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1.  Identification and analysis of potential targets in Streptococcus sanguinis using computer aided protein data analysis 
Purpose
Streptococcus sanguinis is a Gram-positive, facultative aerobic bacterium that is a member of the viridans streptococcus group. It is found in human mouths in dental plaque, which accounts for both dental cavities and bacterial endocarditis, and which entails a mortality rate of 25%. Although a range of remedial mediators have been found to control this organism, the effectiveness of agents such as penicillin, amoxicillin, trimethoprim–sulfamethoxazole, and erythromycin, was observed. The emphasis of this investigation was on finding substitute and efficient remedial approaches for the total destruction of this bacterium.
Materials and methods
In this computational study, various databases and online software were used to ascertain some specific targets of S. sanguinis. Particularly, the Kyoto Encyclopedia of Genes and Genomes databases were applied to determine human nonhomologous proteins, as well as the metabolic pathways involved with those proteins. Different software such as Phyre2, CastP, DoGSiteScorer, the Protein Function Predictor server, and STRING were utilized to evaluate the probable active drug binding site with its known function and protein–protein interaction.
Results
In this study, among 218 essential proteins of this pathogenic bacterium, 81 nonhomologous proteins were accrued, and 15 proteins that are unique in several metabolic pathways of S. sanguinis were isolated through metabolic pathway analysis. Furthermore, four essentially membrane-bound unique proteins that are involved in distinct metabolic pathways were revealed by this research. Active sites and druggable pockets of these selected proteins were investigated with bioinformatic techniques. In addition, this study also mentions the activity of those proteins, as well as their interactions with the other proteins.
Conclusion
Our findings helped to identify the type of protein to be considered as an efficient drug target. This study will pave the way for researchers to develop and discover more effective and specific therapeutic agents against S. sanguinis.
doi:10.2147/AABC.S67336
PMCID: PMC4250024  PMID: 25473301
Streptococcus sanguinis; essential proteins; unique metabolic pathways; therapeutic targets
2.  The hemagglutinin of the influenza A(H1N1)pdm09 is mutating towards stability 
The last influenza A pandemic provided an excellent opportunity to study the adaptation of the influenza A(H1N1)pdm09 virus to the human host. Particularly, due to the availability of sequences taken from isolates since the beginning of the pandemic until date, we could monitor amino acid changes that occurred in the hemagglutinin (HA) as the virus spread worldwide and became the dominant H1N1 strain. HA is crucial to viral infection because it binds to sialidated cell-receptors and mediates fusion of cell and viral membranes; because antibodies that bind to HA may block virus entry to the cell, this protein is subjected to high selective pressure. Multiple alignment analysis of sequences of the HA from isolates taken since 2009 to date allowed us to find amino acid changes that were positively selected as the pandemic progressed. We found nine changes that became prevalent: HA1 subunits D104N, K166Q, S188T, S206T, A259T, and K285E; and HA2 subunits E47K, S124N, and E172K. Most of these changes were located in areas involved in inter- and intrachain interactions, while only two (K166Q and S188T) were located in known antigenic sites. We conclude that selective pressure on HA was aimed to improve its functionality and hence virus fitness, rather than at avoidance of immune recognition.
doi:10.2147/AABC.S68934
PMCID: PMC4198066  PMID: 25328411
influenza A; hemagglutinin evolution; virus fitness
3.  In silico predictive model to determine vector-mediated transport properties for the blood–brain barrier choline transporter 
The blood–brain barrier choline transporter (BBB-ChT) may have utility as a drug delivery vector to the central nervous system (CNS). We therefore initiated molecular docking studies with the AutoDock and AutoDock Vina (ADVina) algorithms to develop predictive models for compound screening and to identify structural features important for binding to this transporter. The binding energy predictions were highly correlated with r2=0.88, F=692.4, standard error of estimate =0.775, and P-value<0.0001 for selected BBB-ChT-active/inactive compounds (n=93). Both programs were able to cluster active (Gibbs free energy of binding <−6.0 kcal*mol−1) and inactive (Gibbs free energy of binding >−6.0 kcal*mol−1) molecules and dock them significantly better than at random with an area under the curve value of 0.86 and 0.84, respectively. In ranking smaller molecules with few torsional bonds, a size-related bias in scoring producing false-negative outcomes was detected. Finally, important blood–brain barrier parameters, such as the logBBpassive and logBBactive values, were assessed to predict compound transport to the CNS accurately. Knowledge gained from this study is useful to better understand the binding requirements in BBB-ChT, and until such time as its crystal structure becomes available, it may have significant utility in developing a highly predictive model for the rational design of drug-like compounds targeted to the brain.
