Insight into drug resistance mechanisms and discovery of potential inhibitors against wild-type and L1196M mutant ALK from FDA-approved drugs

Comprehensive evaluation of the MM-GBSA method on bromodomain-inhibitor sets

MM-PB/GBSA methods represent a higher-level scoring theory than docking. This study reports an extensive testing of different MM-GBSA scoring schemes on two bromodomain (BRD) datasets. The first set is composed of 24 BRPF1 complexes, and the second one is a nonredundant set constructed from the PDBbind and composed of 28 diverse BRD complexes. A variety of MM-GBSA schemes were analyzed to evaluate the performance of four protocols with different numbers of minimization and MD steps, 10 different force fields and three different water models. Results showed that neither additional MD steps nor unfixing the receptor atoms improved scoring or ranking power. On the contrary, our results underscore the advantage of fixing receptor atoms or limiting the number of MD steps not only for a reduction in the computational costs but also for boosting the prediction accuracy. Among Amber force fields tested, ff14SB and its derivatives rather than ff94 or polarized force fields provided the most accurate scoring and ranking results. The TIP3P water model yielded the highest scoring and ranking power compared to the others. Posing power was further evaluated for the BRPF1 set. A slightly better posing power for the protocol which uses both minimization and MD steps with a fixed receptor than the one which uses only minimization with a fully flexible receptor-ligand system was observed. Overall, this study provides insights into the usage of the MM-GBSA methods for screening of BRD inhibitors, substantiating the benefits of shorter protocols and latest force fields and maintaining the crystal waters for accuracy.

Different Positron Emission Tomography Tau Tracers Bind to Multiple Binding Sites on the Tau Fibril: Insight from Computational Modeling

Using the recently reported cryo-EM structure for the tau fibril [ Fitzpatrick et al. (2017) Nature 547, 185-190 ], which is a potential target concerning Alzheimer's disease, we present the first molecular modeling studies on its interaction with various positron emission tomography (PET) tracers. Experimentally, based on the binding assay studies, at least three different high-affinity binding sites have been reported for tracers in the tau fibril. Herein, through integrated modeling using molecular docking, molecular dynamics, and binding free energy calculations, we provide insight into the binding patterns of various tracers to the tau fibril. We suggest that there are four different high-affinity binding sites available for many of the studied tracers showing varying binding affinity to different binding sites. Thus, PBB3 binds most strongly to site 4, and interestingly, this site is not a preferable site for any other tracers. For THK5351, our data show that it strongly binds to sites 3 and 1, the former one being more preferable. We also find that MK6240 and T807 bind to site 1 specifically. The modeling data also give some insight into whether a tracer bound to a specific site can be replaced by others or not. For example, the displacement of T807 by PBB3 as reported experimentally can also be explained and attributed to the larger binding affinity of the latter compound in all binding sites. The binding free energy results explain very well the small binding affinity of THK523 compared to all the aryl quinoline moieties containing THK tracers. The ability of certain tau tracers, like FDDNP and THK523, to bind to amyloid fibrils has also been investigated. Furthermore, such off-target interaction of tau tracers with amyloid beta fibrils has been validated using a quantum mechanical fragmentation approach.

Structure and energy based quantitative missense variant effect analysis provides insights into drug resistance mechanisms of anaplastic lymphoma kinase mutations

Anaplastic lymphoma kinase (ALK) is considered as a validated molecular target in multiple malignancies, such as non-small cell lung cancer (NSCLC). However, the effectiveness of molecularly targeted therapies using ALK inhibitors is almost universally limited by drug resistance. Drug resistance to molecularly targeted therapies has now become a major obstacle to effective cancer treatment and personalized medicine. It is of particular importance to provide an improved understanding on the mechanisms of resistance of ALK inhibitors, thus rational new therapeutic strategies can be developed to combat resistance. We used state-of-the-art computational approaches to systematically explore the mutational effects of ALK mutations on drug resistance properties. We found the activation of ALK was increased by substitution with destabilizing mutations, creating the capacity to confer drug resistance to inhibitors. In addition, results implied that evolutionary constraints might affect the drug resistance properties. Moreover, an extensive profile of drugs against ALK mutations was constructed to give better understanding of the mechanism of drug resistance based on structural transitions and energetic variation. Our work hopes to provide an up-to-date mechanistic framework for understanding the mechanisms of drug resistance induced by ALK mutations, thus tailor treatment decisions after the emergence of resistance in ALK-dependent diseases.

The second-generation ALK inhibitor alectinib effectively induces apoptosis in human neuroblastoma cells and inhibits tumor growth in a TH-MYCN transgenic neuroblastoma mouse model

Activating germline mutations of anaplastic lymphoma kinase (ALK) occur in most cases of hereditary neuroblastoma (NB) and the constitutively active kinase activity of ALK promotes cell proliferation and survival in NB. Therefore, ALK kinase is a potential therapeutic target for NB. In this study, we show that the novel ALK inhibitor alectinib effectively suppressed cell proliferation and induces apoptosis in NB cell lines with either wild-type ALK or mutated ALK (F1174L and D1091N) by blocking ALK-mediated PI3K/Akt/mTOR signaling. In addition, alectinib enhanced doxorubicin-induced cytotoxicity and apoptosis in NB cells. Furthermore, alectinib induced apoptosis in an orthotopic xenograft NB mouse model. Also, in the TH-MYCN transgenic mouse model, alectinib resulted in decreased tumor growth and prolonged survival time. These results indicate that alectinib may be a promising therapeutic agent for the treatment of NB.