Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein-ligand binding challenge.

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 (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy 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 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 "HIV Interaction and Viral Evolution Center".

Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge.

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.

Virtual target screening: validation using kinase inhibitors.

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.

Facile iterative synthesis of 2,5-terpyrimidinylenes as nonpeptidic alpha-helical mimics.

A facile iterative synthesis of 2,5-terpyrimidinylenes that are structurally analogous to alpha-helix mimics is presented. Condensation of amidines with readily prepared alpha,beta-unsaturated alpha-cyanoketones gives 5-cyano-substituted pyrimidines. Iterative transformation of the 5-cyano group into an amidine allows synthesis of 2,5-terpyrimidinylenes with variable groups at the 4-, 4'-, and 4''-positions. These compounds are designed to mimic the i, i + 4, and i + 7 sites of an alpha-helix.

An immunoproteomic approach to characterize the CAR interactome and signalosome.

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 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 antitumor 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 costimulatory 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 of novel optimized immune receptors.

Fighting Cancer with Mathematics and Viruses.

After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex antitumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these research fields to increase the value of the preclinical development and the therapeutic efficacy of the resulting treatments.

In silico characterization of an atypical MAPK phosphatase of Plasmodium falciparum as a suitable target for drug discovery.

Plasmodium falciparum, the causative agent of malaria, contributes to significant morbidity and mortality worldwide. Forward genetic analysis of the blood-stage asexual cycle identified the putative phosphatase from PF3D7_1305500 as an important element of intraerythrocytic development expressed throughout the life cycle. Our preliminary evaluation identified it as an atypical mitogen-activated protein kinase phosphatase. Additional bioinformatic analysis delineated a conserved signature motif and three residues with potential importance to functional activity of the atypical dual-specificity phosphatase domain. A homology model of the dual-specificity phosphatase domain was developed for use in high-throughput in silico screening of the available library of antimalarial compounds from ChEMBL-NTD. Seven compounds from this set with predicted affinity to the active site were tested against in vitro cultures, and three had reduced activity against a ∆PF3D7_1305500 parasite, suggesting PF3D7_1305500 is a potential target of the selected compounds. Identification of these compounds provides a novel starting point for a structure-based drug discovery strategy that moves us closer toward the discovery of new classes of clinical antimalarial drugs. These data suggest that mitogen-activated protein kinase phosphatases represent a potentially new class of P. falciparum drug target.