Evaluation of anti-obesity activities of ethanolic extract of Terminalia paniculata bark on high fat diet-induced obese rats

Background: The prevalence and severity of obesity and associated co-morbidities are rapidly increasing across the world. Natural products-based drug intervention has been proposed as one of the crucial strategies for management of obesity ailments. This study was designed to investigate the anti-obesity activities of ethanolic extract of Terminalia paniculata bark (TPEE) on high fat diet-induced obese rats. Methods: LC-MS/MS analysis was done for ethanolic extract of T. paniculata bark. Male Sprague-Dawley (SD) rats were randomly divided into six groups of six each, normal diet fed (NC), high fat diet-fed (HFD), HFD+ orlistat (standard drug control) administered, and remaining three groups were fed with HFD + TPEE in different doses (100,150 and 200 mg/kg b. wt). For induction of obesity rats were initially fed with HFD for 9 weeks, then, (TPEE) was supplemented along with HFD for 42 days. Changes in body weight, body composition, blood glucose, insulin, tissue and serum lipid profiles, atherogenic index, liver markers, and expression of adipogenesis-related genes such as leptin, adiponectin, FAS, PPARgamma, AMPK-1alpha and SREBP-1c, were studied in experimental rats. Also, histopathological examination of adipose tissue was carried out. Results: Supplementation of TPEE reduced significantly (P < 0.05) body weight, total fat, fat percentage, atherogenic index, blood glucose, insulin, lipid profiles and liver markers in HFD-fed groups, in a dose-dependent manner. The expression of adipogenesis-related genes such as Leptin, FAS, PPARgamma, and SREBP-1c were down regulated while Adiponectin and AMPK-1alpha were up regulated in TPEE + HFD-fed rats. Furthermore, histopathological examination of adipose tissue revealed the alleviating effect of TPEE which is evident by reduced size of adipocytes. Conclusions: Together, the biochemical, histological and molecular studies unambiguously demonstrate the potential anti adipogenic and anti obesity activities of TPEE promoting it as a formidable candidate to develop anti obesity drug.

Structural plasticity in Mycobacterium tuberculosis uracil-DNA glycosylase (MtUng) and its functional implications

17 independent crystal structures of family I uracil-DNA glycosylase from Mycobacterium tuberculosis (MtUng) and its complexes with uracil and its derivatives, distributed among five distinct crystal forms, have been determined. Thermodynamic parameters of binding in the complexes have been measured using isothermal titration calorimetry. The two-domain protein exhibits open and closed conformations, suggesting that the closure of the domain on DNA binding involves conformational selection. Segmental mobility in the enzyme molecule is confined to a 32-residue stretch which plays a major role in DNA binding. Uracil and its derivatives can bind to the protein in two possible orientations. Only one of them is possible when there is a bulky substituent at the 50 position. The crystal structures of the complexes provide a reasonable rationale for the observed thermodynamic parameters. In addition to providing fresh insights into the structure, plasticity and interactions of the protein molecule, the results of the present investigation provide a platform for structure-based inhibitor design.

Bioinspired Reductionistic Peptide Engineering for Exceptional Mechanical Properties

A simple solution-processing and self-assembly approach that exploits the synergistic interactions between multiple hydrogen bonded networks and aromatic interactions was utilized to synthesize molecular crystals of cyclic dipeptides (CDPs), whose molecular weights (similar to 0.2 kDa) are nearly three orders of magnitude smaller than that of natural structural proteins (50-300 kDa). Mechanical properties of these materials, measured using the nanoindentation technique, indicate that the stiffness and strength are comparable and sometimes better than those of natural fibres. The measured mechanical responses were rationalized by recourse to the crystallographic structural analysis and intermolecular interactions in the self-assembled single crystals. With this work we highlight the significance of developing small molecule based bioinspired design strategies to emulate biomechanical properties. A particular advantage of the successfully demonstrated reductionistic strategy of the present work is its amenability for realistic industrial scale manufacturing of designer biomaterials with desired mechanical properties.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

