Genomic alterations may make cancer cells more dependent than normal cells on mechanisms of proteostasis, including protein folding and degradation. This proposition is the basis for the clinical use of proteasome inhibitors to treat multiple myeloma and mantle cell lymphoma. However, proteasome inhibitors have not proved effective in treating other cancers, and this has called into question the general applicability of this approach. Here, I consider possible explanations for this apparently limited applicability, and discuss whether inhibiting other broadly acting components of the ubiquitin-proteasome system - including ubiquitin-activating enzyme and the AAA-ATPase p97/VCP - might be more generally effective in cancer therapy.

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Proteotoxic crisis, the ubiquitin-proteasome system, and cancer therapy

The FDA approval of imatinib in 2001 was a breakthrough in molecularly targeted cancer therapy and heralded the emergence of kinase inhibitors as a key drug class in the oncology area and beyond. Twenty years on, this article analyses the landscape of approved and investigational therapies that target kinases and trends within it, including the most popular targets of kinase inhibitors and their expanding range of indications. There are currently 71 small-molecule kinase inhibitors (SMKIs) approved by the FDA and an additional 16 SMKIs approved by other regulatory agencies. Although oncology is still the predominant area for their application, there have been important approvals for indications such as rheumatoid arthritis, and one-third of the SMKIs in clinical development address disorders beyond oncology. Information on clinical trials of SMKIs reveals that approximately 110 novel kinases are currently being explored as targets, which together with the approximately 45 targets of approved kinase inhibitors represent only about 30% of the human kinome, indicating that there are still substantial unexplored opportunities for this drug class. We also discuss trends in kinase inhibitor design, including the development of allosteric and covalent inhibitors, bifunctional inhibitors and chemical degraders. The FDA approval of imatinib in 2001 heralded the emergence of kinase inhibitors as a key drug class in the oncology area and beyond. This article analyses the landscape of approved and investigational therapies that target kinases and trends within it, including the most popular targets of kinase inhibitors, their expanding range of indications and strategies for kinase inhibitor design.

Trends in kinase drug discovery: targets, indications and inhibitor design.

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review.

Specific small molecule inhibitors of Skp2-mediated p27 degradation.

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

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Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.

The unfolded protein response (UPR) is a coordinated program that promotes cell survival under conditions of endoplasmic reticulum stress and is required in tumor progression as well. To date, no specific small molecule inhibitor targeting this pathway has been identified. Pancreatic endoplasmic reticulum kinase (PERK), one of the UPR transducers, is an eIF2α kinase. Compromising PERK function inhibits tumor growth in mice, suggesting that PERK may be a cancer drug target, but identifying a specific inhibitor of any kinase is challenging. The goal of this study was to identify some pair-wise receptor-ligand atomic contacts that confer selective PERK inhibition. Compounds selectively inhibiting PERK-mediated phosphorylation in vitro were identified using an initial virtual library screen, followed by structure-activity hypothesis testing. The most potent PERK selective inhibitors utilize three specific kinase active site contacts that, when absent from chemically similar compounds, abrogates the inhibition: (i) a strong van der Waals contact with PERK residue Met7, (ii) interactions with the N-terminal portion of the activation loop, and (iii) groups providing electrostatic complementarity to Asp144. Interestingly, the activation loop contact is required for PERK selectivity to emerge. Understanding these structure-activity relationships may accelerate rational PERK inhibitor design.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1747-0285.2010.01048.x

Structural determinants of PERK inhibitor potency and selectivity.

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.

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Can the energy gap in the protein-ligand binding energy landscape be used as a descriptor in virtual ligand screening?

Cellular stress signals activate adaptive signaling pathways of the mammalian integrated stress response (ISR), of which the unfolded protein response (UPR) is a subset. These pathways converge at the phosporylation of eIF2α. Drug-like, potent and selective chemical inhibitors (valid chemical probes) targeting major ISR kinases have been previously identified, with the exception of GCN2. We synthesized and evaluated a series of GCN2 inhibitors based on a triazolo[4,5-d]pyrimidine scaffold. Several compounds potently inhibited GCN2 in vitro and displayed good selectivity over the related kinases PERK, HRI, and IRE1. The compounds inhibited phosporylation of eIF2α in HEK293T cells with an IC50 < 150 nM, validating them as chemical probes for cellular studies. These probes were screened against the National Cancer Institute NCI-60 human cancer cell line panel. Uniform growth inhibition was observed in the leukemia group of cell lines. Growth inhibition in the most sensitive cell lines coincided with high GCN2 mRNA expression levels. Oncomine analysis revealed high GCN2 expression accompanied by lower asparagine synthetase (ASNS) expression in patient-derived acute lymphoblastic leukemias with B-Cell origins (B-ALL) as well. Notably, asparaginase, which depletes amino acids and triggers GCN2 activity, is a licensed, first-line B-ALL treatment. Thus, we hypothesize that leukemias exhibiting high GCN2 expression and low ASNS expression may be susceptible to pharmacologic GCN2 inhibition.

Triazolo[4,5-d]pyrimidines as Validated General Control Nonderepressible 2 (GCN2) Protein Kinase Inhibitors Reduce Growth of Leukemia Cells.

The present invention relates to a method of identifying compounds useful in inhibiting protein kinase-like endoplasmic reticulum protein kinase (PERK). The method comprises providing a first model comprising PERK active domains, where the said active domains are selected from the group consisting of the peptide spanning from amino acid residue Asp144 to amino acid residue Ser191 of SEQ ID NO: 1 and a peptide comprising the amino acid residue at position 7 of SEQ ID NO: 1, providing one or more candidate compounds, evaluating contact between the candidate compounds and the first model to determine which of the one or more candidate compounds have an ability to bind to and/or fit in the first model, and identifying the compounds which, based on said evaluating, have the ability to bind to and/or fit in the first model as compounds potentially useful for inhibiting PERK. The present invention further relates to compounds that can be used for inhibition of PERK, for example human PERK, and methods related to treatment of PERK-mediated diseases.

Hong Wang

Papers

Structural determinants of PERK inhibitor potency and selectivity.

Can the energy gap in the protein-ligand binding energy landscape be used as a descriptor in virtual ligand screening?

Triazolo[4,5-d]pyrimidines as Validated General Control Nonderepressible 2 (GCN2) Protein Kinase Inhibitors Reduce Growth of Leukemia Cells.

Method of treating cancer by inhibition of protein kinase-like endoplasmic reticulum protein kinase