A new software package, ‘kinfitsim’, for fitting and simulating kinetic data is presented. The main goals of the kinfitsim package are to obtain the best-fit parameters—rate constants, amplitudes and others—to a user specified chemical mechanism, plots of the calculated and experimental absorbance versus time, and a report to the user with the results. The kinfitsim package can be used in either chemical research or for educational purposes.
In this paper, the neural network method was applied to predict the content of protein secondary structure elements that was based on ‘pair-coupled amino acid composition’, in which the sequence coupling effects are explicitly included through a series of conditional probability elements. The prediction was examined by a self-consistency test and an independent-dataset. Both indicated good results obtained when using the neural network method to predict the contents of α-helix, β-sheet, parallel β-sheet strand, antiparallel β-sheet strand, β-bridge, 310-helix, π-helix, H-bonded turn, bend, and random coil.
Models of lipid bilayer were extended and dipole structure of polar head in lipid molecules was included. As a result a wavy structure, resembling experimentally observed ‘ripple phase’, was obtained. The discussion on significance of interactions between dipoles that constitute polar part of the model membrane is presented. Assumptions of the model are closer to the real conditions and reflect the real phenomena much better. Dependence of the model system behaviour on dielectric permeability, ionic strength, and temperature was studied. An influence of reduced number of freedom degrees in the dipole system on the membrane properties was also considered. It was proved that if dielectric permeability of membrane polar part is significantly smaller than water dielectric permeability then the membrane model does not have to take into account changeability of dipole tilt towards membrane surface. This assumption becomes more significant for dielectric permeability ε approaching ε=80. Packing degree of hydrocarbon chains in hydrophobic part of the membrane is also responsible for the angle value between dipoles and the membrane surface. The model results are compared to experimental results obtained by means of fluorescence probe fluorescein-PE.
The multistep consecutive ECE–ECE reduction process has been compared with reduction in multicomponent system . A simple method of transformation has been devised to disclose the subtle structure of the complex cyclic voltammetry (CV) responses and illustrated by the ECE–ECE process modeled earlier. The method can be applied to any multi-electron CV experimental curve for which a numerical modeling has been done. Electroreduction processes similar to those considered here are often met in practice. An attempt of unification of consecutive electroreduction and electroreduction of multicomponent system has been made. Interrelation between research and analytical voltammetry aspects of the problem is also discussed.
Stoichiometrically, exact candidate pathways or mechanisms for deriving the rate law of a catalytic or complex reaction can be determined through the synthesis of networks of plausible elementary reactions constituting such pathways. A rigorous algorithmic method is proposed for executing this synthesis, which is exceedingly convoluted due to its combinatorial complexity. Such a method for synthesizing networks of reaction pathways follows the general framework of a highly exacting combinatorial method established by us for process-network synthesis. It is based on the unique graph-representation in terms of P-graphs, a set of axioms, and a group of combinatorial algorithms. In the method, the inclusion or exclusion of a step of each elementary reaction in the mechanism of concern hinges on the general combinatorial properties of feasible reaction networks. The decisions are facilitated by solving linear programming problems comprising a set of mass-balance constraints to determine the existence or absence of any feasible solution. The search is accelerated further by exploiting the inferences of preceding decisions, thereby eliminating redundancy. As a result, all feasible independent reaction networks, i.e. pathways, are generated only once; the pathways violating any first principle of either stoichiometry or thermodynamics are eliminated. The method is also capable of generating those combinations of independent pathways directly, which are not microscopically reversible. The efficiency and efficacy of the method are demonstrated with the identification of the feasible mechanisms of ammonia synthesis involving as many as 14 known elementary reactions.
A mathematica® package, ‘condu.m’, has been developed to find the polynomial in concentration and temperature which best fits conductimetric data of the type (κ, c, T) or (κ, c1, c2, T) of electrolyte solutions (κ: specific conductivity; ci: concentration of component i; T: temperature). In addition, an interface, ‘tkondu’, has been written in the TCL/Tk language to facilitate the use of condu.m by an operator not familiarised with mathematica®. All this software is available on line (UPV/EHU, 2001). ‘condu.m’ has been programmed to: (i) select the optimum grade in c1 and/or c2; (ii) compare models with linear or quadratic terms in temperature; (iii) calculate the set of adjustable parameters which best fits data; (iv) simplify the model by elimination of ‘a priori’ included adjustable parameters which after the regression analysis result in low statistical significance; (v) facilitate the location of outlier data by graphical analysis of the residuals; and (vi) provide quantitative statistical information on the quality of the fit, allowing a critical comparison among different models. Due to the multiple options offered the software allows testing different conductivity models in a short time, even if a large set of conductivity data is being considered simultaneously. Then, the user can choose the best model making use of the graphical and statistical information provided in the output file. Although the program has been initially designed to treat conductimetric data, it can be also applied for processing data with similar structure, e.g. (P, c, T) or (P, c1, c2, T), being P any appropriate transport, physical or thermodynamic property.
