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Computational Biology and Chemistry

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ISSN (Print) 1476-9271
Published by Elsevier

[2565 journals]

A model for the proteolytic regulation of LpxC in the lipopolysaccharide pathway of Escherichia coli
- Abstract: Publication date: Available online 14 June 2013
  Source:Computational Biology and Chemistry
  Author(s): Akintunde Emiola , Paolo Falcarin , Joanne Tocher , John George
  Lipopolysaccharide (LPS) is an essential structural component found in Gram-negative bacteria. The molecule is comprised of a highly conserved lipid A and a variable outer core consisting of various sugars. LPS plays important roles in membrane stability in the bacterial cell and is also a potent activator of the human immune system. Despite its obvious importance, little is understood regarding the regulation of the individual enzymes involved or the pathway as a whole. LpxA and LpxC catalyze the first two steps in the LPS pathway. The reaction catalyzed by LpxA possesses a highly unfavourable equilibrium constant with no evidence of coupling to an energetically favourable reaction. In our model the presence of the second enzyme LpxC was sufficient to abate this unfavourable reaction and confirming previous studies suggesting that this reaction is the first committed step in LPS synthesis. It is believed that the protease FtsH regulates LpxC activity via cleavage. It is also suspected that the activity of FtsH is regulated by a metabolite produced by the LPS pathway; however, it is not known which one. In order to investigate these mechanisms, we obtained kinetic parameters from literature and developed estimates for other simulation parameters. Our simulations suggest that under modest increases in LpxC activity, FtsH is able to regulate the rate of product formation. However, under extreme increases in LpxC activities such as over-expression or asymmetrical cell division then FtsH activation may not be sufficient to regulate this first stage of synthesis.
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  PubDate: 2013-06-15T21:16:28Z
A computational model for enhancing recombinant Penicillin G Acylase production from E. coli DH5α
- Abstract: Publication date: Available online 24 May 2013
  Source:Computational Biology and Chemistry
  Author(s): C. Premalatha , Chandrabose Aravindan , R. Karthikeyan , K. Kannan , M. Surianarayanan
  An attempt was made to develop a computational model based on Artificial Neural Network and Ant Colony Optimization to estimate the composition of medium components for maximizing the productivity of Penicillin G Acylase (PGA) enzyme from E. coli DH5α strain harboring the plasmid pPROPAC. As a first step, an Artificial Neural Network (ANN) model was developed to predict the PGA activity by considering the concentrations of seven important components of the medium. Design of experiments employing Central Composite Design technique was used to obtain the training samples. In the second step, ant colony optimization technique for continuous domain was employed to maximize the PGA activity by finding the optimal inputs for the developed ANN model. Further, the effect of a combination of ant colony optimization for continuous domain with a preferential local search strategy was studied to analyze the performance. For a comparative study, the training samples were fed into the response surface methodology optimization software to maximize the PGA production. The obtained PGA activity (56.94U/mL) by the proposed approach was found to be higher than that of the obtained value (45.60U/mL) with the response surface methodology. The optimum solution obtained computationally was experimentally verified. The observed PGA activity (55.60U/mL) exhibited a close agreement with the model predictions.
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  PubDate: 2013-05-25T21:15:22Z
Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins
- Abstract: Publication date: Available online 17 May 2013
  Source:Computational Biology and Chemistry
  Author(s): Guangya Zhang , Ge Huihua
  Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant resides (Cys and Lys) were missing in the proteins.
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  PubDate: 2013-05-19T16:31:25Z
Prediction and verification of microRNAs related to proline accumulation under drought stress in potato
- Abstract: Publication date: Available online 14 May 2013
  Source:Computational Biology and Chemistry
  Author(s): Jiangwei Yang , Ning Zhang , Congyu Ma , Yun Qu , Huaijun Si , Di Wang
  Proline is an important osmotic adjusting material greatly accumulated under drought stress and can help plant to adapt to osmotic stress. MicroRNAs (miRNAs) are small, endogenous RNAs that play important regulatory roles in plant development and stress response by negatively affecting gene expression at post-transcriptional level. Three genes of pyrroline-5-carboxyla tesynthetase (P5CS), pyrroline-5-carboxylate reductase (P5CR) and proline dehydrogenase (ProDH) are regulating proline metabolism. Until now, little is known about miRNAs regulating proline accumulation. In this work, in order to understand whether miRNAs related to mRNAs of enzymes to regulate proline enrichment under drought stress, we used mRNAs of related enzymes as the targets of miRNAs to search miRBase using BLAST and find many query miRNA sequences. After a range of filtering criteria, 11 known miRNAs classified into 6 miRNA families were predicted. The result from qRT-PCR assay showed that 10 out of 11 predicted miRNAs were successfully detected including 9 down-regulated miRNAs and one up-regulated miRNA. Based on expression and functional analysis, we identified miR172, miR396a, miR396c and miR4233 may regulate P5CS gene, and miR2673 and miR6461 may regulate P5CR and ProDH gene, respectively. The findings can help us make a good understand of the roles of miRNAs in regulation of proline accumulation and provide molecular evidence for involvement process of drought tolerance in potato.
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  PubDate: 2013-05-16T16:24:54Z
Image database analysis of Hodgkin lymphoma
- Abstract: Publication date: Available online 10 May 2013
  Source:Computational Biology and Chemistry
  Author(s): Tim Schäfer , Hendrik Schäfer , Alexander Schmitz , Jörg Ackermann , Norbert Dichter , Claudia Döring , Sylvia Hartmann , Martin-Leo Hansmann , Ina Koch
  Hodgkin lymphoma (hl) is a special type of B cell lymphoma, arising from germinal center b–cells. Morphological and immunohistochemical features of hl as well as the spatial distribution of malignant cells differ from other lymphoma and cancer types. Sophisticated protocols for immunostaining and the acquisition of high–resolution images become routine in pathological labs. Large and daily growing databases of high–resolution digital images are currently emerging. A systematic tissue image analysis and computer-aided exploration may provide new insights into hl pathology. The automated analysis of high resolution images, however, is a hard task in terms of required computing time and memory. Special concepts and pipelines for analyzing high-resolution images can boost the exploration of image databases. In this paper, we report an analysis of digital color images recorded in high–resolution of hl tissue slides. Applying a protocol of cd30 immunostaining to identify malignant cells, we implement a pipeline to handle and explore image data of stained hl tissue images. To the best of our knowledge, this is the first systematic application of image analysis to hl tissue slides. To illustrate the concept and methods we analyze images of two different hl types, nodular sclerosis and mixed cellularity as the most common forms and reactive lymphoid tissue for comparison. We implemented a pipeline which is adapted to the special requirements of whole slide images of hl tissue and identifies relevant regions that contain malignant cells. Using a preprocessing approach, we separate the relevant tissue region from the background. We assign pixels in the images to one of six predefined classes: hematoxylin +, cd30+, nonspecific red, unstained, background, and low intensity, applying a supervised recognition method. Local areas with pixels assigned to the class cd30+ identify regions of interest. As expected, an increased amount of cd30+ pixels is a characteristic feature of nodular sclerosis, and the non-lymphoma cases show a characteristically low amount of cd30+ stain. Images of mixed cellularity samples include cases of high cd30+ coloring as well as cases of low cd30+ coloring.
  Graphical abstract Highlights • We perform automated analysis on a database of Hodgkin Lymphoma (HL) images. • The database contains images of the two HL types nodular sclerosis and mixed cellularity, as well as reactive lymphoid tissue. • Preprocessing narrows down the region of interest that contains malignent cells. • Preprocessing reduces the extensive requirements on computer power and memory for the automated analysis of HL images. • The degree of CD30+ staining is a characteristic feature of HL images.
  
