Protein–ligand interaction studies of retinol-binding protein 3 with herbal molecules using AutoDock for the management of Eales’ disease

Eales’ disease is an idiopathic retinal vasculitis of the eye. The disease is predominantly characterized by recurrent vitreous hemorrhage. Interphotoreceptor retinol-binding protein 3 plays a significant role in the etiopathogenesis of this condition. It transports retinoids between the retinal pigment epithelium and the photoreceptors; hence, this protein is a potential target for docking studies. In silico data reveal that herbal molecules interact with regulatory domains of interphotoreceptor retinol-binding protein 3 (IRBP-3), resulting into significant docking score and also forms H-bond and several hydrophobic interactions between active residues of IRBP-3. These interactions between the active residues may lead to significant conformational change in that particular portion of the protein. This efficacy and suitability of ligand was determined on the basis of binding energy calculations. Ginkgolide showed minimum binding energy calculations among selected 10 other natural ligands. This fact of virtual screening for potential ligand can give new insights toward the therapeutic intonations and alterations toward the advances in treatment for Eales’ disease.     

Metal-binding sites of concanavalin A and their role in the binding of α-methyl d-glucopyranoside

Binding of a transition metal ion to specific sites in concanavalin A induces the formation of specific Ca(2+) ion-binding sites. Sites for binding alpha-methyl d-glucopyranoside exist only when a transition metal ion and Ca(2+) ion are bound.     

Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease

PurposeIt is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson’s disease (PD). Thus, it is critical to improve prediction of outcome in PD patients.MethodsWe systematically probed a range of robust predictor algorithms, aiming to find best combinations of features for significantly improved prediction of motor outcome (MDS-UPDRS-III) in PD. We analyzed 204 PD patients with 18 features (clinical measures; dopamine-transporter (DAT) SPECT imaging measures), performing different randomized arrangements and utilizing data from 64%/6%/30% of patients in each arrangement for training/training validation/final testing. We pursued 3 approaches: i) 10 predictor algorithms (accompanied with automated machine learning hyperparameter tuning) were first applied on 32 experimentally created combinations of 18 features, ii) we utilized Feature Subset Selector Algorithms (FSSAs) for more systematic initial feature selection, and iii) considered all possible combinations between 18 features (262,143 states) to assess contributions of individual features.ResultsA specific set (set 18) applied to the LOLIMOT (Local Linear Model Trees) predictor machine resulted in the lowest absolute error 4.32 ± 0.19, when we firstly experimentally created 32 combinations of 18 features. Subsequently, 2 FSSAs (Genetic Algorithm (GA) and Ant Colony Optimization (ACO)) selecting 5 features, combined with LOLIMOT, reached an error of 4.15 ± 0.46. Our final analysis indicated that longitudinal motor measures (MDS-UPDRS-III years 0 and 1) were highly significant predictors of motor outcome.ConclusionsWe demonstrate excellent prediction of motor outcome in PD patients by employing automated hyperparameter tuning and optimal utilization of FSSAs and predictor algorithms.     

The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis–NIR spectroscopy and comparative methods

The development of a rapid, accurate and cost-effective method for the prediction of constituents related to soil nitrogen (N) supply is considered important. The potential of using visible (Vis) and near infrared reflectance (NIR) spectroscopy (400–2500 nm) as such a method was investigated. Vis–NIR calibrations were performed for organic carbon (C_org) and total N (N_tot) content and their potential mineralisation using 80 grassland soil samples of rather heterogeneous origin. Prediction accuracy was tested using a 'take-out-four' validation strategy (48 samples). Within investigated variables a ratio of standard deviation of reference data to standard error of bias corrected prediction (RPD) within 1.7 (r²=0.65) and 2.7 (r²=0.87) were achieved. Apparent differences in Vis–NIR prediction accuracy among the variables were partly due to errors in the reference values. Thawed moist samples tend to be more accurately predicted than dried samples, and no benefit was derived from the grinding of sieved (4 mm) and dried samples. Prediction accuracy did not differ using two different systems for sample presentation to the Vis–NIR analyses. Comparative predictions of C_org and N_tot and their potential mineralisations were performed using the take-out-four validation strategy and simple linear regression to loss on ignition (LOI) values and hot KCl extracted NH₄-N (N_hotKCl) values as predictors. Likewise, the reference values of C_org and N_tot were also used as predictors for each other and for the potential C and N mineralisation constituents. Accuracy obtained for the Vis–NIR predictions of investigated constituents was in general equal or better than prediction accuracy obtained by these comparative methods. The Vis–NIR method provided promising predictions of variables important for the soil N supply.     

