Biology subjects

Ramanathan, A.

Publications and source records attributed to Ramanathan, A..

At least 19 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↗

bioRxiv2025

3D human skeletal muscle organoids reveal distinct effects of high-dose dihydronicotinamide riboside on muscle development

The evaluation of NAD+-boosting compounds in human skeletal muscle is hindered by limitations of traditional 2D cultures and animal models. Human-relevant, three-dimensional (3D) engineered skeletal muscle organoids offer a promising platform to assess the biological effects of metabolic modulation. Here we engineered 3D human skeletal muscle organoids to investigate the impact of dihydronicotinamide riboside (NRH), a potent NAD+ precursor. Sustained exposure to high NRH concentrations (500 {micro}M) enhanced early differentiation markers, including increased myotube fusion and fast-twitch fiber area, but concurrently induced structural defects such as disrupted sarcomeric organization, enlarged acetylcholine receptor clusters, and impaired acetylcholine-stimulated calcium signaling. These results reveal that excessive and sustained NAD+ elevation can uncouple rapid differentiation from proper maturation in muscle tissue. Our results highlight the importance of dose and duration optimization for NAD+-boosting compounds and establish 3D engineered muscle organoids as a valuable non-animal platform for mechanistic toxicology and preclinical safety assessment.

bioengineering↗

bioRxiv2025

Classification of Human Transcription Factors Based on Their Effector Domains via Unsupervised Learning

TFs combine DBDs, which anchor them to DNA, with EDs that regulate transcription through activation or repression, yet the sequence logic linking ED composition to function remains unclear. Here, we systematically define proxy regions--disordered segments adjacent to DBDs--to enable quantitative analysis of ED-like sequences across the human TF repertoire. Using a biophysically interpretable 22-feature classifier (FALK22) together with an embedding-based model (ESM), we map ED diversity and identify composition and charge-pattern signatures that correspond to regulatory activity along a disorder continuum, separating activation-from repression-associated regions. FALK22 identified classes align well with those identified from ESM while providing transparent, sequence-level features. Proxy regions near C-termini exhibit gradients that track DBD families, suggesting that EDs and DBDs might have co-evolved rather than evolved independently. These results establish proxy regions and FALK22 as a framework to connect sequence features with transcriptional activity and to generate testable hypotheses about effector-domain function and co-evolution with DNA-binding domains. HIGHLIGHTSO_LIWe define proxy regions as systematically identified disordered segments adjacent to DNA-binding domains (DBD), enabling quantitative analysis of effector domain (ED)-like sequences across the human transcription factor (TF) repertoire. C_LIO_LIWe develop FALK22, a 22-feature classification algorithm that classifies transcription factors based on simple sequence properties of their EDs and shows strong alignment with complex embedding-based representations from the Evolutionary Scale Model (ESM). C_LIO_LIFALK22 and ESM uncover distinct amino-acid composition and patterning signatures of EDs that correlate with transcriptional function, separating activation- and repression-associated regions along a disorder continuum. C_LIO_LIProxy regions located at the C-termini exhibit gradients that correspond to their DBD families, suggesting that EDs did not evolve as independent modular units but rather co-evolved with, or became selectively matched to, their DBD contexts. C_LI

bioinformatics↗

bioRxiv2025

Analysis of Intracellular Fatty Acid Metabolism in Senescent Cells using Raman Microscopy

Cellular senescence, a stable growth-arrested state induced by stress or chemotherapeutic agents, is accompanied by metabolic remodeling that supports the senescence-associated secretory phenotype (SASP). Among these pathways, lipid and arachidonic acid (AA) metabolism play central roles in maintaining and propagating the senescent state. Here, we used hyperspectral confocal Raman microscopy to visualize biochemical remodeling in MCF7 human breast adenocarcinoma cells undergoing doxorubicin-induced senescence. Raman spectral analysis and principal-component decomposition revealed time-dependent alterations in lipid-associated vibrational modes--particularly CH2 and C=C stretching--consistent with enhanced lipid accumulation and remodeling between days 10 and 15 after DNA-damage induction. PCA of lipid- rich compartments isolated using true component analysis also confirms progressive increases in triacylglycerol and unsaturated lipid signatures. Using deuterated arachidonic acid (AA-d) and COX-2 inhibition, we further demonstrated real-time intracellular AA metabolism by tracking C=C-D stretching peaks (2220-2254 cm-{superscript 1}) in the Raman-silent window. The ratio of these deuterium bands to CH2 stretching provided a label-free quantitative metric for COX2-dependent AA turnover in senescent cells. Together, these findings establish Raman hyperspectral imaging as a powerful, non-perturbative tool to map lipid and oxylipin metabolism during cellular senescence, offering new avenues to identify metabolic vulnerabilities in senescent tumor cells.