Effective drug dosage design for treatment of infectious diseases using dynamic inversion

A generic nonlinear mathematical model describing the human immunological dynamics is used to design an effective automatic drug administration scheme. Even though the model describes the effects of various drugs on the dynamic system, this work is confined to the drugs that kill the invading pathogen and heal the affected organ. From a system theoretic point of view, the drug inputs can be interpreted as control inputs, which can be designed based on control theoretic concepts. The controller is designed based on the principle of dynamic inversion and is found to be effective in curing the �nominal model patient� by killing the invading microbes and healing the damaged organ. A major advantage of this technique is that it leads to a closed-form state feedback form of control. It is also proved from a rigorous mathematical analysis that the internal dynamics of the system remains stable when the proposed controller is applied. A robustness study is also carried out for testing the effectiveness of the drug administration scheme for parameter uncertainties. It is observed from simulation studies that the technique has adequate robustness for many �realistic model patients� having off-nominal parameter values as well.

Oxovanadium(IV)-based near-IR PDT agents: design to biological evaluation

An oxovanadium(IV) complex of dipyridophenazine, as a potent metal-based PDT agent, shows efficient DNA photocleavage activity at near-IR region and high photocytotoxicity in both UV-A and visible light in HeLa cells.

Molecule Modeling of Human Pentameric alpha(7) Neuronal Nicotinic Acetylcholine Receptor and Its Interaction with its Agonist and Competitive Antagonist

The nicotinic Acetylcholine Receptor (nAChR) is the major class of neurotransmitter receptors that is involved in many neurodegenerative conditions such as schizophrenia, Alzheimer's and Parkinson's diseases. The N-terminal region or Ligand Binding Domain (LBD) of nAChR is located at pre- and post-synaptic nervous system, which mediates synaptic transmission. nAChR acts as the drug target for agonist and competitive antagonist molecules that modulate signal transmission at the nerve terminals. Based on Acetylcholine Binding Protein (AChBP) from Lymnea stagnalis as the structural template, the homology modeling approach was carried out to build three dimensional model of the N-terminal region of human alpha(7)nAChR. This theoretical model is an assembly of five alpha(7) subunits with 5 fold axis symmetry, constituting a channel, with the binding picket present at the interface region of the subunits. alpha-netlrotoxin is a potent nAChR competitive antagonist that readily blocks the channel resulting in paralysis. The molecular interaction of alpha-Bungarotoxin, a long chain alpha-neurotoxin from (Bungarus multicinctus) and human alpha(7)nAChR seas studied. Agonists such as acetylcholine, nicotine, which are used in it diverse array of biological activities, such as enhancements of cognitive performances, were also docked with the theoretical model of human alpha(7)nAChR. These docked complexes were analyzed further for identifying the crucial residues involved i interaction. These results provide the details of interaction of agonists and competitive antagonists with three dimensional model of the N-terminal region of human alpha(7)nAChR and thereby point to the design of novel lead compounds.

Structural Basis of Drug Resistance by Genetic Variants of HIV Type 1 Clade C Protease from India

Using computer modeling of three-dimensional structures and structural information available on the crystal structures of HIV-1 protease, we investigated the structural effects of mutations, in treatment-naive and treatment-exposed individuals from India and postulated mechanisms of resistance in clade C variants. A large number of models (14) have been generated by computational mutation of the available crystal structures of drug bound proteases. Localized energy minimization was carried out in and around the sites of mutation in order to optimize the geometry of interactions present. Most of the mutations result in structural differences at the flap that favors the semiopen state of the enzyme. Some of the mutations were also found to confer resistance by affecting the geometry of the active site. The E35D mutation affects the flap structure in clade B strains and E35N and E35K mutation, seen in our modeled strains, have a more profound effect. Common polymorphisms at positions 36 and 63 in clade C also affected flap structure. Apart from a few other residues Gln-58, Asn-83, Asn-88, and Gln-92 and their interactions are important for the transition from the closed to the open state. Development of protease inhibitors by structure-based design requires investigation of mechanisms operative for clade C to improve the efficacy of therapy.