  PubDate: 2013-05-13T21:15:09Z
Estimating structure quality trends in the Protein Data Bank by equivalent resolution
- Abstract: Publication date: Available online 11 May 2013
  Source:Computational Biology and Chemistry
  Author(s): Anurag Bagaria , Victor Jaravine , Peter Güntert
  The quality of protein structures obtained by different experimental and ab-initio calculation methods varies considerably. The methods have been evolving over time by improving both experimental designs and computational techniques, and since the primary aim of these developments is the procurement of reliable and high-quality data, better techniques resulted on average in an evolution towards higher quality structures in the Protein Data Bank (PDB). Each method leaves a specific quantitative and qualitative “trace” in the PDB entry. Certain information relevant to one method (e.g. dynamics for NMR) may be lacking for another method. Furthermore, some standard measures of quality for one method cannot be calculated forother experimental methods, e.g. crystal resolution or NMR bundle RMSD. Consequently, structures are classified in the PDB by the method used. Here we introduce a method to estimate a measure of equivalent X-ray resolution (e-resolution), expressed in units ofÅ, to assess the quality of any type ofmonomeric,single-chain protein structure, irrespective of the experimentalstructure determination method. We show and compare the trends in the quality of structures in the Protein Data Bank over the last two decades for fivedifferent experimental techniques, excluding theoretical structure predictions. We observe that as new methods are introduced, they undergo a rapid methoddevelopment evolution: within several years the e-resolution score becomes similar for structures obtained from the five methods and they improve from initially poor performance to acceptable quality, comparable with previously established methods, the performance of which is essentially stable.
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  PubDate: 2013-05-13T21:15:09Z
Rare Variants Analysis by Risk-based Variable-threshold Method
- Abstract: Publication date: Available online 9 May 2013
  Source:Computational Biology and Chemistry
  Author(s): Hongyan Fang , Bo Hou , Qi Wang , Yaning Yang
  Genome-wide association studies, as a powerful approach for detecting common variants associated with diseases, have revealed many disease-associated loci. However, the traditional association analysis methods do not have enough power for detecting the effects of rare variants with limited sample size. As a solution to this problem, pooling rare variants by their functions into a composite variant provides an alternative way for identifying susceptible genes. In this paper, we propose a new pooling method to test the variant-disease association and to identify the functional rare variants related with the disease. Variants with smaller and larger risk measures defined as the ratio of allele frequencies between cases and controls are pooled and a chi-square test of the resultant pooled table is calculated. We vary the threshold of pooling over all possible values and use the maximal chi-square as test statistic. The maximal chi-square is in fact the global maximum over all possible poolings. Our approach is similar to the existing variable-threshold method, but we threshold on the risk measure instead of allele frequencies of controls. Simulation results show that our method performs better in both association testing and variant selection.
  Graphical abstract Highlights • We propose a risk-based variable-threshold method in association analysis of rare variants. • Existing methods of pooling similar rare variants were based on quantities calculated from control group. Our method pools rare variants with similar relative risk calculated based on the ratio of variant frequencies between cases and controls. • Our method effectively provides the global optimal pooling/grouping method among all possible ways of grouping. • Simulation results show that our method gains more power than existing methods.
  
  PubDate: 2013-05-10T16:26:19Z
Effect of Acetone Accumulation on Structure and Dynamics of Lipid Membranes Studied by Molecular Dynamics Simulations
- Abstract: Publication date: Available online 7 May 2013
  Source:Computational Biology and Chemistry
  Author(s): Yevgen O. Posokhov , Alexander Kyrychenko
  The modulation of the properties and function of cell membranes by small volatile substances is important for many biomedical applications. Despite available experimental results, molecular mechanisms of action of inhalants and organic solvents, such as acetone, on lipid membranes remain not well understood. To gain a better understanding of how acetone interacts with membranes, we have performed a series of molecular dynamics (MD) simulations of a POPC bilayer in aqueous solution in the presence of acetone, whose concentration was varied from 2.8 to 11.2mol %. The MD simulations of passive distribution of acetone between a bulk water phase and a lipid bilayer show that acetone favors partitioning into the water-free region of the bilayer, located near the carbonyl groups of the phospholipids and at the beginning of the hydrocarbon core of the lipid membrane. Using MD umbrella sampling, we found that the permeability barrier of ∼0.5kcal/mol exists for acetone partitioning into the membrane. In addition, a Gibbs free energy profile of the acetone penetration across a bilayer demonstrates a favorable potential energy well of -3.6kcal/mol, located at 15-16Å from the bilayer center. The analysis of the structural and dynamics properties of the model membrane revealed that the POPC bilayer can tolerate the presence of acetone in the concentration range 2.8-5.6mol %. The accumulation of the higher acetone concentration of 11.2mol % results, however, in drastic disordering of phospholipid packing and the increase in the membrane fluidity. The acetone molecules push the lipid heads apart and, hence, act as spacers in the headgroup region. This effect leads to the increase in the average headgroup area per molecule. In addition, the acyl tail region of the membrane also becomes less dense. We suggest, therefore, that the molecular mechanism of acetone action on the phospholipid bilayer has many common features with the effects of short chain alcohols, DMSO, and chloroform.
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  PubDate: 2013-05-10T16:26:19Z
A structural comparative approach to identifying novel antimalarial inhibitors
- Abstract: Publication date: Available online 2 May 2013
  Source:Computational Biology and Chemistry
  Author(s): Jimmy Franco , Margaret A.L. Blackie , David Toth , Peter J. Smith , Joseph Capuano , Kurt Fastnacht , Charlotte Berkes
  Malaria continues to affect millions of people annually. With the rise of drug resistant strains, the need for alternative treatments has become increasingly urgent. Recently, PfUCHL3 was identified as an essential deubiquitinating enzyme. The increasing number of drug target structures being solved has increased the feasibility of utilizing a structural comparative approach to identifying novel inhibitors. Using AutoDock Vina we recently screened the NCI library of about 320,000 compounds against the crystal structure of PfUCHL3. The top hits were subsequently screened against its human ortholog UCHL3 as to identify compounds that could specifically target the PfUCHL3 over its human counterpart. This method was used to identify small molecule inhibitors that can preferentially inhibit the parasitic enzyme. Several compounds were identified that demonstrated significant binding affinity preference for the malaria target over the human enzyme. Two of these compounds demonstrated ng/mL activity.
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  PubDate: 2013-05-04T13:26:54Z
Expression patterns of photoperiod and temperature regulated heading date genes in Oryza sativa
- Abstract: Publication date: Available online 20 April 2013
  Source:Computational Biology and Chemistry
  Author(s): Mallikarjuna Rao Kovi , Gaurav Sablok , XuFeng Bai , Micael Wendell , Odd-Arne Rognli , HuiHui Yu , YongZhong Xing
  In plants, flowering is a major biological phenomenon, which is regulated by an array of interactions occurring between biotic and abiotic factors. In our study, we have compared the expression profiles of flowering genes involved in the flowering pathway, which are influenced by conditions like photoperiod and temperature from seedling to heading developmental stages in two Oryza sativa indica varieties, viz., Zhenshan 97 and Minghui 63 using a expression network approach. Using the network expression approach, we found 17 co-expressed genes having the same expression profile pattern as three key photoperiod flowering genes Hd1, Ehd1 and Hd3a. We also demonstrated that these three co-expressed genes have a similar simulation pattern as temperature flowering genes. Based on our observations, we hypothesize that photoperiod and temperature regulate flowering pathways independently. The present study provides a basis for understanding the network of co-expressed genes involved in flowering pathway and presents a way to demonstrate the behavior of specific gene sets in specific cultivars.
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  PubDate: 2013-04-22T16:31:09Z
A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes
- Abstract: Publication date: Available online 18 April 2013
  Source:Computational Biology and Chemistry
  Author(s): Xue Wu Zhang
  In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modeling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.
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  PubDate: 2013-04-19T16:06:53Z
Editorial Board
- Abstract: June 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry, Volume 44
  
