Molecular Basis of Inactive B-RAFWT and B-RAFV600E Ligand Inhibition, Selectivity and Conformational Stability: An in Silico Study

Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation

High-risk tumors are genomically heterogeneous, harboring gene amplifications and mutations. The activation status of mutated proteins in cancer can profoundly impact disease progression, patient response, and drug sensitivity. Yet, outside of a few hotspot mutations, functional studies of clinically observed mutations are not commonly pursued. We report a combined experimental profiling and computational analysis of the effects of clinically observed and “test” mutations in the kinase domain of anaplastic lymphoma kinase (ALK), a known oncogenic driver in pediatric neuroblastoma. We find that the activation status of the mutated protein is a good indicator of the transforming ability in NIH 3T3 cells. We also report biophysical as well as data-driven models with predictive power to profile these mutant kinases in silico.

Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently mutated in cancer and are the targets of a large number of specific inhibitors. Surveys of cancer genome atlases reveal that kinase domains, which consist of 300 amino acids, can harbor numerous (150 to 200) single-point mutations across different patients in the same disease. This preponderance of mutations—some activating, some silent—in a known target protein make clinical decisions for enrolling patients in drug trials challenging since the relevance of the target and its drug sensitivity often depend on the mutational status in a given patient. We show through computational studies using molecular dynamics (MD) as well as enhanced sampling simulations that the experimentally determined activation status of a mutated kinase can be predicted effectively by identifying a hydrogen bonding fingerprint in the activation loop and the αC-helix regions, despite the fact that mutations in cancer patients occur throughout the kinase domain. In our study, we find that the predictive power of MD is superior to a purely data-driven machine learning model involving biochemical features that we implemented, even though MD utilized far fewer features (in fact, just one) in an unsupervised setting. Moreover, the MD results provide key insights into convergent mechanisms of activation, primarily involving differential stabilization of a hydrogen bond network that engages residues of the activation loop and αC-helix in the active-like conformation (in >70% of the mutations studied, regardless of the location of the mutation).

Computational algorithms for in silico profiling of activating mutations in cancer

Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods-including those based on machine learning techniques and evolutionary methods-are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent.

The effect of a widespread cancer-causing mutation on the inactive to active dynamics of the B-Raf kinase

Protein kinases play a key role in regulating cellular processes. Kinase dysfunction can lead to disease, making them an attractive target for drug design. The B-Raf kinase is a key target for the treatment of melanoma since a single mutation (V600E) is found in more than 50% of all malignant melanomas. Despite the importance of B-Raf in melanoma treatment, the molecular mechanism by which the mutation increases kinase activity remains elusive. Since kinases are tightly regulated by a conformational transition between an active and inactive state, which is difficult to capture experimentally, large-scale enhanced-sampling simulations are performed to examine the mechanism by which the V600E mutation enhances the activity of the B-Raf monomer. The results reveal that the mutation has a twofold effect. First, the mutation increases the barrier of the active to inactive transition trapping B-Raf in the active state. The mutation also increases the flexibility of the activation loop which might speed-up the rate-limiting step of phosphorylation. Both effects can be explained by the formation of salt-bridges with the Glu600 residue.

Insight into the structural features of pyrazolopyrimidine- and pyrazolopyridine-based B-Raf(V600E) kinase inhibitors by computational explorations

Presently, both ligand-based and receptor-based 3D-QSAR modelings were performed on 107 pyrazolopyrimidine- and pyrazolopyridine-based inhibitors of B-Raf(V600E) kinase. The optimal model is successful to predict the inhibitors' activity with Q(2) of 0.504, R(2) ncv of 0.960, and R(2) pred of 0.872. Besides, the 3D contour maps explain well the structural requirements of the interaction between the ligand and the receptor. Furthermore, molecular docking and MD were also carried out to study the binding mode. Our findings are the following: (i) Bulky substituents at position 3, 10 and ring D improve the inhibitory activity, but impair the activity at position 5, 11, and 19. (ii) Electropositive groups at position 10, 13 and 20 and electronegative groups at position 2 increase the biological activity. (iii) Hydrophobic substituents at ring C are beneficial to improve the biological activity, while hydrophilic substituents at position 11 and ring D are good for the activity. (4) This scaffold of inhibitors may bind to the B-Raf kinase with an 'L' conformation and belong to type III binding mode, which is fixed by hydrophobic interaction and hydrogen bonds with residues from hinge region and DFG motif. These results may be a guidance to develop new B-Raf(V600E) kinase inhibitors.

Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers

Every malignant tumor has a unique spectrum of genomic alterations including numerous protein mutations. There are also hundreds of personal germline variants to be taken into account. The combinatorial diversity of potential cancer-driving events limits the applicability of statistical methods to determine tumor-specific "driver" alterations among an overwhelming majority of "passengers". An alternative approach to determining driver mutations is to assess the functional impact of mutations in a given tumor and predict drivers based on a numerical value of the mutation impact in a particular context of genomic alterations.Recently, we introduced a functional impact score, which assesses the mutation impact by the value of entropic disordering of the evolutionary conservation patterns in proteins. The functional impact score separates disease-associated variants from benign polymorphisms with an accuracy of ~80%. Can the score be used to identify functionally important non-recurrent cancer-driver mutations? Assuming that cancer-drivers are positively selected in tumor evolution, we investigated how the functional impact score correlates with key features of natural selection in cancer, such as the non-uniformity of distribution of mutations, the frequency of affected tumor suppressors and oncogenes, the frequency of concurrent alterations in regions of heterozygous deletions and copy gain; as a control, we used presumably non-selected silent mutations. Using mutations of six cancers studied in TCGA projects, we found that predicted high-scoring functional mutations as well as truncating mutations tend to be evolutionarily selected as compared to low-scoring and silent mutations. This result justifies prediction of mutations-drivers using a shorter list of predicted high-scoring functional mutations, rather than the "long tail" of all mutations.

Investigation of the differences in activity between hydroxycycloalkyl N1 substituted pyrazole derivatives as inhibitors of B-Raf kinase by using docking, molecular dynamics, QM/MM, and fragment-based de novo design: study of binding mode of diastereomer compounds

N1 substituted pyrazole derivatives show diverse B-Raf kinase inhibitory activities when different hydroxy-substituted cycloalkyl groups are placed at this position. Docking, molecular dynamics (MD) simulations, and hybrid calculation methods (Quantum Mechanics/Molecular Mechanics (QM/MM)) were performed on the complexes, in order to explain these differences. Docking of the inhibitors showed the same orientation that X-ray crystal structure of the analogous (1E)-5-[1-(4-piperidinyl)-3-(4-pyridinyl)-1H-pyrazol-4-yl]-2,3-dihydro-1H-inden-1-one oxime. MD simulations of the most active diastereomer compounds containing cis- and trans-3-hydroxycyclohexyl substituents showed stable interactions with residue Ile463 at the entrance of the B-Raf active site. On the other hand, the less active diastereomer compounds containing cis- and trans-2-hydroxycyclopentyl substituents showed interactions with inner residues Asn580 and Ser465. We found that the differences in activity can be explained by considering the dynamic interactions between the inhibitors and their surrounding residues within the B-Raf binding site. We also explained the activity trend by using a testing scoring function derived from more reliable QM/MM calculations. In addition, we search for new inhibitors from a virtual screening carried out by fragment-based de novo design. We generated a set of approximately 200 virtual compounds, which interact with Ile463 and fulfill druglikeness properties according to Lipinski, Veber, and Ghose rules.

Molecular simulation study of the effect of various additives on salbutamol sulfate crystal habit

The effects of polyvinylpyrrolidone (PVP), hydroxypropyl methyl cellulose (HPMC), and lecithin additives on salbutamol sulfate (SS) crystal growth are studied using molecular dynamics (MD) simulation, to provide an insight into the interaction between the additives and SS crystal faces at the atomistic level. The interaction energy between additives and crystal faces is presented. The intermolecular contacts between the additives and the crystal faces are analyzed by calculating the average number of contacts between O atoms of the additives and the H atoms of the first layer of the SS crystal. The mobility of each additive on SS crystal faces is also reported by determining the mean square displacement. Our results suggest that PVP is the most effective among the three additives for the inhibition of SS crystal growth. The methodology used in this study could be a powerful tool for selection of habit-modifying additives in other crystallization systems.

