Current Bioinformatics - Volume 8, Issue 4, 2013

In this paper we review and at the same time investigate some stochastic models for tumor-immune systems. To describe these models, we used a Wiener process, as the noise has a stabilization effect. Their dynamics are studied in terms of stochastic stability around the equilibrium points, by constructing the Lyapunov exponent, depending on the parameters that describe the model. Stochastic stability was also proved by constructing a Lyapunov function and the second order moments. We have studied and analyzed a Kuznetsov-Taylor like stochastic model and a Bell stochastic model for tumor-immune systems. These stochastic models are studied from stability point of view and they were graphically represented using the second order Euler scheme and Maple 12 software.

The Randic index is a well known topological index (TI) used in QSAR/QSPR studies to quantify the molecular structure represented by a graph. In this work we review some aspects of this TI with special emphasis on the generalizations introduced by Kier & Hall and more recently by Estrada. Next, we introduced a new generalization using a Markov chain in order to obtain a new family of TIs called the Markov-Randic indices of order k-th (1χk). Later, we applied these new indices to seek models useful to calculate numerical quality scores S(Lij) for network links Lij (connectivity) in known complex networks. The linear models obtained produced the following results in terms of overall accuracy for network re-construction: Metabolic networks (70.48%), Parasite-Host networks (90.86%), CoCoMac brain cortex co-activation network (81.59%), NW Spain Fasciolosis spreading network (99.04%). Spanish financial law network (71.83%). This work opens a new door to the computational re-evaluation of network connectivity quality (collation) in different complex systems.

A large number of naturally origin alkaloids are reported to be potential against cholinesterases (e.g., acetylcholinesterase, butyrylcholinestarase, etc.). Some of them are from chemical subclass of steroidal alkaloids. Here in this paper docking calculations and the possible intermolecular and atomic interactions have been studied and presented from some of the natural and semisynthetic steroidal alkaloids. These alkaloids were found to be potent inhibitors against both the acetyl- (AChE) and butyrylcholinestarase (BChE). Some (like Terminaline, Hookerianamide I, Chonemorphine, etc.) of them were interestingly found to be quite selective towards the BChE over AChE. For the docking calculations ICMTM docking module and for the study of the intermolecular interactions the program LigPlot have been used. During the docking studies the compounds showed good correlations with the in vitro activity profiles (IC50 values) and the docking (Edocking) and calculated binding energies (ΔG). When docked into AChE the correlation coefficient (R2) 0.808 and 0.813, respectively and when docked into BChE the R2 values were found to be 0.873 and 0.768, respectively. These correlations revealed remarkable agreements of the docking studies with the activity found from in vitro experiments. Majority and the large part of the compounds exhibited hydrogen bonds as well as hydrophobic interactions at the peripheral anionic subsite (PAS), which is at the entrance of the gorge. A number of compounds exhibited interesting interactions both the PAS and acyl-binding sites.

The study of complex systems such as proteins/DNA/RNA or dynamics of tax law systems can be carried out with the complex network theory. This allows the numerical quantification of the significant information contained by the sequences of amino acids, nucleotides or types of tax laws. In this paper we describe S2SNet, a new Python tool with a graphical user interface that can transform any sequence of characters or numbers into series of invariant star network topological indices. The application is based on Python reusable processing procedures that perform different functions such as reading sequence data, transforming numerical series into character sequences, changing letter codification of strings and drawing the star networks of each sequence using Graphviz package as graphical back-end. S2SNet was previously used to obtain classification models for natural/random proteins, breast/colon/prostate cancer-related proteins, DNA sequences of mycobacterial promoters and for early detection of diseases and drug-induced toxicities using the blood serum proteome mass spectrum. In order to show the extended practical potential of S2SNet, this work presents several examples of application for proteins, DNA/RNA, blood proteome mass spectra and time evolution of the financial law recurrence. The obtained topological indices can be used to characterize systems by creating classification models, clustering or pattern search with statistical, Neural Network or Machine Learning methods. The free availability of S2SNet, the flexibility of analyzing diverse systems and the Python portability make it an ideal tool in fields such as Bioinformatics, Proteomics, Genomics, and Biomedicine or Social, Economic and Political Sciences.

