Activating mutations drive human MEK1 kinase using a gear-shifting mechanism.

There is an unmet need to classify cancer-promoting kinase mutations in a mechanistically cognizant way. The challenge is to understand how mutations stabilize different kinase configurations to alter function, and how this influences pathogenic potential of the kinase and its responses to therapeutic inhibitors. This goal is made more challenging by the complexity of the mutational landscape of diseases, and is further compounded by the conformational plasticity of each variant where multiple conformations coexist. We focus here on the human MEK1 kinase, a vital component of the RAS/MAPK pathway in which mutations cause cancers and developmental disorders called RASopathies. We sought to explore how these mutations alter the human MEK1 kinase at atomic resolution by utilizing enhanced sampling simulations and free energy calculations. We computationally mapped the different conformational stabilities of individual mutated systems by delineating the free energy landscapes, and showed how this relates directly to experimentally quantified developmental transformation potentials of the mutations. We conclude that mutations leverage variations in the hydrogen bonding network associated with the conformational plasticity to progressively stabilize the active-like conformational state of the kinase while destabilizing the inactive-like state. The mutations alter residue-level internal molecular correlations by differentially prioritizing different conformational states, delineating the various modes of MEK1 activation reminiscent of a gear-shifting mechanism. We define the molecular basis of conversion of this kinase from its inactive to its active state, connecting structure, dynamics, and function by delineating the energy landscape and conformational plasticity, thus augmenting our understanding of MEK1 regulation.

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

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).

Multiscale modeling of protein membrane interactions for nanoparticle targeting in drug delivery.

Nanoparticle (NP)-based imaging and drug delivery systems for systemic (e.g. intravenous) therapeutic and diagnostic applications are inherently a complex integration of biology and engineering. A broad range of length and time scales are essential to hydrodynamic and microscopic molecular interactions mediating NP (drug nanocarriers, imaging agents) motion in blood flow, cell binding/uptake, and tissue accumulation. A computational model of time-dependent tissue delivery, providing in silico prediction of organ-specific accumulation of NPs, can be leveraged in NP design and clinical applications. In this article, we provide the current state-of-the-art and future outlook for the development of predictive models for NP transport, targeting, and distribution through the integration of new computational schemes rooted in statistical mechanics and transport. The resulting multiscale model will comprehensively incorporate: (i) hydrodynamic interactions in the vascular scales relevant to NP margination; (ii) physical and mechanical forces defining cellular and tissue architecture and epitope accessibility mediating NP adhesion; and (iii) subcellular and paracellular interactions including molecular-level targeting impacting NP uptake.

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.

EEG-based classification of bilingual unspoken speech using ANN.

The ability to interpret unspoken or imagined speech through electroencephalography (EEG) is of therapeutic interest for people suffering from speech disorders and `lockedin' syndrome. It is also useful for brain-computer interface (BCI) techniques not involving articulatory actions. Previous work has involved using particular words in one chosen language and training classifiers to distinguish between them. Such studies have reported accuracies of 40-60% and are not ideal for practical implementation. Furthermore, in today's multilingual society, classifiers trained in one language alone might not always have the desired effect. To address this, we present a novel approach to improve accuracy of the current model by combining bilingual interpretation and decision making. We collect data from 5 subjects with Hindi and English as primary and secondary languages respectively and ask them 20 `Yes'/`No' questions (`Haan'/`Na' in Hindi) in each language. We choose sensors present in regions important to both language processing and decision making. Data is preprocessed, and Principal Component Analysis (PCA) is carried out to reduce dimensionality. This is input to Support Vector Machine (SVM), Random Forest (RF), AdaBoost (AB), and Artificial Neural Networks (ANN) classifiers for prediction. Experimental results reveal best accuracy of 85.20% and 92.18% for decision and language classification respectively using ANN. Overall accuracy of bilingual speech classification is 75.38%.