Computers in molecular biology: current applications and emerging trends

Protobiotic Systems Chemistry Analyzed by Molecular Dynamics

Systems chemistry has been a key component of origin of life research, invoking models of life's inception based on evolving molecular networks. One such model is the graded autocatalysis replication domain (GARD) formalism embodied in a lipid world scenario, which offers rigorous computer simulation based on defined chemical kinetics equations. GARD suggests that the first pre-RNA life-like entities could have been homeostatically-growing assemblies of amphiphiles, undergoing compositional replication and mutations, as well as rudimentary selection and evolution. Recent progress in molecular dynamics has provided an experimental tool to study complex biological phenomena such as protein folding, ligand-receptor interactions, and micellar formation, growth, and fission. The detailed molecular definition of GARD and its inter-molecular catalytic interactions make it highly compatible with molecular dynamics analyses. We present a roadmap for simulating GARD's kinetic and thermodynamic behavior using various molecular dynamics methodologies. We review different approaches for testing the validity of the GARD model by following micellar accretion and fission events and examining compositional changes over time. Near-future computational advances could provide empirical delineation for further system complexification, from simple compositional non-covalent assemblies towards more life-like protocellular entities with covalent chemistry that underlies metabolism and genetic encoding.

Current state-of-the-art molecular dynamics methods and applications

Molecular dynamics simulations are used to describe the patterns, strength, and properties of protein behavior, drug-receptor interactions, the solvation of molecules, the conformational changes that a protein or molecule may undergo under various conditions, and other events that require the systematic evaluation of molecular properties in dynamic molecular systems. Only few years ago proteins were considered to be rigid body structures with very limited conformational flexibility. However, it is now clear that proteins are highly dynamic structures, the internal organization of which is the key to their 3D spatial arrangement and hence biological function. The study of protein dynamics in the lab is a very complicated, expensive, and time-consuming process. Therefore, a lot of effort and hope lies with the computers and the in silico study of protein structure and molecular dynamics. Herein, an effort has been made to describe the ever-evolving field of molecular dynamics, the different algorithms, and force fields that are being used as well as to provide some insight on what the near future holds for this auspicious field of computational structural biology.

Charge-Transfer Salts of Octamethylferrocenyl Thioethers and [M(mnt)(2)](-) Complexes (M = Ni, Co, Pt). Synthesis, Structure, and Physical Properties

The new ferrocene derivatives Fe(eta(5)-C(5)Me(4)SR)(2) (R = Me, 4: R = t-Bu, 5) have been prepared from the corresponding cyclopentadiene and FeCl(2). 5 may be converted to the bis(thiobenzoate) 6, a protected form of dithiol 8. From 6 the octamethyl trithiaferrocenophane 9 may be obtained in good yields. Compounds 4 and 5 are easily oxidized and form paramagnetic salts containing [M(mnt)(2)](-) anions (M = Co, Ni, Pt). The derivatives [Fe(eta(5)-C(5)Me(4)SMe)(2)][Ni(mnt)(2)], 14, [Fe(eta(5)-C(5)Me(4)SMe)(2)][Pt(mnt)(2)], 15, [Fe(eta(5)-C(5)Me(4)SMe)(2)][Co(mnt)(2)], 16, [Fe(eta(5)-C(5)Me(4)St-Bu)(2)][Ni(mnt)(2)], 17, [Fe(eta(5)-C(5)Me(4)St-Bu)(2)][Pt(mnt)(2)], 18, and [Fe(eta(5)-C(5)Me(4)St-Bu)(2)][Co(mnt)(2)], 19, have been prepared and fully characterized. X-ray crystal structural studies of 14 and 16-19 have been carried out. 14 and 16 display stacks of strongly interacting [M(mnt)(2)](-) anions, whereas 19 contains discrete [Co(mnt)(2)] dimers. 17 and 18 are isomorphous and display a typical D(+)A(-)D(+)A(-) structural motif. SQUID susceptibility measurements indicate a weak ferromagnetic ordering at low temperature for these two compounds.