Linear time geometrical design rule checker based on quadtree representation of VLSI mask layouts

An efficient geometrical design rule checker is proposed, based on operations on quadtrees, which represent VLSI mask layouts. The time complexity of the design rule checker is O(N), where N is the number of polygons in the mask. A pseudoPascal description is provided of all the important algorithms for geometrical design rule verification.

Validation-Based Sparse Gaussian Process Classifier Design

Gaussian processes (GPs) are promising Bayesian methods for classification and regression problems. Design of a GP classifier and making predictions using it is, however, computationally demanding, especially when the training set size is large. Sparse GP classifiers are known to overcome this limitation. In this letter, we propose and study a validation-based method for sparse GP classifier design. The proposed method uses a negative log predictive (NLP) loss measure, which is easy to compute for GP models. We use this measure for both basis vector selection and hyperparameter adaptation. The experimental results on several real-world benchmark data sets show better orcomparable generalization performance over existing methods.

Design of laterally loaded piles in clays based on cone penetration test data: a reliability-based approach

The behaviour of laterally loaded piles is considerably influenced by the uncertainties in soil properties. Hence probabilistic models for assessment of allowable lateral load are necessary. Cone penetration test (CPT) data are often used to determine soil strength parameters, whereby the allowable lateral load of the pile is computed. In the present study, the maximum lateral displacement and moment of the pile are obtained based on the coefficient of subgrade reaction approach, considering the nonlinear soil behaviour in undrained clay. The coefficient of subgrade reaction is related to the undrained shear strength of soil, which can be obtained from CPT data. The soil medium is modelled as a one-dimensional random field along the depth, and it is described by the standard deviation and scale of fluctuation of the undrained shear strength of soil. Inherent soil variability, measurement uncertainty and transformation uncertainty are taken into consideration. The statistics of maximum lateral deflection and moment are obtained using the first-order, second-moment technique. Hasofer-Lind reliability indices for component and system failure criteria, based on the allowable lateral displacement and moment capacity of the pile section, are evaluated. The geotechnical database from the Konaseema site in India is used as a case example. It is shown that the reliability-based design approach for pile foundations, considering the spatial variability of soil, permits a rational choice of allowable lateral loads.

Structure-Based Design of DevR Inhibitor Active against Nonreplicating Mycobacterium tuberculosis

Antitubercular treatment is directed against actively replicating organisms. There is an urgent need to develop drugs targeting persistent subpopulations of Mycobacterium tuberculosis. The DevR response regulator is believed to play a key role in bacterial dormancy adaptation during hypoxia. We developed a homology-based model of DevR and used it for the rational design of inhibitors. A phenylcoumarin derivative (compound 10) identified by in silico pharmacophore-based screening of 2.5 million compounds employing protocols with some novel features including a water-based pharmacophore query, was characterized further. Compound 10 inhibited DevR binding to target DNA, down-regulated dormancy genes transcription, and drastically reduced survival of hypoxic but not nutrient-starved dormant bacteria or actively growing organ ` isms. Our findings suggest that compound 10 ``locks'' DevR in an inactive conformation that is unable to bind cognate DNA and induce the dormancy regulon. These results provide proof-of-concept for DevR as a novel target to develop molecules with sterilizing activity against tubercle bacilli.

Gabapentin: A Stereochemically Constrained gamma Amino Acid Residue in Hybrid Peptide Design

