As part of the SAMPL4 blind challenge, filtered AutoDock Vina ligand docking predictions and large scale binding energy distribution analysis method binding free energy calculations have been applied to the virtual screening of a focused library of candidate binders to the LEDGF site of the HIV integrase protein. The computational protocol leveraged docking and high level atomistic models to improve enrichment. The enrichment factor of our blind predictions ranked best among all of the computational submissions, and second best overall. This work represents to our knowledge the first example of the application of an all-atom physics-based binding free energy model to large scale virtual screening. A total of 285 parallel Hamiltonian replica exchange molecular dynamics absolute protein-ligand binding free energy simulations were conducted starting from docked poses. The setup of the simulations was fully automated, calculations were distributed on multiple computing resources and were completed in a 6-weeks period. The accuracy of the docked poses and the inclusion of intramolecular strain and entropic losses in the binding free energy estimates were the major factors behind the success of the method. Lack of sufficient time and computing resources to investigate additional protonation states of the ligands was a major cause of mispredictions. The experiment demonstrated the applicability of binding free energy modeling to improve hit rates in challenging virtual screening of focused ligand libraries during lead optimization.

Adoptive transfer of T cells that express a chimeric antigen receptor (CAR) is an approved immunotherapy that may be curative for some hematological cancers. To better understand the therapeutic mechanism of action, we systematically analyzed prostate cancer-specific CAR signaling in human primary T cells by mass spectrometry. When we compared the interactomes and the signaling pathways activated by distinct CAR-T cells that shared the same antigen-binding domain but differed in their intracellular domains and their in vivo anti-tumor efficacy, we found that only second-generation CARs induced the expression of a constitutively phosphorylated form of CD3ζ that resembled the endogenous species. This phenomenon was independent of the choice of co-stimulatory domains, or the hinge/transmembrane region. Rather, it was dependent on the size of the intracellular domains. Moreover, the second-generation design was also associated with stronger phosphorylation of downstream secondary messengers, as evidenced by global phosphoproteome analysis. These results suggest that second-generation CARs can activate additional sources of CD3ζ signaling, and this may contribute to more intense signaling and superior antitumor efficacy that they display compared to third-generation CARs. Moreover, our results provide a deeper understanding of how CARs interact physically and/or functionally with endogenous T cell molecules, which will inform the development novel optimized immune receptors.

To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly-discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein-ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new “Rank Difference Ratio” metric. The first stage of SAMPL4 involved using Virtual Screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: 1) AutoDock Vina plus visual inspection; 2) a new Common Pharmacophore Engine; 3) BEDAM replica exchange re-scoring simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4’s very challenging compound library that displayed a significantly lower amount of structural diversity than most of the libraries that are conventionally employed in prospective Virtual Screens, these approaches produced hit rates of 24%, 25%, 34%, and 27%, respectively, on a set with 19% declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly-created, multi-institution, NIH-funded center called the “HIVE” (for HIV Interaction and Viral Evolution Center).

Screening of the NCI Diversity Set-1 identified PI-083 (NSC-45382) a proteasome inhibitor selective for cancer over normal cells. Focused libraries of novel compounds based on PI-083 chloronaphthoquinone and sulfonamide moieties were synthesized to gain a better understanding of the structure activity relationship responsible for chymotrypsin-like proteasome inhibitory activity. This led to the demonstration that the chloronaphthoquinone and the sulfonamide moieties are critical for inhibitory activity. The pyridyl group in PI-083 can be replaced with other heterocyclic groups without significant loss of activity. Molecular modeling studies were also performed to explore the detailed interactions of PI-083 and its derivatives with the β5 and β6 subunits of the 20S proteasome. The refined model showed an H-bond interaction between the Asp-114 and the sulfonamide moiety of the PI-083 in the β6 subunit.

Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term “Virtual Target Screening (VTS)”, a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.