Application of AlphaFold models in evaluating ligandable cysteines across E3 ligases.

Proteolysis Targeting Chimeras (PROTACs) are an emerging therapeutic modality and chemical biology tools for Targeted Protein Degradation (TPD). PROTACs contain a ligand targeting the protein of interest, a ligand recruiting an E3 ligase and a linker connecting these two ligands. There are over 600 E3 ligases known so far, but only a handful have been exploited for TPD applications. A key reason for this is the scarcity of ligands binding various E3 ligases and the paucity of structural data available, which complicates ligand design across the family. In this study, we aim to progress PROTAC discovery by proposing a shortlist of E3 ligases that can be prioritized for covalent targeting by performing systematic structural ligandability analysis on a chemoproteomic dataset of potentially reactive cysteines across hundreds of E3 ligases. One of the goals of this study is to apply AlphaFold (AF) models for ligandability evaluations, as for a vast majority of these ligases an experimental structure is not available in the protein data bank (PDB). Using a combination of pocket features, AF model quality and additional aspects, we propose a shortlist of E3 ligases and corresponding cysteines that can be prioritized to potentially discover covalent ligands and expand the PROTAC toolbox.

Development of small molecule inhibitors of natural killer group 2D receptor (NKG2D).

Natural killer group 2D (NKG2D) is a homodimeric activating immunoreceptor whose function is to detect and eliminate compromised cells upon binding to the NKG2D ligands (NKG2DL) major histocompatibility complex (MHC) molecules class I-related chain A (MICA) and B (MICB) and UL16 binding proteins (ULBP1-6). While typically present at low levels in healthy cells and tissue, NKG2DL expression can be induced by viral infection, cellular stress or transformation. Aberrant activity along the NKG2D/NKG2DL axis has been associated with autoimmune diseases due to the increased expression of NKG2D ligands in human disease tissue, making NKG2D inhibitors an attractive target for immunomodulation. Herein we describe the discovery and optimization of small molecule PPI (protein-protein interaction) inhibitors of NKG2D/NKG2DL. Rapid SAR was guided by structure-based drug design and accomplished by iterative singleton and parallel medicinal chemistry synthesis. These efforts resulted in the identification of several potent analogs (14, 21, 30, 45) with functional activity and improved LLE.

Identification of small-molecule protein-protein interaction inhibitors for NKG2D.

NKG2D (natural-killer group 2, member D) is a homodimeric transmembrane receptor that plays an important role in NK, γδ+, and CD8+ T cell-mediated immune responses to environmental stressors such as viral or bacterial infections and oxidative stress. However, aberrant NKG2D signaling has also been associated with chronic inflammatory and autoimmune diseases, and as such NKG2D is thought to be an attractive target for immune intervention. Here, we describe a comprehensive small-molecule hit identification strategy and two distinct series of protein-protein interaction inhibitors of NKG2D. Although the hits are chemically distinct, they share a unique allosteric mechanism of disrupting ligand binding by accessing a cryptic pocket and causing the two monomers of the NKG2D dimer to open apart and twist relative to one another. Leveraging a suite of biochemical and cell-based assays coupled with structure-based drug design, we established tractable structure-activity relationships with one of the chemical series and successfully improved both the potency and physicochemical properties. Together, we demonstrate that it is possible, albeit challenging, to disrupt the interaction between NKG2D and multiple protein ligands with a single molecule through allosteric modulation of the NKG2D receptor dimer/ligand interface.

A computational approach yields selective inhibitors of human excitatory amino acid transporter 2 (EAAT2).

Excitatory amino acid transporters (EAATs) represent a protein family that is an emerging drug target with great therapeutic potential for managing central nervous system disorders characterized by dysregulation of glutamatergic neurotransmission. As such, it is of significant interest to discover selective modulators of EAAT2 function. Here, we applied computational methods to identify specific EAAT2 inhibitors. Utilizing a homology model of human EAAT2, we identified a binding pocket at the interface of the transport and trimerization domain. We next conducted a high-throughput virtual screen against this site and identified a selective class of EAAT2 inhibitors that were tested in glutamate uptake and whole-cell electrophysiology assays. These compounds represent potentially useful pharmacological tools suitable for further exploration of the therapeutic potential of EAAT2 and may provide molecular insights into mechanisms of allosteric modulation for glutamate transporters.

Breaking the Glass Ceiling in Simulation and Modeling: Women in Pharmaceutical Discovery.

The topic of gender equality within the United States workforce is receiving a great deal of attention. The field of chemistry is no exception and is increasingly focused on taking steps to achieve gender diversity within the chemistry workforce. Over the past several years, many computational chemistry groups within large pharmaceutical companies have realized growth in the number of women, and here we discuss the key factors that we believe have played a role in attracting and retaining the authors of this review as computational chemists in pharma. Furthermore, we combine our professional experiences in the context of how computational methodology and technology have evolved over the past decades and how that evolution has facilitated the inclusion of more women into the field. Our hope is to be a part of a solution and provide insight that will allow the chemistry workforce to continue to make steps forward in attaining gender diversity in the workplace.