  PubDate: 2013-04-07T21:18:45Z
Title page
- Abstract: June 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry, Volume 44
  
  PubDate: 2013-04-07T21:18:45Z
Assessment of high–confidence protein–protein interactome in yeast
- Abstract: Available online 26 March 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  The identification of protein–protein interactions (PPIs) and their networks is vitally important to systemically define and understand the roles of proteins in biological systems. In spite of development of numerous experimental systems to detect PPIs and diverse research on assessment of the quality of the obtained data, a consensus–highly reliable, almost complete–interactome of Saccharomyces cerevisiae is not presented yet. In this work, we proposed an unsupervised statistical approach to create a high–confidence yeast PPI network. For this, we assembled databases of interacting protein pairs for yeast and obtained an extremely large PPI dataset which comprises of 135 154 non-redundant interactions between 6191 yeast proteins. A scoring scheme considering eight heterogeneous biological features resulted with a broad score distribution and a highly-reliable network consisting of 29 046 physical interactions with scores higher than the threshold value of 0.85, for which sensitivity, specificity and coverage were 86%, 68%, and 72%, respectively. We evaluated our method by comparing it with other scoring schemes and showed that reducing the noise inherent in experimental PPIs via our scoring scheme further increased the accuracy. Current study is expected to increase the efficiency of the methodologies in biological research which make use of protein interaction networks.
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  PubDate: 2013-03-26T17:24:10Z
A Dynamic View to the Modulation of Phosphorylation and O-GlcNAcylation by Inhibition of O-GlcNAcase
- Abstract: Available online 23 March 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  Protein phosphorylation and O-GlcNAcylation are reciprocally regulated. As hyperphosphorylation is implicated in tau pathology, approaches have been exploited to reduce the magnitude of tau phosphorylation by increasing the level of tau O-GlcNAcylation. With mathematic models constructed to describe different kinetic scenarios, we analyzed the temporal change of an O-GlcNAcylated protein in contrast to that of the phosphorylated form upon inhibition of O-GlcNAcase (OGA). The analyses indicate that when degradation of the modified protein is negligible relative to the naked one, the magnitude of O-GlcNAcylated protein increase is proportional to the level of inhibition, while the extent of phosphorylated protein decline varies due to other factors. Furthermore, the increase of O-GlcNAcylated protein parallels with the decrease of phosphorylated form upon acute or short-term inhibition of OGA, as observed in many in vitro and short term in vivo studies. However, phosphorylated protein is predicted to return to its initial level while O-GlcNAcylated protein to achieve a higher steady level under sustained inhibition. This simulated result is in line with a recent report on long-term inhibition of OGA in transgenic mice. Noticeably, inhibition withdrawal is anticipated to cause a transient rise of phosphorylated protein. If degradation of modified proteins proceeds in addition to the naked one, the characteristic temporal profiles of each form in response to OGA inhibition would depend on the relative importance of individual degradation pathways. The models described herein may serve as a useful investigational tool that will provide insight into pharmacological intervention for tauopathies in particular and for reciprocally modulated reactions in general.
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  PubDate: 2013-03-26T17:24:10Z
Using Volcano Plots and Regularized-Chi Statistics in Genetic Association Studies
- Abstract: Available online 22 March 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  Labor intensive experiments are typically required to identify the causal disease variants from a list of disease associated variants in the genome. For designing such experiments, candidate variants are ranked by their strength of genetic association with the disease. However, the two commonly used measures of genetic association, the odds-ratio (OR) and p-value may rank variants in different order. To integrate these two measures into a single analysis, here we transfer the volcano plot methodology from gene expression analysis to genetic association studies. In its original setting, volcano plots are scatter plots of fold-change and t-test statistic (or -log of the p-value), with the latter being more sensitive to sample size. In genetic association studies, the OR and Pearson's chi-square statistic (or equivalently its square root, chi; or the standardized log(OR)) can be analogously used in a volcano plot, allows for their visual inspection. Moreover, the geometric interpretation of these plots leads to an intuitive method for filtering results by a combination of both OR and chi-square statistic, which we term “regularized-chi”. This method selects associated markers by a smooth curve in the volcano plot instead of the right-angled lines which corresponds to independent cutoffs for OR and chi-square statistic. The regularized-chi incorporates relatively more signals from variants with lower minor-allele-frequencies than chi-square test statistic. As rare variants tend to have stronger functional effects, regularized-chi is better suited to the task of prioritization of candidate genes.
  
  PubDate: 2013-03-23T17:15:30Z
Circular code motifs in transfer RNAs
- Abstract: Available online 15 March 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  In 1996, a trinucleotide circular code X is identified in genes of prokaryotes and eukaryotes (Arquès and Michel, 1996). In 2012, X motifs are identified in the transfer RNA (tRNA) Phe and 16S ribosomal RNA(Michel, 2012). A statistical analysis of X motifsin all available tRNAs of prokaryotes and eukaryotes in the genomic tRNA database (September 2012, http://lowelab.ucsc.edu/GtRNAdb/, Lowe and Eddy, 1997) is carried out here. For this purpose, a search algorithm of X motifs in a DNA sequence is developed. Two definitions allow to determine the occurrence probabilities of X motifs and the circular codes X, X 1 =℘(X) and X 2 =℘2(X) (℘ being a circular permutation map applied on X) in a population of tRNAs. This approach identifies X motifs in the 5’ and/or 3’ regions of 16isoaccepting tRNAs (except for the tRNAs Arg, His, Ser and Trp). The statistical analyses are performed on different and large tRNA populations according to the taxonomy (prokaryotes and eukaryotes), tRNA length and tRNA score. Finally, a circular code property observed in genes of prokaryotes and eukaryotes is identified in the 3’ regions of 19 isoaccepting tRNAs of prokaryotes and eukaryotes (except for the tRNA Leu). The identification of X motifs and a gene circular code property in tRNAs strengthens the concept proposed in Michel (2012) of a possible translation (framing) code based on a circular code.
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  PubDate: 2013-03-17T13:52:36Z
Normal mode analysis based on an elastic network model for biomolecules in the Protein Data Bank, which uses dihedral angles as independent variables
- Abstract: Available online 14 March 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  We have developed a computer program, named PDBETA, that performs normal mode analysis (NMA) based on an elastic network model that uses dihedral angles as independent variables. Taking advantage of the relatively small number of degrees of freedom required to describe a molecular structure in dihedral angle space and a simple potential-energy function independent of atom types, we aimed to develop a program applicable to a full-atom system of any molecule in the Protein Data Bank (PDB). The algorithm for NMA used in PDBETA is the same as the computer program FEDER/2, developed previously. Therefore, the main challenge in developing PDBETA was to find a method that can automatically convert PDB data into molecular structure information in dihedral angle space. Here, we illustrate the performance of PDBETA with a protein-DNA complex, a protein-tRNA complex, and some non-protein small molecules, and show that the atomic fluctuations calculated by PDBETA reproduce the temperature factor data of these molecules in the PDB. A comparison was also made with elastic-network-model based NMA in a Cartesian-coordinate system.
  Graphical abstract Highlights • A program that performs normal mode analysis of molecules in the PDB was developed. • It uses dihedral angles as independent variables, rather than Cartesian coordinates. • It is based on an elastic network model. • It is applicable to a full-atom system of any molecule within the PDB. • Atomic fluctuations (B-factors in the PDB data) are adequately reproduced.
  
