Biology subjects

Ramanathan, A.

Publications and source records attributed to Ramanathan, A..

26 records · Page 2Linked to original sources

bioRxiv2021

Proteome plasticity in response to persistent environmental change

Temperature is a variable component of the environment and all organisms must deal with or adapt to temperature change. Acute temperature change activates cellular stress responses resulting in the refolding or removal of damaged proteins. However, how organisms adapt to long-term temperature change remains largely unexplored. Here, we report that budding yeast responds to long-term high temperature challenge by switching from chaperone induction to the reduction of temperature sensitive proteins and re-localizing a portion of its proteome. Surprisingly, we also find many proteins adopt an alternative conformation. Using Fet3p as an example, we find that the temperature-dependent conformational difference is accompanied by distinct thermostability, subcellular localization, and importantly, cellular functions. We postulate that in addition to the known mechanisms of adaptation, conformational plasticity allows some polypeptides to acquire new biophysical properties and functions when environmental change endures.

molecular biology↗

bioRxiv2021

ARYL HYDROCARBON RECEPTOR BLOCKS AGING-INDUCED SENESCENCE IN THE LIVER AND FIBROBLAST CELLS

Aging induces progressive organ degeneration and worsening of tissue homeostasis leading to multiple pathologies. Yet, little is known about the mechanisms and molecular intermediates involved. Here, we report that aged aryl hydrocarbon receptor-null mice (AhR-/-) had exacerbated senescence and larger numbers of liver progenitor cells. Senescence-associated markers {beta}-galactosidase (SA-{beta}-Gal), p16Ink4a and p21Cip1 and genes of the senescence-associated secretory phenotype (SASP) TNF and IL1 were overexpressed in aged AhR-/- livers. AhR binding to the promoter of those genes, as shown by chromatin immunoprecipitation, likely had a repressive effect maintaining their physiological levels in AhR+/+ livers. Furthermore, factors secreted by senescent cells MCP-2, MMP12 and FGF were also produced at higher levels in aged AhR-null livers. Supporting the linkage between senescence and stemness, liver progenitor cells were more abundant in AhR-/- mice, which could probably contribute to their increased hepatocarcinoma burden. These roles of AhR are not liver-specific since adult and embryonic AhR-null fibroblasts acquired cellular senescence upon culturing with overexpression of SA-{beta}-Gal, p16Ink4a and p21Cip1. Notably, depletion of senescent cells with the senolytic agent navitoclax restored basal expression of senescent markers in AhR-/- fibroblasts. Oppositely, senescence promoter palbociclib induced an AhR-null like phenotype in AhR+/+ fibroblasts. Moreover, doxycycline-induced senescence reduced AhR levels while depletion of p16Ink4a-expressing senescent cells restored basal AhR levels in mouse lungs. Thus, AhR is needed to restrict age-induced senescence, and such activity seems to correlate with a more differentiated phenotype and with increased resistance to liver tumorigenesis.

cancer biology↗

bioRxiv2020

AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spikes full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems. ACM Reference FormatLorenzo Casalino1{dagger}, Abigail Dommer1{dagger}, Zied Gaieb1{dagger}, Emilia P. Barros1, Terra Sztain1, Surl-Hee Ahn1, Anda Trifan2,3, Alexander Brace2, Anthony Bogetti4, Heng Ma2, Hyungro Lee5, Matteo Turilli5, Syma Khalid6, Lillian Chong4, Carlos Simmerling7, David J. Hardy3, Julio D. C. Maia3, James C. Phillips3, Thorsten Kurth8, Abraham Stern8, Lei Huang9, John McCalpin9, Mahidhar Tatineni10, Tom Gibbs8, John E. Stone3, Shantenu Jha5, Arvind Ramanathan2*, Rommie E. Amaro1*. 2020. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics. In Supercomputing 20: International Conference for High Performance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI

biophysics↗

bioRxiv2019

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).