biochemistry↗

bioRxiv2025

Sequence-Based Generative AI-Guided Design of Versatile Tryptophan Synthases

Enzymes offer unparalleled selectivity and sustainability for chemical synthesis, yet their widespread industrial application is often hindered by the slow and uncertain process of discovering and optimizing suitable biocatalysts. While directed evolution remains the gold standard for enzyme optimization, its success hinges on the availability of a starting enzyme with measurable activity, a persistent bottleneck for many desired functions. Designing libraries likely to contain such functional starting points remains a major challenge. In this work, we use the GenSLM protein language model (PLM) along with a series of filters to generate novel sequences of the {beta}-subunit of tryptophan synthase (TrpB) that express in Escherichia coli, are stable, and are catalytically active in the absence of a TrpA partner. Many generated TrpBs also demonstrated significant substrate promiscuity, accepting non-canonical substrates typically inaccessible to natural TrpBs. Remarkably, several outperformed both natural and laboratory-optimized TrpBs on native and non-canonical substrates. Comparative analysis of the most active and promiscuous generated TrpB and its closest natural homolog confirmed that this enhanced functional versatility does not stem from the natural enzyme, highlighting the creative potential of generative models. Our results demonstrate that the model can generate enzymes which not only preserve natural structure and function but also acquire non-natural properties, establishing PLMs as powerful tools for biocatalyst discovery and engineering, with the potential in some cases to bypass further optimization.

biochemistry↗

bioRxiv2024

Fatty acid desaturation guides cellular decisions between ferroptosis and cellular senescence

When subject to damage or stress, cells develop responses in order to maintain tissue homeostasis. Two such decisions are ferroptosis and cellular senescence, but how cells decide between these outcomes remains unclear. Here we show that senescent cells increase levels of multiple membrane-bound polyunsaturated fatty acids (PUFAs), but a specific PUFA, dihomo-gamma-linolenic acid (DGLA, 20:3{omega}-3) is reduced. Exogenous repletion of DGLA or inhibition of delta-5-desaturase, the enzyme that metabolizes DGLA, instead results in cell death by ferroptosis. Senescent cells had elevated levels of other fezzroptosis sensitizers, including labile iron and expression of lipoxygenases - but also increased Gpx4 levels to prevent ferroptosis. Oral DGLA lowered senescent cell burden in aged mice and improved age-related functional outcomes. Finally, obese humans with lowered DGLA desaturation rates showed lower markers of adipose tissue senescence. Together, our data implicate DGLA and its desaturation as a major driver of decisions between senescence and ferroptosis.

cell biology↗

bioRxiv2024

Unsupervised learning of progress coordinates during weighted ensemble simulations: Application to millisecond protein folding

A major challenge for many rare-event sampling strategies is the identification of progress coordinates that capture the slowest relevant motions. Machine-learning methods that can identify progress coordinates in an unsupervised manner have therefore been of great interest to the simulation community. Here, we developed a general method for identifying progress coordinates "on-the-fly" during weighted ensemble (WE) rare-event sampling via deep learning (DL) of outliers among sampled conformations. Our method identifies outliers in a latent space model of the systems sampled conformations that is periodically trained using a convolutional variational autoencoder. As a proof of principle, we applied our DL-enhanced WE method to simulate a millisecond protein folding process. To enable rapid tests, our simulations propagated discrete-state synthetic molecular dynamics trajectories using a generative, fine-grained Markov state model. Results revealed that our on-the-fly DL of outliers enhanced the efficiency of WE by >3-fold in estimating the folding rate constant. Our efforts are a significant step forward in the unsupervised learning of slow coordinates during rare event sampling.

