Biology subjects

Vaidehi, N.

Publications and source records attributed to Vaidehi, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Allosteric pathways govern Gα protein coupling selectivity at promiscuous GPCRs

G protein-coupled receptors (GPCRs) regulate diverse physiological responses by engaging distinct heterotrimeric G proteins, yet the basis of G selectivity in promiscuous receptors remains unclear. Although ligand bias holds therapeutic promise, selectivity has been assumed to reside mainly in the ligand-binding site (LBS) or G protein interface (GPI). Here, we combine whole-receptor mutagenesis, functional G protein assays, molecular dynamics simulations, interpretable machine learning method, and Bayesian network modeling to identify residue networks governing Gq/11 and G12/13 coupling and to define the molecular basis of G protein preference and promiscuity at two vasopressor GPCRs, the angiotensin II type 1 and prostaglandin F2 receptors. While residues within the LBS and GPI domains contribute to coupling efficiency and subtype discrimination, we find that long-range allosteric communication across the receptor, including from structurally unresolved domains, is the principal determinant of G protein preference and promiscuity. These allosteric pathways integrate multiple receptor domains, confer signaling robustness to mutation, and hierarchically govern coupling preferences. Our findings suggest that G protein selectivity is an allosterically encoded property of GPCRs and provide a conceptual framework for designing ligands and receptors with tailored G protein-biased signaling.

pharmacology and toxicology↗

bioRxiv2026

Interpretable Machine Learning Model of Receptor Dynamics Reveals AT1R Allostery and a Negative Allosteric Modulator

Allosteric modulation of G protein-coupled receptors (GPCRs) offers major advantages in receptor selectivity and signaling control; yet systematic approaches to identify allosteric modulators, define their binding sites, and map the underlying allosteric networks remain limited. Current molecular dynamics (MD) and machine learning (ML)-based methods often rely on correlation-driven or black-box models that provide limited mechanistic insight. We developed an interpretable probabilistic framework that extracts residue-level dependencies from MD ensembles using Bayesian network modeling (BNM). By representing each residue through its local interaction energy, BNM identifies both local and long-range energetic couplings and maps the allosteric communication pathways linking the AngII binding site to the G-protein interface in the angiotensin II type 1 receptor (AT1R). To functionally prioritize these pathways, we integrated BNM with comprehensive mutational analysis, combining whole-receptor alanine mutagenesis data with exhaustive in silico deep mutational scanning to validate BNM-predicted hotspots. This approach recovered state-dependent allosteric communities, revealed residues in noncanonical regions that regulate Gq coupling and identified positions whose functional importance emerged only with specific, predicted substitutions, as well as highlighted a cryptic intracellular pocket enriched in communication hubs. Guided by these network-derived residues and pocket geometries, structure-based virtual screening identified a small, fragment-like molecule negative allosteric modulator (NAM) named Q2 that attenuates AngII-mediated Gq signaling. Mutational mapping supports Q2 binding adjacent to the G-protein interface, consistent with its mechanism of action. Together, these results establish a generalizable and interpretable framework for uncovering GPCR allosteric communication networks and discovering modulators that exploit these networks.

molecular biology↗

bioRxiv2026

Allosteric Gating Mechanism Regulates Odorant Selectivity and Antagonism in Odorant Receptors

Odorant receptors (ORs) belong to class A G protein coupled receptors that detect diverse small molecules, yet the steps that link odorant association to receptor mediated selectivity remains incompletely defined. Here we combined 1.26 milliseconds of all-atom odorant association Molecular Dynamics simulations with Markov state modeling and cell-based cAMP measurements to examine two human ORs receptors that recognize chemically distinct odorants. In the class I receptor OR51E2, propionate associates via two extracellular pathways gated for selectivity by residues in the extracellular loop 2 and 3. The longer alkyl chain heptanoate occupies this gate and reduces propionate association and signaling which is consistent with the observed antagonist behavior of heptanoate. Pocket-expanding mutations at F155 and L158 allosterically regulate ECL2-ECL3 gate by permitting longer-chain fatty acids to adopt fully inserted poses that support gate closure, while also attenuating propionate responses. In the class II receptor OR1A1, hydrophobic odorants partition into the membrane and reach the orthosteric site mostly through multiple transmembrane paths. A mutational scan of gating residues and analysis of intermediate-state occupancies suggest that an orthosteric substitution at G108 can allosterically bias odorant association path choice. Together, these results support a model in which odorant association paths and gate residence, together with allosteric coupling between the orthosteric site and entry gates, contribute to odorant specificity and antagonism in odorant receptors.