Accelerating Lead Identification by High Throughput Virtual Screening: Prospective Case Studies from the Pharmaceutical Industry.

At the onset of a drug discovery program, the goal is to identify novel compounds with appropriate chemical features that can be taken forward as lead series. Here, we describe three prospective case studies, Bruton Tyrosine Kinase (BTK), RAR-Related Orphan Receptor γ t (RORγt), and Human Leukocyte Antigen DR isotype (HLA-DR) to illustrate the positive impact of high throughput virtual screening (HTVS) on the successful identification of novel chemical series. Each case represents a project with a varying degree of difficulty due to the amount of structural and ligand information available internally or in the public domain to utilize in the virtual screens. We show that HTVS can be effectively employed to identify a diverse set of potent hits for each protein system even when the gold standard, high resolution structural data or ligand binding data for benchmarking, is not available.

Dynamic Structural Differences between Human and Mouse STING Lead to Differing Sensitivity to DMXAA.

The stimulator-of-interferon-genes (STING) protein is involved in innate immunity. It has recently been shown that modulation of STING can lead to an aggressive antitumor response. DMXAA is an antitumor agent that had shown great promise in murine models but failed in human clinical trials. The molecular target of DMXAA was subsequently shown to be murine STING (mSTING); however, human STING (hSTING) is insensitive to DMXAA. Molecular dynamics simulations were employed to investigate the differences between hSTING and mSTING that could influence DMXAA binding. An initial set of simulations was performed to investigate a single lid region mutation G230I in hSTING (corresponding residue in mSTING is an Ile), which rendered the protein sensitive to DMXAA. The simulations found that an Ile side chain was enough to form a steric barrier that prevents exit of DMXAA, whereas in WT hSTING, the Gly residue that lacks a side chain formed a porous lid region that allowed DMXAA to exit. A second set of molecular dynamics simulations compared the tendency of STING to be in an open-inactive conformation or a closed-active conformation. The results show that hSTING prefers to be in an open-inactive conformation even with cGAMP, the native ligand, bound. On the other hand, mSTING prefers a closed-active conformation even without a ligand bound. These results highlight the challenges in translating a mouse active STING compound into a human active compound, while also providing avenues to pursue for designing a small-molecule drug targeting human STING.

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.

The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant's method and the setup protocol we used. This problem is inherent to decoys, and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock,1 Glide-XP,2 PLANTS,3 Wilma,4 Gold,5 SMINA,6 Glide-XP2/PELE,7 FlexX,8 and MedusaDock.9 In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term,11 SMINA6 using Autodock-Vina,12,13 FlexX8 using HYDE,14 and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity.16 Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.

A Prospective Virtual Screening Study: Enriching Hit Rates and Designing Focus Libraries To Find Inhibitors of PI3Kδ and PI3Kγ.

Here, we report a high-throughput virtual screening (HTVS) study using phosphoinositide 3-kinase (both PI3Kγ and PI3Kδ). Our initial HTVS results of the Janssen corporate database identified small focused libraries with hit rates at 50% inhibition showing a 50-fold increase over those from a HTS (high-throughput screen). Further, applying constraints based on "chemically intuitive" hydrogen bonds and/or positional requirements resulted in a substantial improvement in the hit rates (versus no constraints) and reduced docking time. While we find that docking scoring functions are not capable of providing a reliable relative ranking of a set of compounds, a prioritization of groups of compounds (e.g., low, medium, and high) does emerge, which allows for the chemistry efforts to be quickly focused on the most viable candidates. Thus, this illustrates that it is not always necessary to have a high correlation between a computational score and the experimental data to impact the drug discovery process.

CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge.

Community Structure-Activity Resource (CSAR) conducted a benchmark exercise to evaluate the current computational methods for protein design, ligand docking, and scoring/ranking. The exercise consisted of three phases. The first phase required the participants to identify and rank order which designed sequences were able to bind the small molecule digoxigenin. The second phase challenged the community to select a near-native pose of digoxigenin from a set of decoy poses for two of the designed proteins. The third phase investigated the ability of current methods to rank/score the binding affinity of 10 related steroids to one of the designed proteins (pKd = 4.1 to 6.7). We found that 11 of 13 groups were able to correctly select the sequence that bound digoxigenin, with most groups providing the correct three-dimensional structure for the backbone of the protein as well as all atoms of the active-site residues. Eleven of the 14 groups were able to select the appropriate pose from a set of plausible decoy poses. The ability to predict absolute binding affinities is still a difficult task, as 8 of 14 groups were able to correlate scores to affinity (Pearson-r > 0.7) of the designed protein for congeneric steroids and only 5 of 14 groups were able to correlate the ranks of the 10 related ligands (Spearman-ρ > 0.7).

