Proximate composition,amino acid and fatty acid composition of fish maws

Fish maws are commonly recommended and consumed in Asia over many centuries because it is believed to have some traditional medical properties. This study highlights and provides new information on the proximate composition, amino acid and fatty acid composition of fish maws of Cynoscion acoupa, Congresox talabonoides and Sciades proops. The results indicated that fish maws were excellent protein sources and low in fat content. The proteins in fish maws were rich in functional amino acids (FAAs) and the ratio of FAAs and total amino acids in fish maws ranged from 0.68 to 0.69. Among species, croaker C. acoupa contained the most polyunsaturated fatty acids, arachidonic acid, docosahexaenoic acid and eicosapntemacnioc acid, showing the lowest value of index of atherogenicity and index of thrombogenicity, showing the highest value of hypocholesterolemic/hypercholesterolemic ratio, which is the most desirable.     

Interfacial composition, structural parameters and thermodynamic properties of water-in-oil microemulsions

The formation and structural characteristics of water-in-oil microemulsions comprising hexadecylpyridinium chloride (CPC), alkanols (C₄–C₆) and alkanes (C₅, C₈–C₁₀) have been investigated by the method of dilution. The compositions of the surfactant and the cosurfactant in the interfacial region (interphase) of the microemulsion droplets have been determined. The thermodynamics of transfer of the cosurfactants (alkanols) from the continuous oil (alkane) phase to the interface have been evaluated from dilution measurements at different temperatures. The structural parameters, radii of the droplet and the waterpool, aggregation numbers of CPC and the alkanols in the interphase of a droplet, and the nanoparticle density of solution have been estimated assuming monodispersity of the droplets. The thermodynamics and structural parameters have been examined in terms of the chain lengths of the alkanols and alkanes. Received: 12 September 2000 Accepted: 27 October 2000     

Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes

Using the pseudo amino acid (PseAA) composition to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. However, how to optimally formulate the PseAA composition is an important problem yet to be solved. In this article the grey modeling approach is introduced that is particularly efficient in coping with complicated systems such as the one consisting of many proteins with different sequence orders and lengths. On the basis of the grey model, four coefficients derived from each of the protein sequences concerned are adopted for its PseAA components. The PseAA composition thus formulated is called the "grey-PseAA" composition that can catch the essence of a protein sequence and better reflect its overall pattern. In our study we have demonstrated that introduction of the grey-PseAA composition can remarkably enhance the success rates in predicting the protein structural class. It is anticipated that the concept of grey-PseAA composition can be also used to predict many other protein attributes, such as subcellular localization, membrane protein type, enzyme functional class, GPCR type, protease type, among many others.     

Free and total amino acid composition in blue-green algae

A simple, accurate and reproducible analytical method is described for the extraction and the simultaneous determination of 18 amino acids in different for geographical origin Spirulina alga samples using phenylisothiocianate as derivatizating agent in natural feed. The best experimental hydrolysis conditions have been studied varying the temperature, the time and the hydrolyzing reagent. The separation and the quantitative analysis of the by-products have been carried out by HPLC analysis and UV detection. An amino acid pattern is compared with that proposed by the Food Agriculture Organization (FAO) for an ideal protein and with those of some traditional feed.     

Theoretical studies on the structural,spectroscopic, thermodynamic,and electronic properties of zoledronic acid

The structure, spectroscopic, thermodynamic, and electronic properties of zoledronic acid (ZL, 1-hydroxy- 2-(1H-imidazol-1-yl)ethane-1,1-diyldiphosphonic acid), typical third-generation nitrogen-containing bisphosphonates (N-BPs), have been investigated systematically. Six conformations are taken into account, including three unprotonated and three protonated structures. They are optimized by four different density functional theory (DFT) methods combined with four different basis sets to evaluate their performance in predicting the structural and spectral features of ZL. Thermodynamic properties are calculated based on the harmonic vibrational analysis, including the standard heat capacity (C _p,m ⁰ ), entropy (S _m ⁰ ), and enthalpy (S _m ⁰ ). The ¹H and ¹³C NMR chemical shifts are calculated using the GIAO method and compared with the experimental data. Molecular electrostatic potential (MEP) and frontier molecular orbital (FMO) analyses are also performed to study the electronic characteristics of the title compound.     