Synthesis, Structure, and Magnetic Properties of Transition Metal Complexes of the Nitroxide 2,5-Dihydro-4,5,5-trimethyl-2,2-bis(2-pyridyl)imidazole-1-oxyl

With the radical 2,5-dihydro-4,5,5-trimethyl-2,2-bis(2-pyridyl)imidazole-1-oxyl (L) a series of transition metal complexes have been prepared: [ML(2)](SbF(6))(2) with M(2+) = Mn(2+) (1), Fe(2+) (2), Co(2+) (3), Ni(2+) (4), Cu(2+) (5), and Zn(2+) (6), Cu(L)(Cl)(2)(MeOH) (7), and Cu(L)SO(4).H(2)O (8). The structures of 1, 3, and 6 were determined by X-ray structural analyses. In these compounds the tridendate L is coordinated to the metal ion by the two pyridine nitrogen donors and by the oxygen atom of the nitroxide group. The N-O bond distances are 1.25(2) Å (1), 1.267(13) Å (3), and 1.260(11) Å (6). The M-O-N angles are 117.0(10) degrees (1), 114.5(8) degrees (3), and 114.2(7) degrees (6). Crystal data: space group P2(1)/n, for 1, 3, and 6; compound 1, a = 10.806(3) Å, b = 14.101(6) Å, c = 14.253(4) Å, beta = 108.82(2) degrees, V = 2055.7(12) Å(3), Z = 2, R(1) = 0.0677, wR(2) = 0.1512. Compound 3, a = 10.761(4) Å, b = 14.253(6) Å, c = 14.108(5) Å, beta = 111.16(3) degrees, V = 2017.9(13) Å(3), Z = 2, R(1) = 0.0702, wR(2) = 0.1460; compound 6, a = 10.788(2) Å, b = 14.147(3) Å, c = 14.196(3) Å, beta = 109.93(3) degrees, V = 2036.8(7) Å(3), Z = 2, R(1) = 0.0573, wR(2) = 0.1194. Magnetic measurements of 1, 2, 5, and 8 show strong antiferromagnetic interaction between the spin of the metal ion and the spin of the radical which increases at lower temperatures. For 6 the magnetic moment corresponds to two noninteracting spins in the temperature range 60-300 K.

Mathematical characterization of Chaos Game Representation. New algorithms for nucleotide sequence analysis

Chaos Game Representation (CGR) can recognize patterns in the nucleotide sequences, obtained from databases, of a class of genes using the techniques of fractal structures and by considering DNA sequences as strings composed of four units, G, A, T and C. Such recognition of patterns relies only on visual identification and no mathematical characterization of CGR is known. The present report describes two algorithms that can predict the presence or absence of a stretch of nucleotides in any gene family. The first algorithm can be used to generate DNA sequences represented by any point in the CGR. The second algorithm can simulate known CGR patterns for different gene families by setting the probabilities of occurrence of different di- or trinucleotides by a trial and error process using some guidelines and approximate rules-of-thumb. The validity of the second algorithm has been tested by simulating sequences that can mimic the CGRs of vertebrate non-oncogenes, proto-oncogenes and oncogenes. These algorithms can provide a mathematical basis of the CGR patterns obtained using nucleotide sequences from databases.

Determination of eukaryotic protein coding regions using neural networks and information theory