  PubDate: 2013-03-14T17:19:36Z
Extending the applicability of the O-ring theory to protein-DNA complexes
- Abstract: Available online 14 March 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  Many biological processes depend on protein-based interactions, which are governed by central regions with higher binding affinities, the hot-spots. The O-ring theory or the “Water Exclusion” hypothesis states that the more deeply buried central regions are surrounded by areas, the null-spots, whose role would be to shelter the hot-spots from the bulk solvent. Although this theory is well-established for protein-protein interfaces, its applicability to other protein interfaces remains unclear. Our goal was to verify its applicability to protein-DNA interfaces. We performed Molecular Dynamics simulations in explicit solvent of several protein-DNA complexes and measured a variety of Solvent Accessible Surface Area (SASA) features, as well as, radial distribution functions of hot-spots and null-spots. Our aim was to test the influence of water in their coordination sphere. Our results show that hot-spots tend to have fewer water molecules in their neighborhood when compared to null-spots, and higher values of ΔSASA, which confirms their occlusion from solvent. This study provides evidence in support of the O-ring theory with its applicability to a new type of protein-based interfa protein-DNA.
  Graphical abstract Highlights • The O-ring theory is a well-established theory for protein-protein interfaces. • Lacks confirmation for other protein-based systems. • Molecular Dynamics simulations of several p-DNA complexes. • High ΔSASA values for hot-spots and fewer water molecules around them. • The work confirms the applicability of the O-ring theory to p-DNA interfaces.
  
  PubDate: 2013-03-14T17:19:36Z
Editorial
- Abstract: Available online 22 February 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  PubDate: 2013-02-24T15:00:41Z
Analysis of the NCI-60 dataset for cancer-related microRNA and mRNA using expression profiles
- Abstract: Available online 19 February 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  Background Recent studies have indicated that microRNA (miRNA) may play an oncogenic or tumor suppressor role in human cancer. To study the regulatory role of miRNAs in tumorigenesis, an integrated platform has been set up to provide a user friendly interface for query. The main advantage of the present platform is that all the miRNA target genes’ information and disease records are drawn from experimentally verified or high confidence records. Results MiRNA target gene results are annotated with reference to the disease gene as well as the pathway database. The correlation strength between miRNA and target gene expression profile is quantified by computing the correlation coefficient using the NCI-60 expression profiling data. Comprehensive analysis of the NCI-60 data found that the cumulative percentage of negative correlation coefficients for cleavage regulation is slightly higher than its positive counterpart; which indicated that the mRNA degradation mechanism is slightly dominant. In addition, the RNAHybrid and TargetScans scores are computed which potentially served as quantitative estimators for miRNA-mRNA binding events. Three scores are defined for each miRNA-mRNA pair, which are based on the disease gene and pathway information. These three scores allow user to sort out high confidence cancer-related miRNA-mRNA pairs. Statistical tests were applied to investigate the relations of three chromosomal features, i.e. CpG island, fragile site, and miRNA cluster, with cancer-related miRNAs. A web-based interface has been set up for query, which can be accessed at: http://ppi.bioinfo.asia.edu.tw/mirna_target/. Conclusions The main advantage of the present platform on miRNA-mRNA targeting information is that all the target genes’ information and disease records are experimentally verified. Although this may limit the number of miRNA-mRNA relationships, the results provided here are more solid and have fewer false positive events. Certain novel cancer-related miRNA-mRNA pairs are identified and confirmed in the literature. Fisher's exact test suggests that CpG island and fragile site associated miRNAs tend to associate with cancer formation. In summary, the present platform provides an easy means of investigating cancer-related miRNAs.
  Graphical abstract Highlights ► Quantify the miRNA and target gene expression profile by computing the correlation coefficient. ► This work provides a mean of sorting out highly confident cancer-related miRNA-mRNA pairs. ► Statistical test suggests that near CpG island miRNA and fragile site proximal miRNA may be associated with cancer formation. ► The platform is set up by integrating a rather wide range of datasets; such as, human miRNA and target genes data, with the cancerous genes, transcription factor, pathway, CpG island, fragile site and miRNA cluster information.
  
  PubDate: 2013-02-20T17:12:27Z
Editorial Board
- Abstract: April 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry, Volume 43
  
  PubDate: 2013-02-20T17:12:27Z
Title Page
- Abstract: April 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry, Volume 43
  
  PubDate: 2013-02-20T17:12:27Z
Semantically Predicting Protein Functions Based on Protein Functional Connectivity
- Abstract: Available online 29 January 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  Background The current availability of public protein-protein interaction (PPI) databases which are usually modeled as PPI networks has led to the rapid development of protein function prediction approaches. The existing network-based prediction approaches mainly focus on the topological similarities between immediately interacting proteins, neglecting the protein functional connectivity which is the functional tightness between proteins. In this paper, we attempt to predict the functions of unannotated proteins based on PPI networks by incorporating the protein functional connectivity, as well as the similarity of protein functions, into the prediction procedure. Results An approach named Semantic Protein Function Prediction based on protein Functional Connectivity (SPFC) is proposed to achieve a higher accuracy in predicting functions of unannotated protein. We define the functional connectivity and function addition for each protein, and incorporate them into the prediction. We evaluated the SPFC on real PPI datasets and the experiment results show that the SPFC method is more effective in function prediction than other network-based approaches. Conclusion Incorporating the functional connectivity of each protein into the function prediction can significantly improve the accuracy of protein prediction.
  Graphical abstract Highlights ► We defined functional connectivity in protein-protein interaction network to measure the semantic relationships between proteins and its neighbours. ► We defined function addition to measure the strength of a function correlating with other functions in a protein. ► We integrate functional connectivity and function addition to effectively predict protein functions.
  
  PubDate: 2013-01-30T01:40:32Z
Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization
- Abstract: Available online 25 January 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  Background Finding candidate genes associated with a disease is an important issue in biomedical research. Recently, many network-based methods have been proposed that implicitly utilize the modularity principle, which states that genes causing the same or similar diseases tend to form physical or functional modules in gene/protein relationship networks. Of these methods, the random walk with restart (RWR) algorithm is considered to be a state-of-the-art approach, but the modularity principle has not been fully considered in traditional RWR approaches. Therefore, we propose a novel method called ORIENT (neighbor-favoring weight reinforcement) to improve the performance of RWR through proper intensification of the weights of interactions close to the known disease genes. Results Through extensive simulations over hundreds of diseases, we observed that our approach performs better than the traditional RWR algorithm. In particular, our method worked best when the weights of interactions involving only the nearest neighbor genes of the disease genes were intensified. Interestingly, the performance of our approach was negatively related to the probability with which the random walk will restart, whereas the performance of RWR without the weight-reinforcement was positively related in dense gene/protein relationship networks. We further found that the density of the disease gene-projected sub-graph and the number of paths between the disease genes in a gene/protein relationship network may be explanatory variables for the RWR performance. Finally, a comparison with other well-known gene prioritization tools including Endeavour, ToppGene, and BioGraph, revealed that our approach shows significantly better performance. Conclusion Taken together, these findings provide insight to efficiently guide RWR in disease gene prioritization.
  Graphical abstract Highlights ► Our proposed the neighbor-favoring weight reinforcement (ORIENT) uses a weighting-adjustment scheme to improve performance of traditional the random-walk with restart (RWR). ► ORIENT works best when only direct interactions to disease genes are intensified. ► Performance of ORIENT is negatively related to the back probability in RWR. ► Topological properties of disease genes affect RWR performance. ► ORIENT outperforms Endeavour, ToppGene, and BioGraph.
  