Origins and suppression of oscillations in a computational model of Parkinson’s disease

Efficacy of deep brain stimulation (DBS) for motor signs of Parkinson’s disease (PD) depends in part on post-operative programming of stimulus parameters. There is a need for a systematic approach to tuning parameters based on patient physiology. We used a physiologically realistic computational model of the basal ganglia network to investigate the emergence of a 34 Hz oscillation in the PD state and its optimal suppression with DBS. Discrete time transfer functions were fit to post-stimulus time histograms (PSTHs) collected in open-loop, by simulating the pharmacological block of synaptic connections, to describe the behavior of the basal ganglia nuclei. These functions were then connected to create a mean-field model of the closed-loop system, which was analyzed to determine the origin of the emergent 34 Hz pathological oscillation. This analysis determined that the oscillation could emerge from the coupling between the globus pallidus external (GPe) and subthalamic nucleus (STN). When coupled, the two resonate with each other in the PD state but not in the healthy state. By characterizing how this oscillation is affected by subthreshold DBS pulses, we hypothesize that it is possible to predict stimulus frequencies capable of suppressing this oscillation. To characterize the response to the stimulus, we developed a new method for estimating phase response curves (PRCs) from population data. Using the population PRC we were able to predict frequencies that enhance and suppress the 34 Hz pathological oscillation. This provides a systematic approach to tuning DBS frequencies and could enable closed-loop tuning of stimulation parameters.     

Ligand recognition in μ opioid receptor: experimentally based modeling of μ opioid receptor binding sites and their testing by ligand docking

For three-dimensional understanding of the mechanisms that control potency and selectivity of the ligand binding at the atomic level, we have analysed opioid receptor-ligand interaction based on the receptor's 3D model. As a first step, we have constructed molecular models for the multiple opioid receptor subtypes using bacteriorhodopsin as a template. The S-activated dihydromorphine derivatives should serve as powerful tools in mapping the three-dimensional structure of the μ opioid receptor, including the nature of the agonist-mediated conformational change that permits G protein-coupling to ‘second messenger’ effector molecules, and in identifying specific ligand-binding contacts with the μ opioid receptor. The analyses of the interactions of some opioid ligands with the predicted ligand binding sites are consistent with the results of the affinity labeling experiments.     

Localization and pharmacological characterization of nicotinic-cholinergic binding sites in cockroach brain using α- and neuronal bungarotoxin

[¹²⁵I]α-Bungarotoxinisusedasaprobetostudythenicotinic-cholinergicreceptorinmembrane preparations of the cockroach brain. Binding is restricted mainly to particulate fractions of brain homogenates, is time dependent and is saturable above 2 nM with very low non-specific binding. Scatchard analysis indicates that binding is associated with a single affinity site (K_d = 1.09 nM) having a B_max of 8926 fmol/mg protein which is the highest concentration of binding sites yet reported in insects. Association kinetics are best fit by a mono-exponential model with a k_obs = 4.37 × 10⁻³s⁻¹. Dissociation is best described by a bi-exponential model giving dissociation constants of 1.18 × 10⁻⁵ and 9.94 × 10⁻⁵s⁻¹. The K_ds calculated from kinetic data are 0.029 and 0.25 nM suggesting the possibility of heterogeneous binding sites not detected by saturation studies. Displacement studies indicate that binding follows a nicotinic pharmacology and demonstrate the high affinity of methyllycaconitine and the anthelmintics, morantel and pyrantel. Displacement by neuronal bungarotoxin shows the presence of two distinct binding sites not differentiated by α-bungarotoxin. Autoradiographic studies show α-bungarotoxin to be binding to neuropile regions of the brain, to be displaced from these regions by agents effective in binding studies and demonstrate that the neuronal bungarotoxin binding sites can be regionally localized.     

