Biology subjects

RODIN, A. S.

Publications and source records attributed to RODIN, A. S..

7 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↗

bioRxiv2025

Reliable Bayesian Network Structure Learning in BiomedicalApplications: Model Uncertainty Criterion and Its Operating Characteristics.

Bayesian network (BN) structure learning (BNSL) from heterogeneous data is a classical problem in probabilistic machine learning and knowledge discovery. A variety of computational methods exist for score-based BNSL, but all inherit limitations imposed by the underlying model selection paradigms. With finite, often scarce data, imperfect conversion of observations into structural evidence can lead to miscalibration, structural bias, amplification of statistical errors, and unstable or poorly interpretable learned networks. The recently introduced Minimum Uncertainty (MU) principle was proposed to mitigate these problems for discrete BNSL, but its scoring criterion remained a preliminary prototype requiring generalization and calibration. Here, we derive a resolution-aware, calibrated MU criterion under broad perturbation assumptions and develop a framework for evaluating the discriminative power of BN scoring criteria. Across parameter regimes and dependence modes, comparison with MDL, BDeu, and K2 shows that the calibrated MU criterion maintains balanced operating characteristics and improves structure recovery performance, with negligible computational overhead; these gains are also confirmed on real biomedical data. The evaluation framework additionally provides edge-level specificity, sensitivity, and evidence-quality estimates, supporting both practical interpretation of learned networks and principled comparison of scoring criteria. Together, the calibrated MU criterion and the discriminative power evaluation framework provide a general approach for more robust, interpretable, and statistically calibrated score-based structure learning across diverse settings and domains.

bioinformatics↗

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↗

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

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↗