Pratul K. Agarwal

The sigma-2 receptor (σ₂R/TMEM97) is a clinically relevant membrane protein involved in cholesterol regulation and overexpressed in cancer and neurodegenerative diseases. Despite its therapeutic potential, the dynamic mechanisms underlying σ₂R function and ligand binding remain poorly understood. Here, we combined adaptive sampling molecular dynamics simulations with quasi-anharmonic analysis and unsupervised machine learning method to investigate the conformational behavior of the σ₂R homodimer in both apo and cholesterol-bound states. Our results reveal asymmetric dynamics between the two protomers. This asymmetry is driven by anticorrelated helical motions and mutually exclusive salt bridge formation, including a switching mechanism between K₅₅-E₁₃₉ and D₁₂₂-R₁₄₀. Cholesterol binding further enhances this asymmetry by stabilizing one protomer and altering the dynamics of the other. Species-specific allosteric interaction between D₅₆-R₁₃₃ may be essential for the human σ₂R function. Additional lipid-protein interaction analysis highlights asymmetric membrane coupling in the bound state. These findings provide a plausible explanation for the receptor's dimeric nature, suggesting that ligand binding at one site may allosterically influence the apo protomer, thereby modulating receptor function. Our work provides new mechanistic insight into σ₂R function and highlights the importance of asymmetric dynamics.

Functional segregation among protein isoforms depends on the interplay of their overall structures and the molecular dynamics of these structures. Thermotoga maritima maltose-binding protein (tmMBP) isoforms show size-dependent differential binding of maltose and malto-oligomers while maintaining remarkable fold conservation. This differential behavior needs detailed characterization in native-like aqueous conditions to understand the effects of protein dynamics on ligand binding and recognition. Small-angle neutron scattering (SANS), neutron spin echo (NSE) spectroscopy, and dynamic light scattering (DLS) were used in conjunction with previously published computational molecular dynamics (MD) simulations to understand the dynamic behavior of tmMBPs experimentally. SANS provided information on the overall structure of the molecules, while NSE was used to determine the dynamics in the nanosecond time scale. Both tmMBP2 and tmMBP3 have a bidomain architecture linked with a flexible hinge, with the binding pocket sitting in the cleft between the two domains. tmMBP2 and tmMBP3 showed different solution dynamics, with the translational and rotational components dominating the dynamics of both systems, resulting in a clear differentiation of their diffusion pattern. A faster dynamics component was also observed and was attributed to segmental dynamics. Differences observed between the ligand-free (apo) and ligand-bound (holo) states of the two proteins are attributed to conformational entropy. Our results highlight the intricacies of how structure and dynamics can together shape binding to a repertoire of substrates in structurally similar proteins.

Antimicrobial resistance (AMR) is a global threat, with Burkholderia species contributing significantly to difficult-to-treat infections. The Pen family of β-lactamases are produced by all Burkholderia spp., and their mutation or overproduction leads to the resistance of β-lactam antibiotics. Here we investigate the dynamic differences among four Pen β-lactamases (PenA, PenI, PenL and PenP) using machine learning driven enhanced sampling molecular dynamics simulations, Markov State Models (MSMs), convolutional variational autoencoder-based deep learning (CVAE) and the BindSiteS-CNN model. In spite of sharing the same catalytic mechanisms, these enzymes exhibit distinct dynamic features due to low sequence identity, resulting in different substrate profiles and catalytic turnover. The BindSiteS-CNN model further reveals local active site dynamics, offering insights into the Pen β-lactamase evolutionary adaptation. Our findings reported here identify critical mutations and propose new hot spots affecting Pen β-lactamase flexibility and function, which can be used to fight emerging resistance in these enzymes.