Quantitative structure–activity relationship (QSAR) models have application in bioorganic chemistry mainly to the study of small sized molecules while applications to biopolymers remain not very developed. MicroRNAs (miRNAs), which are non-coding small RNAs, regulate a variety of biological processes and constitute good candidates to scale up the application of QSAR and complex network (CN). In this work, we selected microRNAs and predicted activity profile subsequently represented as a large network, which may be used to identify stem cell microRNAs with similar action. The propensity of a small RNA sequence to act as miRNA depends on its secondary structure, which one can explain in terms of folding thermodynamic and topological parameters; these can be used for fast identification of miRNAs at early stages of development of stem cells, and gain clarity inside cellular differentiation processes and diseases such as cancer. First, we calculated thermodynamic parameters and topological descriptors for 432 small RNA sequences. The model correctly recognized 203 of smiRNAs (94.0 %) and 216 of non-smiRNAs (100.0 %) divided into both training and validation series used to extend model validation for network construction. ROC curve analysis (area = 0.99) demonstrated that the present model significantly differentiates from a random classifier. In addition, a double ordinate cartesian plot of cross-validated residuals, standard residuals and leverages defined the domain of applicability of the model as a squared area within ±2 band for residuals and a leverage threshold of h = 0.0466. Last, we accounted for the methodology to combine QSAR and CN to carry out a study that would allow us to differentiate the activity of smiRNAs. The network predicted has 216 nodes (smiRNAs), 1948 edges (pairs of smiRNAs with similar activity), and low coverage density d = 8.4%. Comparative studies with real networks reveal that our network apparently has not only an ideal behavior but also resembles the known network models in different aspects. The combination of QSAR and CN is used for quickly accurate selection of new smiRNAs with potential use in bioorganic and medicinal chemistry.

Human immunodeficiency virus (HIV) is the responsible causal agent of acquired immunodeficiency syndrome (AIDS), a condition in humans where the immune system begins to fail, permitting the entry of diverse opportunistic infections. Until now, there is currently no available vaccine or cure for HIV or AIDS. Thus, the search for new anti-HIV therapies is a very active area. The viral infection takes place through a phenomenon called entry process, and proteins known as gp120, CCR5 and CXCR4 are essential for the prevention of the HIV entry. Bioinformatics has emerged as a powerful science to provide better understanding of biochemical or biological processes or phenomena, where 3D-QSAR methodologies and molecular modeling techniques have served as strong support. The present review is focused on the 3D-QSAR methodologies and molecular modeling techniques as parts of Bioinformatics for the rational design of entry inhibitors. Also, we propose here, a chemo-bioinformatic approach which is based on a model using substructural descriptors and allowing the prediction of multi-target (mt) inhibitors against five proteins related with the HIV entry process. By employing the model we calculated the quantitative contributions of some fragments to the inhibitory activity against all the proteins. This allowed us to automatically extract the desirable fragments for design of new, potent and versatile entry inhibitors.

Chronic hepatitis C virus (HCV) infections are a significant health problem worldwide. The NS5B Polymerase of HCV plays a central role in virus replication and is a prime target for the discovery of new treatment options. The urgent need to develop novel anti-HCV agents has provided an impetus for understanding the structure-activity relationship of novel Hepatitis C virus (HCV) NS5B polymerase inhibitors. Towards this objective, multiple linear regression (MLR) and support vector machine (SVM) were used to develop quantitative structure-activity relationship (QSAR) models for a dataset of 34 Tetrahydrobenzothiophene derivatives. The statistical analysis showed that the models derived from both SVM (R2 = 0.9784, SE=0.2982, R2cv = 0.92) and MLR (R2=0.9684, SE=0.1171, R2cv= 0.955) have a good internal predictivity. The models were also validated using external test set validation and Y-scrambling, the results demonstrated that MLR has a significant predictive ability for the external dataset as compared to SVM. Also the model is found to yield reliable clues for further optimization of Tetrahydrobenzothiophene derivatives in the data set.

DNA polymerases are essential enzymes for DNA replication, repair and recombination. The high number of possible candidates creates the necessity of Quantitative Structure-Activity Relationship models in order to guide the search for DNA polymerase inhibitors. In this work, we revised different computational studies for a very large and heterogeneous series of DNA polymerase inhibitors. Methods using bioinformatics, molecular docking, and quantitative structure-activity relationship (QSAR) were applied to develop new DNA polymerase inhibitors. First, we revised three servers like ChEMBL, PDB or PubMed to obtain databases of DNA polymerase inhibitors. Next, we reviewed previous works based on 2D-QSAR, 3D-QSAR, CoMFA, CoMSIA and Docking techniques, which studied different compounds to find out the structural requirements. And finally, we surveyed the more recent studies of alignments of DNA polymerase.

It is well known that transcription factors can induce deformations in their DNA-binding sites upon complex formation. However, few attempts have been made to investigate the extent to which induced structural deformations in the DNA molecule are conserved between different members of the same transcription factor family. In this article, we used the CRoSSeD methodology for describing DNA structural properties to extract common features in the binding sites of different LacI-GalR family members. The most significant feature identified in this way was located at the center of the binding sites, which is also the most likely location for an induced DNA deformation following an amino acid interdigitation. This feature was related further to specific elements present in the protein structure and was used to identify and characterize deviant family members. A general family-wide binding site model was constructed and applied to screen for unknown member binding sites.

