Wednesday, October 5, 2016

Phosphonate emerging zinc binding group in matrix metalloproteinase inhibitors



Phosphonate emerging zinc binding group in matrix metalloproteinase inhibitors

Matrix metalloproteinases (MMPs) are zinc-dependent endopeptidases, responsible for the degradation of the extracellular matrix (ECM). Their involvement has been demonstrated in several diseases, among which chronic inflammation and cancer; therefore, they have been considered interesting therapeutic targets. The design of MMP inhibitors (MMPIs) has largely focused on development of various compounds containing a zinc binding group (ZBG) in their structure, with the hydroxamate being the most potent one.
Currently, there are no MMPI containing hydroxamate function as ZBG in the market, mainly due to their proven toxicity. An alternative function chelating the zinc ion would ensure a better selectivity. This latter aspect is mandatory to obtain clinically exploitable compounds. In this review the phosphonate and bis-phosphonate groups as the ZBG are considered in the development of selective and potent MMPIs.
The authors have been working since a long time on MMP inhibitors containing alternative zinc-binding group to hydroxamate, focusing in particular on phosphonic acids, thus contributing to this still interesting research field.
Phosphonate-based inhibitors are classified on peptide phosphonate; sulphonamide and sulfonyl phosphonate, the most potent; carbamoyl phosphonate, and bisphosphonate that have been identified more recently with an interesting activity on bone resorption.
Moreover, the binding with the MMP active site is discussed in the research paper, ‘Phosphonate Emerging Zinc Binding Group in Matrix Metalloproteinase Inhibitors’, published in Current Drug Targets.
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Mind and molecules — Fingerprinting psychiatric illness


Mind and molecules Fingerprinting psychiatric illness

Diagnosis of mood and psychotic disorders depend solely on relatively subjective assessment of symptoms and psychometric evaluations, upon which a decision is made to prescribe one or more standardised treatment regimen. Treatment response in turn is evaluated on the same principles. All this in spite of decades’ worth of research efforts aimed at understanding the neurobiological underpinnings of these disorders.
Research into mood (depression, bipolar disorder) and psychotic disorders has advanced to the extent where biochemical hypotheses explaining the aetiology of a particular illness may be individualised to more accurately target one or more underlying pathology in a specific patient or subgroup of patients, hence achieving more effective disease modifying therapy. A “one-size fits all” paradigm is no longer a viable approach. Rather a customized regime based on individual biological abnormalities would pave the way toward more effective treatment.
In reviewing the clinical and preclinical literature, this paper discusses the most highly regarded pathophysiologic processes in mood and psychotic disorders by exploring various biomarkers relating to neuroanatomy, neuro-circuitry, neuronal growth and resilience as well as markers associated with oxidative stress and inflammation. A brief overview of prominent markers in the fields of genetics and proteomics also offers additional insight. Scrutinizing prominent and more equivocal biological markers of mood and psychotic disorders aids to address the urgent need to identify neurobiological targets of a disease as well as its associated biomarkers that will improve the current classification, diagnosis and treatment of these disorders. Ultimately, this knowledge will inform on the development of biomarker panels that in turn will customize treatment regimens for better therapeutic outcomes. The identified biomarkers should accurately reflect pathophysiologic processes in these disorders that will enable practitioners to stratify patients on a biological basis into more homogeneous clinically distinct subgroups, allowing the prescribing of target-specific therapy.

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Recently Published Issue of the Journal Current Bioinformatics



Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews, drug clinical trial studies and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science.
Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
Following are the articles from the Journal of Current Bioinformatics, 10 Issue 2:
Article: Investigating Power and Limitations of Ensemble Motif Finders Using Metapredictor CE3
Author(s): Mauro Leoncini, Manuela Montagnero and Karina Panucia Tillán

Article: Protein Sequence Annotation by Means of Community Detection
Author(s): Giuseppe Profiti, Damiano Piovesan, Pier Luigi Martelli, Piero Fariselli and Rita Casadio