Probing the binding mechanisms of α-tocopherol to trypsin and pepsin using isothermal titration calorimetry,spectroscopic, and molecular modeling methods

α-Tocopherol is a required nutrient for a variety of biological functions. In this study, the binding of α-tocopherol to trypsin and pepsin was investigated using isothermal titration calorimetry (ITC), steady-state and time-resolved fluorescence measurements, circular dichroism (CD) spectroscopy, and molecular modeling methods. Thermodynamic investigations reveal that α-tocopherol binds to trypsin/pepsin is synergistically driven by enthalpy and entropy. The fluorescence experimental results indicate that α-tocopherol can quench the fluorescence of trypsin/pepsin through a static quenching mechanism. The binding ability of α-tocopherol with trypsin/pepsin is in the intermediate range, and one molecule of α-tocopherol combines with one molecule of trypsin/pepsin. As shown by circular dichroism (CD) spectroscopy, α-tocopherol may induce conformational changes of trypsin/pepsin. Molecular modeling displays the specific binding site and gives information about binding forces and α-tocopherol-tryptophan (Trp)/tyrosine (Tyr) distances. In addition, the inhibition rate of α-tocopherol on trypsin and pepsin was studied. The study provides a basic data set for clarifying the binding mechanisms of α-tocopherol with trypsin and pepsin and is helpful for understanding its biological activity in vivo.     

A review of major Crohn’s disease susceptibility genes and their role in disease pathogenesis

Crohn’s disease (CD) is a chronic inflammatory bowel disease whose relevance is increasing in industrialized society. Recent genome wide association studies revealed over seventy one loci associated with disease penetrance. Several variants that increase disease risk encode for altered proteins that diminish bacterial host defense. NOD2 alters intracellular bacterial sensing while ATG16L1 is thought to diminish bacterial clearance by impairing autophagy. Additionally, changes in the IBD5 locus are thought to diminish barrier function. Alternatively, recent data indicate a gain of function genetic variant of IL23R is protective amongst European CD patients. These recent genetic discoveries contradict historical theories that Crohn’s disease results from overactive auto-aggressive responses. Rather, new genetic data suggest disease-associated variants encode for dysfunctional proteins that diminish essential innate immune responses against commensal organisms. This review provides an overview of these critical discoveries and places them in their biological context.     

Tumor necrosis factor-α-mediated severity of idiopathic retinal periphlebitis in young adults (Eales’ disease): implication for anti-TNF-α therapy

Tumor necrosis factor-alpha (TNF-α) is a pleiotropic inflammatory cytokine. Tumor necrosis factor-alpha was evaluated in the serum samples of patients with idiopathic retinal periphlebitis in young adults (Eales’ disease). Retinal periphlebitis was graded according to a new grading system based on severity of inflammation (grade 1–4). Quantification of the TNF-α levels was carried out using ELISA kit in the serum samples of young adults with idiopathic retinal periphlebitis (n = 17) and healthy controls (n = 17) of similar age. Tumor necrosis factor-α level was found to be significantly raised in cases with retinal periphlebitis as compared with controls (p < 0.001). Higher levels of TNF-α were found to be associated with increased severity of retinal periphlebitis. Tumor necrosis factor-α represents a novel target for controlling inflammatory activity in idiopathic retinal periphlebitis. Higher levels of TNF-α, in association with the increased severity of retinal periphlebitis, have implications for early anti-TNF-α therapy.     

Regulation of integrin αIIbβ3 ligand binding and signaling by the metal ion binding sites in the β I domain

The ability of αIIbβ3 to bind ligands and undergo outside-in signaling is regulated by three divalent cation binding sites in the β I domain. Specifically, the metal ion-dependent adhesion site (MIDAS) and the synergistic metal binding site (SyMBS) are thought to be required for ligand binding due to their synergy between Ca(2+) and Mg(2+). The adjacent to MIDAS (ADMIDAS) is an important ligand binding regulatory site that also acts as a critical link between the β I and hybrid domains for signaling. Mutations in this site have provided conflicting results for ligand binding and adhesion in different integrins. We have mutated the β3 SyMBS and ADMIDAS. The SyMBS mutant abolished ligand binding and outside-in signaling, but when an activating glycosylation mutation in the αIIb Calf 2 domain was introduced, the ligand binding affinity and signaling were restored. Thus, the SyMBS is important but not absolutely required for integrin bidirectional signaling. The ADMIDAS mutants showed reduced ligand binding affinity and abolished outside-in signaling, and the activating glycosylation mutation could fully restore integrin signaling of the ADMIDAS mutant. We propose that the ADMIDAS ion stabilizes the low-affinity state when the integrin headpiece is in the closed conformation, whereas it stabilizes the high-affinity state when the headpiece is in the open conformation with the swung-out hybrid domain.     