Understanding the cellular behavior from a systems perspective requires the identification of functional and physical interactions among diverse molecular entities in a cell (i.e. DNA/RNA, proteins and metabolites). Powerful and scalable technologies enabled the generation of genome-wide datasets that describe cellular systems by capturing the interactions of their building blocks under different environmental stimuli. The most straightforward way to represent such datasets is by means of molecular networks of which nodes correspond to molecular entities and edges to the interactions amongst those entities. In this review we give an overview of the different functional and physical interaction networks in bacteria that have been or potentially can be built by the integration of diverse omics datasets.

Alzheimer’s disease is a complex disease, and no single “magic bullet” is likely to prevent or cure it. That’s why current treatments focus on several different aspects, including helping people maintain mental function; managing behavioral symptoms; and slowing, delaying, or preventing the disease. Four medications are approved by the U.S. Food and Drug Administration to treat Alzheimer’s. Donepezil, rivastigmine, and galantamine are used to treat mild to moderate Alzheimer’s. Memantine is used to treat moderate to severe Alzheimer’s. These drugs work by regulating neurotransmitters (the chemicals that transmit messages between neurons). Treatment of AD by ACh precursors and cholinergic agonists was ineffective or caused severe side effects. ACh hydrolysis by AChE causes termination of cholinergic neurotransmission. Therefore, compounds which inhibit AChE might significantly increase the levels of ACh depleted in AD. However, these drugs don’t change the underlying disease process and may help only for a few months to a few years. In this sense, quantitative structure-activity relationships (QSAR) could play an important role in studying these AChE inhibitors. QSAR models are necessary in order to guide the AChE synthesis. In this work, we revised different bioinformatics and theoretical studies of Acetylcholinesterase inhibitors, design and computational studies for a very large and heterogeneous series of AChE inhibitors. First, we review 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking and new theoretical methodology with different compound to find out the structural requirements. Next, we revised QSAR studies using method of Linear Discriminant Analysis (LDA) in order to understand the essential structural requirement for binding with receptor for AChE inhibitors.

In the metagenomics, long metagenome contigs can either improve metagenome gene prediction or metagenome sequence binning. Moreover, metagenome contigs can also make gene function annotation more accurate because they provide a lot of genome context information. Because of repetitive sequences of either intra-genomes or inter-genomes, metagenome contigs are probably wrongly assembled. Therefore, it is essential to develop a method to validate metagenome contigs. Here, we propose a computational method to validate metagenome contigs. After realigning raw sequencing reads onto one contig, we first compute a contig-ECDF (empirical cumulative probability distribution functions) and its corresponding reference using a computational simulation-based method. Because a reference of the contig-ECDF is changeless given some parameters, we use the distinction between them to check whether or not a contig is bona fide. The less the distinction is, the more likely a contig is bona fide. For wrongly assembled metagenome contigs, using simulated metagenome datasets, our method was shown to have a good capacity to identify them. After applying the method to a real metagenome dataset, which was sequenced from an in vitro-simulated microbial community with known constituted genomes, we showed that our method had a strong ability to identify bona fide contigs, and further demonstrated that small distinctions between contig-ECDFs and their references were significantly correlated with bona fide contigs. A computational method is developed to validate metagenome contigs. For each metagenome contig, our method gives it a score, and the smaller the score is, the more likely a contig is bona fide. After validation using both simulated and real datasets, our method was shown to have good performances.

Mangrove Infoline Database contains information about the medicinal uses of mangrove plants with protein/enzyme sequences. Mangrove Infoline Database is a comprehensive dynamic web-based database, used to facilitate retrieval of information related to the mangrove medicinal species. Most of the enzyme sequences extracted from mangroves are taken from NCBI’s protein sequence database, whereas the information related to physical characteristics, geographical distribution, common/vernacular names, taxonomy IDs, medicinal uses, parts used, chemical components extracted from various mangrove plants which can be used as a drug molecules have been collected from various literatures and scientific journals available in the text form. NCBI’s BLAST link has also been provided for the further comparative study. So there was a need to build database where users could get all specific information about mangrove medicinal plants at one place. The current database contains information about 100 Mangrove medicinal species out of which 40 are True mangroves, 30 Minor mangroves and, 30 Associate mangroves. This database would be useful to explore mangrove medicinal plant information through Web based database which would be helpful to derive the information for Researchers, Scientists, Pharmacologists, Biologists, Chemists, Doctors/Pharmacists, Teaching (Students and Teachers from universities and schools), Home-User, Botanical interested Persons, Farmers, and finally mangrove lovers. This attempt would create social awareness among the users about the important applications, uses and conservation of mangrove species around the world.