biophysics↗

bioRxiv2024

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare event problems, the computational costs and/or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information of transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. We also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

biophysics↗

bioRxiv2023

Senescent cells inhibit muscle differentiation via the lipid- SASP 15d-PGJ2 mediated modification and control of HRas

Senescent cells, which are characterized by multiple features such as increased expression of Senescence-Associated {beta}-galactosidase activity (SA {beta}-gal) and cell cycle inhibitors such as p21 or p16, accumulate with tissue damage and dysregulate tissue homeostasis. In the context of skeletal muscle, it is known that agents used for chemotherapy such as Doxorubicin cause buildup of senescent cells, leading to the inhibition of tissue regeneration. Senescent cells influence the neighboring cells via numerous secreted factors which form the senescence-associated secreted phenotype (SASP). Lipids are emerging as a key component of SASP that can control tissue homeostasis. Arachidonic acid-derived lipids have been shown to accumulate within senescent cells, specifically 15d-PGJ2, which is an electrophilic lipid produced by the non-enzymatic dehydration of the prostaglandin PGD2. In this study, we show that 15d-PGJ2 is also released by Doxorubicin-induced senescent cells as a SASP factor. Treatment of skeletal muscle myoblasts with the conditioned medium from these senescent cells inhibits myoblast fusion during differentiation. Inhibition of L-PTGDS, the enzyme that synthesizes PGD2, diminishes the release of 15d-PGJ2 by senescent cells and restores muscle differentiation. We further show that this lipid post-translationally modifies Cys184 of HRas in skeletal muscle cells, causing a reduction in the localization of HRas to the Golgi, increased HRas binding to RAF RBD, and activation of cellular MAPK-Erk signaling (but not the Akt signaling). Mutating C184 of HRas prevents the ability of 15d- PGJ2 to inhibit the differentiation of muscle cells and control the activity of HRas. This work shows that 15d-PGJ2 released from senescent cells could be targeted to restore muscle homeostasis after chemotherapy.

biochemistry↗

bioRxiv2023

C-terminal cysteines of HRas control Erk signaling and 15-deoxy-Δ12,14-prostaglandin J2 (15d-PGJ2) mediated inhibition of myoblast differentiation.

HRas is an important node that controls cellular signaling, proliferation, and differentiation. Mutants of HRas (e.g., the constitutively active HRas V12) can be oncogenic, and can also inhibit myoblast differentiation. The C-terminal cysteines of HRas (Cys181 and Cys184) serve as substrates for intra-cellular reversible palmitoylation and de-palmitoylation reactions, which control its subcellular distribution. The relationship between the C-terminal cysteines of HRas, its intracellular distribution, and its cellular activity has remained unclear. Understanding this relationship has important implications for targeting HRas in pathogenic states where it is activated. In this study, we show that a mutation in the C-terminal of HRas, C181S, is sufficient to cause increased levels of HRas V12 in the Golgi, decreased HRas V12-driven Akt and Erk signaling and reverse the ability of HRas V12 to inhibit myoblast differentiation. This demonstrates the importance of C-terminal cysteines in controlling HRas V12. It has been previously shown that Cys184 can also be irreversibly modified by an electrophilic prostaglandin lipid 15d-PGJ2. This lipid is released by senescent cells as a part of senescence-associated secretory phenotype (SASP). In this study, we show that 15d-PGJ2 is secreted by senescent myoblasts formed by treatment with Doxorubicin. We also show that 15d-PGJ2 causes decreased levels of HRas within Golgi, activates Erk signaling (but not Akt signaling), and inhibits differentiation of C2C12 myoblasts in an HRas Cys184-dependent fashion. Chemotherapeutics such as Doxorubicin drive senescence and loss of skeletal muscle homeostasis in cancer patients. This study suggests that targeting the senescence-derived synthesis of 15-PGJ2 might be a target to promote muscle homeostasis after chemotherapy.

cell biology↗

bioRxiv2023

PROTEOMICS OF HYPOTHERMIC ADAPTATION REVEALS THAT RBM3 ENHANCES MITOCHONDRIAL METABOLISM AND MUSCLE STEM-CELL DIFFERENTIATION

