Binding site comparisons for target-centered drug discovery

Introduction: The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs.

Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites.

Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein–protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.     

基于多向药理学的BET Bromodomain抑制剂及降解剂研究进展

溴结构域和末端外结构域(BET)Bromodomain已成为可用于治疗癌症和其他人类疾病的新靶标.目前已发现了几类有效的选择性小分子BET Bromodomain抑制剂,且多种已处于临床开发中.临床前和临床数据已证明BET Bromodomain抑制剂有良好的前景,但也存在耐药性等潜在缺陷.目前人们正在尝试将具有不同作用机制的靶标与BET Bromodomain组合,开发基于多向药理学的BET Bromodomain抑制剂及降解剂.本文综述了激酶/BET小分子抑制剂、组蛋白去乙酰化酶/BET小分子抑制剂及BET蛋白降解剂,为后续针对BET蛋白更深入的研究提供思路.     

Cell context-dependent activities of parthenolide in primary and metastatic melanoma cells

Background and purpose:

Growing evidence implicates NF-κB as an important contributor to metastasis and increased chemoresistance of melanoma. Here, we report the effects of parthenolide on either untreated, cisplatin- or TNFα-treated melanoma cell lines A375, 1205Lu and WM793, exhibiting different levels of constitutive NF-κB activity.

Experimental approach:

Electrophoretic mobility shift assay was used to assess changes in NF-κB activity, and real-time PCR to evaluate expression of NF-κB-regulated genes. Cell cycle arrest and apoptosis were assessed by flow cytometry. Cell death was also visualized by fluorescence microscopy. Migration was determined by scratch assay and invasiveness by Matrigel assay.

Key results:

Parthenolide suppressed both constitutive and induced NF-κB activity in melanoma cells. This was accompanied by down-regulation of cancer-related genes, with NF-κB-binding sites in their promoters, including: Bcl-X_L, survivin, cyclin D1, interleukin 8 and matrix metalloproteinase 9. When the various effects of 6 µM parthenolide were compared, apoptosis associated with loss of mitochondrial membrane potential was most efficiently induced in 1205Lu cells, cell cycle arrest in G₀/G₁ phase was observed in WM793 cells, and high metastatic potential was markedly reduced in A375 cells. These findings not only reflected differences between melanoma cell lines in basal expression of NF-κB-regulated genes, but also suggested other parthenolide targets involved in cell cycle progression, migration, invasiveness and survival.

Conclusions:

Inhibition of constitutive and therapeutically induced NF-κB pathway by parthenolide might be useful in the treatment of melanoma, although the diversity of changes induced in melanoma cells with different genetic backgrounds indicate context-dependent poly-pharmacological properties of this compound.     

Impossible or Merely Difficult? Two Grand Challenges from a Biologist’s Perspective

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Identifying mechanism-of-action targets for drugs and probes

Notwithstanding their key roles in therapy and as biological probes, 7% of approved drugs are purported to have no known primary target, and up to 18% lack a well-defined mechanism of action. Using a chemoinformatics approach, we sought to "de-orphanize" drugs that lack primary targets. Surprisingly, targets could be easily predicted for many: Whereas these targets were not known to us nor to the common databases, most could be confirmed by literature search, leaving only 13 Food and Drug Administration-approved drugs with unknown targets; the number of drugs without molecular targets likely is far fewer than reported. The number of worldwide drugs without reasonable molecular targets similarly dropped, from 352 (25%) to 44 (4%). Nevertheless, there remained at least seven drugs for which reasonable mechanism-of-action targets were unknown but could be predicted, including the antitussives clemastine, cloperastine, and nepinalone; the antiemetic benzquinamide; the muscle relaxant cyclobenzaprine; the analgesic nefopam; and the immunomodulator lobenzarit. For each, predicted targets were confirmed experimentally, with affinities within their physiological concentration ranges. Turning this question on its head, we next asked which drugs were specific enough to act as chemical probes. Over 100 drugs met the standard criteria for probes, and 40 did so by more stringent criteria. A chemical information approach to drug-target association can guide therapeutic development and reveal applications to probe biology, a focus of much current interest.     

Targeting the off-targets: a computational bioinformatics approach to understanding the polypharmacology of nelfinavir

Evaluation of: Xie L, Evangelidis T, Xie L, Bourne PE. Drug discovery using chemical systems biology: weak inhibition of multiple kinases may contribute to the anti-cancer effect of nelfinavir. PLoS Comput Biol. 7(4), e1002037 (2011).

In recent years, the identification of new pharmacological effects of already established or abandoned drugs has become a valuable tool for drug repositioning purposes. The HIV drug nelfinavir belongs to those drugs for which empirical data indicate additional pharmacological applications for various diseases, including cancer. To identify and confirm binding partners of nelfinavir other than HIV-1 protease, Xie et al. performed a systematic computational analysis to identify possible structural similarities between the nelfinavir-binding pocket of HIV-1 protease and 5985 protein database entries. Of 126 possible binding partners to nelfinavir, a remarkably high percentage of protein kinases were identified. Further in-depth computational ligand-binding studies indicated the EGF receptor and cytosolic protein kinase B as the most likely off-targets of nelfinavir. Astonishingly, these in silico data are in accordance with previous data obtained by experimental in vitro analysis, indicating a high predictive value of the computer-based approach developed and applied by Xie et al. The computational approach and the authors’ results, with respect to their integration in systems biology, are presented and discussed.     

Exploiting PubChem for virtual screening

Importance of the field: PubChem is a public molecular information repository, a scientific showcase of the National Institutes of Health Roadmap Initiative. The PubChem database holds > 27 million records of unique chemical structures of compounds (compound ID) derived from nearly 70 million substance depositions (substance ID), and contains > 449,000 bioassay records with thousands of in vitro biochemical and cell-based screening bioassays established targeting > 7000 proteins and genes linking to > 1.8 million of substances.

Areas covered in this review: This review builds on recent PubChem-related computational chemistry research reported by other authors while providing readers with an overview of the PubChem database, focusing on its increasing role in cheminformatics, virtual screening and toxicity prediction modeling.

What the reader will gain: These publicly available data sets in PubChem provide great opportunities for scientists to perform cheminformatics and virtual screening research for computer-aided drug design. However, the high volume and complexity of the data sets, in particular the bioassay-associated false positives/negatives and highly imbalanced data sets in PubChem, also create major challenges. Several approaches regarding the modeling of PubChem data sets and development of virtual screening models for bioactivity and toxicity predictions are also reviewed.

Take home message: Novel data-mining cheminformatics tools and virtual screening algorithms are being developed and used to retrieve, annotate and analyze the large-scale and highly complex PubChem biological screening data for drug design.