doi:10.2147/AABC.S63749
PMCID: PMC4159400  PMID: 25214795
blood–brain barrier choline transporter; central nervous system; drug delivery vector; molecular docking; virtual screening; Gibbs free energy of binding; diffusion
4.  Three-dimensional quantitative structure–activity relationship and docking studies in a series of anthocyanin derivatives as cytochrome P450 3A4 inhibitors 
The cytochrome P450 (CYP)3A4 enzyme affects the metabolism of most drug-like substances, and its inhibition may influence drug safety. Modulation of CYP3A4 by flavonoids, such as anthocyanins, has been shown to inhibit the mutagenic activity of mammalian cells. Considering the previous investigations addressing CYP3A4 inhibition by these substances, we studied the three-dimensional quantitative structure–activity relationship (3D-QSAR) in a series of anthocyanin derivatives as CYP3A4 inhibitors. For the training dataset (n=12), comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) yielded crossvalidated and non-crossvalidated models with a q2 of 0.795 (0.687) and r2 of 0.962 (0.948), respectively. The models were also validated by an external test set of four compounds with r2 of 0.821 (CoMFA) and r2 of 0.812 (CoMSIA). The binding affinity modes associated with experimentally derived IC50 (half maximal inhibitory concentration) values were confirmed by molecular docking into the CYP3A4 active site with r2 of 0.66. The results obtained from this study are useful for a better understanding of the effects of anthocyanin derivatives on inhibition of carcinogen activation and cellular DNA damage.
doi:10.2147/AABC.S56478
PMCID: PMC3970920  PMID: 24741320
three-dimensional quantitative structure–activity relationship; cytochrome P450 3A4; comparative molecular field analysis; comparative molecular similarity index analysis; anthocyanin derivatives; molecular docking; carcinogen activation
5.  In silico structure-based screening of versatile P-glycoprotein inhibitors using polynomial empirical scoring functions 
P-glycoprotein (P-gp) is an ATP (adenosine triphosphate)-binding cassette transporter that causes multidrug resistance of various chemotherapeutic substances by active efflux from mammalian cells. P-gp plays a pivotal role in limiting drug absorption and distribution in different organs, including the intestines and brain. Thus, the prediction of P-gp–drug interactions is of vital importance in assessing drug pharmacokinetic and pharmacodynamic properties. To find the strongest P-gp blockers, we performed an in silico structure-based screening of P-gp inhibitor library (1,300 molecules) by the gradient optimization method, using polynomial empirical scoring (POLSCORE) functions. We report a strong correlation (r2=0.80, F=16.27, n=6, P<0.0157) of inhibition constants (Kiexp or pKiexp; experimental Ki or negative decimal logarithm of Kiexp) converted from experimental IC50 (half maximal inhibitory concentration) values with POLSCORE-predicted constants (KiPOLSCORE or pKiPOLSCORE), using a linear regression fitting technique. The hydrophobic interactions between P-gp and selected drug substances were detected as the main forces responsible for the inhibition effect. The results showed that this scoring technique might be useful in the virtual screening and filtering of databases of drug-like compounds at the early stage of drug development processes.
