Analyzing Kinase Similarity in Small Molecule and Protein Structural Space to Explore the Limits of Multi-Target Screening.

While selective inhibition is one of the key assets for a small molecule drug, many diseases can only be tackled by simultaneous inhibition of several proteins. An example where achieving selectivity is especially challenging are ligands targeting human kinases. This difficulty arises from the high structural conservation of the kinase ATP binding sites, the area targeted by most inhibitors. We investigated the possibility to identify novel small molecule ligands with pre-defined binding profiles for a series of kinase targets and anti-targets by in silico docking. The candidate ligands originating from these calculations were assayed to determine their experimental binding profiles. Compared to previous studies, the acquired hit rates were low in this specific setup, which aimed at not only selecting multi-target kinase ligands, but also designing out binding to anti-targets. Specifically, only a single profiled substance could be verified as a sub-micromolar, dual-specific EGFR/ErbB2 ligand that indeed avoided its selected anti-target BRAF. We subsequently re-analyzed our target choice and in silico strategy based on these findings, with a particular emphasis on the hit rates that can be expected from a given target combination. To that end, we supplemented the structure-based docking calculations with bioinformatic considerations of binding pocket sequence and structure similarity as well as ligand-centric comparisons of kinases. Taken together, our results provide a multi-faceted picture of how pocket space can determine the success of docking in multi-target drug discovery efforts.

The yin-yang of kinase activation and unfolding explains the peculiarity of Val600 in the activation segment of BRAF.

Many driver mutations in cancer are specific in that they occur at significantly higher rates than - presumably - functionally alternative mutations. For example, V600E in the BRAF hydrophobic activation segment (AS) pocket accounts for >95% of all kinase mutations. While many hypotheses tried to explain such significant mutation patterns, conclusive explanations are lacking. Here, we use experimental and in silico structure-energy statistical analyses, to elucidate why the V600E mutation, but no other mutation at this, or any other positions in BRAF's hydrophobic pocket, is predominant. We find that BRAF mutation frequencies depend on the equilibrium between the destabilization of the hydrophobic pocket, the overall folding energy, the activation of the kinase and the number of bases required to change the corresponding amino acid. Using a random forest classifier, we quantitatively dissected the parameters contributing to BRAF AS cancer frequencies. These findings can be applied to genome-wide association studies and prediction models.

Insight into molecular dynamics simulation of BRAF(V600E) and potent novel inhibitors for malignant melanoma.

BRAF inhibitors have changed the standard therapeutic protocol for advanced or metastatic melanoma which harbored notorious BRAF(V600E) single mutation. However, drug resistance to BRAF inhibitors happens just like other cancer treatment. In this study, we constructed the ideal BRAF(V600E)-modeled structure through homology modeling and introduced the method of structure-based docking or virtual screening from the large compound database. Through certain methods of molecular dynamics simulation, we realized that BRAF(V600E) had quite prominent difference of molecular character or structural variation from the wild-type BRAF protein. It might confer the metamorphic character of advanced melanoma for the patients who harbored BRAF(V600E) mutation. By the methods of ligand-based quantitative structure-activity relationship and molecular dynamics simulation, we further recommend that aknadicine and 16beta-hydroxy-19s-vindolinine N-oxide from the traditional Chinese medicine are potent novel inhibitors for the management of malignant melanoma in the future.

[Dabrafenib: the new inhibitor of hyperactive B-RAF kinase]. / Dabrafenib: nový inhibitor hyperaktivní kinázy B-RAF.

