Biology subjects

Chennubhotla, S. C.

Publications and source records attributed to Chennubhotla, S. C..

5 recordsLinked to original sources

bioRxiv2026

Mechanistic insights into the association and activation of the SARS-CoV-2 2'-O-Methyltransferase (NSP16)

The nsp16 2-O-Methyltransferase is an essential non-structural protein of SARS-CoV-2, which methylates the viral mRNA cap structure, enabling it to evade the host immune response for higher translation efficiency. However, nsp16 is only active when it is bound to its cofactor, namely the non-structural protein-10 (nsp10). Understanding how nsp10 binds to and activates nsp16 function can help to develop targeted inhibitors; however, given the varying degree of disorder in both nsp10 and nsp16, characterizing this interaction has been challenging. Using long-timescale molecular dynamics simulations and AI/ML methods, we posit that the nsp16/nsp10 binding process is mediated by a hydrophobic latch formed with Leu4298 from nsp10 and a hydrophobic concave on the nsp16 protein surface. Our study highlights how the nsp16 S-adenosyl-L-methionine (SAM) pocket closes in its monomer state, which in turn deactivates the MTase function. We also observe that the nsp16/nsp10 complex allows for the RNA binding site to open with the empty SAM pocket. The results reveal how the SAM pocket loops facilitate SAM binding while allowing for the by-product S-adenosyl-L-homocysteine (SAH) to exit. Our study thus provides valuable atomistic-level mechanistic insights into understanding the activation of nsp16 MTase function while highlighting the challenges of studying protein-protein interactions mediated by largely flexible/disordered regions. SIGNIFICANCENsp16 carries out the methylation of the viral mRNA to gain immune evasion and translation efficiency. Understanding its complex molecular machinery can help us develop better therapeutic treatments. Here, we explore the key activation conditions for the SARS-CoV-2 nsp16 function via molecular dynamics simulation and AI/ML methods. The results demonstrate the role of nsp16 loops in different stages preparing for the methylation reaction from nsp16/nsp10 binding, (de)activation of nsp16 function and how the nsp16 SAM binding pocket can affect the RNA binding loops. This research explains the role of the nsp16 loops, which orchestrate its molecular function, and provides valuable insight to develop more targeted therapeutic approaches to disrupt viral immune evasion activity.

biophysics↗

bioRxiv2021

Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

biophysics↗

bioRxiv2021

High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel non-covalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 {micro}M [95% CI 2.2, 4.0]. Further, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple {micro}s-timescale molecular dynamics (MD) simulations, and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design. O_TEXTBOXSignificance StatementThe ongoing novel coronavirus pandemic (COVID-19) has prompted a global race towards finding effective therapeutics that can target the various viral proteins. Despite many virtual screening campaigns in development, the discovery of validated inhibitors for SARS-CoV-2 protein targets has been limited. We discover a novel inhibitor against the SARS-CoV-2 main protease. Our integrated platform applies downstream biochemical assays, X-ray crystallography, and atomistic simulations to obtain a comprehensive characterization of its inhibitory mechanism. Inhibiting Mpro can lead to significant biomedical advances in targeting SARS-CoV-2 treatment, as it plays a crucial role in viral replication. C_TEXTBOX

biophysics↗

bioRxiv2020

Unsupervised cellular phenotypic hierarchy enables spatial intratumor heterogeneity characterization, recurrence-associated microdomains discovery, and harnesses network biology from hyperplexed in-situ fluorescence images of colorectal carcinoma

LEAPH is an unsupervised machine learning algorithm for characterizing in situ phenotypic heterogeneity in tissue samples. LEAPH builds a phenotypic hierarchy of cell types, cell states and their spatial configurations. The recursive modeling steps involve determining cell types with low-ranked mixtures of factor analyzers and optimizing cell states with spatial regularization. We applied LEAPH to hyperplexed (51 biomarkers) immunofluorescence images of colorectal carcinoma primary tumors (N=213). LEAPH, combined with pointwise mutual information (PMI), enables the discovery of phenotypically distinct microdomains, composed of spatially configured computational phenotypes. LEAPH identified a subset of microdomains visualized as the spatial configuration of recurrence-specific signaling networks whose intracellular and intercellular interactions support cancer stem cell maintenance and immunosuppression in the evolving tumor microenvironment. The LEAPH framework, when combined with microdomain discovery and microdomain-specific network biology, has the potential to provide insights into pathophysiological mechanisms, identify novel drug targets and inform therapeutic strategies for individual patients.

cancer biology↗

bioRxiv2019

A spatial analytics computational and systems biology platform predicts risk of colorectal cancer recurrence and identifies emergent spatial domain networks associated with recurrence

An unmet clinical need in solid tumor cancers is the ability to harness the intrinsic spatial information in primary tumors that can be exploited to optimize prognostics, diagnostics and therapeutic strategies for precision medicine. We have developed a transformational spatial analytics (SpAn) computational and systems biology platform that predicts clinical outcomes and captures emergent spatial biology that can potentially inform therapeutic strategies. Here we apply SpAn to primary tumor tissue samples from a cohort of 432 chemo-naive colorectal cancer (CRC) patients iteratively labeled with a highly multiplexed (hyperplexed) panel of fifty-five fluorescently tagged antibodies. SpAn predicted the 5-year risk of CRC recurrence with a mean area under the ROC curve of 88.5% (SE of 0.1%), significantly better than current state-of-the-art methods. SpAn also inferred the emergent network biology of the tumor spatial domains revealing a synergistic role of known features from CRC consensus molecular subtypes that will enhance precision medicine.

cancer biology↗