doi:10.2147/AABC.S56046
PMCID: PMC3969253  PMID: 24711707
ATP-binding cassette transporter; P-gp inhibitors; multidrug resistance; molecular docking; POLSCORE
6.  Hypergeometric analysis of tiling-array and sequence data: detection and interpretation of peaks 
Probing protein-deoxyribonucleic acid (DNA) is gaining popularity as it sheds light on molecular mechanisms that regulate the expression of genes. Currently, tiling-arrays and next-generation sequencing technology can be used to measure these interactions. Both methods generate a signal over the genome in which contiguous regions of peaks on the genome represent the presence of an interacting molecule. Many methods do exist to identify functional regions of interest (ROIs) on the genome. However the detection of ROIs are often not an end-point in research questions and it therefore requires data dragging between tools to relate the ROIs to information present in databases, such as gene-ontology, pathway information, or enrichment of certain genomic content. We introduce hypergeometric analysis of tiling-array and sequence data (HATSEQ), a powerful tool that accurately identifies functional ROIs on the genome where a genomic signal significantly deviates from the general genome-wide behavior. HATSEQ also includes a number of built-in post-analyses with which biological meaning can be attached to the detected ROIs in terms of gene pathways and de-novo motif analysis, and provides different visualizations and statistical summaries for the detected ROIs. In addition, HATSEQ has an intuitive graphic user interface that lowers the barrier for researchers to analyze their data without the need of scripting languages. We compared the results of HATSEQ against two other popular chromatin immunoprecipitation sequencing (ChIP-Seq) methods and observed overlap in the detected ROIs but HATSEQ is more specific in delineating the peak boundaries. We also discuss the versatility of HATSEQ by using a Signal Transducer and Activator of Transcription 1 (STAT1) ChIP-Seq data-set, and show that the detected ROIs are highly specific for the expected STAT1 binding motif. HATSEQ is freely available at: http://hema13.erasmusmc.nl/index.php/HATSEQ.
doi:10.2147/AABC.S51271
PMCID: PMC3810201  PMID: 24187504
bioinformatics; NGS analysis; ChIP-Seq; peak detection
7.  Design of multiligand inhibitors for the swine flu H1N1 neuraminidase binding site 
Viral neuraminidase inhibitors such as oseltamivir and zanamivir prevent early virus multiplication by blocking sialic acid cleavage on host cells. These drugs are effective for the treatment of a variety of influenza subtypes, including swine flu (H1N1). The binding site for these drugs is well established and they were designed based on computational docking studies. We show here that some common natural products have moderate inhibitory activity for H1N1 neuraminidase under docking studies. Significantly, docking studies using AutoDock for biligand and triligand forms of these compounds (camphor, menthol, and methyl salicylate linked via methylene bridges) indicate that they may bind in combination with high affinity to the H1N1 neuraminidase active site. These results also indicate that chemically linked biligands and triligands of these natural products could provide a new class of drug leads for the prevention and treatment of influenza. This study also highlights the need for a multiligand docking algorithm to understand better the mode of action of natural products, wherein multiple active ingredients are present.
doi:10.2147/AABC.S49503
PMCID: PMC3751451  PMID: 23983477
neuraminidase; influenza; H1N1; multiligand; binding energy; molecular docking; virus
8.  Antigenic heterogeneity of capsid protein VP1 in foot-and-mouth disease virus (FMDV) serotype Asia 1 
Foot and mouth disease virus (FMDV), with its seven serotypes, is a highly contagious virus infecting mainly cloven-hoofed animals. The serotype Asia1 occurs mainly in Asian regions. An in-silico approach was taken to reveal the antigenic heterogeneities within the capsid protein VP1 of Asia1. A total of 47 VP1 sequences of Asia1 isolates from different countries of South Asian regions were selected, retrieved from database, and were aligned. The structure of VP1 protein was modeled using a homology modeling approach. Several antigenic sites were identified and mapped onto the three-dimensional protein structure. Variations at these antigenic sites were analyzed by calculating the protein variability index and finding mutation combinations. The data suggested that vaccine escape mutants have derived from only few mutations at several antigenic sites. Five antigenic peptides have been identified as the least variable epitopes, with just fewer amino acid substitutions. Only a limited number of serotype Asia1 antigenic variants were found to be circulated within the South Asian region. This emphasizes a possibility of formulating synthetic vaccines for controlling foot-and-mouth disease by Asia1 serotypes.
doi:10.2147/AABC.S49587
PMCID: PMC3751384  PMID: 23983476
protein modeling; antigenic sites; sequence variation
9.  Approaches to the detection of recessive effects using next generation sequencing data from outbred populations 
Conventional methods to analyze genome-wide association studies and whole exome or whole genome sequencing studies would be prone to overlook variants which might exert a recessive effect on risk of disease, either as homozygotes or compound heterozygotes. It is plausible that such effects may be common even in outbred populations. An approach is described which is based on identifying a set of variants in a gene as being potentially of interest and then testing whether there is an excess of cases who are either homozygotes or complex heterozygotes for these variants. Methods based on departure from Hardy–Weinberg equilibrium are more powerful than those which compare cases to controls. However, linkage disequilibrium between variants can be difficult to deal with if phase is unknown. A simple approach for discarding variants apparently in strong linkage disequilibrium with others is proposed. The procedure is simple and quick to apply so can be used in the context of whole genome or exome sequencing studies and is implemented in the SCOREASSOC program.