  PubDate: 2013-01-27T11:48:54Z
ProteinLasso: A Lasso regression approach to protein inference problem in shotgun proteomics
- Abstract: Available online 12 January 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry
  
  Protein inference is an important issue in proteomics research. Its main objective is to select a proper subset of candidate proteins that best explain the observed peptides. Although many methods have been proposed for solving this problem, several issues such as peptide degeneracy and one-hit wonders still remain unsolved. Therefore, the accurate identification of proteins that are truly present in the sample continues to be a challenging task. Based on the concept of peptide detectability, we formulate the protein inference problem as a constrained Lasso regression problem, which can be solved very efficiently through a coordinate descent procedure. The new inference algorithm is named as ProteinLasso, which explores an ensemble learning strategy to address the sparsity parameter selection problem in Lasso model. We test the performance of ProteinLasso on three datasets. As shown in the experimental results, ProteinLasso outperforms those state-of-the-art protein inference algorithms in terms of both identification accuracy and running efficiency. In addition, we show that ProteinLasso is stable under different parameter specifications. The source code of our algorithm is available at: http://sourceforge.net/projects/proteinlasso.
  Graphical abstract Highlights ► The accurate identification of proteins that are truly present in the sample continues to be a challenging task. ► We use a constrained Lasso regression model to formulate the protein inference problem. ► The model can be solved very efficiently through a coordinate descent algorithm. ► An ensemble learning strategy is adopted to address the parameter estimation problem.
  
  PubDate: 2013-01-18T00:48:33Z
Editorial Board
- Abstract: February 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry, Volume 42
  
  PubDate: 2013-01-08T00:37:48Z
Title page
- Abstract: February 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry, Volume 42
  
  PubDate: 2013-01-08T00:37:48Z
Publisher's note
- Abstract: February 2013
  Publication year: 2013
  Source:Computational Biology and Chemistry, Volume 42
  
  PubDate: 2013-01-08T00:37:48Z
Multi Objective SNP Selection Using Pareto Optimality
- Abstract: Available online 31 December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  Biomarker discovery is a challenging task of bioinformatics especially when targeting high dimensional problems such as SNP (Single Nucleotide Polymorphism) datasets. Various types of feature selection methods can be applied to accomplish this task. Typically, using features versus class labels of samples in the training dataset, these methods aim at selecting feature subsets with maximal classification accuracies. Although finding such class-discriminative features is crucial, selection of relevant SNPs for maximizing other properties that exist in the nature of population genetics such as the correlation between genetic diversity and geographical distance of ethnic groups can also be equally important. In this work, a methodology using a multi objective optimization technique called Pareto Optimal is utilized for selecting SNP subsets offering both high classification accuracy and correlation between genomic and geographical distances. In this method, discriminatory power of an SNP is determined using mutual information and its contribution to the genomic-geographical correlation is estimated using its loadings on principal components. Combining these objectives, the proposed method identifies SNP subsets that can better discriminate ethnic groups than those obtained with sole mutual information and yield higher correlation than those obtained with sole principal components on the Human Genome Diversity Project (HGDP) SNP dataset.
  Graphical abstract Highlights . ► Twelve ethnic groups selected from HGDP dataset are used to demonstrate multi objective SNP selection. ► Pareto Optimal is utilized for selecting SNP subsets offering both high classification accuracy and correlation between genomic and geographical distances. ► Pairwise genomic distances of ethnic groups are highly correlated with their geographical distances. ► Assessing each chromosome individually, chromosome 11 was found to be the one that possessed SNPs yielding to both high correlation and accuracy values.
  
  PubDate: 2013-01-01T22:47:18Z
Statistical analysis of combinatorial transcriptional regulatory motifs in human intron-containing promoter sequences
- Abstract: Available online 31 December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  The combinatorial regulation is an important feature of transcriptional regulation of genes. Although a number of researches have been done, the general properties of combinatorial transcriptional regulatory elements, including their patterns and distributions, are still not well understood. Specially, the understanding for laws of transcriptional regulation involving introns is challenging. Here, we performed statistical analyses on potentially interacting motif pairs in human housekeeping genes and tissue-specific genes of some tissues. We first identified transcriptional regulatory motifs in promoter sequences, including the first introns, using a sequence-based approach which incorporates several different sequence backgrounds. And then we predicted the potentially interacting motif pairs using Hypergeometric distribution and Mann–Whitney U-test. The validation of the predicted interacting motif pairs has been carried out via analyses of orthologous conservation and enrichment. The distribution characteristics of the identified motif pairs were examined using a probability model. We found that interacting motif pairs have some significant preferences in location, distance and orientation in housekeeping and tissue-specific genes. The results suggest that the features of transcriptional regulation in housekeeping genes are different from the tissue-specific genes. Besides, location preferences of the motif pairs provide clues to the way of the introns participating in transcriptional regulation in different tissue-specific genes.
  Graphical abstract Highlights ► We performed statistical analyses on potentially interacting motif pairs in human housekeeping genes and tissue-specific genes of some tissues. ► The distribution characteristics of the identified motif pairs were examined using a probability model. ► The results provide clues to the way of the introns participating in transcriptional regulation in different tissue-specific genes.
  
  PubDate: 2013-01-01T22:47:18Z
ProCoCoA: a quantitative approach for analyzing protein core composition
- Abstract: Available online 31 December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  Defining the amino acid composition of protein cores is fundamental for understanding protein folding, as different architectures might achieve structural stability only in the presence of specific amino acid networks. Quantitative characterization of protein cores in relation to the corresponding structures and dynamics is needed to increase the reliability of protein engineering procedures. Unambiguous criteria based on atom depth considerations were established to assign amino acid residues to protein cores and, hence, for classifying inner and outer molecular moieties. These criteria were summarized in a new tool named ProCoCoA, Protein Core Composition Analyzer. An user-friendly web interface was developed, available at the URL: http//www.sbl.unisi.it/prococoa. An accurate estimate of protein core composition for six protein architectures selected from the CATH database of solved structures has been carried out, and the obtained results indicate the presence of specific patterns of amino acid core composition in different protein folds.
  Graphical abstract Highlights ► Unambiguous criteria are established for defining protein cores from PDB files;. ► Atom depth calculations yield structure-based residue assignment to protein cores;. ► A possible relationship between protein folds and core composition was studied;. ► Different protein folds were found to have specific amino acid core compositions;. ► URL: http//www.sbl.unisi.it/prococoa gives an user-friendly interface for ProCoCoA.
  
  PubDate: 2013-01-01T22:47:18Z
On the geometric modeling approach to empirical null distribution estimation for empirical Bayes modeling of multiple hypothesis testing
- Abstract: Available online 22 December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  We study the geometric modeling approach to estimating the null distribution for the empirical Bayes modeling of multiple hypothesis testing. The commonly used method is a nonparametric approach based on the Poisson regression, which however could be unduly affected by the dependence among test statistics and perform very poorly under strong dependence. In this paper, we explore a finite mixture model based geometric modeling approach to empirical null distribution estimation and multiple hypothesis testing. Through simulations and applications to two public microarray data, we will illustrate its competitive performance.
  Graphical abstract Highlights Power comparison based on FDR (summarized over 500 simulations). ► A flexible modeling approach to estimate empirical null distribution for appropriate control of false positives. ► Detailed simulation studies demonstrating the very competitive performance of the proposed method. ► Applications to two microarray data illustrating the favorable performance of the proposed method.
  