On the relation between thermodynamic,transport and structural properties of electrolyte solutions

The relation between thermodynamic, transport and structural properties of electrolyte solutions is explored for volumes and radii of ions in solution, water structure making and breaking by ions, ion pairing, and electromotive force of cells with transport, and preferential solvation of ions in mixed solvents. Published in Russian in Elektrokhimiya, 2008, Vol. 44, No. 1, pp. 18–31. The text was submitted by the authors in English.     

Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor

The structural class is an important feature widely used to characterize the overall folding type of a protein. How to improve the prediction quality for protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. Based on the concept of the pseudo amino acid composition [Chou, K. C. Proteins Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60], a novel approach for measuring the complexity of a protein sequence was introduced. The advantage by incorporating the complexity measure factor into the pseudo amino acid composition as one of its components is that it can catch the essence of the overall sequence pattern of a protein and hence more effectively reflect its sequence-order effects. It was demonstrated thru the jackknife crossvalidation test that the overall success rate by the new approach was significantly higher than those by the others. It has not escaped our notice that the introduction of the complexity measure factor can also be used to improve the prediction quality for, among many other protein attributes, subcellular localization, enzyme family class, membrane protein type, and G-protein couple receptor type.     

Electrochemical and structural coupling of the naphthoquinone amino acid

As a prelude to engineering artificial energy conversion proteins emulating biology, we examine the inclusion of a synthetic naphthoquinone amino acid in a characterized host-guest protein and determine the effects of its quinone and hydroquinone forms on the helix-coil distribution.     

Interfacial composition, thermodynamic properties, and structural parameters of water-in-oil microemulsions stabilized by 1-pentanol and mixed surfactants

The present study is focused on the evaluation of the interfacial composition, thermodynamic properties, and structural parameters of water-in-oil mixed surfactant microemulsions [(cetylpyridinium chloride, CPC+polyoxyethylene (20) cetyl ether, Brij-58 or polyoxyethylene (20) stearyl ether, Brij-78)/1-pentanol/n-heptane, or n-decane] under various physicochemical environments by the Schulman method of cosurfactant titration of the oil/water interface. The estimation of the number of moles of 1-pentanol at the interface (n(a)(i)) and bulk oil (n(a)(o)) and its distribution between these two domains at the threshold level of stability have been emphasized. The thermodynamics of transfer of 1-pentanol from the continuous oil phase to the interface have been evaluated. n(a)(i),n(a)(i), standard Gibbs free energy (ΔG(t)(0)), standard enthalpy (ΔH(t)(0)), and standard entropy (ΔG(t)(0)) of transfer process have been found to be dependent on the molar ratio of water to surfactant (ω), type of nonionic surfactant and its content (X(Brij-58 or Brij-78)), oil and temperature. A correlation between (ΔH(t)(0)) and (ΔS(t)(0)) is examined at different experimental temperatures. Bulk surfactant composition dependent temperature insensitive microemulsions have been reported. Associated structural parameters, such as droplet dimensions and aggregation number of surfactant and cosurfactant at the droplet interface have been evaluated using a mathematical model after suitable modifications for mixed surfactant systems. In light of these parameters, the prospect of using these microemulsion systems for the synthesis of nanoparticles and the modulation of enzyme activity has been discussed. Correlations of the results in terms of the evaluated physicochemical parameters have been attempted.     

Molecular dynamics study of structural and thermodynamic characteristics of nanodroplets of simple fluid

Equilibrium configurations of Lennard-Jones nanodroplets composed of 10–15000 spherically symmetric molecules placed in the center of a spherical container are studied at constant temperature by the molecular dynamics method. The distribution of local density is found and size dependences of density in the center of droplet, first coordination number, and energy surface tension coinciding for equimolecular dividing surface with specific excess free energy of droplet are studied. Radial distribution function is also determined. It is established that the passage of structural characteristics to their macroscopic values is observed for droplets containing as little as about 300 molecules, while, for energy surface tension, analogous passage for energy surface tension occurs for droplets containing 700–6000 molecules.     