doi:10.2147/AABC.S44332
PMCID: PMC3685401  PMID: 23807854
association; sequence; DNA
10.  Inferences on the biochemical and environmental regulation of universal stress proteins from Schistosomiasis parasites 
Background
Human schistosomiasis is a freshwater snail-transmitted disease caused by parasitic flatworms of the Schistosoma genus. Schistosoma haematobium, Schistosoma mansoni, and Schistosoma japonicum are the three major species infecting humans. These parasites undergo a complex developmental life cycle, in which they encounter a plethora of environmental signals. The presence of genes encoding the universal stress protein (USP) domain in the genomes of Schistosoma spp. suggests these flatworms are equipped to respond to unfavorable conditions. Though data on gene expression is available for USP genes, their biochemical and environmental regulation are incompletely understood. The identification of additional regulatory molecules for Schistosoma. USPs, which may be present in the human, snail, or water environments, could also be useful for schistosomiasis interventions.
Methods
We developed a protocol that includes a visual analytics stage to facilitate integration, visualization, and decision making, from the results of sequence analyses and data collection on a set of 13 USPs from S. mansoni and S. japonicum.
Results
Multiple sequence alignment identified conserved sites that could be key residues regulating the function of USPs of the Schistosoma spp. Based on the consistency and completeness of sequence annotation, we prioritized for further research the gene for a 184-amino-acid-long USP that is present in the genomes of the three human-infecting Schistosoma spp. Calcium, zinc, and magnesium ions were predicted to interact with the protein product of the gene.
Conclusion
Given that the initial effects of praziquantel on schistosomes include the influx of calcium ions, additional investigations are required to (1) functionally characterize the interactions of calcium ions with the amino acid residues of Schistosoma USPs; and (2) determine the transcriptional response of Schistosoma. USP genes to praziquantel. The data sets produced, and the visual analytics views that were developed, can be easily reused to develop new hypotheses.
doi:10.2147/AABC.S37191
PMCID: PMC3656623  PMID: 23696708
ATP binding protein; calcium; functional sites; praziquantel; Schistosoma; schistosomiasis
11.  Genetic and chemical knockdown: a complementary strategy for evaluating an anti-infective target 
The equity of a drug target is principally evaluated by its genetic vulnerability with tools ranging from antisense- and microRNA-driven knockdowns to induced expression of the target protein. In order to upgrade the process of antibacterial target identification and discern its most effective type of inhibition, an in silico toolbox that evaluates its genetic and chemical vulnerability leading either to stasis or cidal outcome was constructed and validated. By precise simulation and careful experimentation using enolpyruvyl shikimate-3-phosphate synthase and its specific inhibitor glyphosate, it was shown that genetic knockdown is distinct from chemical knockdown. It was also observed that depending on the particular mechanism of inhibition, viz competitive, uncompetitive, and noncompetitive, the antimicrobial potency of an inhibitor could be orders of magnitude different. Susceptibility of Escherichia coli to glyphosate and the lack of it in Mycobacterium tuberculosis could be predicted by the in silico platform. Finally, as predicted and simulated in the in silico platform, the translation of growth inhibition to a cidal effect was able to be demonstrated experimentally by altering the carbon source from sorbitol to glucose.
doi:10.2147/AABC.S39198
PMCID: PMC3572760  PMID: 23413046
knockdown; inhibition; in silico; vulnerability
12.  A novel biclustering approach with iterative optimization to analyze gene expression data 
Video abstract
Video
Objective
With the dramatic increase in microarray data, biclustering has become a promising tool for gene expression analysis. Biclustering has been proven to be superior over clustering in identifying multifunctional genes and searching for co-expressed genes under a few specific conditions; that is, a subgroup of all conditions. Biclustering based on a genetic algorithm (GA) has shown better performance than greedy algorithms, but the overlap state for biclusters must be treated more systematically.