  PubDate: 2012-12-24T01:34:03Z
Protein function prediction using Neighbor Relativity in protein-protein interaction network
- Abstract: Available online 20 December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called “Neighbor Relativity Coefficient” (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network.
  Graphical abstract Highlights ► In this article, we try to predict protein function utilizing protein interaction network. ► A new measure of proteins relativity in the network has been defined called NRC. ► NRC combines three different network topological features. ► We propose to use different relativity coefficient for each GO function type. ► NRC has better results in protein function prediction compared with previous methods.
  
  PubDate: 2012-12-21T17:02:02Z
Editorial Board
- Abstract: October 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry, Volume 40
  
  PubDate: 2012-12-15T03:10:14Z
A novel divide-and-merge classification for high dimensional datasets
- Abstract: Available online 31 October 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  High dimensional datasets contain up to thousands of features, and can result in immense computational costs for classification tasks. Therefore, these datasets need a feature selection step before the classification process. The main idea behind feature selection is to choose a useful subset of features to significantly improve the comprehensibility of a classifier and maximize the performance of a classification algorithm. In this paper, we propose a one-per-class model for high dimensional datasets. In the proposed method, we extract different feature subsets for each class in a dataset and apply the classification process on the multiple feature subsets. Finally, we merge the prediction results of the feature subsets and determine the final class label of an unknown instance data. The originality of the proposed model is to use appropriate feature subsets for each class. To show the usefulness of the proposed approach, we have developed an application method following the proposed model. From our results, we confirm that our method produces higher classification accuracy than previous novel feature selection and classification methods.
  Graphical abstract Highlights ► High dimensional dataset is difficult for classification analysis. ► Transform multiclass problem to multiple binary-class problem is well known method. ► In this study, we attempted to apply feature selection to above method. ► Our strategy is to select different proper features for each class.
  
  PubDate: 2012-12-15T03:10:14Z
Parameter sensitivity analysis of stochastic models: application to catalytic reaction networks.
- Abstract: Available online 6 November 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  A general numerical methodology for parametric sensitivity analysis is proposed, which allows to determine the parameters exerting the greatest influence on the output of a stochastic computational model, especially when the knowledge about the actual value of a parameter is insufficient. An application of the procedure is performed on a model of protocell, in order to detect the kinetic rates mainly affecting the capability of a catalytic reaction network enclosed in a semi-permeable membrane to retain material from its environment and to generate a variety of molecular species within its boundaries. It is shown that the former capability is scarcely sensitive to variations in the model parameters, whereas a kinetic rate responsible for profound modifications of the latter can be identified and it depends on the specific reaction network. A faster uptaking of limited resources from the environment may have represented a significant advantage from an evolutionary point of view and this result is a first indication in order to decipher which kind of structures are more suitable to achieve a viable evolution.
  Graphical abstract Highlights ► Methodology for parameter sensitivity analysis of discrete stochastic models. ► Impact of parameters on the distribution probability of the model output. ► Sensitivity assessed in different regions of the parameter space. ► Parameters ranked according to the sensitivity. ► Procedure performed on a model of protocell. ► Parameters determining its capability to retain material from the environment.
  
  PubDate: 2012-12-15T03:10:14Z
Computational determination of the orientation of a heat repeat-like domain of DNA-PKcs
- Abstract: Available online 19 November 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  DNA dependent protein kinase catalytic subunit (DNA-PKcs) is an important regulatory protein in non-homologous end joining a process used to repair DNA double strand breaks. Medium resolution structures both from cryoEM and X-ray crystallography show the general topology of the protein and positions of helices in parts of DNA-PKcs. EM-Fold, an algorithm developed for building protein models into medium resolution density maps has been used to generate models for the heat repeat-like “Ring structure” of the molecule. We were able to computationally corroborate placement of the N-terminus of the domain that supports a previously published hypothesis. Targeted experiments are suggested to test the model.
  Graphical abstract Highlights ► Computational determination of orientation of heat repeat domain of DNA-PKcs. ► Presentation of structural model for heat repeat domain to enable design of experiments. ► Adaption of EM-Fold to work with X-ray density maps.
  
  PubDate: 2012-12-15T03:10:14Z
Understanding the General Packing Rearrangements Required for Successful Template Based Modeling of Protein Structure from a CASP Experiment
- Abstract: Available online 22 November 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  As an alternative to the common template based protein structure prediction methods based on main-chain position, a novel side-chain centric approach has been developed. Together with a Bayesian loop modeling procedure and a combination scoring function, the Stone Soup algorithm was applied to the CASP9 set of template based modeling targets. Although the method did not generate as large of perturbations to the template structures as necessary, the analysis of the results gives unique insights into the differences in packing between the target structures and their templates. Considerable variation in packing is found between target and template structures even when the structures are close, and this variation is found due to 2 and 3 body packing interactions. Outside the inherent restrictions in packing representation of the PDB, the first steps in correctly defining those regions of variable packing have been mapped primarily to local interactions, as the packing at the secondary and tertiary structure are largely conserved. Of the scoring functions used, a loop scoring function based on water structure exhibited some promise for discrimination. These results present a clear structural path for further development of a side-chain centered approach to template based modeling.
  Graphical abstract Highlights ► Similar (CaRMSD
  PubDate: 2012-12-15T03:10:14Z
A more accurate relationship between ‘effective number of codons’ and GC3s under assumptions of no selection
- Abstract: Available online 23 November 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  The ‘effective number of codons’ (Nc) introduced by Frank Wright in 1990 is one of the best measures to show the state of codon usage biases in genes and genomes. Although estimate methods of Nc have been improved by several investigators since then, no one noticed that the relationship between Nc and GC3s under assumptions of no selection given by Wright has a little but significant deviation. Since the curve showing such a relationship in Nc-plot is a useful reference line to display the main features of codon usage pattern for a number of genes, its high accuracy is important and necessary. Under ideal and ultimate conditions listed in this text a computational sample of Nc versus GC3s was derived and calculated. By nonlinear regression analysis, the relationship between Nc and GC3s without synonymous codon selection can be approximated by: N c = 2.5 − s + 29.5 / ( s 2 + ( 1 − s ) 2 ) , instead of Wright's: N c = 2 + s + 29 / ( s 2 + ( 1 − s ) 2 ) , where s denotes GC3s. The goodness of fit analysis of both confirmed that the new formula presented in this text is more accurate than the original one. In addition, in the case of using the same estimate method of Nc, the situation in overestimation is decreased to a certain extent by using the new reference line in comparison with Wright's one.
  Graphical abstract Highlights ► Ultimate conditions to build the relationship between Nc and GC3s were proposed. ► Codon homozygosities and a computational sample were derived and calculated. ► Based on the sample a new formula representing such a relationship was created. ► Analyses confirmed that new formula gives an accurate reference line in Nc-plot.
  
