Biology subjects

Mukhaleva, E.

Publications and source records attributed to Mukhaleva, E..

5 recordsLinked to original sources

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

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

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↗

bioRxiv2023

An Ionic Lock Mediates Interdomain Interactions in G protein αi subunits and Determines Gαi Subtype Signaling Specificity

Highly homologous members of the Gi family, Gi1-3, have distinct tissue distributions and physiological functions, yet the functional properties of these proteins with respect to GDP/GTP binding and regulation of adenylate cyclase are very similar. We recently identified PDZ-RhoGEF (PRG) as a novel Gi1 effector, however, it is poorly activated by Gi2. Here, in a proteomic proximity labeling screen we observed a strong preference for Gi1 relative to Gi2 with respect to engagement of a broad range of potential targets. We investigated the mechanistic basis for this selectivity using PRG as a representative target. Substitution of either the helical domain (HD) from Gi1 into Gi2 or substitution of a single amino acid, A230 in Gi2 to the corresponding D in Gi1, largely rescues PRG activation and interactions with other Gi targets. Molecular dynamics simulations combined with Bayesian network models revealed that in the GTP bound state, dynamic separation at the HD-Ras-like domain (RLD) interface is prevalent in Gi2 relative to Gi1 and that mutation of A230s4h3.3 to D in Gi2 stabilizes HD-RLD interactions through formation of an ionic interaction with R145HD.11 in the HD. These interactions in turn modify the conformation of Switch III. These data support a model where D229s4h3.3 in Gi1 interacts with R144HD.11 stabilizes a network of interactions between HD and RLD to promote protein target recognition. The corresponding A230 in Gi2 is unable to form the "ionic lock" to stabilize this network leading to an overall lower efficacy with respect to target interactions. This study reveals distinct mechanistic properties that could underly differential biological and physiological consequences of activation of Gi1 or Gi2 by GPCRs.

pharmacology and toxicology↗