biophysics↗

bioRxiv2026

Conserved Residues in the Gα interface show subtype specificity in Gβγ coupling

Although the structural basis of selective G-protein coupling to G protein coupled receptors (GPCRs) is well characterized, the mechanisms underlying selective interactions between distinct G subtypes (Gs, Gi, Gq, and G12/13) and G{beta}{gamma} remain poorly understood. While conserved residues in G subtypes are often assumed to have similar functions, they may instead modulate coupling selectivity by altering the frequency and stability of contacts at the G:G{beta}{gamma} interface. Using molecular dynamics (MD) simulations combined with the interpretable machine learning method, Bayesian Network Model (BNM), and protein-protein proximity (BRET) assays, we show that conserved residues in the two closely related Gi/o and Gq/11 subfamilies contribute differentially to G{beta}{gamma} coupling. These conserved residue "hotspots" on Gi and Gq produced divergent functional effects on G{beta}{gamma} coupling, indicating that conservation does not ensure functional equivalence. These findings suggest that local microenvironment and paralog-specific allosteric coupling shape how conserved interface residues contribute to protein-protein coupling. The framework provides a systematic approach for dissecting subtype-specific mechanisms, with implications for drug design and for annotating the functional relevance of disease-associated variants. The computational methods used here are broadly applicable to other homologous protein families.

molecular biology↗

bioRxiv2026

Mapping the Ligand-dependent Remodeling of the Conformational Entropy Landscape in Neurotensin Receptor 1 by NMR-guided Molecular Simulations

This study presents a comprehensive analysis of the dynamic properties and allosteric regulation mechanisms of Class A G protein-coupled receptors (GPCRs) by integrating molecular dynamics (MD) simulations with nuclear magnetic resonance (NMR) relaxation measurements. Utilizing generalized order parameters derived from NMR data and MD trajectories, we quantitatively assess conformational entropy changes that occur during receptor activation and ligand binding events. This approach enables a detailed characterization of protein flexibility at multiple timescales, revealing how dynamic fluctuations contribute to allosteric signal transmission within the receptor. Our results demonstrate that conformational entropy plays a pivotal role in modulating the functional states of Class A GPCRs, influencing the equilibrium between inactive and active conformations. By elucidating the interplay between structural dynamics and allostery, this work advances the molecular-level understanding of GPCR function and highlights the importance of entropy-driven effects in receptor signaling. The integrative methodology and findings provide a valuable framework for future investigations aimed at targeting receptor dynamics in drug discovery and rational design of allosteric modulators.

molecular biology↗

bioRxiv2025

Stabilization versus flexibility: detergent-dependent trade-offs in neurotensin receptor 1 GPCR ensembles

Detergents provide essential membrane-mimetic environments for studying G protein-coupled receptors (GPCRs), but their molecular impact on receptor energetics remains incompletely understood. We combined ligand binding, thermostability measurements and atomistic molecular dynamics to dissect detergent- versus ligand-driven stabilization in a thermostabilized neurotensin receptor 1 (enNTS1). Circular dichroism and ligand binding assays revealed that apo enNTS1 becomes progressively more stable in decyl maltoside (DM), dodecyl maltoside (DDM), and lauryl maltose neopentyl glycol (LMNG). Yet this gain in baseline stability was accompanied by a paradox: LMNG, the most stabilizing detergent, supported the weakest neurotensin agonist binding affinity. Thermodynamic analysis resolved this contradiction by partitioning stability into detergent-driven conformational rigidity ({Delta}Gconf) and ligand-induced stabilization ({Delta}Gligand). In DM, {Delta}Gligand contributions were large, consistent with the receptors engineered background. In contrast, LMNG maximized {Delta}Gconf, constraining conformational flexibility and reducing {Delta}Gligand. Molecular dynamics simulations corroborated these results, showing that LMNG formed denser, less mobile detergent shells around the receptor, enhancing protein-detergent interaction energies while limiting conformational flexibility. Redistribution of ligand contacts, particularly at neurotensin residue Y11, further underscored detergent-dependent modulation of the binding pocket. These results highlight a fundamental trade-off: LMNG provides exceptional receptor stabilization, supporting structural studies, but may mask conformational states relevant to signaling. In contrast, less rigid detergents preserve ligand-induced transitions at the expense of stability. These findings emphasize that detergent choice should be guided by whether the goal is structural resolution or dynamic characterization.