Molecular modeling of zinc and copper binding with Alzheimer’s amyloid β-peptide

Aggregation of the amyloid β-peptide (Aβ) into insoluble fibrils is a key pathological event in Alzheimer’s disease. Cu(II) and Zn(II) ions were reported to be able to induce Aβ aggregation at nearly physiological concentrations in vitro. In this study, the binding modes of Cu(II) and Zn(II) in this process were explored by molecular modeling. In the pre-associated Aβ, Nτ atom of imidazole ring of His14, O atom of carbonyl of main-chain and two O atoms of water occupied the four ligand positions of the complex. While in the aggregated form of Aβ, the His13(N)–Metals–His14(N) bridges were formed through metal cross-linking action. These results would be helpful to put insight on revealing the formation mechanism of pathogenic Aβ aggregates in brain.     

Fluid–structure interaction modeling of compliant aortic valves using the lattice Boltzmann CFD and FEM methods

Biomechanics and Modeling in Mechanobiology - The lattice Boltzmann method (LBM) has been increasingly used as a stand-alone CFD solver in various biomechanical applications. This study proposes a...     

Prevalence of distal diabetic polyneuropathy using quantitative sensory methods in a population with diabetes of more than 10 years’ disease duration

ObjectivesResults of studies on the prevalence of distal diabetic polyneuropathy (DPN) are contradictory. Conventional methods used for the diagnosis of DPN in clinical practice have limited effectiveness. The present study aimed to assess the prevalence of DPN in a population with long-standing diabetes (more than 10 years disease duration) by measuring vibratory, thermal and tactile sensitivities with quantitative sensory devices, as well as their relationship with associated clinical risk factors.Patients and methodsA total of 1011 diabetic patients were evaluated in a multicenter, cross-sectional, observational study. The three sensitivities were assessed by ultrabiothesiometer, aesthesiometer and thermoskin devices, respectively. The prevalence of neuropathic pain was validated by the DN4 questionnaire.ResultsOf the 1011 cases included, 400 (39.6%) met the diagnostic criteria of DPN, while no DPN was found in the remaining 611 (60.4%). Of the 400 patients with DPN, 253 (63.2%) showed clinical manifestations, while 147 (36.8%) were diagnosed as subclinical DPN. The prevalence of DPN increased with disease duration. There was a progressive loss of the three sensitivities with increased disease duration, particularly thermal and vibratory sensitivities. This loss was statistically significant for the latter two sensitivities. Among patients with clinical DPN, 84.2% had painful neuropathic symptoms. The prevalence of DPN was positively related to micro- and macroangiopathic complications and with dyslipidemia.ConclusionThis study reveals a high degree of underdiagnosis of DPN, most likely due to the asymptomatic nature of the disease in a considerable proportion of patients. Our observations provide evidence of the usefulness of specific equipment for quantitative and objective assessment of polyneuropathy.     

Prediction of Colles’ fracture load in human radius using cohesive finite element modeling

Osteoporotic and age-related fractures are a significant public health problem. One of the most common osteoporotic fracture sites in the aging population is distal radius. There is evidence in the literature that distal radius fractures (Colles’ fracture) are an indicative of increased risk of future spine and hip fractures. In this study, a nonlinear fracture mechanics-based finite element method is applied to human radius to assess its fracture load as a function of cortical bone geometry and material properties. Seven three-dimensional finite element models of radius were created and the fracture loads were determined by using cohesive finite element modeling which explicitly represents the crack and the fracture process zone behavior. The fracture loads found in the simulations (731–6793 N) were in the range of experimental values reported in the literature. The fracture loads predicted by the simulations decreased by 4–5% per decade based only on material level changes and by 6–20% per decade when geometrical changes were also included. Cortical polar moment of inertia at 15% distal radius showed the highest correlation to fracture load (r²=0.97). These findings demonstrate the strength of fracture mechanics-based finite element modeling and show that combining geometrical and material properties provides a better assessment of fracture risk in human radius.     

Investigations of Takeout proteins’ ligand binding and release mechanism using molecular dynamics simulation

Takeout (To) proteins exist in a diverse range of insect species. They are involved in many important processes of insect physiology and behaviors. As the ligand carriers, To proteins can transport the small molecule to the target tissues. However, ligand release mechanism of To proteins is unclear so far. In this contribution, the process and pathway of the ligand binding and release are revealed by conventional molecular dynamics simulation, steered molecular dynamics simulation and umbrella sampling methods. Our results show that the α4-side of the protein is the unique gate for the ligand binding and release. The structural analysis confirms that the internal cavity of the protein has high rigidity, which is in accordance with the recent experimental results. By using the potential of mean force calculations in combination with residue cross correlation calculation, we concluded that the binding between the ligand and To proteins is a process of conformational selection. Furthermore, the conformational changes of To proteins and the hydrophobic interactions both are the key factors for ligand binding and release.