Adaptation to hypothermic stress is important for skeletal muscle cells, but a comprehensive knowledge of molecular mediators is lacking. We show that adaptation to mild hypothermia (320C) improves the ability of skeletal muscle myoblasts to differentiate into myotubes in vitro. We performed proteomic analysis of mouse myoblasts exposed to mild hypothermia for various time points and identified dynamic changes in mitochondrial metabolism and proteostasis. This revealed that RBM3, an RNA-binding protein, increases progressively with acute and chronic exposure to hypothermic stress, and is necessary for the enhanced differentiation upon hypothermic adaptation. We also demonstrate that overexpression of RBM3 at physiological temperatures is sufficient to (i) enhance mitochondrial metabolism as judged by a decrease in the AMPK energy-sensing pathway, (ii) increase levels of proteins associated with translation and increase levels of 4E-BP1 phosphorylation, (iii) increase stem cell markers (MyoD1, PAX7), and improve differentiation of myoblasts from both young and aged mice.

biochemistry↗

bioRxiv2022

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.

We seek to transform how new and emergent variants of pandemiccausing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pretraining on over 110 million prokaryotic gene sequences and finetuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data.

bioinformatics↗

bioRxiv2022

BigNeuron: A resource to benchmark and predict best-performing algorithms for automated reconstruction of neuronal morphology

BigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets. Our results show that image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. By benchmarking automatic reconstruction algorithms, we observed that diverse algorithms can provide complementary information toward obtaining accurate results and developed a novel algorithm to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms. Finally, to aid users in predicting the most accurate automatic reconstruction results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic reconstructions.

bioinformatics↗

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

Mechanisms of Coupling between Angiotensin Converting Enzyme 2 and Nicotinic Acetylcholine Receptors

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an RNA virus encapsulated by a spike (S) glycoprotein envelope, binds with high affinity to angiotensin converting enzyme 2 (ACE2) during cell entry of a susceptible host. Recent studies suggest nicotinic acetylcholine receptors (nAChRs) play a role in functional ACE2 regulation and nicotine may contribute to the progression of coronavirus disease 2019 (COVID-19). Here, we present evidence for coupling between ACE2 and nAChR through bioinformatic analysis and cell culture experiments. Following molecular and structural protein comparison of over 250 ACE2 vertebrate orthologues, a region of human ACE2 at positions C542-L554 was identified to have sequence similarity to nAChR-binding neurotoxin and rabies virus glycoproteins (RBVG). Furthermore, experiments conducted in PC12 cells indicate a potential for physical interaction between ACE2 and 7 nAChR proteins. Our findings support a model of nAChR involvement in in COVID-19.

neuroscience↗

bioRxiv2021

The misleading certainty of uncertain data in biological network processes

Mathematical models are often used to explore network-driven cellular processes from a systems perspective. However, a dearth of quantitative data suitable for model calibration leads to models with parameter unidentifiability and questionable predictive power. Here we introduce a Bayesian and Machine-Learning based Measurement Model approach to explore how quantitative and non-quantitative data constrain models of apoptosis execution within a missing data context. We find two orders of magnitude more ordinal (e.g. immunoblot) data are necessary to achieve accuracy comparable to quantitative (e.g. fluorescence) data. Notably, ordinal and nominal (e.g. immunostain) non-quantitative data synergize to reduce model uncertainty and improve accuracy. Further, model prediction accuracy and certainty strongly depend on rigorous data-driven formulations of the measurement, and the size and make-up of the datasets. Finally, we demonstrate the potential of a data-driven Measurement Model approach to identify model features that could lead to informative experimental measurements and improve model predictive power.

systems biology↗

bioRxiv2021

Structural dynamics of the β-coronavirus Mpro protease ligand binding sites

{beta}-coronaviruses alone have been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a back-up against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensible role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all non-redundant ligand-binding sites available for SARS-CoV2, SARS-CoV and MERS-CoV Mpro. Extensive adaptive sampling has been used to explore conformational dynamics employing convolutional variational auto encoder-based deep learning, and investigates structural conservation of the ligand binding sites using Markov state models across {beta}-coronavirus homologs. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across {beta}-coronavirus homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.

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↗