biophysics↗

bioRxiv2025

Allostery between distant structural regions dictates selectivity in GPCR:G protein coupling.

Selectivity in GPCR-G protein coupling is widely regarded as a solved problem that derives from a combination of structural hotspot pairings and dynamics at the protein-protein interface. Here, using interpretable machine learning Bayesian Network model with Molecular Dynamics simulations and experiments, we reveal the influence of distant residue communities within the G protein core on coupling selectivity. We observed distinct cooperative hotspot residues across different G protein subtypes, including key regions such as the N-terminus, h4s6 loop, and H5 helix. These results demonstrate the intricate allosteric dependencies between the core and the H5 helix in stabilizing selective interactions. The functional significance of these cooperative regions is validated through subtype swapping mutations. By introducing targeted Gq like mutations in the Gs core, we successfully altered receptor coupling profile to signal through Gq. Our findings emphasize that cooperative interactions in the G core are not only crucial for selectivity but can also be leveraged to engineer G proteins with tailored coupling preferences. TeaserAllosteric interactions in the G protein core shape GPCR selectivity and can be engineered to rewire receptor signaling preferences.

biophysics↗

bioRxiv2025

DRUMBEAT: Temporally resolved interpretable machine learning model for characterizing state transitions in protein dynamics

Conformational transitions are central to protein function, yet their mechanistic analysis remains challenging due to the multi-dimensionality and timescales underlying the molecular motions. While interpretable network models such as Bayesian networks have advanced the identification of key residue interactions in molecular dynamics (MD) data, they lack temporal resolution and cannot capture the sequence of events during transitions. Here, we introduce Dynamically Resolved Universal Model for BayEsiAn network Tracking or DRUMBEAT, a machine learning approach that combines a universal graph topology with sliding-window rescoring to generate interpretable, time-resolved maps of cooperative events in MD trajectories. Applying DRUMBEAT to the benchmark Fip35 WW domain folding trajectories from DE Shaw Research Group, we recover both major folding pathways and critical residues previously highlighted by experiment. Importantly, DRUMBEAT provides new insight in two ways: (1) uncover unknown protein features important for transition, and (2) dissect the order and timing of conformational changes, revealing the precise sequence of residue contact closures during individual folding events. Robustness analysis demonstrates that both the universal graph and time-resolved results are highly consistent across multiple sampling replicates. These findings establish DRUMBEAT as a scalable and interpretable machine learning framework for dissecting the dynamics of protein folding and other conformational transitions, offering a generalizable tool for the mechanistic study of biomolecular dynamics.

biochemistry↗

bioRxiv2025

Protein Frustration Reveals Active Sites in Co-Evolved GPCR:G Protein Complexes and in Engineered Targeted Degrader Complexes

The folded structure of a protein is understood to be an optimal energy state. However, previous studies have shown that certain amino acid residue positions that play a critical role in protein function are often in a suboptimal energy state or "frustrated". Here, we leverage over 1200 three-dimensional structures of G protein-coupled receptors (GPCRs) to demonstrate that residues at the interface between GPCR and its ligand or G protein contain a higher density of frustrated residues compared to other structural regions in the receptor. Likewise, the G subunit of the trimeric G proteins shows multiple clusters of highly frustrated residues on its surface that overlap with their effector protein (G{beta}{gamma}, RGS, Adenyl cyclase, Ric8) binding interfaces. Compared to the co-evolved GPCR:G protein complexes, engineered protein complexes, such as those facilitated by molecular degraders, show a much greater density of highly frustrated residues in the degrader interface. Our study highlights the use of protein frustration as an invaluable tool to evaluate both native protein-protein interfaces and design strategies to facilitate engineered protein complexes.