bioinformatics↗

bioRxiv2019

Steering Molecular Dynamics Simulations of Membrane-Associated Proteins with Neutron Reflection Results

We present a method to incorporate structural results from neutron reflectometry, a technique that determines interfacial structures such as protein-membrane complexes at a solid surface, into molecular dynamics simulations. By analyzing component volume occupancy profiles, which describe the one-dimensional distribution of a particular molecular component within an interfacial architecture, we construct a real-space constraint in the form of a biasing potential for the simulation that vanishes when the simulated and experimental profiles agree. This approach improves the correspondence between simulation and experiment, as shown for an earlier investigation where an NR-derived structure was well captured by an independent MD simulation, and may lead to faster equilibration of ensemble structures. We further show that time averaging of the observable when biasing with this approach permits fluctuations about the average, which are necessary for conformational exploration of the protein. The method described here also provides insights into systems that are characterized by NR and MD when the two show slight differences in their profiles. This is particularly valuable for studies of proteins at interfaces that contain disordered regions since the conformation of such regions is difficult to judge from the analysis of one-dimensional experimental profiles and take prohibitively long to equilibrate in simulations.

biophysics↗

bioRxiv2019

Biochemical and structural analysis reveals the Neurofibromin (NF1) protein forms a high-affinity dimer

Neurofibromin is the protein product of the NF1 gene which is mutated in the Rasopathy disease Neurofibromatosis Type I. Defects in NF1 lead to aberrant signaling through the RAS-MAPK pathway due to disruption of the Neurofibromin GTPase-activating function on RAS family small GTPases. Very little is known about the function of the majority of Neurofibromin--to date, biochemical and structural data exist only for the GAP domain and the region containing a Sec-PH motif. To better understand the role of this large protein, we carried out a series of biochemical and biophysical studies which demonstrate that full length Neurofibromin forms a high-affinity dimer. Neurofibromin dimerization also occurs in cells, and likely has biological and clinical implications. Analysis of purified full-length and truncated variants of Neurofibromin by negative stain electron microscopy reveals the overall architecture of the dimer and predicts the potential interactions which contribute to the dimer interface. Structures resembling high-affinity full-length dimers could be reconstituted by mixing N- and C-terminal protein domains in vitro. Taken together these data suggest how Neurofibromin dimers might form and be stabilized within the cell.

biochemistry↗

bioRxiv2019

Cardiolipin-dependent Properties of Model Mitochondrial Membranes from Molecular Dynamics Simulations

Cardiolipin is a unique anionic lipid found in mitochondrial membranes where it contributes to various mitochondrial functions, including metabolism, mitochondrial membrane fusion/fission dynamics, and apoptosis. Dysregulation of cardiolipin synthesis and remodeling have also been implicated in several diseases, such as diabetes, heart disease and Barth Syndrome. Although cardiolipins structural and dynamic roles have been extensively studied in binary mixtures with other phospholipids, the biophysical properties of cardiolipin in ternary lipid mixtures are still not well resolved. Here, we used molecular dynamics simulations to investigate the cardiolipin-dependent properties of ternary lipid bilayer systems that mimic the major components of mitochondrial membranes. We found that changes to cardiolipin concentration only resulted in minor changes to bilayer structural features, but that the lipid diffusion was significantly affected by those alterations. We also found that cardiolipin position along the bilayer surfaces correlated to negative curvature deflections, consistent with the induction of negative curvature stress in the membrane monolayers. This work contributes to a foundational understanding of the role of CL in altering the properties in ternary lipid mixtures composed of the major mitochondrial phospholipids, providing much needed insights to help understand how cardiolipin concentration modulates the biophysical properties of mitochondrial membranes.

biophysics↗

bioRxiv2018

Deep clustering of protein folding simulations

We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes. In this paper, we use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely the Fs-peptide (14 s aggregate sampling), villin head piece (single trajectory of 125 s) and the mixed {beta}-{beta}- (BBA) protein (223 + 102 s sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features. Together, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.

biophysics↗