Computers & chemistry最新文献

The proteins involved in a single biological process may form a stable supra-molecular assembly or be transiently in interaction. Although, the first annotation steps of a complete genome may allow the identification of the different partners, their assembly in a functional system, referred to as an integrated system, is a domain where methodological effort has to be done. Indeed, the knowledge required to assemble partners of such systems should be explicitly included in annotation software. The availability of a complete genome, and therefore of all the proteins encoded by that genome, motivated the development of automated approaches through the coordinated combination of different bio-informatic methods allowing the identification of the different partners, their assembly and the classification of the reconstructed systems in functional categories. In this data flux, the identification of the sequence partners represents the principal bottleneck. Here, we describe and compare the results obtained with different classes of methods (blastp2, psi-blast, mast and Meta-meme) applied to the identification in complete genomes of a given family of integrated systems: the ABC transporters. psi-blast appears to significantly outperform motif-based methods, and the results are discussed according to the nature of the proteins and the structure of the sub-families.

The Z-value (Comput. Chem. 23 (1999) 333) is an extension of the Z-score that is classically used to compare sets of biological sequences. The Z-value has been successfully used to handle complete genome studies as well as analyze large sets of proteins. The Z-value computation is based on a Monte Carlo approach to estimate the statistical significance of a Smith & Waterman alignment score. Comet et al. (Comput. Chem. 23 (1999) 333) have shown that, in contrast to the alignment score, the Z-value largely reduces the bias due to the lengths and compositions of the sequences. They also described an estimator of the deviation of Z-values, that we extend in this paper in order to optimize Z-values computation. The incremental algorithm described here provides two characteristics which are usually incompatible: (i) it improves the accuracy of Z-values calculation; (ii) it reduces the time complexity (this algorithm has been named incremental because it iteratively adds random sequences to the Monte-Carlo process when needed). Results are presented, originating from the all-by-all comparison of the proteins from Saccharomyces cerevisiae and Escherichia coli.

This paper presents an algorithm, DCFold, that automatically predicts the common secondary structure of a set of aligned homologous RNA sequences. It is based on the comparative approach. Helices are searched in one of the sequences, called the ‘target sequence’, and compared to the helices in the other sequences, called the ‘test sequences’. Our algorithm searches in the target sequence for palindromes that have a high probability to define helices that are conserved in the test sequences. This selection of significant palindromes is based on criteria that take into account their length and their mutation rate. A recursive search of helices, starting from these likely ones, is implemented using the ‘divide and conquer’ approach. Indeed, as pseudo-knots are not searched by DCFold, a selected palindrome (p, p′) makes possible to divide the initial sequence into two sequences, the internal one and the one resulting from the concatenation of the two external ones. New palindromes can be searched independently in these subsequences. This algorithm was run on ribosomal RNA sequences and recovered very efficiently their common secondary structures.

Most evolutionary studies based on molecular data refer to the portion of genomes encoding for proteins. Today, however, more and more attention is paid to the so-called ‘non-coding’ regions, which constitute a notable portion of the metazoan nuclear genome. Among them, the untranslated regions of messenger RNAs (mRNA UTRs) are particularly important, as they are involved in the regulation of gene expression, controlling translation efficiency as well as mRNA localization and stability. Up to now, only few studies have focused on the analysis of the compositional and structural features of UTRs, or carried out to investigate quantitatively their evolutionary dynamics. For this reason we have carried out an inter-order study on the evolutionary rate of 5′ and 3′ UTRs with respect to the corresponding coding region in 93 triplets of orthologous genes (selected through a phylogenetic approach, for a total of 645 625 nt) belonging to Primates (Homo sapiens), Artiodactyla (Bos taurus) and Rodentia (Mus spp.). Our study, that considered only likely orthologous genes, has revealed interesting features on the evolution of these regions concerning nucleotide substitution rate and indels and repetitive element distribution. UTRs from different genes showed a remarkable heterogeneity in the evolutionary dynamics, with some homologous so highly divergent to prevent their alignment, and other rather conserved, at least in some regions; most divergent sequence pairs were excluded from our analysis. The comparison between the nucleotide substitution rates calculated for 5′ and 3′ UTRs with those calculated on synonymous coding position allowed us to verify and measure the existence of functional constraints acting upon the UTRs of different genes which have shown, in many cases, a positive selection driven evolutionary dynamics.