Nature has used the all-alpha-polypeptide backbone of proteins to create a remarkable diversity of folded structures. Sequential patterns of 20 distinct amino adds, which differ only in their side chains, determine the shape and form of proteins. Our understanding of these specific secondary structures is over half a century old and is based primarily on the fundamental elements: the Pauling alpha-helix and beta-sheet. Researchers can also generate structural diversity through the synthesis of polypeptide chains containing homologated (omega) amino acid residues, which contain a variable number of backbone atoms. However, incorporating amino adds with more atoms within the backbone introduces additional torsional freedom into the structure, which can complicate the structural analysis. Fortunately, gabapentin (Gpn), a readily available bulk drug, is an achiral beta,beta-disubstituted gamma amino add residue that contains a cyclohexyl ring at the C-beta carbon atom, which dramatically limits the range of torsion angles that can be obtained about the flanking C-C bonds. Limiting conformational flexibility also has the desirable effect of increasing peptide crystallinity, which permits unambiguous structural characterization by X-ray diffraction methods. This Account describes studies carried out in our laboratory that establish Gpn as a valuable residue in the design of specifically folded hybrid peptide structures. The insertion of additional atoms into polypeptide backbones facilitates the formation of intramolecular hydrogen bonds whose directionality is opposite to that observed in canonical alpha-peptide helices. If hybrid structures mimic proteins and biologically active peptides, the proteolytic stability conferred by unusual backbones can be a major advantage in the area of medicinal chemistry. We have demonstrated a variety of internally hydrogen-bonded structures in the solid state for Gpn-containing peptides, including the characterization of the C-7 and C-9 hydrogen bonds, which can lead to ribbons in homo-oligomeric sequences. In hybrid alpha gamma sequences, district C-12 hydrogen-bonded turn structures support formation of peptide helices and hairpins in longer sequences. Some peptides that include the Gpn residue have hydrogen-bond directionality that matches alpha-peptide helices, while others have the opposite directionality. We expect that expansion of the polypeptide backbone will lead to new classes of foldamer structures, which are thus far unknown to the world of alpha-polypeptides. The diversity of internally hydrogen-bonded structures observed in hybrid sequences containing Gpn shows promise for the rational design of novel peptide structures incorporating hybrid backbones.

Agents Based Algorithms for Design Parameter Estimation in Contaminant Transport Inverse Problems

A considerable amount of work has been dedicated on the development of analytical solutions for flow of chemical contaminants through soils. Most of the analytical solutions for complex transport problems are closed-form series solutions. The convergence of these solutions depends on the eigen values obtained from a corresponding transcendental equation. Thus, the difficulty in obtaining exact solutions from analytical models encourages the use of numerical solutions for the parameter estimation even though, the later models are computationally expensive. In this paper a combination of two swarm intelligence based algorithms are used for accurate estimation of design transport parameters from the closed-form analytical solutions. Estimation of eigen values from a transcendental equation is treated as a multimodal discontinuous function optimization problem. The eigen values are estimated using an algorithm derived based on glowworm swarm strategy. Parameter estimation of the inverse problem is handled using standard PSO algorithm. Integration of these two algorithms enables an accurate estimation of design parameters using closed-form analytical solutions. The present solver is applied to a real world inverse problem in environmental engineering. The inverse model based on swarm intelligence techniques is validated and the accuracy in parameter estimation is shown. The proposed solver quickly estimates the design parameters with a great precision.

Optimum Design of Bridge Abutments under Seismic Conditions: Reliability-Based Approach

The paper focuses on the reliability-based design optimization of gravity wall bridge abutments when subjected to active condition during earthquakes. An analytical study considering the effect of uncertainties in the seismic analysis of bridge abutments is presented. Planar failure surface has been considered in conjunction with the pseudostatic limit equilibrium method for the calculation of the seismic active earth pressure. Analysis is conducted to evaluate the external stability of bridge abutments when subjected to earthquake loads. Reliability analysis is used to estimate the probability of failure in three modes of failure viz. sliding failure of the wall on its base, overturning failure about its toe (or eccentricity failure of the resultant force) and bearing failure of foundation soil below the base of wall. The properties of backfill and foundation soil below the base of abutment are treated as random variables. In addition, the uncertainties associated with characteristics of earthquake ground motions such as horizontal seismic acceleration and shear wave velocity propagating through backfill soil are considered. The optimum proportions of the abutment needed to maintain the stability are obtained against three modes of failure by targeting various component and system reliability indices. Studies have also been made to study the influence of various parameters on the seismic stability.