Apurinic/Apyrimidinic (AP)-sites are common and highly mutagenic DNA lesions that can arise spontaneously or as intermediates during Base Excision Repair (BER). The enzyme apurinic/apyrimidinic endonuclease 1 (APE1) initiates repair of AP-sites by cleaving the DNA backbone at the AP-site via its endonuclease activity. Here, we investigated the functional role of the APE1 active site residue N174 that contacts the AP-site during catalysis. We analyzed the effects of three rationally designed APE1 mutations that alter the hydrogen bonding potential, size, and charge of N174: N174A, N174D, and N174Q. We found impaired catalysis of the APE1 _N174A and APE1 _N174D mutants due to disruption of hydrogen bonding and electrostatic interactions between residue 174 and the AP-site. In comparison, the APE1 _N174Q mutant was less impaired due to retaining similar hydrogen bonding and electrostatic characteristics as N174 in wild-type APE1. Structures and computational simulations further revealed that the AP-site was destabilized within the active sites of the APE1 _N174A and APE1 _N174D mutants due to loss of hydrogen bonding between residue 174 and the AP-site. Cumulatively, we show that N174 stabilizes the AP-site within the APE1 active site through hydrogen bonding and electrostatic interactions to enable effective catalysis. These findings highlight the importance of N174 in APE1’s function and provide new insights into the molecular mechanism by which APE1 processes AP-sites during DNA repair.

The target DNA (tDNA) cleavage catalyzed by the CRISPR Cas9 enzyme is a critical step in the Cas9-based genome editing technologies. Previously, the tDNA cleavage from an active SpyCas9 enzyme conformation was modeled by Palermo and co-workers (Nierzwicki et al., Nat. Catal. 2022 5, 912) using ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulations, where the free energy barrier was found to be more favorable than that from a pseudoactive enzyme conformation. In this work, we performed ai-QM/MM simulations based on another catalytically active conformation (PDB 7Z4J) of the Cas9 HNH domain from cryo-electron microscopy experiments. For the wildtype enzyme, we acquired a free energy profile for the tDNA cleavage that is largely consistent with the previous report. Furthermore, we explored the role of the active-site K866 residue on the catalytic efficiency by modeling the K866A mutant and found that the K866A mutation increased the reaction free energy barrier, which is consistent with the experimentally observed reduction in the enzyme activity.

The target DNA (tDNA) cleavage catalyzed by the CRISPR Cas9 enzyme is a critical step in the Cas9-based genome editing technologies. Previously, the tDNA cleavage from an active SpyCas9 enzyme conformation was modeled by Palermo and coworkers [Nierzwicki et al., Nat. Catal. 5, 912 (2022)] using ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulations, where the free energy barrier was found to be more favorable than that from a pseudoactive enzyme conformation. In this work, we performed ai-QM/MM simulations based on another catalytically active conformation (PDB 7Z4J) of the Cas9 HNH domain from cryo-electron microscopy experiments.For the wildtype enzyme, we acquired a free energy profile for the tDNA cleavage that is largely consistent with the previous report. Furthermore, we explored the role of the active-site K866 residue on the catalytic efficiency by modeling the K866A mutant, and found that the K866A mutation increased the reaction free energy barrier, which is consistent with the experimentally observed reduction in the enzyme activity.

The coronavirus SARS-CoV-2 protects its RNA from being recognized by host immune responses by methylation of its 5’ end, also known as capping. This process is carried out by two enzymes, non-structural protein 16 (NSP16) containing 2’-O-methyltransferase and NSP14 through its N7 methyltransferase activity, which are essential for the replication of the viral genome as well as evading the host’s innate immunity. NSP10 acts as a crucial cofactor and stimulator of NSP14 and NSP16. To further understand the role of NSP10, we carried out a comprehensive analysis of >13 million globally collected whole-genome sequences (WGS) of SARS-CoV-2 obtained from the Global Initiative Sharing All Influenza Data (GISAID) and compared it with the reference genome Wuhan/WIV04/2019 to identify all currently known variants in NSP10. T12I, T102I, and A104V in NSP10 have been identified as the three most frequent variants and characterized using X-ray crystallography, biophysical assays, and enhanced sampling simulations. In contrast to other proteins such as spike and NSP6, NSP10 is significantly less prone to mutation due to its crucial role in replication. The functional effects of the variants were examined for their impact on the binding affinity and stability of both NSP14-NSP10 and NSP16-NSP10 complexes. These results highlight the limited changes induced by variant evolution in NSP10 and reflect on the critical roles NSP10 plays during the SARS-CoV-2 life cycle. These results also indicate that there is limited capacity for the virus to overcome inhibitors targeting NSP10 via the generation of variants in inhibitor binding pockets.

