Diabetes mellitus caused by mutations in human insulin: analysis of impaired receptor binding of insulins Wakayama, Los Angeles and Chicago using pharmacoinformatics

Implicit Solvent with Explicit Ions Generalized Born Model in Molecular Dynamics: Application to DNA

The ion atmosphere surrounding highly charged biomolecules, such as nucleic acids, is crucial for their dynamics, structure, and interactions. Here, we develop an approach for the explicit treatment of ions within an implicit solvent framework suitable for atomistic simulations of biomolecules. The proposed implicit solvent/explicit ions model, GBION, is based on a modified generalized Born (GB) model; it includes separate, modified GB terms for solute-ion and ion-ion interactions. The model is implemented in the AMBER package (version 24), and its performance is thoroughly investigated in atomistic molecular dynamics (MD) simulations of double-stranded DNA on a microsecond time scale. The aggregate characteristics of monovalent (Na+ and K+) and trivalent (Cobalt Hexammine, CoHex3+) counterion distributions around double-stranded DNA predicted by the model are in reasonable agreement with the experiment (where available), all-atom explicit water MD simulations, and the expectation from the Manning condensation theory. The radial distributions of monovalent cations around DNA are reasonably close to the ones obtained using the explicit water model: expressed in units of energy, the maximum deviations of local ion concentrations from the explicit solvent reference are within 1 kBT, comparable to the corresponding deviations expected between different established explicit water models. The proposed GBION model is able to simulate DNA fragments in a large volume of solvent with explicit ions with little additional computational overhead compared with the fully implicit GB treatment of ions. Ions simulated using the developed model explore conformational space at least 2 orders of magnitude faster than in the explicit solvent. These advantages allowed us to observe and explore an unexpected "stacking" mode of DNA condensation in the presence of trivalent counterions (CoHex3+) that was revealed by recent experiments.

Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains

The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.

Changes in Cells Associated with Insulin Resistance

Insulin is a polypeptide hormone synthesized and secreted by pancreatic β-cells. It plays an important role as a metabolic hormone. Insulin influences the metabolism of glucose, regulating plasma glucose levels and stimulating glucose storage in organs such as the liver, muscles and adipose tissue. It is involved in fat metabolism, increasing the storage of triglycerides and decreasing lipolysis. Ketone body metabolism also depends on insulin action, as insulin reduces ketone body concentrations and influences protein metabolism. It increases nitrogen retention, facilitates the transport of amino acids into cells and increases the synthesis of proteins. Insulin also inhibits protein breakdown and is involved in cellular growth and proliferation. On the other hand, defects in the intracellular signaling pathways of insulin may cause several disturbances in human metabolism, resulting in several chronic diseases. Insulin resistance, also known as impaired insulin sensitivity, is due to the decreased reaction of insulin signaling for glucose levels, seen when glucose use in response to an adequate concentration of insulin is impaired. Insulin resistance may cause, for example, increased plasma insulin levels. That state, called hyperinsulinemia, impairs metabolic processes and is observed in patients with type 2 diabetes mellitus and obesity. Hyperinsulinemia may increase the risk of initiation, progression and metastasis of several cancers and may cause poor cancer outcomes. Insulin resistance is a health problem worldwide; therefore, mechanisms of insulin resistance, causes and types of insulin resistance and strategies against insulin resistance are described in this review. Attention is also paid to factors that are associated with the development of insulin resistance, the main and characteristic symptoms of particular syndromes, plus other aspects of severe insulin resistance. This review mainly focuses on the description and analysis of changes in cells due to insulin resistance.

Insulin Resistance: The Increased Risk of Cancers

Insulin resistance, also known as impaired insulin sensitivity, is the result of a decreased reaction of insulin signaling to blood glucose levels. This state is observed when muscle cells, adipose tissue, and liver cells, improperly respond to a particular concentration of insulin. Insulin resistance and related increased plasma insulin levels (hyperinsulinemia) may cause metabolic impairments, which are pathological states observed in obesity and type 2 diabetes mellitus. Observations of cancer patients confirm that hyperinsulinemia is a major factor influencing obesity, type 2 diabetes, and cancer. Obesity and diabetes have been reported as risks of the initiation, progression, and metastasis of several cancers. However, both of the aforementioned pathologies may independently and additionally increase the cancer risk. The state of metabolic disorders observed in cancer patients is associated with poor outcomes of cancer treatment. For example, patients suffering from metabolic disorders have higher cancer recurrence rates and their overall survival is reduced. In these associations between insulin resistance and cancer risk, an overview of the various pathogenic mechanisms that play a role in the development of cancer is discussed.

