Computers & chemistry最新文献

A new method for the prediction of enthalpy of alkanes between C₆ and C₁₀ from molecular structures has been proposed. Thirty five calculated descriptors were selected for the description of molecular structures. The first four scores of Principle Component Analysis on the calculated descriptors were used as inputs to predict the enthalpy of alkanes. Models relating relationships between molecular structure descriptors and enthalpy of alkanes were constructed by means of radial basis function neural networks. To get the best prediction results, some strategies were also employed to optimise the learning parameters of the radial basis function neural networks. For the test set, a predictive correlation coefficient of R=0.9913 and root mean squared error of 0.5876 were obtained.

The application of fourth order discretisations of the second derivative of concentration with respect to distance from the electrode, in electrochemical digital simulations, is examined. In the bulk of the diffusion space, a central five-point scheme is used, and six-point asymmetric schemes are used at the edges. In this paper, the scheme is applied to the extrapolation technique, based on the backward implicit (BI) algorithm for temporal integration, which (with extrapolation) allows higher orders in time as well. The method is found to be stable, using both the von Neumann and matrix methods. Exceptional efficiency is obtained both for Cottrell and chronopotentiometry simulations, requiring as few as 3–5 steps in time, starting at the dimensionless time t=0 to gain four-decimal accuracy at t=1.

Heats of formation of energetic materials were calculated by Dewar's AM1 and Stewart's PM3 methods. In order to compare the theoretical results with the experimental ones, some correlation models were proposed in this study. Correlations were evaluated by multivariable linear regression method, considering the number of nitro groups and the use of quadratic relations involving the number of carbon, hydrogen, nitrogen, and oxygen atoms. Results indicated very precise correlations. Based on these correlations, heats of formation of some aliphatic nitro compounds can be predicted at 95% predictive interval without experimental analysis.

The transmittance signal of a precipitant system measured with a focused laser beam carries associated noise coming from several sources. In this work, we have studied the influence of the focal parameters (wavelength, focal length and prefocused radius of the beam) on the maximum noise reached in equivalent nucleation processes. For this purpose, a simulation program of precipitating systems, designed in fortran 90, has been developed. The program generates simulated transmittances, which are processed by another computer program to extract associated noise.Wide ranges of values of the focal parameters have been analysed, finding relationships between the maximum noise and the focal parameters. They have been justified in connection with the changes observed in the radial parameters, which define the size and shape of the focused path.

There exist no methodical studies concerning non-equilibrium systems in cellular biology. This paper is an attempt to partially fill this shortcoming. We have undertaken an extensive data-mining operation in the existing scientific literature to find scattered information about non-equilibrium subcellular systems, in particular concerning fast proteins, i.e. those with short turnover half-time. We have advanced the hypothesis that functionality in fast proteins emerges as a consequence of their intrinsic physical instability that arises due to conformational strains resulting from co-translational folding (the interdependence between chain elongation and chain folding during biosynthesis on ribosomes). Such intrinsic physical instability, a kind of conformon (Klonowski–Klonowska conformon, according to Ji, (Molecular Theories of Cell Life and Death, Rutgers University Press, New Brunswick, 1991)) is probably the most important feature determining functionality and timing in these proteins. If our hypothesis is true, the turnover half-time of fast proteins should be positively correlated with their molecular weight, and some experimental results (Ames et al., J. Neurochem. 35 (1980) 131) indeed demonstrated such a correlation. Once the native structure (and function) of a fast protein macromolecule is lost, it may not be recovered—denaturation of such proteins will always be irreversible; therefore, we searched for information on irreversible denaturation. Only simulation and modeling of protein co-translational folding may answer the questions concerning fast proteins (Ruggiero and Sacile, Med. Biol. Eng. Comp. 37 (Suppl. 1) (1999) 363). Non-equilibrium structures may also be built up of protein subunits, even if each one taken by itself is in thermodynamic equilibrium (oligomeric proteins; sub-cellular sol–gel dissipative network structures).

‘Silicon-based’ biology has gathered momentum as the world-wide sequencing projects have made possible the investigation and comparative analysis of complete genomes. Central to the quest to elucidate and characterise the genes and gene products encoded within genomes are pivotal concepts concerning the processes of evolution, the mechanisms of protein folding, and, crucially, the manifestation of protein function. Our use of computers to model such concepts is limited by, and must be placed in the context of, the current limits of our understanding of these biological processes. It is important to recognise that we do not have a common understanding of what constitutes a gene; we cannot invariably say that a particular sequence or fold has arisen via divergence or convergence; we do not fully understand the rules of protein folding, so we cannot predict protein structure; and we cannot invariably diagnose protein function, given knowledge only of its sequence or structure in isolation. Accepting what we cannot do with computers plays an essential role in forming an appreciation of what we can do. Without this understanding, it is easy to be misled, as spurious arguments are often used to promote over-enthusiastic notions of what particular programs can achieve. There are valuable lessons to be learned here from the field of artificial intelligence, principal among which is the realisation that capturing and representing complex knowledge is time consuming, expensive and hard. If bioinformatics is to tackle biological complexity meaningfully, the road ahead must therefore be paved with caution, rigour and pragmatism.

The determination of a protein's structure from the knowledge of its linear chain is one of the important problems that remains as a bottleneck in interpreting the rapidly increasing repository of genetic sequence data. One approach to this problem that has shown promise and given a measure of success is threading. In this approach contact energies between different amino acids are first determined by statistical methods applied to known structures. These contact energies are then applied to a sequence whose structure is to be determined by threading it through various known structures and determining the total threading energy for each candidate structure. That structure that yields the lowest total energy is then considered the leading candidate among all the structures tested. Additional information is often needed in order to support the results of threading studies, as it is well known in the field that the contact potentials used are not sufficiently sensitive to allow definitive conclusions. Here, we investigate the hypothesis that the environment of an amino acid residue realized as all those residues not local to it on the chain but sufficiently close spatially can supply information predictive of the type of that residue that is not adequately reflected in the individual contact energies. We present evidence that confirms this hypothesis and suggests a high order cooperativity between the residues that surround a given residue and how they interact with it. We suggest a possible application to threading.