Molecular dynamics simulation, free energy calculation and structure-based 3D-QSAR studies of B-RAF kinase inhibitors

(V600E)B-RAF kinase is the most frequent onco-genic protein kinase mutation in melanoma and is a promising target to treat malignant melanoma. In this work, a molecular modeling study combining QM-polarized ligand docking, molecular dynamics, free energy calculation, and three-dimensional quantitative structure-activity relationships (3D-QSAR) was performed on a series of pyridoimidazolone compounds as the inhibitors of (V600E)B-RAF kinase to understand the binding mode between the inhibitors and (V600E)B-RAF kinase and the structural requirement for the inhibiting activity. 3D-QSAR models, including CoMFA and CoMSIA, were developed from the conformations obtained by QM-polarized ligand docking strategy. The obtained models have a good predictive ability in both internal and external validation. Furthermore, molecular dynamics simulation and free energy calculations were employed to determine the detailed binding process and to compare the binding mode of the inhibitors with different activities. The binding free energies calculated by MM/PBSA gave a good correlation with the experimental biological activity. The decomposition of free energies by MM/GBSA indicates the van der Waals interaction is the major driving force for the interaction between the inhibitors and (V600E)B-RAF kinase. The hydrogen bond interactions between the inhibitors with Glu501 and Asp594 of the (V600E)B-RAF kinase help to stabilize the DFG-out conformation. The results from this study can provide some insights into the development of novel potent (V600E)B-RAF kinase inhibitors.

Computational study on the interaction of N1 substituted pyrazole derivatives with B-raf kinase: an unusual water wire hydrogen-bond network and novel interactions at the entrance of the active site

Docking and molecular dynamics (MD) simulations of N1 substituted pyrazole derivatives complexed with B-Raf kinase were performed to gain insight into the structural and energetic preferences of these inhibitors. First, a comparative study of fully automated docking programs AutoDock, ICM, GLIDE, and Surflex-Dock in closely approximating the X-ray crystal structure of the inhibitor (1E)-5-[1-(4-piperidinyl)-3-(4-pyridinyl)-1H-pyrazol-4-yl]-2,3-dihydro-1H-inden-1-one oxime was performed. Afterward, the dynamics of the above-mentioned compound and the less active analogous compounds with 1-methyl-4-piperidinyl and tetrahydro-2H-pyran-4-yl groups at position N1 of pyrazole ring inside the B-Raf active site were analyzed by MD simulations. We found that the most active compound has stable interactions with residues Ile463 and His539 at the entrance of the B-Raf active site. Those interactions were in very good agreement with more reliable quantum mechanics/molecular mechanics calculations performed on the torsional angle phi between the pyrazole ring and the substituents at position N1. In addition, we identified a water wire connecting N2 of the pyrazole ring, Cys532, and Ser536, which is composed of three water molecules for the most active compound. We found some differences in the water wire hydrogen-bond network formed by less active compounds. We suggest that the differences between these structural features are responsible for the differences in activity among the studied compounds.

Molecular basis of the interaction for an essential subunit PA-PB1 in influenza virus RNA polymerase: insights from molecular dynamics simulation and free energy calculation

The emergence of the extremely aggressive influenza recently has highlighted the urgent need for new effective treatments. The influenza RNA-dependent RNA polymerase (RdRp) heterotrimer including PA, PB1 and PB2 has crucial roles in viral RNA replication and transcription. The highly conserved PB1 binding site on PA can be considered as a novel potential drug target site. The interaction between PB1 binding site and PA is crucial to many functions of the virus. In this study, to understand the detailed interaction profile and to characterize the binding hot spots in the interactions of the PA-PB1 complex, an 8 ns molecular dynamics simulation of the subunit PA-PB1 combined with MM-PBSA (molecular mechanics Poisson-Boltzmann surface area), MM-GBSA (molecular mechanics generalized Born surface area) computations and virtual alanine scanning were performed. The results from the free energy decomposition indicate that the intermolecular van der Waals interaction and the nonpolar solvation term provide the driving force for binding process. Through the pair interaction analysis and virtual alanine scanning, we identified the binding hot spots of PA and the basic binding motif of PB1. This information can provide some insights for the structure-based RNA-dependent RNA polymerase inhibitors design. The identified binding motif can be used as the starting point for the rational design of small molecules or peptide mimics. This study will also lead to new opportunities toward the development of new generation therapeutic agents exhibiting specificity and low resistance to influenza virus.