biophysics↗

bioRxiv2025

Temporally Resolved and Interpretable Machine Learning Model of GPCR Conformational Transition

Identifying target-specific drugs remains a challenge in pharmacology, especially for highly homologous proteins such as dopamine receptors D2R and D3R. Differences in target-specific cryptic druggable sites for such receptors arise from the distinct conformational ensembles underlying their dynamic behavior. While Molecular Dynamics (MD) simulations has emerged as a powerful tool for dissecting protein dynamics, the sheer volume of MD data requires scalable and unbiased data analysis strategies to pinpoint residue communities regulating conformational state ensembles. We have developed the Dynamically Resolved Universal Model for BayEsiAn network Tracking (DRUMBEAT) interpretable machine learning algorithm and validated it by identifying residue communities that enable the deactivation of the {beta}2-adrenergic receptor. Further, upon analyzing dopamine receptor dynamics we identified distinct and non-conserved residue communities around the contacts F1704.62_F172ECL2 and S1464.38_G14134.56 that are specific to D3R conformational transitions compared to D2R. This information can be tapped to design subtype-specific drugs for neuropsychiatric and substance use disorders.

biochemistry↗

bioRxiv2025

Insights From Protein Frustration Analysis Of BRD4-Cereblon Degrader Ternary Complexes Show Separation of Strong from Weak Degraders

PROteolysis TArgeting Chimeras (PROTACs), also known as Ligand-Directed Degraders (LDDs), are an innovative class of small molecules that leverage the ubiquitin-proteasome system to induce the degradation of target proteins. Structure based design methods are not readily applicable for designing LDDs due to the dynamic nature of the ternary complexes. This study investigates the dynamic properties of five LDD-mediated BRD4-Cereblon complexes, focusing on the challenges of evaluating linker efficiency due to the difficulty in identifying suitable computational metrics that correlate well with the cooperativity or degradation propensity of LDDs. We uncovered that protein frustration, a concept originally developed to understand protein folding, calculated for the residues in the protein-protein interface of the LDD-mediated ternary complexes recapitulate the strength of degradation of the LDDs. Our findings indicated that hydrophobic residues in the interface are among the highly frustrated residues pairs, and they are crucial in distinguishing strong degraders from weak ones. By analyzing frustration patterns, we identified key residues and interactions critical to the effectiveness of the ternary complex. These insights provide practical guidelines for designing and prioritizing more efficient degraders, paving the way for the development of next-generation LDDs with improved therapeutic potential.

biophysics↗

bioRxiv2024

Frustration in the Protein-Protein interface Plays a Central Role in the Cooperativity of PROTAC Ternary Complexes

Targeted protein degradation of a protein of interest (POI) by Proteolysis Targeting Chimeras (PROTACs) is an attractive approach for dealing with formerly undruggable protein targets. PROTACs are heterobifunctional molecules that connect a POI-binding and an E3-ligase (E3) binding motif with a linker. The simultaneous binding and formation of a ternary POI::PROTAC::E3 complex (TC) induces proximity between the POI and the E3, allowing for POI-ubiquitination and subsequent induction of proteasomal degradation. Despite the availability of many three-dimensional structures of TCs, unveiling the structure-function relationships for the design of PROTACs remains a challenge. This is because the TCs can be dynamic with a complex conformational landscape that individual crystal structures may not capture. In this work, we used SMARCA2 as the POI and VHL as the E3-ligase and solved the X-ray crystal structures of the respective ternary complexes with four different PROTACs. Molecular dynamics (MD) simulations were used to show that the SMARCA2-VHL interface is flexible with multiple energy minima. The protein-protein (POI-E3) interactions are largely formed by residues located in structurally disordered loops in both VHL and SMARCA2. The residue pairs in the SMARCA2-VHL interface are frustrated, i.e., adopt a suboptimal energetic state. The number of frustrated residue pairs averaged over the MD ensemble shows a positive correlation with the experimentally determined cooperativity of the PROTACs. This indicates that protein-protein interface frustration can play an important role in PROTAC function. The TC ensembles of VHL, SMARCA2 and 11 different PROTACs were modeled by comparative modeling followed by MD. The frustration was subsequently calculated from the MD trajectories and correlated with the cooperativity. We found that identification of the dynamic protein-protein contacts and frustrated residue pairs in the interface can provide a rational framework for the structure-based design of PROTACs.

