How does conformational flexibility influence key structural features involved in activation of anaplastic lymphoma kinase?

Comparative Atomistic Insights on Apo and ATP-I1171N/S/T in Nonsmall-Cell Lung Cancer

Anaplastic lymphoma kinase (ALK) rearrangements occur in about 5% of nonsmall cell lung cancer (NSCLC) patients. Despite being first recognized as EML4-ALK, fusions with several additional genes have been identified, all of which cause constitutive activation of the ALK kinase and subsequently lead to tumor development. ALK inhibitors first-line crizotinib, second-line ceritinib, and alectinib are effective against NSCLC patients with these rearrangements. Patients progressing on crizotinib had various mutations in the ALK kinase domain. ALK fusion proteins are activated by oligomerization through the fusion partner, which leads to the autophosphorylation of the kinase's domain and consequent downstream activation. The proposed computational study focuses on understanding the activation mechanism of ALK and ATP binding of wild-type (WT) and I1171N/S/T mutations. We analyzed the conformational change of ALK I1171N/S/T mutations and ATP binding using molecular docking and molecular dynamics simulation approaches. According to principal component analysis and free energy landscape, it is clear that I1171N/S/T mutations in Apo and ATP showed different energy minima/unstable structures compared to WT-Apo. The results revealed that I1171N/S/T mutations and ATP binding significantly supported a change toward an active-state conformation, whereas WT-Apo remained inactive. We demonstrated that I1171N/S/T mutations are persistent in an active state and independent of ATP. The I1171S/T mutations showed greater intermolecular H-bonds with ATP than WT-ATP. The molecular mechanics Poisson-Boltzmann surface area analysis revealed that the I1171N/S/T mutation binding energy was similar to that of WT-ATP. This study shows that I1171N/S/T can form stable bonds with ATP and may contribute to a constitutively active kinase. Based on the Y1278-C1097 H-bond and E1167-K1150 salt bridge interaction, I1171N strongly promotes the constitutively active kinase independent of ATP. This structural mechanism study will aid in understanding the oncogenic activity of ALK and the basis for improving the ALK inhibitors.

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.

Understanding Miro GTPases: Implications in the Treatment of Neurodegenerative Disorders

The Miro GTPases represent an unusual subgroup of the Ras superfamily and have recently emerged as important mediators of mitochondrial dynamics and for maintaining neuronal health. It is now well-established that these enzymes act as essential components of a Ca²⁺-sensitive motor complex, facilitating the transport of mitochondria along microtubules in several cell types, including dopaminergic neurons. The Miros appear to be critical for both anterograde and retrograde mitochondrial transport in axons and dendrites, both of which are considered essential for neuronal health. Furthermore, the Miros may be significantly involved in the development of several serious pathological processes, including the development of neurodegenerative and psychiatric disorders. In this review, we discuss the molecular structure and known mitochondrial functions of the Miro GTPases in humans and other organisms, in the context of neurodegenerative disease. Finally, we consider the potential human Miros hold as novel therapeutic targets for the treatment of such disease.

Effects of Mutations on Structure-Function Relationships of Matrix Metalloproteinase-1

Matrix metalloproteinase-1 (MMP-1) is one of the most widely studied enzymes involved in collagen degradation. Mutations of specific residues in the MMP-1 hemopexin-like (HPX) domain have been shown to modulate activity of the MMP-1 catalytic (CAT) domain. In order to reveal the structural and conformational effects of such mutations, a molecular dynamics (MD) study was performed of in silico mutated residues in the X-ray crystallographic structure of MMP-1 complexed with a collagen-model triple-helical peptide (THP). The results indicate an important role of the mutated residues in MMP-1 interactions with the THP and communication between the CAT and the HPX domains. Each mutation has a distinct impact on the correlated motions in the MMP-1•THP. An increased collagenase activity corresponded to the appearance of a unique anti-correlated motion and decreased correlated motions, while decreased collagenase activity corresponded both to increased and decreased anti-correlated motions.

Dimerization and ligand binding in tyrosylprotein sulfotransferase-2 are influenced by molecular motions

Tyrosylprotein sulfotransferase-2 catalyses important, but a less explored posttranslational modification of proteins.