How to obtain statistically converged MM/GBSA results

The molecular mechanics/generalized Born surface area (MM/GBSA) method has been investigated with the aim of achieving a statistical precision of 1 kJ/mol for the results. We studied the binding of seven biotin analogues to avidin, taking advantage of the fact that the protein is a tetramer with four independent binding sites, which should give the same estimated binding affinities. We show that it is not enough to use a single long simulation (10 ns), because the standard error of such a calculation underestimates the difference between the four binding sites. Instead, it is better to run several independent simulations and average the results. With such an approach, we obtain the same results for the four binding sites, and any desired precision can be obtained by running a proper number of simulations. We discuss how the simulations should be performed to optimize the use of computer time. The correlation time between the MM/GBSA energies is approximately 5 ps and an equilibration time of 100 ps is needed. For MM/GBSA, we recommend a sampling time of 20-200 ps for each separate simulation, depending on the protein. With 200 ps production time, 5-50 separate simulations are required to reach a statistical precision of 1 kJ/mol (800-8000 energy calculations or 1.5-15 ns total simulation time per ligand) for the seven avidin ligands. This is an order of magnitude more than what is normally used, but such a number of simulations is needed to obtain statistically valid results for the MM/GBSA method.

The phosphorylation specificity of B-RAF WT, B-RAF D594V, B-RAF V600E and B-RAF K601E kinases: an in silico study

Phosphorylation of the B-RAF kinase is an essential process in tumour induction and maintenance in several cancers. Herein the phosphorylation specificity of the activation segment of the wild type B-RAF kinase and the B-RAF(D594V), B-RAF(V600E) and B-RAF(K601E) mutants was examined by molecular dynamics (MD) simulations and GRID molecular interaction field analysis. According to our analysis, Thr599 and Ser602 were the only residues in the activation segment in B-RAF(WT) that were well exposed to ATP binding, which is in agreement with the experimental results, and provide a molecular basis of the observed phosphorylation. The phosphorylation specificity was altered significantly for each of the three different mutants studied due to the large conformational changes and subsequent alterations in the electrostatic forces between several residues for each of these mutants. Thus the analysis revealed limited phosphorylation potential of the non-active B-RAF(D594V) mutant and several potential ATP binding sites were identified for the highly active B-RAF(V600E) mutant. The Lys601 residue, which is specific to RAF and not present in the activation segment of other similar kinases, was identified to potentially be of major importance to the observed differences in the phosphorylation specificity of the mutants. Our results indicate that Lys601 might be a specific ATP coordinating residue, contributing to the B-RAF phosphorylation specificity. The overall results can be helpful for the understanding of the B-RAF phosphorylation processes on a molecular level.

An in silico study of the molecular basis of B-RAF activation and conformational stability

BACKGROUND

B-RAF kinase plays an important role both in tumour induction and maintenance in several cancers and it is an attractive new drug target. However, the structural basis of the B-RAF activation is still not well understood.

RESULTS

In this study we suggest a novel molecular basis of B-RAF activation based on molecular dynamics (MD) simulations of B-RAFWT and the B-RAFV600E, B-RAFK601E and B-RAFD594V mutants. A strong hydrogen bond network was identified in B-RAFWT in which the interactions between Lys601 and the well known catalytic residues Lys483, Glu501 and Asp594 play an important role. It was found that several mutations, which directly or indirectly destabilized the interactions between these residues within this network, contributed to the changes in B-RAF activity.

CONCLUSION

Our results showed that the above mechanisms lead to the disruption of the electrostatic interactions between the A-loop and the alphaC-helix in the activating mutants, which presumably contribute to the flipping of the activation segment to an active form. Conversely, in the B-RAFD594V mutant that has impaired kinase activity, and in B-RAFWT these interactions were strong and stabilized the kinase inactive form.