Article: Discretization of Expression Quantitative Trait Loci in Association Analysis Between Genotypes and Expression Data§
Author(s): Andrés R. Masegosa, Rubén Armañanzas, María M. Abad-Grau, Víctor Potenciano, Serafín Moral, Pedro Larrañaga, Concha Bielza and Fuencisla Matesanz

Article: Lexical Characterisation of Bio-Ontologies by the Inspection of Regularities in Labels
Author(s): Manuel Quesada-Martínez, Jesualdo Tomás Fernández-Breis and Robert Stevens

Article: Incremental Construction of Biological Networks by Relation Extraction from Literature
Author(s): Dragana Miljkovic, Vid Podpečan, Tjaša Stare, Igor Mozetič, Kristina Gruden and Nada Lavrač
Article: Efficient and Error-Tolerant Sequencing Read Mapping
Author(s): Piotr Jaroszyński and Norbert Dojer

Article: A Hierarchical Classification for the Selection of the Most Suitable Multiple Sequence Alignment Methodology
Author(s): Francisco M. Ortuño, Hector Pomares, Olga Valenzuela, Carolina Torres and Ignacio Rojas

Article: Regulation of Meiosis Initiation before the Commitment Point in Budding Yeast: A Review of Biology, Molecular Mechanisms and Related Mathematical Models
Author(s): Clampi T. Wannige, Don Kulasiri and Sandhya Samarasinghe

Article: PuzzleCluster: A Novel Unsupervised Clustering Algorithm for Binning DNA Fragments in Metagenomics
Author(s): Kyler Siegel, Kristen Altenburger, Yu-Sing Hon, Jessey Lin and Chenglong Yu

Article: Effect of Hubs in Amino Acid Network on Iron Superoxide Dismutase Stability
Author(s): Yanrui Ding, Xueqin Wang and Zhaolin Mou
For details, please visit: http://bit.ly/1dJDm9V

courtesy by : Bentham Insight
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Major Article Contributions by some of the Indian Authors in Bentham Science Publishers Journal; Current Bioinformatics:



