

AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions.

SUMMARY: The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein-ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations. AVAILABILITY AND IMPLEMENTATION: AutoDock Bias is distributed with MGLTools (since version 1.5.7), and freely available on the web at http://ccsb.scripps.edu/mgltools/ or http://autodockbias.wordpress.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrative modeling of the HIV-1 ribonucleoprotein complex.

A coarse-grain computational method integrates biophysical and structural data to generate models of HIV-1 genomic RNA, nucleocapsid and integrase condensed into a mature ribonucleoprotein complex. Several hypotheses for the initial structure of the genomic RNA and oligomeric state of integrase are tested. In these models, integrase interaction captures features of the relative distribution of gRNA in the immature virion and increases the size of the RNP globule, and exclusion of nucleocapsid from regions with RNA secondary structure drives an asymmetric placement of the dimerized 5'UTR at the surface of the RNP globule.

Structural basis for the stabilization of amyloidogenic immunoglobulin light chains by hydantoins.

Misfolding and aggregation of immunoglobulin light chains (LCs) leads to the degeneration of post-mitotic tissue in the disease immunoglobulin LC amyloidosis (AL). We previously reported the discovery of small molecule kinetic stabilizers of the native dimeric structure of full-length LCs, which slow or stop the LC aggregation cascade at the outset. A predominant structural category of kinetic stabilizers emerging from the high-throughput screen are coumarins substituted at the 7-position, which bind at the interface between the two variable domains of the light chain dimer. Here, we report the binding mode of another, more polar, LC kinetic stabilizer chemotype, 3,5-substituted hydantoins. Computational docking, solution nuclear magnetic resonance experiments, and x-ray crystallography show that the aromatic substructure emerging from the hydantoin 3-position occupies the same LC binding site as the coumarin ring. Notably, the hydantoin ring extends beyond the binding site mapped out by the coumarin hits. The hydantoin ring makes hydrogen bonds with both LC monomers simultaneously. The alkyl substructure at the hydantoin 5-position partially occupies a novel binding pocket proximal to the pocket occupied by the coumarin substructure. Overall, the hydantoin structural data suggest that a larger area of the LC variable-domain-variable-domain dimer interface is amenable to small molecule binding than previously demonstrated, which should facilitate development of more potent full-length LC kinetic stabilizers.

Initial Analysis of the Arylomycin D Antibiotics.

The arylomycins are a class of natural product antibiotics that inhibit bacterial type I signal peptidase and are under development as therapeutics. Four classes of arylomycins are known, arylomycins A-D. Previously, we reported the synthesis and analysis of representatives of the A, B, and C classes and showed that their spectrum of activity has the potential to be much broader than originally assumed. Along with a comparison of the mechanism of acquired and innate resistance, this led us to suggest that the arylomycins are latent antibiotics, antibiotics that once possessed broad-spectrum activity, but which upon examination today, have only narrow spectrum activity due to prior selection for resistance in the course of the competition with other microorganisms that drove their evolution in the first place. Interestingly, actinocarbasin, the only identified member of the arylomycin D class, has been reported to have activity against MRSA. To confirm and understand this activity, several actinocarbasin derivatives were synthesized. We demonstrate that the previously reported structure of actinocarbasin is incorrect, identify what is likely the correct scaffold, confirm that scaffold has activity against MRSA, and determine the origin of this activity.