Results
We developed a new biclustering algorithm (binary-iterative genetic algorithm [BIGA]), based on an iterative GA, by introducing a novel, ternary-digit chromosome encoding function. BIGA searches for a set of biclusters by iterative binary divisions that allow the overlap state to be explicitly considered. In addition, the average of the Pearson’s correlation coefficient was employed to measure the relationship of genes within a bicluster, instead of the mean square residual, the popular classical index. As compared to the six existing algorithms, BIGA found highly correlated biclusters, with large gene coverage and reasonable gene overlap. The gene ontology (GO) enrichment showed that most of the biclusters are significant, with at least one GO term over represented.
Conclusion
BIGA is a powerful tool to analyze large amounts of gene expression data, and will facilitate the elucidation of the underlying functional mechanisms in living organisms.
doi:10.2147/AABC.S32622
PMCID: PMC3459542  PMID: 23055751
biclustering; microarray data; genetic algorithm; Pearson’s correlation coefficient
13.  Contact-based ligand-clustering approach for the identification of active compounds in virtual screening 
Evaluation of docking results is one of the most important problems for virtual screening and in silico drug design. Modern approaches for the identification of active compounds in a large data set of docked molecules use energy scoring functions. One of the general and most significant limitations of these methods relates to inaccurate binding energy estimation, which results in false scoring of docked compounds. Automatic analysis of poses using self-organizing maps (AuPosSOM) represents an alternative approach for the evaluation of docking results based on the clustering of compounds by the similarity of their contacts with the receptor. A scoring function was developed for the identification of the active compounds in the AuPosSOM clustered dataset. In addition, the AuPosSOM efficiency for the clustering of compounds and the identification of key contacts considered as important for its activity, were also improved. Benchmark tests for several targets revealed that together with the developed scoring function, AuPosSOM represents a good alternative to the energy-based scoring functions for the evaluation of docking results.
doi:10.2147/AABC.S30881
PMCID: PMC3459543  PMID: 23055752
scoring; docking; virtual screening; CAR; AuPosSOM
14.  B-Pred, a structure based B-cell epitopes prediction server 
The ability to predict immunogenic regions in selected proteins by in-silico methods has broad implications, such as allowing a quick selection of potential reagents to be used as diagnostics, vaccines, immunotherapeutics, or research tools in several branches of biological and biotechnological research. However, the prediction of antibody target sites in proteins using computational methodologies has proven to be a highly challenging task, which is likely due to the somewhat elusive nature of B-cell epitopes. This paper proposes a web-based platform for scoring potential immunological reagents based on the structures or 3D models of the proteins of interest. The method scores a protein’s peptides set, which is derived from a sliding window, based on the average solvent exposure, with a filter on the average local model quality for each peptide. The platform was validated on a custom-assembled database of 1336 experimentally determined epitopes from 106 proteins for which a reliable 3D model could be obtained through standard modeling techniques. Despite showing poor sensitivity, this method can achieve a specificity of 0.70 and a positive predictive value of 0.29 by combining these two simple parameters. These values are slightly higher than those obtained with other established sequence-based or structure-based methods that have been evaluated using the same epitopes dataset. This method is implemented in a web server called B-Pred, which is accessible at http://immuno.bio.uniroma2.it/bpred. The server contains a number of original features that allow users to perform personalized reagent searches by manipulating the sliding window’s width and sliding step, changing the exposure and model quality thresholds, and running sequential queries with different parameters. The B-Pred server should assist experimentalists in the rational selection of epitope antigens for a wide range of applications.
doi:10.2147/AABC.S30620
PMCID: PMC3413014  PMID: 22888263
B-cell epitopes; immunoinformatics; bioinformatics; web server; epitope prediction
15.  A rapid method for combined analysis of common and rare variants at the level of a region, gene, or pathway 
Previously described methods for the combined analysis of common and rare variants have disadvantages such as requiring an arbitrary classification of variants or permutation testing to assess statistical significance. Here we propose a novel method which implements a weighting scheme based on allele frequencies observed in both cases and controls. Because the test is unbiased, scores can be analyzed with a standard t-test. To test its validity we applied it to data for common, rare, and very rare variants simulated under the null hypothesis. To test its power we applied it to simulated data in which association was present, including data using the observed allele frequencies of common and rare variants in NOD2 previously reported in cases of Crohn’s disease and controls. The method produced results that conformed well to those expected under the null hypothesis. It demonstrated more power to detect association when rare and common variants were analyzed jointly, the power further increasing when rare variants were assigned higher weights. 20,000 analyses of a gene containing 62 variants could be performed in 80 minutes on a laptop. This approach shows promise for the analysis of data currently emerging from genome wide sequencing studies.