biophysics↗

bioRxiv2024

BaNDyT: Bayesian Network modeling of molecular Dynamics Trajectories

Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical datasets. Concurrently, our ability to perform long-timescale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. However, the analysis of MD simulation trajectories has not been data-driven but rather dependent on the users prior knowledge of the systems, thus limiting the scope and utility of the MD simulations. Recently, we pioneered using BNM for analyzing the MD trajectories of protein complexes. The resulting BN models yield novel fully data-driven insights into the functional importance of the amino acid residues that modulate proteins function. In this report, we describe the BaNDyT software package that implements the BNM specifically attuned to the MD simulation trajectories data. We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the softwares uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. This provides a powerful and versatile mechanism for users to control the workflow. As an application example, we have utilized this methodology and associated software to study how membrane proteins, specifically the G protein-coupled receptors, selectively couple to G proteins. The software can be used for analyzing MD trajectories of any protein as well as polymeric materials.

bioinformatics↗

bioRxiv2023

Cellular Lipids Regulate the Conformational Ensembles of the Disordered Intracellular Loop 3 in beta-2 Adrenergic Receptor

The structurally disordered intracellular loops (ICLs) of G protein-coupled receptors (GPCRs) play a critical role in G protein coupling. In our previous work, we used a combination of FRET-based and computational methodologies to show that the third intracellular loop (ICL3) modulates the activity and G protein coupling selectivity in GPCRs. In the current study, we have uncovered the role of several lipid components in modulating the conformational ensemble of ICL3 of the {beta}2-adrenergic receptor ({beta}2AR). Our findings indicate that phosphatidylinositol 4,5-bisphosphate (PIP2) in the inner leaflet of the membrane bilayer acts as a stabilizing anchor for ICL3, opening the intracellular cavity to facilitate G protein coupling. This interaction between PIP2 and ICL3 causes tilting of {beta}2AR within the cellular membrane. Notably, this tilting of the receptor is supported by ganglioside GM3 stabilizing the extracellular loops on the outer leaflet of the bilayer, thereby exerting an allosteric effect on the orthosteric ligand binding pocket. Our results underscore the significance of lipids in modulating GPCR activity, proposing an allosteric mechanism that occurs through the receptors orientation within the membrane.

biophysics↗

bioRxiv2023

Engineered odorant receptors illuminate structural principles of odor discrimination

A central challenge in olfaction is understanding how the olfactory system detects and distinguishes odorants with diverse physicochemical properties and molecular configurations. Vertebrate animals perceive odors via G protein-coupled odorant receptors (ORs). In humans, [~]400 ORs enable the sense of smell. The OR family is composed of two major classes: Class I ORs are tuned to carboxylic acids while Class II ORs, representing the vast majority of the human repertoire, respond to a wide variety of odorants. How ORs recognize chemically diverse odorants remains poorly understood. A fundamental bottleneck is the inability to visualize odorant binding to ORs. Here, we uncover fundamental molecular properties of odorant-OR interactions by employing engineered ORs crafted using a consensus protein design strategy. Because such consensus ORs (consORs) are derived from the 17 major subfamilies of human ORs, they provide a template for modeling individual native ORs with high sequence and structural homology. The biochemical tractability of consORs enabled four cryoEM structures of distinct consORs with unique ligand recognition properties. The structure of a Class I consOR, consOR51, showed high structural similarity to the native human receptor OR51E2 and yielded a homology model of a related member of the human OR51 family with high predictive power. Structures of three Class II consORs revealed distinct modes of odorant-binding and activation mechanisms between Class I and Class II ORs. Thus, the structures of consORs lay the groundwork for understanding molecular recognition of odorants by the OR superfamily.