The novel vertex degree vm for heteroatom in molecular graph is derived on the basis of the valence connectivity δv of Kier–Hall. The newly proposed atom-type AI indices and previously proposed Xu index, are further modified for compounds with heteroatoms by replacing the vertex-degree of heteroatom by the proposed vm. The multiple linear regression using the modified Xu index and AI indices can provide high-quality QSPR models for the normal boiling points (BP), molar volumes (MV), molar refractions (MR), and molecular total surface areas (TSA) of alcohols with up to 17 non-hydrogen atoms. The results imply that these physical properties may be expressed as a linear combination of the individual indices related to molecular size and atom-types. For each of the four properties, the correlation coefficient r is greater than 0.996 and particularly the decrease in the standard error is within the range of 61–83% compared with the simple linear models based on the modified Xu index, and the standard errors are 3.814, 0.939, 0.187, and 3.348 for BP, MV, MR, and TSA, respectively. The final models correspond to a fit error of 2.33, 0.70, 0.53, and 0.95% for BP, MV, MR, and TSA, respectively. The more general leave-n-out method is used to do the cross-validation. The cross-validation demonstrates the outstanding predictive power of the final models. The contributions of individual indices are used to illustrate the role of the molecular size and individual groups in molecules. The results indicate that physical properties of alcohols are dominated by the molecular size. On the other hand, although the hydrogen-bonding interactions caused by the OH group play an important role in determining the normal BPs, the branching seems to be a more important factor influencing the MVs, MRs, and TSAs than the hydrogen-bonding interaction. The contribution of individual atom type or group to properties is not a constant and depends on its structural environment in a molecule.
The Euclid distance discriminant method is used to find protein coding genes in the yeast genome, based on the single nucleotide frequencies at three codon positions in the ORFs. The method is extremely simple and may be extended to find genes in prokaryotic genomes or eukaryotic genomes with less introns. Six-fold cross-validation tests have demonstrated that the accuracy of the algorithm is better than 93%. Based on this, it is found that the total number of protein coding genes in the yeast genome is less than or equal to 5579 only, about 3.8–7.0% less than 5800–6000, which is currently widely accepted. The base compositions at three codon positions are analyzed in details using a graphic method. The result shows that the preference codons adopted by yeast genes are of the RW type, where R, and W indicate the bases of purine, non-G and A/T, whereas the ‘codons’ in the intergenic sequences are of the form NNN, where N denotes any base. This fact constitutes the basis of the algorithm to distinguish between coding and non-coding ORFs in the yeast genome. The names of putative non-coding ORFs are listed here in detail.
In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a result, high rates of both self-consistency and jackknife test are obtained. This indicates that the structural class of a protein inconsiderably correlated with its amino acid composition, and the support vector machine can be referred as a powerful computational tool for predicting the structural classes of proteins.
Post Hartree–Fock and density functional theory (DFT) methods were used to study the different conformers of nitrosoethylene HCHCHNO, and substituted compounds of the nitrosoethylene RCHCHNO (R=Cl, NH2, N(CH3)2, OH, OCH3). The molecules were optimized at MP2/6-31G* level of theory of ab initio and B3LYP/6-31G* and B3PW91/6-31G* levels of theory of DFT. Special emphasis has been given to the effect of substitution of π-electron donor groups NH2, N(CH3)2, OH, and OCH3, which play a major role in modifying the geometrical parameters of NO group by the electronic transmission effects through the central group CHCH. The ability of DFT methods to predict the bond length adjacent to the atoms having lone pair electrons has been discussed. The conformational stabilities have been studied using the relative energies and DFT parameters such as chemical hardness and chemical potential. The role of intra-molecular hydrogen bond on the equilibrium structure has been discussed. The vibrational spectra for the different conformers of the nitrosoethylene and substituted compounds have been generated using the MP2/6-31G* level of theory.
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