doi:10.2147/AABC.S33049
PMCID: PMC3413013  PMID: 22888262
common; rare; variant; sequence; genome; exome
16.  Role of Shwachman-Bodian-Diamond syndrome protein in translation machinery and cell chemotaxis: a comparative genomics approach 
Shwachman-Bodian-Diamond syndrome (SBDS) is linked to a mutation in a single gene. The SBDS proinvolved in RNA metabolism and ribosome-associated functions, but SBDS mutation is primarily linked to a defect in polymorphonuclear leukocytes unable to orient correctly in a spatial gradient of chemoattractants. Results of data mining and comparative genomic approaches undertaken in this study suggest that SBDS protein is also linked to tRNA metabolism and translation initiation. Analysis of crosstalk between translation machinery and cytoskeletal dynamics provides new insights into the cellular chemotactic defects caused by SBDS protein malfunction. The proposed functional interactions provide a new approach to exploit potential targets in the treatment and monitoring of this disease.
doi:10.2147/AABC.S23510
PMCID: PMC3202468  PMID: 22046100
Shwachman-Bodian-Diamond syndrome; wybutosine; tRNA; chemotaxis; translation; genomics; gene proximity
17.  SNP analysis of follistatin gene associated with polycystic ovarian syndrome 
Follistatin has been reported as a candidate gene for polycystic ovarian syndrome (PCOS) based on linkage and association studies. In this study, investigation of polymorphisms in the FST gene was done to determine if genetic variation is associated with susceptibility to PCOS. The nucleotide sequence of human follistatin and the protein sequence of human follistatin were retrieved from the NCBI database using Entrez. The follistatin protein of human was retrieved from the Swiss-Prot database. There are 344 amino acids and the molecular weight is 38,007 Da. The ProtParam analysis shows that the isoelectric point is 5.53 and the aliphatic index is 61.25. The hydropathicity is −0.490. The domains in FST protein are as follows: Pfam-B 5005 domain from 1 to 92; EGF-like subdomain from 93 to 116; Kazal 1 domain, occurred in three places, namely, 118–164, 192–239, and 270–316. There are 31 single-nucleotide polymorphisms (SNPs) for this gene. Some are nonsynonymous, some occur in the intron region, and some in an untranslated region. Two nonsynonymous SNPs, namely, rs11745088 and rs1127760, were taken for analysis. In the SNP rs11745088, the change is E152Q. Likewise, in rs1127760, the change is C239S. SIFT (Sorting Intolerant from Tolerant) showed positions of amino acids and the single letter code of amino acids that can be tolerated or deleterious for each position. There were six SNP results and each result had links to it. The dbSNP id, primary database id, and the type of mutation whether silent and if occurring in coding region are given as phenotype alterations. The FASTA format of protein was given to the nsSNP Analyzer tool, and the variation E152Q and C239S were given as inputs in the SNP data field. E152Q change was neutral and C239S causes disease. Using PANTHER for evolutionary analysis of coding SNPs, the protein sequence was given as input and analyzed for the E152Q and C239S SNPs for deleterious effect on protein function. The genetic association database results showed that FST gene SNPs are linked to PCOS coming under the disease class of metabolic disorders. The list of intronic and synonymous SNPs, with their nucleotide position, amino acid change information, and dbSNP link, is provided for further analysis.
doi:10.2147/AABC.S11013
PMCID: PMC3170008  PMID: 21918632
FST; polycystic ovarian syndrome; single-nucleotide polymorphism analysis
18.  A kinetic platform for in silico modeling of the metabolic dynamics in Escherichia coli 
Background
A prerequisite for a successful design and discovery of an antibacterial drug is the identification of essential targets as well as potent inhibitors that adversely affect the survival of bacteria. In order to understand how intracellular perturbations occur due to inhibition of essential metabolic pathways, we have built, through the use of ordinary differential equations, a mathematical model of 8 major Escherichia coli pathways.
