Structural Ensemble of the Insulin Monomer

Characterization and modulation of human insulin degrading enzyme conformational dynamics to control enzyme activity

Insulin degrading enzyme (IDE) is a dimeric 110 kDa M16A zinc metalloprotease that degrades amyloidogenic peptides diverse in shape and sequence, including insulin, amylin, and amyloid-β, to prevent toxic amyloid fibril formation. IDE has a hollow catalytic chamber formed by four homologous subdomains organized into two ~55 kDa N- and C- domains (IDE-N and IDE-C, respectively), in which peptides bind, unfold, and are repositioned for proteolysis. IDE is known to transition between a closed state, poised for catalysis, and an open state, able to release cleavage products and bind new substrate. Here, we present five cryoEM structures of the IDE dimer at 3.0-4.1 Å resolution, obtained in the presence of a sub-saturating concentration of insulin. Analysis of the heterogeneity within the particle populations comprising these structures combined with all-atom molecular dynamics (MD) simulations permitted a comprehensive characterization of IDE conformational dynamics. Our analysis identified the structural basis and key residues for these dynamics that were not revealed by IDE static structures. Notably arginine-668 serves as a molecular latch mediating the open-close transition and facilitates key protein motions through charge-swapping interactions at the IDE-N/C interface. Our size-exclusion chromatography-coupled small-angle X-ray scattering and enzymatic assays of an arginine-668 to alanine mutant indicate a profound alteration of conformational dynamics and catalytic activity. Taken together, this work highlights the power of integrating experimental and computational methodologies to understand protein dynamics, offers the molecular basis of unfoldase activity of IDE, and provides a new path forward towards the development of substrate-specific modulators of IDE activity.

Analysis of the Dynamics of a Complex, Multipathway Reaction: Insulin Dimer Dissociation

The protein hormone insulin forms a homodimer that must dissociate to bind to its receptor. Understanding the kinetics and mechanism of dissociation is essential for the rational design of therapeutic analogs. In addition to its physiological importance, this dissociation process serves as a paradigm for coupled (un)folding and (un)binding. Based on previous free energy simulations, insulin dissociation is thought to involve multiple pathways with comparable free energy barriers. Here, we analyze the mechanism of insulin dimer dissociation using a recently developed computational framework for estimating kinetic statistics from short-trajectory data. These statistics indicate that the likelihood of dissociation (the committor) closely tracks the decrease in the number of (native and nonnative) intermonomer contacts and the increase in the number of water contacts at the dimer interface; the transition state with equal likelihood of association and dissociation corresponds to an encounter complex with relatively few native contacts and many nonnative contacts. We identify four pathways out of the dimer state and quantify their contributions to the rate as well as their exchange by computing reactive fluxes. We show that both the pathways and their extents of exchange can be understood in terms of rotations around three axes of the dimer structure. Our results provide insights into the kinetics of insulin analogs and, more generally, how to characterize complex, multipathway processes.

Analysis of the dynamics of a complex, multipathway reaction: Insulin dimer dissociation

The protein hormone insulin forms a homodimer that must dissociate to bind to its receptor. Understanding the kinetics and mechanism of dissociation is essential for rational design of therapeutic analogs. In addition to its physiological importance, this dissociation process serves as a paradigm for coupled (un)folding and (un)binding. Based on previous free energy simulations, insulin dissociation is thought to involve multiple pathways with comparable free energy barriers. Here, we analyze the mechanism of insulin dimer dissociation using a recently developed computational framework for estimating kinetic statistics from short-trajectory data. These statistics indicate that the likelihood of dissociation (the committor) closely tracks the decrease in the number of (native and nonnative) intermonomer contacts and the increase in the number of water contacts at the dimer interface; the transition state with equal likelihood of association and dissociation corresponds to an encounter complex with relatively few native contacts and many nonnative contacts. We identify four pathways out of the dimer state and quantify their contributions to the rate, as well as their exchange, by computing reactive fluxes. We show that both the pathways and their extents of exchange can be understood in terms of rotations around three axes of the dimer structure. Our results provide insights into the kinetics of insulin analogues and, more generally, how to characterize complex, multipathway processes.