Correlation between the reactivity and the structural and thermal properties of supported metal nitrates

Montmorillonite-supported iron(III) nitrate and copper(II) nitrate reagents, and other supported metal nitrates prepared in the same way, were investigated by thermal and X-ray powder diffraction methods. The metal nitrates are present on the support in the form of crystalline hydrate and not as acetone solvate as supposed earlier. Thermal decomposition of metal nitrates that are active in model reactions proceeds in a different way from that of the practically inactive nitrates. In the former case, water release and nitrate decomposition itself are simultaneous process. These and other results contributed to determination of suitable reaction conditions for the montmorillonite-supported reagents.     

Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components

The proteins structure can be mainly classified into four classes: all-alpha, all-beta, alpha/beta, and alpha + beta protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discriminant analysis, is presented to study and predict protein structural class. On the basis of the concept of the pseudo amino acid composition (Chou, Proteins: Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60), 400 dipeptide components and 20 amino acid composition are, respectively, selected as parameters of diversity source. Total of 204 nonhomologous proteins constructed by Chou (Chou, Biochem Biophys Res Commun 1999, 264, 216) are used for training and testing the predictive model. The predicted results by using the pseudo amino acids approach as proposed in this paper can remarkably improve the success rates, and hence the current method may play a complementary role to other existing methods for predicting protein structural classification.     

A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction

During last few decades accurate determination of protein structural class using a fast and suitable computational method has been a challenging problem in protein science. In this context a meaningful representation of a protein sample plays a key role in achieving higher prediction accuracy. In this paper based on the concept of Chou's pseudo amino acid composition (Chou, K.C., 2001. Proteins 43, 246-255), a new feature representation method is introduced which is composed of the amino acid composition information, the amphiphilic correlation factors and the spectral characteristics of the protein. Thus the sample of a protein is represented by a set of discrete components which incorporate both the sequence order and the length effect. On the basis of such a statistical framework a simple radial basis function network based classifier is introduced to predict protein structural class. A set of exhaustive simulation studies demonstrates high success rate of classification using the self-consistency and jackknife test on the benchmark datasets.     

Relations between thermodynamic,physical and structural properties of alkali halides at their melting points

Linear relations of the general form Λ = a + mτ have been presented to describe the connection between the characteristic energetic and geometric parameters of alkali halides at their respective melting points. Inference has been made that these presented relations may be associated with the structure of the alkali halide liquids.     

Supersecondary structure prediction using Chou's pseudo amino acid composition

Supersecondary structures (SSSs) are the building blocks of protein 3D structures. Accurate prediction of SSSs can be one important step toward building a tertiary structure from the specified secondary structure. How to improve the accuracy of prediction of SSSs by effectively incorporating the sequence order effects is an important and challenging problem. Based on a different form of Chou's pseudo amino acid composition, a novel approach for feature representation of SSSs is proposed. Amino acid basic compositions, dipeptide components, and amino acid composition distribution are incorporated to represent the compositional features of proteins. Each supersecondary structural motif is characterized as a vector of 36 dimensions. In addition, we propose a novel prediction system by using SVM and IDQD algorithm as classifiers. Our method is trained and tested on ArchDB40 dataset containing 3088 proteins. The highest overall accuracy for the training dataset and the independent testing dataset are 77.7 and 69.4%, respectively. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011     

Use of amino acid composition to predict ligand-binding sites

A novel method for predicting the binding sites for druglike compounds on the surface of proteins was developed on the basis of the specific amino acid composition observed at the ligand-binding sites of ligand-protein complexes determined by X-ray analysis. A profile representing the preference of each of the 20 standard amino acids at the binding sites of druglike molecules was obtained for a small set of high-quality complex structures. An index termed propensity for ligand binding (PLB) was created from these profiles. The PLB index was used to predict the propensity of binding for 804 ligands at all potential binding sites on the proteins whose structures were determined by X-ray analysis. If the sites with the first two highest PLB indices are taken into consideration, the successfully predicted sites reached a high percentage of 86. The PLB prediction is relatively simple, but the validation study showed that it is both fast and accurate to detect ligand-binding sites, especially the binding sites of druglike molecules. Therefore, the PLB index can be used to predict the ligand-binding sites of uncharacterized protein structures and also to identify novel drug-binding sites of known drug targets.