Results
Individual in vitro enzyme kinetic parameters published in the literature were used to build the network of pathways in such a way that the flux distribution matched that reported from whole cells. Gene regulation at the transcription level as well as feedback regulation of enzyme activity was incorporated as reported in the literature. The unknown kinetic parameters were estimated by trial and error through simulations by observing network stability. Metabolites, whose biosynthetic pathways were not represented in this platform, were provided at a fixed concentration. Unutilized products were maintained at a fixed concentration by removing excess quantities from the platform. This approach enabled us to achieve steady state levels of all the metabolites in the cell. The output of various simulations correlated well with those previously published.
Conclusion
Such a virtual platform can be exploited for target identification through assessment of their vulnerability, desirable mode of target enzyme inhibition, and metabolite profiling to ascribe mechanism of action following a specific target inhibition. Vulnerability of targets in the biosynthetic pathway of coenzyme A was evaluated using this platform. In addition, we also report the utility of this platform in understanding the impact of a physiologically relevant carbon source, glucose versus acetate, on metabolite profiles of bacterial pathogens.
doi:10.2147/AABC.S14368
PMCID: PMC3170011  PMID: 21918631
antibacterial drug; mathematical model; kinetic platform; metabolic dynamics; Escherichia coli
19.  Construction of random perfect phylogeny matrix 
Purpose
Interest in developing methods appropriate for mapping increasing amounts of genome-wide molecular data are increasing rapidly. There is also an increasing need for methods that are able to efficiently simulate such data.
Patients and methods
In this article, we provide a graph-theory approach to find the necessary and sufficient conditions for the existence of a phylogeny matrix with k nonidentical haplotypes, n single nucleotide polymorphisms (SNPs), and a population size of m for which the minimum allele frequency of each SNP is between two specific numbers a and b.
Results
We introduce an O(max(n2, nm)) algorithm for the random construction of such a phylogeny matrix. The running time of any algorithm for solving this problem would be Ω (nm).
Conclusion
We have developed software, RAPPER, based on this algorithm, which is available at http://bioinf.cs.ipm.ir/softwares/RAPPER.
doi:10.2147/AABC.S13397
PMCID: PMC3170006  PMID: 21918630
perfect phylogeny; minimum allele frequency (MAF); tree; recursive algorithm
20.  Efficient algorithms for multidimensional global optimization in genetic mapping of complex traits 
We present a two-phase strategy for optimizing a multidimensional, nonconvex function arising during genetic mapping of quantitative traits. Such traits are believed to be affected by multiple so called quantitative trait loci (QTL), and searching for d QTL results in a d-dimensional optimization problem with a large number of local optima. We combine the global algorithm DIRECT with a number of local optimization methods that accelerate the final convergence, and adapt the algorithms to problem-specific features. We also improve the evaluation of the QTL mapping objective function to enable exploitation of the smoothness properties of the optimization landscape. Our best two-phase method is demonstrated to be accurate in at least six dimensions and up to ten times faster than currently used QTL mapping algorithms.
doi:10.2147/AABC.S9240
PMCID: PMC3170002  PMID: 21918629
global optimization; QTL mapping; DIRECT
21.  An unsupervised strategy for biomedical image segmentation 
Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In this paper, we present an unsupervised strategy for biomedical image segmentation using an algorithm based on recursively applying mean shift filtering, where entropy is used as a stopping criterion. This strategy is proven with many real images, and a comparison is carried out with manual segmentation. With the proposed strategy, errors less than 20% for false positives and 0% for false negatives are obtained.
doi:10.2147/AABC.S11918
PMCID: PMC3170003  PMID: 21918628
segmentation; mean shift; unsupervised segmentation; entropy
22.  Modeling of thermodynamic and physico-chemical properties of coumarins bioactivity against Candida albicans using a Levenberg–Marquardt neural network 
In recent years, due to vital need for novel fungicidal agents, investigation on natural antifungal resources has been increased. The special features exhibited by neural network classifiers make them suitable for handling complex problems like analyzing different properties of candidate compounds in computer-aided drug design. In this study, by using a Levenberg–Marquardt (LM) neural network (the fastest of the training algorithms), the relation between some important thermodynamic and physico-chemical properties of coumarin compounds and their biological activities (tested against Candida albicans) has been evaluated. A set of already reported antifungal bioactive coumarin and some well-known physical descriptors have been selected and using LM training algorithm the best architecture of neural model has been designed for forecasting the new bioactive compounds.