Article Title: A Computational Prediction of Conserved MicroRNA Targets of Ion Channels in Vertebrates
Author(s): Priyadarshan Kathirvel, Gopal Ramesh Kumar and Kavitha
Sankaranarayanan Abstract: Ion channels are integral membrane proteins that are responsible for most physiological functions including the electrical activity of excitable cells. Their expression and function are regulated by a variety of means including microRNAs (miRNAs). MicroRNAs are small non coding RNAs, 22 nucleotides in length, involved in regulation of gene expression. In this study, we attempt to predict the miRNA targets of ion channel genes conserved across 4 species using TargetScan algorithm. From the results, many miRNA targets were found to be conserved among mammals. The expression profile of identified miRNA targets of certain genes that are implicated in channelopathies of brain, heart and skeletal muscle in Homo sapiens are explored. Further a final correlation of channel genes and miRNAs expressed in specific tissues is obtained by comparison of expression profiles using mimiRNA. miR-302a and miR-9 were found to be positively correlated with many channelopathy associated genes, while miR-143 and miR-29a were predicted to exhibit negative correlation. In this paper we summarize a list of all possible miRNAs linked with channelopathies. For details, visit: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=106272
Article Title: Mangrove Infoline Database: A Database of Mangrove Plants with Protein Sequence Information Author(s): Sambhaji B. Thakar and Kailas D. Sonawane Abstract: 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. For more information, kindly visit:http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=114022
Article Title: Credential Role of van der Waal Volumes and Atomic Masses in Modeling Hepatitis C Virus NS5B Polymerase Inhibition by Tetrahydrobenzo- Thiophenes Using SVM and MLR Aided QSAR Studies Author(s): Kirti Khuntwal, Mukesh Yadav, Anuraj Nayarisseri, Shobha Joshi, Deepika Sharma and Smita Suhane Abstract: 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, R2 cv = 0.92) and MLR (R2=0.9684, SE=0.1171, R2 cv= 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. For more details, click: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=114016
Article Title: Development of an Engineering Method to Optimize Polyamine Metabolic Pathways. Author(s): Mouli Das, Subhasis Mukhopadhyay and Rajat K. De Abstract: In this article, we determine an optimal set of enzymes which is needed to be expressed at a specific level, in order to maximize the production of the target metabolite, spermine, from the substrate, ornithine, in the polyamine metabolic pathway. The pathway thus obtained is compared with the optimal pathway obtained using the existing extreme pathway analysis method. The results are appropriately validated through literature. Finally, we discuss potential applications of this approach in the field of metabolic and genetic engineering. It is hoped that the engineering of this pathway can produce secondary metabolites having commercial values, and finally drugs. For more details click: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=116191
Article Title: PredictFold-PSS-3D1D: A Protein Fold Recognition Server for Predicting Folds from the Twilight Zone Sequences. Author(s): Kaliappan Ganesan and Subbiah Parthasarathy Abstract: The PredictFold-PSS-3D1D is an online protein fold recognition web server used to predict the possible folds from the twilight zone protein sequences. In this server, an improved 3D1D profile method (Ganesan and Parthasarathy, J. Struct. Funct. Genomics, 12, 181-189, 2011) is employed, wherein, the inclusion of predicted secondary structure information improves fold recognition. The PredictFold-PSS-3D1D server accepts amino acid sequences and their predicted secondary structure data as input and aligns them with the 3D1D profiles of known SCOP folds in a database. The alignments are ranked by the z-values and P-values. The top 5 ranks of the SCOP folds from the database are listed along with a link to ‘View SCOP details’. The folds with z-values ≥3.0 and P-values ≤0.05 are indicated as ‘Predicted Fold’ for the given query twilight zone protein sequence. This server is available in our PredictFold web server athttp://bioinfo.bdu.ac.in/pss3d1d/. For more information, visit: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=116188
Article Title: An In Silico Identification of Human Promoters: A Soft Computing Based Approach Author(s): Sutapa Datta and Subhasis Mukhopadhyay Abstract: Promoter region of a gene sequence of Eukaryotes is very important as it helps us to understand the mechanism of transcription regulation. The identification of this region is a complex problem as the signature for identification turns out to be fuzzy. Several in silico methods are available for identifying the promoter region, but the scope for new methods still exists. Reasonable prediction of promoter sequence (that can be tested by comparing with the wet-lab data) from a mixed database of promoters and nonpromoters is thus a challenge that any new method would have to face. In this communication we propose a composite method that utilizes clustering of known promoter and non-promoter sequences in their respective clusters based on their relative distances, and then classifying the max similarity scores obtained from a group of new sequences and the clusters, to predict the true promoters among the new set of sequences. The in silico experiment is carried out on different databases constructed by us from the available primary sequence databanks to demonstrate the advantage of the proposed approach. For more information on this journal, click:http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=111148
courtesy by : Bentham Insight
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Japanese authors have made important contributions to Bentham Science Journal, Current Bioinformatics



Journal Name:Current Bioinformatics
Article Title: Signal-Dependent Noise Induces Muscle Co-Contraction to Achieve Required Movement Accuracy: A Simulation Study with an Optimal Control
Author(s): Yuki Ueyama and Eizo Miyashita
Abstract
Simultaneous activation of the agonist and antagonist muscles surrounding a joint, called co-contraction, is suggested to play a role in increasing joint stiffness to improve movement accuracy. However, it has not been clarified how co-contraction is related to movement accuracy, as most models for motor planning and control cannot deal with muscle co-contraction. In this study, the muscle activation and joint stiffness in reaching movements were studied under three different requirement levels of endpoint accuracy using a two-joint six-muscle model and an approximately optimal control. We carried out simulations of biological arm movements for a center-out reaching task under different accuracy demands with different types of motor noise and demonstrated time-varying co-contraction and a double-peaked jointstiffness profile. Furthermore, we showed that the strength of co-contraction and joint stiffness increased depending on the required accuracy level under signal-dependent noise, the magnitude of which was proportional to the motor command but not to additive Gaussian noise. We concluded that the optimal control is a valid model for the human motor control system and that signal-dependent noise is essential to induce co-contraction depending on accuracy demands.
For more information, visit:http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=106262
courtesy by :Bentham Insight
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