Arsen Grigoryan

Specific Small Molecule Inhibitors of Skp2-Mediated p27 Degradation

In the ubiquitin proteasome system, the E3 ligase SCF-Skp2 and its accessory protein, Cks1, promote proliferation largely by inducing the degradation of the CDK inhibitor p27. Overexpression of Skp2 in human cancers correlates with poor prognosis, and deregulation of SCF-Skp2-Cks1 promotes tumorigenesis in animal models. We identified small molecule inhibitors specific to SCF-Skp2 activity using in silico screens targeted to the binding interface for p27. These compounds selectively inhibited Skp2-mediated p27 degradation by reducing p27 binding through key compound-receptor contacts. In cancer cells, the compounds induced p27 accumulation in a Skp2-dependent manner and promoted cell-type-specific blocks in the G1 or G2/M phases. Designing SCF-Skp2-specific inhibitors may be a novel strategy to treat cancers dependent on the Skp2-p27 axis.

Specific Increase in Potency via Structure-Based Design of a TCR

Adoptive immunotherapy with Ag-specific T lymphocytes is a powerful strategy for cancer treatment. However, most tumor Ags are nonreactive “self” proteins, which presents an immunotherapy design challenge. Recent studies have shown that tumor-specific TCRs can be transduced into normal PBLs, which persist after transfer in ∼30% of patients and effectively destroy tumor cells in vivo. Although encouraging, the limited clinical responses underscore the need for enrichment of T cells with desirable antitumor capabilities prior to patient transfer. In this study, we used structure-based design to predict point mutations of a TCR (DMF5) that enhance its binding affinity for an agonist tumor Ag–MHC (peptide–MHC [pMHC]), Mart-1 (27L)-HLA-A2, which elicits full T cell activation to trigger immune responses. We analyzed the effects of selected TCR point mutations on T cell activation potency and analyzed cross-reactivity with related Ags. Our results showed that the mutated TCRs had improved T cell activation potency while retaining a high degree of specificity. Such affinity-optimized TCRs have demonstrated to be very specific for Mart-1 (27L), the epitope for which they were structurally designed. Although of somewhat limited clinical relevance, these studies open the possibility for future structural-based studies that could potentially be used in adoptive immunotherapy to treat melanoma while avoiding adverse autoimmunity-derived effects.

Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes

Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/.

Historeceptomic Fingerprints for Drug-Like Compounds

Most drugs exert their beneficial and adverse effects through their combined action on several different molecular targets (polypharmacology). The true molecular fingerprint of the direct action of a drug has two components: the ensemble of all the receptors upon which a drug acts and their level of expression in organs/tissues. Conversely, the fingerprint of the adverse effects of a drug may derive from its action in bystander tissues. The ensemble of targets is almost always only partially known. Here we describe an approach improving upon and integrating both components: in silico identification of a more comprehensive ensemble of targets for any drug weighted by the expression of those receptors in relevant tissues. Our system combines more than 300,000 experimentally determined bioactivity values from the ChEMBL database and 4.2 billion molecular docking scores. We integrated these scores with gene expression data for human receptors across a panel of human tissues to produce drug-specific tissue-receptor (historeceptomics) scores. A statistical model was designed to identify significant scores, which define an improved fingerprint representing the unique activity of any drug. These multi-dimensional historeceptomic fingerprints describe, in a novel, intuitive, and easy to interpret style, the holistic, in vivo picture of the mechanism of any drugs action. Valuable applications in drug discovery and personalized medicine, including the identification of molecular signatures for drugs with polypharmacologic modes of action, detection of tissue-specific adverse effects of drugs, matching molecular signatures of a disease to drugs, target identification for bioactive compounds with unknown receptors, and hypothesis generation for drug/compound phenotypes may be enabled by this approach. The system has been deployed at drugable.org for access through a user-friendly web site.

Computational Prediction of Neutralization Epitopes Targeted by Human Anti-V3 HIV Monoclonal Antibodies

The extreme diversity of HIV-1 strains presents a formidable challenge for HIV-1 vaccine design. Although antibodies (Abs) can neutralize HIV-1 and potentially protect against infection, antibodies that target the immunogenic viral surface protein gp120 have widely variable and poorly predictable cross-strain reactivity. Here, we developed a novel computational approach, the Method of Dynamic Epitopes, for identification of neutralization epitopes targeted by anti-HIV-1 monoclonal antibodies (mAbs). Our data demonstrate that this approach, based purely on calculated energetics and 3D structural information, accurately predicts the presence of neutralization epitopes targeted by V3-specific mAbs 2219 and 447-52D in any HIV-1 strain. The method was used to calculate the range of conservation of these specific epitopes across all circulating HIV-1 viruses. Accurately identifying an Ab-targeted neutralization epitope in a virus by computational means enables easy prediction of the breadth of reactivity of specific mAbs across the diversity of thousands of different circulating HIV-1 variants and facilitates rational design and selection of immunogens mimicking specific mAb-targeted epitopes in a multivalent HIV-1 vaccine. The defined epitopes can also be used for the purpose of epitope-specific analyses of breakthrough sequences recorded in vaccine clinical trials. Thus, our study is a prototype for a valuable tool for rational HIV-1 vaccine design.

Can the Energy Gap in the Protein-Ligand Binding Energy Landscape Be Used as a Descriptor in Virtual Ligand Screening?

The ranking of scores of individual chemicals within a large screening library is a crucial step in virtual screening (VS) for drug discovery. Previous studies showed that the quality of protein-ligand recognition can be improved using spectrum properties and the shape of the binding energy landscape. Here, we investigate whether the energy gap, defined as the difference between the lowest energy pose generated by a docking experiment and the average energy of all other generated poses and inferred to be a measure of the binding energy landscape sharpness, can improve the separation power between true binders and decoys with respect to the use of the best docking score. We performed retrospective single- and multiple-receptor conformation VS experiments in a diverse benchmark of 40 domains from 38 therapeutically relevant protein targets. Also, we tested the performance of the energy gap on 36 protein targets from the Directory of Useful Decoys (DUD). The results indicate that the energy gap outperforms the best docking score in its ability to discriminate between true binders and decoys, and true binders tend to have larger energy gaps than decoys. Furthermore, we used the energy gap as a descriptor to measure the height of the native binding phase and obtained a significant increase in the success rate of near native binding pose identification when the ligand binding conformations within the boundaries of the native binding phase were considered. The performance of the energy gap was also evaluated on an independent test case of VS-identified PKR-like ER-localized eIF2α kinase (PERK) inhibitors. We found that the energy gap was superior to the best docking score in its ability to more highly rank active compounds from inactive ones. These results suggest that the energy gap of the protein-ligand binding energy landscape is a valuable descriptor for use in VS.

In silico prediction of the neutralization range of human anti-HIV monoclonal antibodies

Results We optimized the method for each of the two Abs by determining an optimal docking model (optimal boundaries of a docking peptide and an optimal Ab crystallographic conformation) giving the largest area under the prediction ROC curve (AUC) on the training set of 59 psVs. The prediction accuracy for the optimized method was then estimated: the AUC was equal to 0.96 (95% CI (0.91; 1)) for 2219, and to 0.88 (95% CI (0.79; 0.97)) for 447-52D. Conclusion The method accurately predicts the neutralization of any HIV-1 strain by mAbs 2219 or 447-52D based solely on neutralization assay independent energetics and 3D structural parameters. The neutralization range of these antiV3 mAbs can therefore be precisely determined in silico. Furthermore, given the fact that mAbs 2219 and 447-52D have completely different binding modes, we anticipate that our approach is extensible to other antibody-viral complexes with known structure.