PMCID: PMC3170013  PMID: 21918627
Levenberg/Marquardt algorithm; coumarin; neural network
23.  Molecular biocoding of insulin 
This paper discusses cyberinformation studies of the amino acid composition of insulin, in particular the identification of scientific terminology that could describe this phenomenon, ie, the study of genetic information, as well as the relationship between the genetic language of proteins and theoretical aspects of this system and cybernetics. The results of this research show that there is a matrix code for insulin. It also shows that the coding system within the amino acid language gives detailed information, not only on the amino acid “record”, but also on its structure, configuration, and various shapes. The issue of the existence of an insulin code and coding of the individual structural elements of this protein are discussed. Answers to the following questions are sought. Does the matrix mechanism for biosynthesis of this protein function within the law of the general theory of information systems, and what is the significance of this for understanding the genetic language of insulin? What is the essence of existence and functioning of this language? Is the genetic information characterized only by biochemical principles or it is also characterized by cyberinformation principles? The potential effects of physical and chemical, as well as cybernetic and information principles, on the biochemical basis of insulin are also investigated. This paper discusses new methods for developing genetic technologies, in particular more advanced digital technology based on programming, cybernetics, and informational laws and systems, and how this new technology could be useful in medicine, bioinformatics, genetics, biochemistry, and other natural sciences.
PMCID: PMC3170004  PMID: 21918626
human insulin; insulin model; biocode; genetic code; amino acids
24.  Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms 
Chronic hepatitis C (CHC) patients often stop pursuing interferon-alfa and ribavirin (IFN-alfa/RBV) treatment because of the high cost and associated adverse effects. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the IFN-alfa/RBV treatments. Single nucleotide polymorphisms (SNPs) can be used to understand the relationship between genetic inheritance and IFN-alfa/RBV therapeutic response. The aim in this study was to establish a predictive model based on a pharmacogenomic approach. Our study population comprised Taiwanese patients with CHC who were recruited from multiple sites in Taiwan. The genotyping data was generated in the high-throughput genomics lab of Vita Genomics, Inc. With the wrapper-based feature selection approach, we employed multilayer feedforward neural network (MFNN) and logistic regression as a basis for comparisons. Our data revealed that the MFNN models were superior to the logistic regression model. The MFNN approach provides an efficient way to develop a tool for distinguishing responders from nonresponders prior to treatments. Our preliminary results demonstrated that the MFNN algorithm is effective for deriving models for pharmacogenomics studies and for providing the link from clinical factors such as SNPs to the responsiveness of IFN-alfa/RBV in clinical association studies in pharmacogenomics.
PMCID: PMC3170005  PMID: 21918625
chronic hepatitis C; artificial neural networks; interferon; pharmacogenomics; ribavirin; single nucleotide polymorphisms
25.  Simultaneous use of solution, solid-state NMR and X-ray crystallography to study the conformational landscape of the Crh protein during oligomerization and crystallization 
We explore, using the Crh protein dimer as a model, how information from solution NMR, solid-state NMR and X-ray crystallography can be combined using structural bioinformatics methods, in order to get insights into the transition from solution to crystal. Using solid-state NMR chemical shifts, we filtered intra-monomer NMR distance restraints in order to keep only the restraints valid in the solid state. These filtered restraints were added to solid-state NMR restraints recorded on the dimer state to sample the conformational landscape explored during the oligomerization process. The use of non-crystallographic symmetries then permitted the extraction of converged conformers subsets. Ensembles of NMR and crystallographic conformers calculated independently display similar variability in monomer orientation, which supports a funnel shape for the conformational space explored during the solution-crystal transition. Insights into alternative conformations possibly sampled during oligomerization were obtained by analyzing the relative orientation of the two monomers, according to the restraint precision. Molecular dynamics simulations of Crh confirmed the tendencies observed in NMR conformers, as a paradoxical increase of the distance between the two β1a strands, when the structure gets closer to the crystallographic structure, and the role of water bridges in this context.
PMCID: PMC3170007  PMID: 21918624
structural bioinformatics; NMR structure calculation; ARIA; non-crystallographic symmetry; crystallographic ensemble refinement; molecular dynamics simulation

Results 1-25 (28)