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Ramanathan, Arvind

Publications and source records attributed to Ramanathan, Arvind.

43 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Biochemical and structural analyses reveal that the tumor suppressor neurofibromin (NF1) forms a high-affinity dimer

Neurofibromin is a tumor suppressor encoded by the NF1 gene, which is mutated in Rasopathy disease neurofibromatosis type I. Defects in NF1 lead to aberrant signaling through the RAS–mitogen-activated protein kinase pathway due to disruption of the neurofibromin GTPase-activating function on RAS family small GTPases. Very little is known about the function of most of the neurofibromin protein; to date, biochemical and structural data exist only for its GAP domain and a region containing a Sec-PH motif. To better understand the role of this large protein, here we carried out a series of biochemical and biophysical experiments, including size-exclusion chromatography–multiangle light scattering (SEC-MALS), small-angle X-ray and neutron scattering, and analytical ultracentrifugation, indicating that full-length neurofibromin forms a high-affinity dimer. We observed that neurofibromin dimerization also occurs in human cells and likely has biological and clinical implications. Analysis of purified full-length and truncated neurofibromin variants by negative-stain EM revealed the overall architecture of the dimer and predicted the potential interactions that contribute to the dimer interface. We could reconstitute structures resembling high-affinity full-length dimers by mixing N- and C-terminal protein domains in vitro. The reconstituted neurofibromin was capable of GTPase activation in vitro, and co-expression of the two domains in human cells effectively recapitulated the activity of full-length neurofibromin. Taken together, these results suggest how neurofibromin dimers might form and be stabilized within the cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

Chae, Junghoon↗
DOE OSTI2019

DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding

Simulations of biological macromolecules are important in understanding the physical basis of complex processes such as protein folding. However, even with increasing computational capacity and specialized architectures, the ability to simulate protein folding at atomistic scales still remains challenging. This stems from the dual aspects of high dimensionality of protein conformational landscapes, and the inability of atomistic molecular dynamics (MD) simulations to sufficiently sample these landscapes to observe folding events. Machine learning/deep learning (ML/DL) techniques, when combined with atomistic MD simulations offer the opportunity to potentially overcome these limitations by: (1) effectively reducing the dimensionality of MD simulations to automatically build latent representations that correspond to biophysically relevant reaction coordinates (RCs), and (2) driving MD simulations to automatically sample potentially novel conformational states based on these RCs. We examine how coupling DL approaches with MD simulations can lead to effective approaches to fold small proteins on supercomputers. In particular, we study the computational costs and effectiveness of scaling DL-coupled MD workflows implemented using RADICAL-Cybertools in folding two prototypical systems, namely Fs-peptide and the fast-folding variant of the villin head piece protein. We demonstrate that a DL-coupled MD workflow is able to effectively learn latent representations and drive adaptive simulations. Compared to traditional MD-based approaches, our approach achieves an effective performance gain in sampling the folded states by at least 2.3x. Together, our study provides quantitative basis to understand how coupling DL approaches to MD simulations, can lead to effective performance gains and reduced times to solution on supercomputing resources.

Lee, Hyungro↗
DOE OSTI2019

Classifying Cancer Pathology Reports with Hierarchical Self-Attention Networks

We introduce a deep learning architecture, hierarchical self-attention networks (HiSANs), designed for classifying pathology reports and show how its unique architecture leads to a new state-of-the-art in accuracy, faster training, and clear interpretability. We evaluate performance on a corpus of 374,899 pathology reports obtained from the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program. Each pathology report is associated with five clinical classification tasks – site, laterality, behavior, histology, and grade. We compare the performance of the HiSAN against other machine learning and deep learning approaches commonly used on medical text data – Naive Bayes, logistic regression, convolutional neural networks, and hierarchical attention networks (the previous state-of-the-art). We show that HiSANs are superior to other machine learning and deep learning text classifiers in both accuracy and macro F-score across all five classification tasks. Compared to the previous state-of-the-art, hierarchical attention networks, HiSANs not only are an order of magnitude faster to train, but also achieve about 1% better relative accuracy and 5% better relative macro F-score.

60 APPLIED LIFE SCIENCES↗