Our previous work applied neural network techniques to the problem of discriminating open reading frame (ORF) sequences taken from introns versus exons. The method counted the codon frequencies in an ORF of a specified length, and then used this codon frequency representation of DNA fragments to train a neural net (essentially a Perceptron with a sigmoidal, or "soft step function", output) to perform this discrimination. After training, the network was then applied to a disjoint "predict" set of data to assess accuracy. The resulting accuracy in our previous work was 98.4%, exceeding accuracies reported in the literature at that time for other algorithms. Here, we report even higher accuracies stemming from calculations of mutual information (a correlation measure) of spatially separated codons in exons, and in introns. Significant mutual information exists in exons, but not in introns, between adjacent codons. This suggests that dicodon frequencies of adjacent codons are important for intron/exon discrimination. We report that accuracies obtained using a neural net trained on the frequency of dicodons is significantly higher at smaller fragment lengths than even our original results using codon frequencies, which were already higher than simple statistical methods that also used codon frequencies. We also report accuracies obtained from including codon and dicodon statistics in all six reading frames, i.e. the three frames on the original and complement strand. Inclusion of six-frame statistics increases the accuracy still further. We also compare these neural net results to a Bayesian statistical prediction method that assumes independent codon frequencies in each position. The performance of the Bayesian scheme is poorer than any of the neural based schemes, however many methods reported in the literature either explicitly, or implicitly, use this method. Specifically, Bayesian prediction schemes based on codon frequencies achieve 90.9% accuracy on 90 codon ORFs, while our best neural net scheme reaches 99.4% accuracy on 60 codon ORFs. "Accuracy" is defined as the average of the exon and intron sensitivities. Achievement of sufficiently high accuracies on short fragment lengths can be useful in providing a computational means of finding coding regions in unannotated DNA sequences such as those arising from the mega-base sequencing efforts of the Human Genome Project. We caution that the high accuracies reported here do not represent a complete solution to the problem of identifying exons in "raw" base sequences. The accuracies are considerably lower from exons of small length, although still higher than accuracies reported in the literature for other methods. Short exon lengths are not uncommon.(ABSTRACT TRUNCATED AT 400 WORDS)

Use of conditional probabilities for determining relationships between amino acid sequence and protein secondary structure

The conditional probability, P(sigma/x), is a statement of the probability that the value of sigma will be found given the prior information that a value of x has been observed. Here sigma represents any one of the secondary structure types, alpha, beta, tau, and rho for helix, sheet, turn, and random, respectively, and x represents a sequence attribute, including, but not limited to: (1) hydropathy; (2) hydrophobic moments assuming helix and sheet; (3) Richardson and Richardson helical N-cap and C-cap values; (4) Chou-Fasman conformational parameters for helix, P alpha, for sheet, P beta, and for turn, P tau; and (5) Garnier, Osguthorpe, and Robson (GOR) information values for helix, I alpha, for sheet, I beta, for turn, I tau, and for random structure, I rho. Plots of P(sigma/x) vs. x are demonstrated to provide information about the correlation between structure and attribute, sigma and x. The separations between different P(sigma/x) vs. x curves indicate the capacity of a given attribute to discriminate between different secondary structural types and permit comparison of different attributes. P(alpha/x), P(beta/x), P(tau/x) and P(rho/x) vs. x plots show that the most useful attributes for discriminating helix are, in order: hydrophobic moment assuming helix greater than P alpha much greater than N-cap greater than C-cap approximately I alpha approximately I tau. The information value for turns, I tau, was found to discriminate helix better than turns. Discrimination for sheet was found to be in the following order: I beta much greater than P beta approximately hydropathy greater than I rho approximately hydrophobic moment assuming sheet. Three attributes, at their low values, were found to give significant discrimination for the absence of helix: I alpha approximately P alpha approximately hydrophobic moment assuming helix. Also, three other attributes were found to indicate the absence of sheet: P beta much greater than I rho approximately hydropathy. Indications of the absence of sigma could be as useful for some applications as the indication of the presence of sigma.

On research subsystems and their integration in the computer-supported part of hospital information systems

Taking as an example the special research programme ('Sonderforschungsbereich') in leukemia research and immunogenetics (SFB120) of the University of Tübingen, we intend to discuss how research subsystems should be built up, which functions they should take over and which network architecture is suitable. Furthermore, we examined to what extent research subsystems can be integrated in the computer-supported part of hospital information systems. It can easily be seen that no general solution exists for the structuring. The structure depends rather upon the specific problems and information requirements of individual research units. The clinical environment must be taken into account if research subsystems are to be integrated into the computer-supported part of a hospital information system. Especially in regard to the data protection aspect, we must keep in mind that the connection between subsystems must not necessarily result in the setup of a network for the corresponding computer systems.