neuroscience↗

bioRxiv2023

Bayesian network models identify co-operative GPCR:G protein interactions that contribute to G protein coupling

Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network-like behavior of interface interactions and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces it is challenging to determine which amino acid pair interactions are cooperative. In this work we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein-coupled receptor (GPCR) complexes with G proteins. Our results reveal a strong co-dependency in the formation of interface GPCR:G protein contacts. This observation indicates that cooperativity of GPCR:G protein interactions is necessary for the coupling and selectivity of G proteins and is thus critical for receptor function. We have identified subnetworks containing polar and hydrophobic interactions that are common among multiple GPCRs coupling to different G protein subtypes (Gs, Gi and Gq). These common subnetworks along with G protein-specific subnetworks together confer selectivity to the G protein coupling. This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components.

biophysics↗

bioRxiv2023

Oligo-PROTAC strategy for cell-selective and targeted degradation of activated STAT3

Decoy-oligodeoxynucleotides (D-ODNs) can target undruggable transcription factors, such as STAT3. However, challenges in D-ODN delivery and potency hampered their translation. To overcome these limitations, we conjugated STAT3-specific D-ODN to thalidomide (Tha), a known ligand to cereblon (CRBN, a component of E3 ubiquitin ligase) to generate a proteolysis-targeting chimera (STAT3DPROTAC). STAT3DPROTAC downregulated STAT3, but not STAT1 or STAT5, in target cells. Computational modeling of the STAT3DPROTAC ternary complex predicted two surface lysines on STAT3, K601 and K626 as potential ubiquitination sites for the PROTAC bound E3 ligase. Accordingly, K601/K626 point mutations in STAT3, as well as proteasome inhibitors, and CRBN deletion alleviated STAT3DPROTAC effect. Next, we conjugated STAT3DPROTAC to a CpG ligand targeting Toll-like receptor 9 (TLR9) to generate myeloid/B-cell-selective C-STAT3DPROTAC conjugate. Naked C-STAT3DPROTAC was spontaneously internalized by TLR9+ myeloid cells, B cells as well as human Ly18 and mouse A20 lymphoma cells, but not by T cells. C-STAT3DPROTAC decreased STAT3 levels to 50% at 250 nM and over 85% at 2 {micro}M dosing in myeloid cells. We also observed significantly improved downregulation of STAT3 target genes involved in lymphoma cell proliferation and/or survival (BCL2L1, CCND2, MYC). Finally, we assessed the antitumor efficacy of C-STAT3DPROTAC compared to C-STAT3D or scrambled control (C-SCR) against human lymphoma xenotransplants. Local C-STAT3DPROTAC administration triggered lymphoma regression while control treatments had limited effects. Our results underscore feasibility of using PROTAC strategy for cell-selective, decoy oligonucleotide-based targeting of STAT3 and potentially other tumorigenic transcription factors for cancer therapy.

cancer biology↗

bioRxiv2023

Molecular recognition of an aversive odorant by the murine trace amine-associated receptor TAAR7f

There are two main families of G protein-coupled receptors that detect odours in humans, the odorant receptors (ORs) and the trace amine-associated receptors (TAARs). Their amino acid sequences are distinct, with the TAARs being most similar to the aminergic receptors such as those activated by adrenaline, serotonin and histamine. To elucidate the structural determinants of ligand recognition by TAARs, we have determined the cryo-EM structure of a murine receptor, mTAAR7f, coupled to the heterotrimeric G protein Gs and bound to the odorant N,N-dimethylcyclohexylamine (DMCH) to an overall resolution of 2.9 [A]. DMCH is bound in a hydrophobic orthosteric binding site primarily through van der Waals interactions and a strong charge-charge interaction between the tertiary amine of the ligand and an aspartic acid residue. This site is distinct and non-overlapping with the binding site for the odorant propionate in the odorant receptor OR51E2. The structure, in combination with mutagenesis data and molecular dynamics simulations suggests that the activation of the receptor follows a similar pathway to that of the {beta}-adrenoceptors, with the significant difference that DMCH interacts directly with one of the main activation microswitch residues.

molecular biology↗