CSAR data set release 2012: ligands, affinities, complexes, and docking decoys.

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) has collected several data sets from industry and added in-house data sets that may be used for this purpose ( www.csardock.org). CSAR has currently obtained data from Abbott, GlaxoSmithKline, and Vertex and is working on obtaining data from several others. Combined with our in-house projects, we are providing a data set consisting of 6 protein targets, 647 compounds with biological affinities, and 82 crystal structures. Multiple congeneric series are available for several targets with a few representative crystal structures of each of the series. These series generally contain a few inactive compounds, usually not available in the literature, to provide an upper bound to the affinity range. The affinity ranges are typically 3-4 orders of magnitude per series. For our in-house projects, we have had compounds synthesized for biological testing. Affinities were measured by Thermofluor, Octet RED, and isothermal titration calorimetry for the most soluble. This allows the direct comparison of the biological affinities for those compounds, providing a measure of the variance in the experimental affinity. It appears that there can be considerable variance in the absolute value of the affinity, making the prediction of the absolute value ill-defined. However, the relative rankings within the methods are much better, and this fits with the observation that predicting relative ranking is a more tractable problem computationally. For those in-house compounds, we also have measured the following physical properties: logD, logP, thermodynamic solubility, and pK(a). This data set also provides a substantial decoy set for each target consisting of diverse conformations covering the entire active site for all of the 58 CSAR-quality crystal structures. The CSAR data sets (CSAR-NRC HiQ and the 2012 release) provide substantial, publically available, curated data sets for use in parametrizing and validating docking and scoring methods.

CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series.

The Community Structure-Activity Resource (CSAR) recently held its first blinded exercise based on data provided by Abbott, Vertex, and colleagues at the University of Michigan, Ann Arbor. A total of 20 research groups submitted results for the benchmark exercise where the goal was to compare different improvements for pose prediction, enrichment, and relative ranking of congeneric series of compounds. The exercise was built around blinded high-quality experimental data from four protein targets: LpxC, Urokinase, Chk1, and Erk2. Pose prediction proved to be the most straightforward task, and most methods were able to successfully reproduce binding poses when the crystal structure employed was co-crystallized with a ligand from the same chemical series. Multiple evaluation metrics were examined, and we found that RMSD and native contact metrics together provide a robust evaluation of the predicted poses. It was notable that most scoring functions underpredicted contacts between the hetero atoms (i.e., N, O, S, etc.) of the protein and ligand. Relative ranking was found to be the most difficult area for the methods, but many of the scoring functions were able to properly identify Urokinase actives from the inactives in the series. Lastly, we found that minimizing the protein and correcting histidine tautomeric states positively trended with low RMSD for pose prediction but minimizing the ligand negatively trended. Pregenerated ligand conformations performed better than those that were generated on the fly. Optimizing docking parameters and pretraining with the native ligand had a positive effect on the docking performance as did using restraints, substructure fitting, and shape fitting. Lastly, for both sampling and ranking scoring functions, the use of the empirical scoring function appeared to trend positively with the RMSD. Here, by combining the results of many methods, we hope to provide a statistically relevant evaluation and elucidate specific shortcomings of docking methodology for the community.

Overcoming sequence misalignments with weighted structural superposition.

An appropriate structural superposition identifies similarities and differences between homologous proteins that are not evident from sequence alignments alone. We have coupled our Gaussian-weighted RMSD (wRMSD) tool with a sequence aligner and seed extension (SE) algorithm to create a robust technique for overlaying structures and aligning sequences of homologous proteins (HwRMSD). HwRMSD overcomes errors in the initial sequence alignment that would normally propagate into a standard RMSD overlay. SE can generate a corrected sequence alignment from the improved structural superposition obtained by wRMSD. HwRMSD's robust performance and its superiority over standard RMSD are demonstrated over a range of homologous proteins. Its better overlay results in corrected sequence alignments with good agreement to HOMSTRAD. Finally, HwRMSD is compared to established structural alignment methods: FATCAT, secondary-structure matching, combinatorial extension, and Dalilite. Most methods are comparable at placing residue pairs within 2 Å, but HwRMSD places many more residue pairs within 1 Å, providing a clear advantage. Such high accuracy is essential in drug design, where small distances can have a large impact on computational predictions. This level of accuracy is also needed to correct sequence alignments in an automated fashion, especially for omics-scale analysis. HwRMSD can align homologs with low-sequence identity and large conformational differences, cases where both sequence-based and structural-based methods may fail. The HwRMSD pipeline overcomes the dependency of structural overlays on initial sequence pairing and removes the need to determine the best sequence-alignment method, substitution matrix, and gap parameters for each unique pair of homologs.