A Review of the Biosynthesis and Structural Implications of Insulin Gene Mutations Linked to Human Disease

The discovery of the insulin hormone over 100 years ago, and its subsequent therapeutic application, marked a key landmark in the history of medicine and medical research. The many roles insulin plays in cell metabolism and growth have been revealed by extensive investigations into the structure and function of insulin, the insulin tyrosine kinase receptor (IR), as well as the signalling cascades, which occur upon insulin binding to the IR. In this review, the insulin gene mutations identified as causing disease and the structural implications of these mutations will be discussed. Over 100 studies were evaluated by one reviewing author, and over 70 insulin gene mutations were identified. Mutations may impair insulin gene transcription and translation, preproinsulin trafficking and proinsulin sorting, or insulin-IR interactions. A better understanding of insulin gene mutations and the resultant pathophysiology can give essential insight into the molecular mechanisms underlying impaired insulin biosynthesis and insulin-IR interaction.

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.

Cadmium-induced conformational changes in type 2 metallothionein of medicinal plant Coptis japonica: insights from molecular dynamics studies of apo, partially and fully metalated forms

Plants play an important role in the removal of excess heavy metals from soil and water. Medicinal plants can also have non-traditional use in phytoremediation technologies. Among the heavy metals, Cadmium (Cd) is the most abundant and readily taken up by the crop plants. Plant metallothioneins (MTs) are small proteins having cysteine-rich residues and appear to play key roles in metal homoeostasis. Plant metallothionein 2 (MT 2) from Coptis japonica (Gold-thread; CjMT 2) is a typical member of this subfamily and features two cysteine-rich regions containing eight and six cysteine residues, respectively, separated by 42 amino acids long linker region. In-silico analysis of MT 2 protein sequences of C. japonica was performed. In this study, ab initio methods were utilised for the prediction of three-dimensional structure of CjMT 2. After structure validation, heavy metal-binding sites were predicted for the selected modelled structures of CjMT 2. To obtain Cd_i-CjMT 2 (i = 1-7), metalated complex individual docking experiments were performed. The stability of the metalated docked structures was assessed by molecular dynamics (MD) simulation studies. Our study showed that CjMT 2 binds up to 4 Cd²⁺ ions in two distinct domains: a N-terminal β-domain that binds to 2 Cd²⁺ ions and a C-terminal α-domain that binds with 2 Cd²⁺ ions. Our analysis revealed that Cys residues of alpha and beta domain and some residues of spacer region of CjMT 2 protein might be important for the cadmium interaction. MD simulation studies provided insight into metal-induced conformational changes and mechanism of metalation of CjMT 2, an intrinsically disordered protein. This study provides useful insights into mechanism of cadmium-type 2 metallothionein interaction.

Insights into unbound-bound states of GPR142 receptor in a membrane-aqueous system using molecular dynamics simulations

G protein coupled receptors (GPCRs) are source machinery in signal transduction pathways and being one of the major therapeutic targets play a significant in drug discovery. GPR142, an orphan GPCR, has been implicated in the regulation of insulin, thereby having a crucial role in Type II diabetes management. Deciphering of the structures of orphan, GPCRs (O-GPCRs) offer better prospects for advancements in research in ion translocation and transduction of extracellular signals. As the crystallographic structure of GPR142 is not available in PDB, therefore, threading and ab initio-based approaches were used for 3D modeling of GPR142. Molecular dynamic simulations (900 ns) were performed on the 3D model of GPR142 and complexes of GPR142 with top five hits, obtained through virtual screening, embedded in lipid bilayer with aqueous system using OPLS force field. Compound 1, 3, and 4 may act as scaffolds for designing potential lead agonists for GPR142. The finding of GPR142 MD simulation study provides more comprehensive representation of the functional properties. The concern for Type II diabetes is increasing worldwide and successful treatment of this disease demands novel drugs with better efficacy.