Genomic surveillance is a vital strategy for preparedness against the spread of infectious diseases and to aid in development of new treatments. In an unprecedented effort, millions of samples from COVID-19 patients have been sequenced worldwide for SARS-CoV-2. Using more than 8 million sequences that are currently available in GenBank’s SARS-CoV-2 database, we report a comprehensive overview of mutations in all 26 proteins and open reading frames (ORFs) from the virus. The results indicate that the spike protein, NSP6, nucleocapsid protein, envelope protein and ORF7b have shown the highest mutational propensities so far (in that order). In particular, the spike protein has shown rapid acceleration in mutations in the post-vaccination period. Monitoring the rate of non-synonymous mutations (K _a ) provides a fairly reliable signal for genomic surveillance, successfully predicting surges in 2022. Further, the external proteins (spike, membrane, envelope, and nucleocapsid proteins) show a significant number of mutations compared to the NSPs. Interestingly, these four proteins showed significant changes in K _a typically 2 to 4 weeks before the increase in number of human infections (“surges”). Therefore, our analysis provides real time surveillance of mutations of SARS-CoV-2, accessible through the project website http://pandemics.okstate.edu/covid19/ . Based on ongoing mutation trends of the virus, predictions of what proteins are likely to mutate next are also made possible by our approach. The proposed framework is general and is thus applicable to other pathogens. The approach is fully automated and provides the needed genomic surveillance to address a fast-moving pandemic such as COVID-19.

Telomere biology disorders (TBDs) are a spectrum of diseases that arise from mutations in genes responsible for maintaining telomere integrity. Human telomerase reverse transcriptase (hTERT) adds nucleotides to chromosome ends and is frequently mutated in individuals with TBDs. Previous studies have provided insight into how relative changes in hTERT activity can lead to pathological outcomes. However, the underlying mechanisms describing how disease-associated variants alter the physicochemical steps of nucleotide insertion remain poorly understood. To address this, we applied single-turnover kinetics and computer simulations to the Tribolium castaneum TERT (tcTERT) model system and characterized the nucleotide insertion mechanisms of six disease-associated variants. Each variant had distinct consequences on tcTERT’s nucleotide insertion mechanism, including changes in nucleotide binding affinity, rates of catalysis, or ribonucleotide selectivity. Our computer simulations provide insight into how each variant disrupts active site organization, such as suboptimal positioning of active site residues, destabilization of the DNA 3′ terminus, or changes in nucleotide sugar pucker. Collectively, this work provides a holistic characterization of the nucleotide insertion mechanisms for multiple disease-associated TERT variants and identifies additional functions of key active site residues during nucleotide insertion.

COVID19 has aptly revealed that airborne viruses such as SARS-CoV-2 with the ability to rapidly mutate combined with high rates of transmission and fatality can cause a deadly worldwide pandemic in a matter of weeks (Plato et al., 2021). Apart from vaccines and post-infection treatment options, strategies for preparedness will be vital in responding to the current and future pandemics. Therefore, there is wide interest in approaches that allow predictions of increase in infections (‘surges’) before they occur. We describe here real-time genomic surveillance particularly based on mutation analysis, of viral proteins as a methodology for a priori determination of surge in number of infection cases. The full results are available for SARS-CoV-2 at http://pandemics.okstate.edu/covid19/, and are updated daily as new virus sequences become available. This approach is generic and will also be applicable to other pathogens.