Evolution of biosynthetic human insulin and its analogues for diabetes management

Hormones play a crucial role in maintaining the normal human physiology. By acting as chemical messengers that facilitate the communication between different organs, tissues and cells of the body hormones assist in responding appropriately to external and internal stimuli that trigger growth, development and metabolic activities of the body. Any abnormalities in the hormonal composition and balance can lead to devastating health consequences. Hormones have been important therapeutic agents since the early 20th century, when it was realized that their exogenous supply could serve as a functional substitution for those hormones which are not produced enough or are completely lacking, endogenously. Insulin, the pivotal anabolic hormone in the body, was used for the treatment of diabetes mellitus, a metabolic disorder due to the absence or intolerance towards insulin, since 1921 and is the trailblazer in hormone therapeutics. At present the largest market share for therapeutic hormones is held by insulin. Many other hormones were introduced into clinical practice following the success with insulin. However, for the six decades following the introduction the first therapeutic hormone, there was no reliable method for producing human hormones. The most common source for hormones were animals, although semisynthetic and synthetic hormones were also developed. However, none of these were optimal because of their allergenicity, immunogenicity, lack of consistency in purity and most importantly, scalability. The advent of recombinant DNA technology was a game changer for hormone therapeutics. This revolutionary molecular biology tool made it possible to synthesize human hormones in microbial cell factories. The approach allowed for the synthesis of highly pure hormones which were structurally and biochemically identical to the human hormones. Further, the fermentation techniques utilized to produce recombinant hormones were highly scalable. Moreover, by employing tools such as site directed mutagenesis along with recombinant DNA technology, it became possible to amend the molecular structure of the hormones to achieve better efficacy and mimic the exact physiology of the endogenous hormone. The first recombinant hormone to be deployed in clinical practice was insulin. It was called biosynthetic human insulin to reflect the biological route of production. Subsequently, the biochemistry of recombinant insulin was modified using the possibilities of recombinant DNA technology and genetic engineering to produce analogues that better mimic physiological insulin. These analogues were tailored to exhibit pharmacokinetic and pharmacodynamic properties of the prandial and basal human insulins to achieve better glycemic control. The present chapter explores the principles of genetic engineering applied to therapeutic hormones by reviewing the evolution of therapeutic insulin and its analogues. It also focuses on how recombinant analogues account for the better management of diabetes mellitus.

PEGylation of Insulin and Lysozyme To Stabilize against Thermal Denaturation: A Molecular Dynamics Simulation Study

Biologic drugs or "biologics" (proteins derived from living organisms) are one of the fastest-growing classes of FDA-approved therapeutics. These compounds are often fragile and require conjugation to polymers for stabilization, with many proteins too ephemeral for therapeutic use. During storage or administration, proteins tend to unravel and lose their secondary structure due to changes in solution temperature, pH, and other external stressors. To enhance their lifetime, protein drugs currently in the market are conjugated with polyethylene glycol (PEG), owing to its ability to increase the stability, solubility, and pharmacokinetics of protein drugs. Here, we perform all-atom molecular dynamics simulations to study the unfolding process of egg-white lysozyme and insulin at elevated temperatures. We test the validity of two force fields─CHARMM36 and Amber ff99SB-ILDN─in the unfolding process. By calculating global and local properties, we capture residues that deteriorate first─these are the "weak links" in the proteins. Next, we conjugate both proteins with PEG and find that PEG preserves the native structure of the proteins at elevated temperatures by blocking water molecules from entering the hydrophobic core, thereby causing the secondary structure to stabilize.

Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction

Understanding dynamics in complex systems is challenging because there are many degrees of freedom, and those that are most important for describing events of interest are often not obvious. The leading eigenfunctions of the transition operator are useful for visualization, and they can provide an efficient basis for computing statistics, such as the likelihood and average time of events (predictions). Here, we develop inexact iterative linear algebra methods for computing these eigenfunctions (spectral estimation) and making predictions from a dataset of short trajectories sampled at finite intervals. We demonstrate the methods on a low-dimensional model that facilitates visualization and a high-dimensional model of a biomolecular system. Implications for the prediction problem in reinforcement learning are discussed.