  PubDate: 2012-12-15T03:10:14Z
Analysis of compensatory substitution and gene evolution on the MAGEA/CSAG-palindrome of the primate X chromosomes
- Abstract: Available online 23 November 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  The human X chromosome contains a large number of inverted repeat DNA palindromes. Although arbitrary substitutions destroyed the inverted repeat structure of MAGEA/CSAG-palindrome during the evolutionary process of the primates, most of the substitutions are compensatory. Using maximum parsimony, it is demonstrated that the compensatory substitutions are prone to occur between bases with similar structures on the human, chimpanzee and orangutan MAGEA/CSAG-palindromes. Furthermore, it is found that MAGEA/CSAG genes also exist in orangutan and rhesus monkey palindromes by homologous searching. This suggests that the MAGEA/CSAG-palindrome might predate the divergence of human and other primate lineages. Comparative sequence analysis of the arms and genes on the primate MAGEA/CSAG-palindromes provides possible evidence of subsequently arm to arm gene conversion. These compensatory substitutions on the MAGEA/CSAG-palindrome of the primate X chromosomes play an important role in maintaining their structural symmetry during the process of formation.
  Graphical abstract Highlights Most of the substitutions on the MAGE/CSAG-palindromes are compensatory and are prone to occur within similar structures. Compensatory substitutions and further phylogenetic analysis of MAGEA/CSAG genes provides possible evidence of subsequently arm to arm gene conversion. ► Most of the substitutions on the MAGE/CSAG-palindromes are compensatory and are prone to occur within similar structures. ► The homologous genes exist in orangutan and rhesus monkey, which suggests that the palindrome might predate the divergence of human and other primate lineages. ► Compensatory substitution and further phylogenetic analysis of MAGEA/CSAG genes provides possible evidence of subsequently arm to arm gene conversion.
  
  PubDate: 2012-12-15T03:10:14Z
Interactions of iron–sulfur clusters with small peptides: Insights into early evolution
- Abstract: December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry, Volume 41
  
  Favoring the stability of iron–sulfur clusters in hydrothermal vents could have been important for the origin of life. It has been postulated that small “nest” peptides with lengths between 3 and 6 residues could have been important to stabilize early iron–sulfur clusters. We present theoretical calculations exploring the sequence and conformational spaces of short peptides able to bind with high affinity the iron–sulfur cluster Fe4S4. Our results indicate that it is unlikely to form stable complexes between Fe4S4 and small peptides at the core of hydrothermal vents. The formation of these complexes is instead favored for peptides of at least 8 residues as they diffused together with the Fe4S4 clusters toward lower temperature regions within the vent-associated temperature gradients.
  Graphical abstract Highlights ► Formation of peptide/iron–sulfur under hydrothermal conditions is explored. ► A novel computational peptide optimization technique is applied to iron–sulfur clusters. ► Iron–sulfur clusters and 8 AA peptides could have form initial non-covalent adducts. ► Stable adducts including Cys could have only occur away from hydrothermal vents.
  
  PubDate: 2012-12-15T03:10:14Z
Analysis of the relationships between evolvability, thermodynamics, and the functions of intrinsically disordered proteins/regions
- Abstract: December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry, Volume 41
  
  The evolvability of proteins is not only restricted by functional and structural importance, but also by other factors such as gene duplication, protein stability, and an organism's robustness. Recently, intrinsically disordered proteins (IDPs)/regions (IDRs) have been suggested to play a role in facilitating protein evolution. However, the mechanisms by which this occurs remain largely unknown. To address this, we have systematically analyzed the relationship between the evolvability, stability, and function of IDPs/IDRs. Evolutionary analysis shows that more recently emerged IDRs have higher evolutionary rates with more functional constraints relaxed (or experiencing more positive selection), and that this may have caused accelerated evolution in the flanking regions and in the whole protein. A systematic analysis of observed stability changes due to single amino acid mutations in IDRs and ordered regions shows that while most mutations induce a destabilizing effect in proteins, mutations in IDRs cause smaller stability changes than in ordered regions. The weaker impact of mutations in IDRs on protein stability may have advantages for protein evolvability in the gain of new functions. Interestingly, however, an analysis of functional motifs in the PROSITE and ELM databases showed that motifs in IDRs are more conserved, characterized by smaller entropy and lower evolutionary rate, than in ordered regions. This apparently opposing evolutionary effect may be partly due to the flexible nature of motifs in IDRs, which require some key amino acid residues to engage in tighter interactions with other molecules. Our study suggests that the unique conformational and thermodynamic characteristics of IDPs/IDRs play an important role in the evolvability of proteins to gain new functions.
  Graphical abstract Highlights ► Intrinsically disordered regions have higher evolutionary rates than ordered regions. ► Mutations in disordered regions cause less stability changes than in ordered regions. ► Functional motifs in disordered regions are more conserved than in ordered regions. ► Evolvability, thermodynamics and function of disordered regions are inter-related.
  
  PubDate: 2012-12-15T03:10:14Z
Identification of novel splice variants and exons of human endothelial cell-specific chemotaxic regulator (ECSCR) by bioinformatics analysis
- Abstract: December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry, Volume 41
  
  Recent discovery of biological function of endothelial cell-specific chemotaxic regulator (ECSCR), previously known as endothelial cell-specific molecule 2 (ECSM2), in modulating endothelial cell migration, apoptosis, and angiogenesis, has made it an attractive molecule in vascular research. Thus, identification of splice variants of ECSCR could provide new strategies for better understanding its roles in health and disease. In this study, we performed a series of blast searches on the human EST database with known ECSCR cDNA sequence (Variant 1), and identified additional three splice variants (Variants 2–4). When examining the ECSCR gene in the human genome assemblies, we found a large unknown region between Exons 9 and 11. By PCR amplification and sequencing, we partially mapped Exon 10 within this previously unknown region of the ECSCR gene. Taken together, in addition to previously reported human ECSCR, we identified three novel full-length splice variants potentially encoding different protein isoforms. We further defined a total of twelve exons and nearly all exon–intron boundaries of the gene, of which only eight are annotated in current public databases. Our work provides new information on gene structure and alternative splicing of the human ECSCR, which may imply its functional complexity. This undoubtedly opens new opportunities for future investigation of the biological and pathological significance of these ECSCR splice variants.
  Graphical abstract Highlights ► We identify four human ECSCR splice variants (Variants 1–4) in this study. ► We define all twelve exons and exon–intron boundaries of human ECSCR gene. ► Exons 2 and 8 are novel and have not been previously annotated in public databases. ► Our data provides new information on ECSCR gene structure and alternative splicing.
  
  PubDate: 2012-12-15T03:10:14Z
On topological indices for small RNA graphs
- Abstract: December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry, Volume 41
  
  The secondary structure of RNAs can be represented by graphs at various resolutions. While it was shown that RNA secondary structures can be represented by coarse grain tree-graphs and meaningful topological indices can be used to distinguish between various structures, small RNAs are needed to be represented by full graphs. No meaningful topological index has yet been suggested for the analysis of such type of RNA graphs. Recalling that the second eigenvalue of the Laplacian matrix can be used to track topological changes in the case of coarse grain tree-graphs, it is plausible to assume that a topological index such as the Wiener index that represents all Laplacian eigenvalues may provide a similar guide for full graphs. However, by its original definition, the Wiener index was defined for acyclic graphs. Nevertheless, similarly to cyclic chemical graphs, small RNA graphs can be analyzed using elementary cuts, which enables the calculation of topological indices for small RNAs in an intuitive way. We show how to calculate a structural descriptor that is suitable for cyclic graphs, the Szeged index, for small RNA graphs by elementary cuts. We discuss potential uses of such a procedure that considers all eigenvalues of the associated Laplacian matrices to quantify the topology of small RNA graphs.
  Graphical abstract Highlights ► For small RNAs a coarse grain tree-graph representation may not suffice and a full graph representation is needed. ► Suggested topological indices for small RNA full graphs contain information from all Laplacian eigenvalues. ► Elementary cuts can be used to calculate topological indices for small RNA full graphs in an intuitive way.
  