In silico identification of potential drug compound against Peroxisome proliferator-activated receptor-gamma by virtual screening and toxicity studies for the treatment of Diabetic Nephropathy

Diabetic Nephropathy is a serious complication of diabetes mellitus. Current therapeutic strategies of Diabetic Nephropathy are based on control of modifiable risks like hypertension, glucose levels, and dyslipidemia. Peroxisome proliferator activated receptor-gamma (PPAR-γ) is implicated in several metabolic syndromes including Diabetic Nephropathy, besides obesity, insulin insensitivity, dislipidemia, inflammation, and hypertension. In the present study, virtual screening of 617 compounds from two different public databases was done against PPAR-γ with an objective to find a possible lead compound. Two softwares, PyRx and iGEMDOCK, were used to achieve the docking accuracy in order to avoid loss of candidate compounds. Rosiglitazone (used to treat Diabetic Nephropathy) was taken as the standard compound. A total of 30 compounds with good binding affinity with PPAR-γ were selected for further filtering, on the basis of absorption, distribution, metabolism, excretion, and toxicity (ADMET). The interaction profiling of these 30 compounds, showed a minimum of one and maximum of three interactions with reference to rosiglitazone (SER-289, HIS-449, HIS-323, TYR-473). The fulfilling of ADMET analysis criteria of 30 compounds led to the selection of four compounds (ZINC ID 00181552, 00276456, 00298314, 00448009). Molecular dynamics simulation of these lead compounds in complex with PPAR-γ revealed that three of the four compounds formed a stable complex in the ligand-binding pocket of PPAR-γ during 20-ns simulation. Hence, these three (ZINC ID 00181552, 00276456, 00298314) of the four compounds are potential candidates for experimental validation of biological activity against PPAR-γ in future drug discovery studies.

In silico modelling and molecular dynamics simulation studies of thiazolidine based PTP1B inhibitors

Protein tyrosine phosphatase 1B (PTP1B) has been identified as a negative regulator of insulin and leptin signalling pathway; hence, it can be considered as a new therapeutic target of intervention for the treatment of type 2 diabetes. Inhibition of this molecular target takes care of both diabetes and obesity, i.e. diabestiy. In order to get more information on identification and optimization of lead, pharmacophore modelling, atom-based 3D QSAR, docking and molecular dynamics studies were carried out on a set of ligands containing thiazolidine scaffold. A six-point pharmacophore model consisting of three hydrogen bond acceptor (A), one negative ionic (N) and two aromatic rings (R) with discrete geometries as pharmacophoric features were developed for a predictive 3D QSAR model. The probable binding conformation of the ligands within the active site was studied through molecular docking. The molecular interactions and the structural features responsible for PTP1B inhibition and selectivity were further supplemented by molecular dynamics simulation study for a time scale of 30 ns. The present investigation has identified some of the indispensible structural features of thiazolidine analogues which can further be explored to optimize PTP1B inhibitors.

Computational study of the activity, dynamics, energetics and conformations of insulin analogues using molecular dynamics simulations: Application to hyperinsulinemia and the critical residue B26

Due to the increasing prevalence of diabetes, finding therapeutic analogues for insulin has become an urgent issue. While many experimental studies have been performed towards this end, they have limited scope to examine all aspects of the effect of a mutation. Computational studies can help to overcome these limitations, however, relatively few studies that focus on insulin analogues have been performed to date. Here, we present a comprehensive computational study of insulin analogues-three mutant insulins that have been identified with hyperinsulinemia and three mutations on the critical B26 residue that exhibit similar binding affinity to the insulin receptor-using molecular dynamics simulations with the aim of predicting how mutations of insulin affect its activity, dynamics, energetics and conformations. The time evolution of the conformers is studied in long simulations. The probability density function and potential of mean force calculations are performed on each insulin analogue to unravel the effect of mutations on the dynamics and energetics of insulin activation. Our conformational study can decrypt the key features and molecular mechanisms that are responsible for an enhanced or reduced activity of an insulin analogue. We find two key results: 1) hyperinsulinemia may be due to the drastically reduced activity (and binding affinity) of the mutant insulins. 2) Y26_BS and Y26_BE are promising therapeutic candidates for insulin as they are more active than WT-insulin. The analysis in this work can be readily applied to any set of mutations on insulin to guide development of more effective therapeutic analogues.