Semiempirical AM1 calculations were carried out for quantum chemically optimized conformations of ATP and ADP and their modified phosphate derivatives with the oxygen atoms intervening between phosphorus atoms substituted by imido or methylene groups or the double-bonded oxygen atoms substituted by sulfur. In addition to the calculation of conventional geometric and energetic parameters, the effect of these substitutions was quantified in terms of conformational ‘strain energy’. The latter has been defined as the energy of transformation of the parent nucleotide (ATP or ADP) from the optimum conformation to the conformation optimized for its phosphate-modified analog. The results of calculations revealed that conformational ‘strain’ of phosphate-modified nucleotides depends not only on the nature of the substituent but also on its position. The respective effect had the largest magnitude when the substitution was made between two terminal phosphorus atoms. Given that the ‘strain energy’ characterizes the geometrical aspects of the interaction of nucleotide molecules with receptors and enzymes, an attempt was made to correlate it with the corresponding biological activities. Such correlation was significant in the case of highly specific binding sites for universal ligands like ATP.

A comparison of elementary (input) and apparent (output) values of the transfer coefficient was carried out for simulated kinetic cyclic voltammetry spectrum from stepwise to concerted mechanism. The resulting patterns of α_app versus E_p plots do not provide sufficient information to discriminate between the two mechanisms. It has been demonstrated that non-linear α_app patterns are characteristic for the pure stepwise mechanism obeying the Butler–Volmer kinetics. The change in α_app is not a sufficient condition for the transition from stepwise to concerted mechanism. This paper contains visualizations of the relationship between the apparent and elementary transfer coefficient. The difference between the potential variabilities of α_app and α_elem (a frequent source of confusion) is also discussed.

Atom-type AI topological indices derived from the topological distance sums and vertex degree further are used to describe different structural environment of each atom-type in a molecule. The multiple linear regression based on combined use of the proposed Xu index and AI indices is performed to develop high quality QSPR models for describing six physical properties (the normal boiling points, heats of vaporization, molar volumes, molar refractions, van der Waals’ constants, and Pitzer's acentric factors) of alkanes with up to nine carbon atoms. For each of six properties, the correlation coefficient r of the final models is larger than 0.995 and particularly the decrease in the standard error (s) is within the range of 45–86% as compared with the simple linear models with Xu index alone. The agreement between calculated and experimental data is quite good. The results indicate the potential of these indices for application to a wide range of physical properties. The role of each of the molecular size and individual groups in the molecules are illustrated by analyzing the relative or fraction contributions of individual indices. The results indicate that the six physical properties of alkanes are dominated by molecular size while AI indices have smaller influence dependent on the studied properties. Moreover, the studies demonstrate that each atomic group contributes an indefinite value to properties dependent on its structural environment in a molecule or other groups present. The cross-validation using the more general leave-n-out method demonstrates the final models to be highly statistically reliable.

Entropy is calculated for a representative set of acyclic and aromatic compounds within the realm of QSAR/QSPR theory. Flexible topological molecular indices are chosen as independent variables in the fitting equations. The comparison with results derived from quantum mechanical calculations shows that the present approach gives better predictions. Some possible future extensions are pointed out.

Values obtained from single molecule techniques exhibit distinct distributions comprising an uncertainty due to random noise convoluted with the intrinsic distribution of the molecule's properties. In the fields of single molecule light microscopy and spectroscopy, force microscopy and spectroscopy as well as other techniques like electrophysiology, sophisticated data analysis algorithms are available which extract the interesting parameters and their uncertainties from the noisy data set. The intrinsic distributions of these parameters contain valuable information about the molecules’ physical and chemical properties, that need to be deconvoluted from the data. Here, we present an expectation–maximisation (EM-) algorithm that estimates the intrinsic distribution in single molecule experiments. The performance is tested by using computer simulations and the application of the method is demonstrated for data from single molecule force spectroscopy.