Investigation of the Captopril-Insulin Interaction by Mass Spectrometry and Computational Approaches Reveals that Captopril Induces Structural Changes in Insulin.

Post-translational modifications remarkably regulate proteins' biological function. Small molecules such as reactive thiols, metabolites, and drugs may covalently modify the proteins and cause structural changes. This study reports the covalent modification and noncovalent interaction of insulin and captopril, an FDA-approved antihypertensive drug, through mass spectrometric and computation-based approaches. Mass spectrometric analysis shows that captopril modifies intact insulin, reduces it into its "A" and "B" chains, and covalently modifies them by forming adducts. Since captopril has a reactive thiol group, it might reduce the insulin dimer or modify it by reacting with cysteine residues. This was proven with dithiothreitol treatment, which reduced the abundance of captopril adducts of insulin A and B chains and intact Insulin. Liquid chromatography tandem mass spectrometric analysis identified the modification of a total of four cysteine residues, two in each of the A and B chains of insulin. These modifications were identified to be Cys6 and Cys7 of the A chain and Cys7 and Cys19 of the B chain. Mass spectrometric analysis indicated that captopril may simultaneously modify the cysteine residues of intact insulin or its subunits A and B chains. Biophysical studies involving light scattering and thioflavin T assay suggested that the binding of captopril to the protein leads to the formation of aggregates. Docking and molecular dynamics studies provided insights into the noncovalent interactions and associated structural changes in insulin. This work is a maiden attempt to understand the detailed molecular interactions between captopril and insulin. These findings suggest that further investigations are required to understand the long-term effect of drugs like captopril.

Mechanistic insights of key host proteins and potential repurposed inhibitors regulating SARS-CoV-2 pathway.

The emergence of pandemic situations originated from severe acute respiratory syndrome (SARS)-CoV-2 and its new variants created worldwide medical emergencies. Due to the non-availability of efficient drugs and vaccines at these emergency hours, repurposing existing drugs can effectively treat patients critically infected by SARS-CoV-2. Finding a suitable repurposing drug with inhibitory efficacy to a host-protein is challenging. A detailed mechanistic understanding of the kinetics, (dis)association pathways, key protein residues facilitating the entry-exit of the drugs with targets are fundamental in selecting these repurposed drugs. Keeping this target as the goal of the paper, the potential repurposing drugs, Nafamostat, Camostat, Silmitasertib, Valproic acid, and Zotatifin with host-proteins HDAC2, CSK22, eIF4E2 are studied to elucidate energetics, kinetics, and dissociation pathways. From an ensemble of independent simulations, we observed the presence of single or multiple dissociation pathways with varying host-proteins-drug systems and quantitatively estimated the probability of unbinding through these specific pathways. We also explored the crucial gateway residues facilitating these dissociation mechanisms. Interestingly, the residues we obtained for HDAC2 and CSK22 are also involved in the catalytic activity. Our results demonstrate how these potential drugs interact with the host machinery and the specific target residues, showing involvement in the mechanism. Most of these drugs are in the preclinical phase, and some are already being used to treat severe COVID-19 patients. Hence, the mechanistic insight presented in this study is envisaged to support further findings of clinical studies and eventually develop efficient inhibitors to treat SARS-CoV-2.

Role of Cholesterol in Transmembrane Dimerization of the ErbB2 Growth Factor Receptor.

The association of ErbB2 growth factor receptors is critical for cell growth and potentiates tumor proliferation in several cancer types. An important aspect in ErbB2 association is the role of lipids such as cholesterol, especially since their metabolism is often reprogrammed in cancer cells. Here, we have coupled metadynamics with coarse-grain simulations to identify cholesterol effects in the transmembrane dimerization of ErbB2 receptors. Overall, cholesterol interactions are observed with the receptor that directly tunes the association energetics. Several dimer conformations are identified both in the presence and absence of cholesterol, although the dimer regime appears to be more favorable in the presence of cholesterol. We observe an overall modulation of the underlying energy profile and the symmetric active and inactive conformational states are not distinguished in the presence of cholesterol. We show that cholesterol binds to the receptor transmembrane domain at a site (CRAC motif) that overlaps with the dimer interface (SmXXXSm motif). The competition between the transmembrane interactions and cholesterol interactions decides the final conformational landscape. Our work is an important step toward characterizing cholesterol effects in ErbB2 membrane receptor function.

Lipid-dependent conformational landscape of the ErbB2 growth factor receptor dimers.

Altered lipid metabolism has been linked to cancer development and progression. Several roles have been attributed to the increased saturation and length of lipid acyl tails observed in tumors, but its effect on signaling receptors is still emerging. In this work, we have analyzed the lipid dependence of the ErbB2 growth factor receptor dimerization that plays an important role in the pathogenesis of breast cancer. We have performed coarse-grain ensemble molecular dynamics simulations to comprehensively sample the ErbB2 monomer-dimer association. Our results indicate a dynamic dimer state with a complex conformational landscape that is modulated with increasing lipid tail length. We resolve the native N-terminal "active" and C-terminal "inactive" conformations in all membrane compositions. However, the relative population of the N-terminal and C-terminal conformers is dependent on length of the saturated lipid tails. In short-tail membranes, additional non-specific dimers are observed which are reduced or absent in long-tailed bilayers. Our results indicate that the relative population as well as the structure of the dimer state is modulated by membrane composition. We have correlated these differences to local perturbations of the membrane around the receptor. Our work is an important step in characterizing ErbB dimers in healthy and diseased states and emphasize the importance of sampling lipid dynamics in understanding receptor association.