Profiling of insulin and resveratrol interaction using multi-spectroscopy and molecular docking study

Background

Resveratrol, a phenolic compound, has various medicinal properties, including anticancer, anti-diabetic, antioxidant, anti-inflammatory, etc. Diabetes is a killing disease, especially for people who cannot maintain a healthy lifestyle. People with diabetes need additional information about a supplement that can prevent and treat diabetes. The present study aims to investigate the interaction of insulin with resveratrol using fluorescence spectroscopy, UV–Vis spectroscopy, CD spectropolarimeter, and molecular docking methods. As an outcome of this study, we expect to understand the contribution of resveratrol in insulin resistance.

Result

The fluorescence spectroscopy results showed that the peak intensity of insulin emission decreased with resveratrol. The interaction of insulin with resveratrol involved a combination of static and dynamic quenching effects. Temperature changes caused the binding constant (K) and the binding site (n) unstable. The interaction occurred through hydrogen bonding, van der Waal, and was hydrophobic. The results of UV–Vis spectroscopy showed that the addition of resveratrol caused a peak in a blueshift, and the absorbance was hyperchromic. Also, there was a reduction in electron transition and the extinction coefficient. The CD spectropolarimeter results showed that the addition of resveratrol affected the secondary structure of insulin. The amount of α-helix and β-sheet slightly change and increase in the secondary structure’s length. The molecular docking study showed that resveratrol interacts via hydrogen bonding with glycine and asparagine. van der Waal interactions occurred in asparagine, phenylalanine, and cysteine. The interaction of electrons occurred through the π orbitals of resveratrol with tyrosine A and B. The binding energy of molecules interaction happened spontaneously on a hydrophobic surface.

Conclusion

Profiling the interaction of insulin and resveratrol shows that resveratrol can stabilize insulin structure and prevent insulin resistance in diabetes.

Progress in Simulation Studies of Insulin Structure and Function

Insulin is a peptide hormone known for chiefly regulating glucose level in blood among several other metabolic processes. Insulin remains the most effective drug for treating diabetes mellitus. Insulin is synthesized in the pancreatic β-cells where it exists in a compact hexameric architecture although its biologically active form is monomeric. Insulin exhibits a sequence of conformational variations during the transition from the hexamer state to its biologically-active monomer state. The structural transitions and the mechanism of action of insulin have been investigated using several experimental and computational methods. This review primarily highlights the contributions of molecular dynamics (MD) simulations in elucidating the atomic-level details of conformational dynamics in insulin, where the structure of the hormone has been probed as a monomer, dimer, and hexamer. The effect of solvent, pH, temperature, and pressure have been probed at the microscopic scale. Given the focus of this review on the structure of the hormone, simulation studies involving interactions between the hormone and its receptor are only briefly highlighted, and studies on other related peptides (e.g., insulin-like growth factors) are not discussed. However, the review highlights conformational dynamics underlying the activities of reported insulin analogs and mimetics. The future prospects for computational methods in developing promising synthetic insulin analogs are also briefly highlighted.

Identifying signatures of proteolytic stability and monomeric propensity in O-glycosylated insulin using molecular simulation

Insulin has been commonly adopted as a peptide drug to treat diabetes as it facilitates the uptake of glucose from the blood. The development of oral insulin remains elusive over decades owing to its susceptibility to the enzymes in the gastrointestinal tract and poor permeability through the intestinal epithelium upon dimerization. Recent experimental studies have revealed that certain O-linked glycosylation patterns could enhance insulin's proteolytic stability and reduce its dimerization propensity, but understanding such phenomena at the molecular level is still difficult. To address this challenge, we proposed and tested several structural determinants that could potentially influence insulin's proteolytic stability and dimerization propensity. We used these metrics to assess the properties of interest from [Formula: see text] aggregate molecular dynamics of each of 12 targeted insulin glyco-variants from multiple wild-type crystal structures. We found that glycan-involved hydrogen bonds and glycan-dimer occlusion were useful metrics predicting the proteolytic stability and dimerization propensity of insulin, respectively, as was in part the solvent-accessible surface area of proteolytic sites. However, other plausible metrics were not generally predictive. This work helps better explain how O-linked glycosylation influences the proteolytic stability and monomeric propensity of insulin, illuminating a path towards rational molecular design of insulin glycoforms.