Linguistic measure of taxonomic and functional relatedness of nucleotide sequences

The frequencies of "words", oligonucleotides within nucleotide sequences, reflect the genetic information contained in the sequence "texts". Nucleotide sequences are characteristically represented by their contrast word vocabularies. Comparison of the sequences by correlating their contrast vocabularies is shown to reflect well the relatedness (unrelatedness) between the sequences. A single value, the linguistic similarity between the sequences, is suggested as a measure of sequence relatedness. Sequences as short as 1000 bases can be characterized and quantitatively related to other sequences by this technique. The linguistic sequence similarity value is used for analysis of taxonomically and functionally diverse nucleotide sequences. The similarity value is shown to be very sensitive to the relatedness of the source species, thus providing a convenient tool for taxonomic classification of species by their sequence vocabularies. Functionally diverse sequences appear distinct by their linguistic similarity values. This can be a basis for a quick screening technique for functional characterization of the sequences and for mapping functionally distinct regions in long sequences.

The distribution of interspersed repetitive DNA sequences in the human genome

The distribution of interspersed repetitive DNA sequences in the human genome has been investigated, using a combination of biochemical, cytological, computational, and recombinant DNA approaches. "Low-resolution" biochemical experiments indicate that the general distribution of repetitive sequences in human DNA can be adequately described by models that assume a random spacing, with an average distance of 3 kb. A detailed "high-resolution" map of the repetitive sequence organization along 400 kb of cloned human DNA, including 150 kb of DNA fragments isolated for this study, is consistent with this general distribution pattern. However, a higher frequency of spacing distances greater than 9.5 kb was observed in this genomic DNA sample. While the overall repetitive sequence distribution is best described by models that assume a random distribution, an analysis of the distribution of Alu repetitive sequences appearing in the GenBank sequence database indicates that there are local domains with varying Alu placement densities. In situ hybridization to human metaphase chromosomes indicates that local density domains for Alu placement can be observed cytologically. Centric heterochromatin regions, in particular, are at least 50-fold underrepresented in Alu sequences. The observed distribution for repetitive sequences in human DNA is the expected result for sequences that transpose throughout the genome, with local regions of "preference" or "exclusion" for integration.

Database and search techniques for two-dimensional gel protein data: a comparison of paradigms for exploratory data analysis and prospects for biological modeling

Two-dimensional (2-D) polyacrylamide gel electrophoresis can detect thousands of polypeptides, separating them by apparent molecular weight (Mr) and isoelectric point (pI). Thus it provides a more realistic and global view of cellular genetic expression than any other technique. This technique has been useful for finding sets of key proteins of biological significance. However, a typical experiment with more than a few gels often results in an unwiedly data management problem. In this paper, the GELLAB-II system is discussed with respect to how data reduction and exploratory data analysis can be aided by computer data management and statistical search techniques. By encoding the gel patterns in a "three-dimensional" (3-D) database, an exploratory data analysis can be carried out in an environment that might be called a "spread sheet for 2-D gel protein data". From such databases, complex parametric network models of protein expression during events such as differentiation might be constructed. For this, 2-D gel databases must be able to include data from other domains external to the gel itself. Because of the increasing complexity of such databases, new tools are required to help manage this complexity. Two such tools, object-oriented databases and expert-system rule-based analysis, are discussed in this context. Comparisons are made between GELLAB and other 2-D gel database analysis systems to illustrate some of the analysis paradigms common to these systems and where this technology may be heading.

The ordering of the nucleotides in DNA: computational problems in molecular biology

Viewing DNA, RNA and proteins as strings of letters, various algorithms designed for their optimal alignments or their secondary structures have been developed. The results emanating from such sequence editing algorithms are often not correlated with the physio-chemical computations for calculating the detailed atomic coordinates of these molecules. These two aspects are often viewed as separate research entities. Here I attempt to relate various computational aspects of modern molecular biology. In particular, I attempt putting these (along with complementary experimental data) in the framework of a very basic biological question--what fixes the order of the bases in the DNA.