Resolving the conformational dynamics of ErbB growth factor receptor dimers.

The combinatorial dimerization of the ErbB growth factor receptors (ErbB1- ErbB4) are critical for their function. Here, we have characterized the conformational dynamics of ErbB transmembrane homo-dimers and hetero-dimers by using a coarse-grain simulation framework. All dimers, except ErbB4-4 and ErbB1-4, exhibit at least two conformations. The reported NMR structures correspond to one of these conformations, representing the N-terminal active state in ErbB1-1 (RH2), ErbB2-2 (RH1) and ErbB4-4 (RH) homo-dimers and the LH dimer in ErbB3-3 homo-dimer, validating the computational approach. Further, we predict a right-handed ErbB3-3 dimer conformer that warrants experimental testing. The five hetero-dimers that have not yet been experimentally resolved display prominent right-handed dimers associating by the SmXXXSm motif. Our results provide insights into the constitutive signaling of ErbB4 after cleavage of the extracellular region. The presence of the inactive-like dimer conformers leading to symmetric kinase domains gives clues on the autoinhibition of the receptor dimers. The dimer states characterized here represent an important step towards understanding the combinatorial cross associations in the ErbB family.

Effect of Membrane Composition on Receptor Association: Implications of Cancer Lipidomics on ErbB Receptors.

The association of single transmembrane receptors, such as the ErbB receptors is a key event in initiating cell signaling networks. The interactions between these receptors have been well characterized for both ligand-driven and pre-formed dimers. However, the role of the membrane in modulating association is less well understood and assumes greater importance in light of altered membrane composition in diseased states. Here, we discuss how membrane composition has been observed to induce both structural and dynamic differences in receptor association. Computational studies, especially those using coarse-grain simulations have been successful in predicting the role of the membrane and calculating the related free energy landscapes. Membrane perturbations and differences in lipid chain order, related to the lipophobic effect, have been shown to play a large role in driving membrane protein association. Further, we review lipid compositions reported in diseased conditions and its effect on transmembrane receptor association, focusing on the ErbB growth factor receptor dimers in cancer. Understanding the role of the membrane in receptor association will provide general design principles driving receptor organization, as well as help to identify novel therapeutic strategies.

Role of spatial inhomogenity in GPCR dimerisation predicted by receptor association-diffusion models.

G protein-coupled receptor (GPCR) association is an emerging paradigm with far reaching implications in the regulation of signalling pathways and therapeutic interventions. Recent super resolution microscopy studies have revealed that receptor dimer steady state exhibits sub-second dynamics. In particular the GPCRs, muscarinic acetylcholine receptor M1 (M1MR) and formyl peptide receptor (FPR), have been demonstrated to exhibit a fast association/dissociation kinetics, independent of ligand binding. In this work, we have developed a spatial kinetic Monte Carlo model to investigate receptor homo-dimerisation at a single receptor resolution. Experimentally measured association/dissociation kinetic parameters and diffusion coefficients were used as inputs to the model. To test the effect of membrane spatial heterogeneity on the simulated steady state, simulations were compared to experimental statistics of dimerisation. In the simplest case the receptors are assumed to be diffusing in a spatially homogeneous environment, while spatial heterogeneity is modelled to result from crowding, membrane micro-domains and cytoskeletal compartmentalisation or 'corrals'. We show that a simple association-diffusion model is sufficient to reproduce M1MR association statistics, but fails to reproduce FPR statistics despite comparable kinetic constants. A parameter sensitivity analysis is required to reproduce the association statistics of FPR. The model reveals the complex interplay between cytoskeletal components and their influence on receptor association kinetics within the features of the membrane landscape. These results constitute an important step towards understanding the factors modulating GPCR organisation.

Thermodynamic and kinetic characterization of transmembrane helix association.

The transient dimerization of transmembrane proteins is an important event in several cellular processes and computational methods are being increasingly used to quantify their underlying energetics. Here, we probe the thermodynamics and kinetics of a simple transmembrane dimer to understand membrane protein association. A multi-step framework has been developed in which the dimerization profiles are calculated from coarse-grain molecular dynamics simulations, followed by meso-scale simulations using parameters calculated from the coarse-grain model. The calculated value of ΔGassoc is approx. -20 kJ mol(-1) and is consistent between three methods. Interestingly, the meso-scale stochastic model reveals low dimer percentages at physiologically-relevant concentrations, despite a favorable ΔGassoc. We identify generic driving forces arising from the protein backbone and lipid bilayer and complementary factors, such as protein density, that govern self-interactions in membranes. Our results provide an important contribution in understanding membrane protein organization and linking molecular, nano-scale computational studies to meso-scale experimental data.