  PubDate: 2012-12-15T03:10:14Z
Overlapping genetic codes for overlapping frameshifted genes in Testudines, and Lepidochelys olivacea as special case
- Abstract: December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry, Volume 41
  
  Mitochondrial genes code for additional proteins after +2 frameshifts by reassigning stops to code for amino acids, which defines overlapping genetic codes for overlapping genes. Turtles recode stops UAR→Trp and AGR→Lys (AGR→Gly in the marine Olive Ridley turtle, Lepidochelys olivacea). In Lepidochelys the +2 frameshifted mitochondrial Cytb gene lacks stops, open reading frames from other genes code for unknown proteins, and for regular mitochondrial proteins after frameshifts according to the overlapping genetic code. Lepidochelys’ inversion between proteins coded by regular and overlapping genetic codes substantiates the existence of overlap coding. ND4 differs among Lepidochelys mitochondrial genomes: it is regular in DQ486893; in NC_011516, the open reading frame codes for another protein, the regular ND4 protein is coded by the frameshifted sequence reassigning stops as in other turtles. These systematic patterns are incompatible with Genbank/sequencing errors and DNA decay. Random mixing of synonymous codons, conserving main frame coding properties, shows optimization of natural sequences for overlap coding; Ka/Ks analyses show high positive (directional) selection on overlapping genes. Tests based on circular genetic codes confirm programmed frameshifts in ND3 and ND4l genes, and predicted frameshift sites for overlap coding in Lepidochelys. Chelonian mitochondria adapt for overlapping gene expression: cloverleaf formation by antisense tRNAs with predicted anticodons matching stops coevolves with overlap coding; antisense tRNAs with predicted expanded anticodons (frameshift suppressor tRNAs) associate with frameshift-coding in ND3 and ND4l, a potential regulation of frameshifted overlap coding. Anaeroby perhaps switched between regular and overlap coding genes in Lepidochelys.
  Graphical abstract Highlights ► Frameshifted turtle mitochondrial proteins match regular proteins. ► Frameshifted stops are reassigned to amino acids. ► AGR→Gly, Lys (Lepidochelys, other turtles); UAR→Trp. ► Lepidochelys CytB gene: 2 reading frames, for cytochrome B, and an unknown protein.
  
  PubDate: 2012-12-15T03:10:14Z
Stochastic synchronization of interacting pathways in testosterone model
- Abstract: December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry, Volume 41
  
  We examine the possibilities of various coupling mechanisms among a group of identical stochastic oscillators via Chemical Langevin formalism where each oscillator is modeled by stochastic model of testosterone (T) releasing pathway. Our results show that the rate of synchrony among the coupled oscillators depends on various parameters namely fluctuating factor, coupling constants ϵ, and interestingly on system size. The results show that synchronization is achieved much faster in classical deterministic system rather than stochastic system. Then we do large scale simulation of such coupled pathways using stochastic simulation algorithm and the detection of synchrony is measured by various order parameters such as synchronization manifolds, phase plots etc and found that the proper synchrony of the oscillators is maintained in different coupling mechanisms and support our theoretical claims. We also found that the coupling constant follows power law behavior with the systems size (V) by ϵ ∼ AV −γ , where γ =1 and A is a constant. We also examine the phase transition like behavior in all coupling mechanisms that we have considered for simulation. The behavior of the system is also investigated at thermodynamic limit; where V→∞, molecular population, N→∞ but N V → finite , to see the role of noise in information processing and found the destructive role in the rate of synchronization.
  Graphical abstract Highlights ► The possible coupling mechanism in the interacting pathways of testosterone hormone is studied theoretically. ► We found coupling constant to follow power law behavior with system size showing the effect of noise in it. ► The destructive role of noise at small system size is seen giving hindrance to synchronization. ► Delay induced synchronization of the interacting pathways is identified in this model.
  
  PubDate: 2012-12-15T03:10:14Z
Editorial Board
- Abstract: December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry, Volume 41
  
  PubDate: 2012-12-15T03:10:14Z
Effects of gene length on the dynamics of gene expression
- Abstract: December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry, Volume 41
  
  In Escherichia coli, the nucleotide length of a gene is bound to affect its expression dynamics. From simulations of a stochastic model of gene expression at the nucleotide and codon levels we show that, within realistic parameter values, the nucleotide length affects RNA and protein mean levels, as well as the expected transient time for RNA and protein numbers to change, following a signal. Fluctuations in RNA and protein numbers are found to be minimized for a small range of lengths, which matches the means of the distributions of lengths found in E. coli of both essential and non-essential genes. The variance of the length distribution for essential genes is found to be smaller than for non-essential genes, implying that these distributions are far from random. Finally, gene lengths are shown to affect the kinetics of a genetic switch, namely, the correlation between temporal proteins numbers, the stability of the two noisy attractors of the switch, and how biased is the choice of noisy attractor. The stability increases with gene length due to increased ‘memory’ about the previous states of the switch. We argue that, by affecting the dynamics of gene expression and of genetic circuits, gene lengths are subject to selection.
  Graphical abstract Highlights ► We simulate and study gene expression kinetics as a function of gene length. ► Fluctuations in protein numbers are minimized for a small range of lengths. ► This range covers the mode and mean of the distribution of gene lengths in E. coli. ► Gene length affects the stability of the kinetics of genetic switches. ► By affecting expression dynamics, gene lengths are likely to be subject to selection.
  
  PubDate: 2012-12-15T03:10:14Z
Communication between the active site and the allosteric site in class A beta-lactamases
- Abstract: Available online 12 December 2012
  Publication year: 2012
  Source:Computational Biology and Chemistry
  
  Bacterial production of beta-lactamases, which hydrolyze beta-lactam type antibiotics, is a common antibiotic resistance mechanism. Antibiotic resistance is a high priority intervention area and one strategy to overcome resistance is to administer antibiotics with beta-lactamase inhibitors in the treatment of infectious diseases. Unfortunately, beta-lactamases are evolving at a rapid pace with new inhibitor resistant mutants emerging every day, driving the design and development of novel beta-lactamase inhibitors. Here, we examined the inhibitor recognition mechanism of two common beta-lactamases using molecular dynamics simulations. Binding of beta-lactamase inhibitor protein (BLIP) caused changes in the flexibility of regions away from the binding site. One of these regions was the H10 helix, which was previously identified to form a lid over an allosteric inhibitor binding site. Closer examination of the H10 helix using sequence and structure comparisons with other beta-lactamases revealed the presence of a highly conserved Trp229 residue, which forms a stacking interaction with two conserved proline residues. Molecular dynamics simulations on the Trp229Ala mutants of TEM-1 and SHV-1 resulted in decreased stability in the apo form, possibly due to loss of the stacking interaction as a result of the mutation. The mutant TEM-1 beta-lactamase had higher H10 fluctuations in the presence of BLIP, higher affinity to BLIP and higher cross-correlations with BLIP. Our results suggest that the H10 helix and specifically W229 are important modulators of the allosteric communication between the active site and the allosteric site.
  Graphical abstract Highlights ► Molecular dynamics simulations on different Beta-lactamase–BLIP systems were performed. ► Comparison of the simulations revealed differences in a site previously identified to bind an allosteric inhibitor. ► W229 residue of the allosteric site was found to be highly conserved. ► Mutation of W229 results in enhanced ligand binding and higher correlations between beta-lactamase and BLIP.
  
  PubDate: 2012-12-15T03:10:14Z