The B-RAF kinase is among major targets of biological therapy of cancer. B-RAF acts in the MAP kinase pathway, being activated by any of the RAS G-proteins. Hyperactive B-RAF is typically detected in chemoresistant and radioresistant malignant metastatic melanoma. In this study, we focus on the reversible ATP-competitive inhibitor dabrafenib (GSK-2118436), which is now in phase III clinical trial for use in subjects with various cancers expressing hyperactive B-RAF. Dabrafenib is selective for B-RAFV600E and B-RAFV600K (less for B-RAFV600D) over wild-type B-RAF. Thus, similarly to vemurafenib (Zelboraf), suggested is mandatory pre-screening for activating B-RAF mutations in the cancer tissue of each subject. Dabrafenib inhibits neoplastic growth at concentrations 53.8 nM in plasma, which corresponds to 30 mg/kg qd p.o., or to --- 3 mg/kg qd i.v. Most of the cancers expressing hyperactive B-RAF respond to dabrafenib treatment, but the complete response is only rarely achieved. Toxic side effects include skin lesions, pyrexia, frequent fatigue, nausea and pain. Resistance to dabrafenib is frequently developed via de novo RAS mutations, leading to the disease relapse. The RAS G-protein is capable of signaling downstream not only through B-RAF, but also through closely related C-RAF, which circumvents the effects of the B-RAF inhibitor. Thus, dabrafenib should not be prescribed to subjects with neoplasias that are positive for activating RAS mutations. Since B-RAF mutations alone cause only the formation of benign naevi, since the tumors frequently and quickly acquire resistance to B-RAF inhibitors, and because the B-RAF-inhibitor-mediated treatment outcomes are severely affected by changes in the activity and expression of a number of signaling molecules (among them PI3K/mTOR, PTEN, AKT, MEK, PDGFRß), it can be anticipated that dabrafenib treatment should be suggested only as a part of combined therapy targeting simultaneously the other pathways responsible for cancer onset and progression.

Identification of novel BRAF kinase inhibitors with structure-based virtual screening.

VRAF murine sarcoma viral oncogene homologue B1 (BRAF) kinase has proved to be a promising target for the development of therapeutics for the treatment of a variety of human cancers. Here, we report the first example of a successful application of the structure-based virtual screening to identify novel BRAF inhibitors. These inhibitors have desirable physicochemical properties as a drug candidate, and compound 1 revealed a submicromolar binding affinity (0.7 µM). Therefore, they may serve as promising lead compounds for further development by structure-activity relationship (SAR) studies to optimize the inhibitory activities. Structural features relevant to the stabilization of the newly identified inhibitors in the ATP-binding site of BRAF are discussed in detail.

Development of novel 3D-QSAR combination approach for screening and optimizing B-Raf inhibitors in silico.

B-Raf is a member of the RAF family of serine/threonine kinases: it mediates cell division, differentiation, and apoptosis signals through the RAS-RAF-MAPK pathway. Thus, B-Raf is of keen interest in cancer therapy, such as melanoma. In this study, we propose the first combination approach to integrate the pharmacophore (PhModel), CoMFA, and CoMSIA models for B-Raf, and this approach could be used for screening and optimizing potential B-Raf inhibitors in silico. Ten PhModels were generated based on the HypoGen BEST algorithm with the flexible fit method and diverse inhibitor structures. Each PhModel was designated to the alignment rule and screening interface for CoMFA and CoMSIA models. Therefore, CoMFA and CoMSIA models could align and recognize diverse inhibitor structures. We used two quality validation methods to test the predication accuracy of these combination models. In the previously proposed combination approaches, they have a common factor in that the number of training set inhibitors is greater than that of testing set inhibitors. In our study, the 189 known diverse series B-Raf inhibitors, which are 7-fold the number of training set inhibitors, were used as a testing set in the partial least-squares validation. The best validation results were made by the CoMFA09 and CoMSIA09 models based on the Hypo09 alignment model. The predictive r(2)(pred) values of 0.56 and 0.56 were derived from the CoMFA09 and CoMSIA09 models, respectively. The CoMFA09 and CoMSIA09 models also had a satisfied predication accuracy of 77.78% and 80%, and the goodness of hit test score of 0.675 and 0.699, respectively. These results indicate that our combination approach could effectively identify diverse B-Raf inhibitors and predict the activity.