James C Burnett1, Bing Li2, Ramdas Pai2, Steven C Cardinale2, Michelle M Butler2, Norton P Peet2, Donald Moir2, Sina Bavari3, Terry Bowlin21Target, Structure-Based Drug Discovery Group, SAIC-Frederick, Inc., National Cancer Institute at Frederick; Frederick, MD 21702, USA; 2Microbiotix, Inc., Worcester, MA 01605, USA; 3Division of Integrated Toxicology,  United States Army Medical Research Institute of Infectious Diseases, Frederick, MD 21702, USAAbstract: Botulinum neurotoxins (BoNTs), and in particular serotype A, are the most poisonous of known biological substances, and are responsible for the flaccid paralysis of the disease state botulism. Because of the extreme toxicity of these enzymes, BoNTs are considered highest priority biothreat agents. To counter BoNT serotype A (BoNT/A) poisoning, the discovery and development of small molecule, drug like inhibitors as post intoxication therapeutic agents is being pursued. Specifically, we are focusing on inhibitors of the BoNT/A light chain (LC) (ie, a metalloprotease) subunit, since such compounds can enter neurons and provide post intoxication protection of the enzyme target substrate. To aid/facilitate this drug development effort, a pharmacophore for inhibition of the BoNT/A LC subunit was previously developed, and is continually being refined via the incorporation of novel and diverse inhibitor chemotypes. Here, we describe several analogs of a promising therapeutic chemotype in the context of the pharmacophore for BoNT/A LC inhibition. Specifically, we describe: 1) the pharmacophoric ‘fits’ of the analogs and how these ‘fits’ rationalize the in vitro inhibitory potencies of the analogs, and 2) pharmacophore refinement via the inclusion of new components from the most potent of the presented analogs.Keywords: botulinum, neurotoxin, inhibitor, pharmacophore, metalloprotease, biothreat

Yang Dai1, Xiaofeng Zhou21Department of Bioengineering, Department of Computer Science, College of Engineering, 2Center for Molecular Biology of Oral Diseases, College of Dentistry, and Graduate College, UIC Cancer Center, University of Illinois at Chicago, Chicago, IL, USAAbstract: MicroRNAs are pivotal regulators of development and cellular homeostasis. They act as post-transcriptional regulators, which control the stability and translation efficiency of their target mRNAs. The prediction of microRNA targets and detection of microRNA-mRNA regulatory modules (MRMs) are crucial components for understanding of microRNA functions. Numerous computational methods for microRNA target prediction have been developed. Computationally-predicted targets have been recently used in the integrative analysis of microRNA and mRNA expression analysis to identify microRNA targets and MRMs. In this article we review these recent developments in the integrative analysis methods. We also discuss the remaining challenges and our insights on future directions.Keywords: microRNA target prediction, integrative analysis, microRNA regulatory mechanism, microRNA profiling, mRNA expression profiles

Eugene Lin1, Po See Chen2,6, I Hui Lee2, Hui Hua Chang3, Po-Wu Gean4, Yen Kuang Yang2, Ru-Band Lu2,51Vita Genomics, Inc., Taipei, Taiwan; 2Department of Psychiatry, 3Institute of Biopharmaceutical Sciences, 4Department of Pharmacology, 5Institute of Behavioral Medicine, 6Department of Psychiatry, National Cheng Kung University Hospital, Dou-Liou Branch, TaiwanAbstract: Due to the varying nature of patient response to different types and even dosages of the same antidepressant, doctors currently prescribe antidepressants on a trial and error basis. Therefore, it is highly desirable, both clinically and economically, to establish tools that distinguish responders from non-responders and to predict possible outcomes of the antidepressant treatments. The overall effectiveness of treatment using antidepressants may thus be optimized. Common genetic polymorphisms, such as single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. In this work we developed a prediction model resulting from the analysis of clinical factors such as SNPs, age, baseline Hamilton Rating Scale for Depression (HAM-D) score, antidepressant groups, and gender of depression patients. We used it to predict the responsiveness of antidepressant treatment. By using candidate genes reported in the literature, we selected four SNPs that were strongly relevant to antidepressant efficacy. Our study population consisted of Taiwanese patients with major depression recruited from the National Cheng Kung University Hospital. 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 showed 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 antidepressants in clinical association studies.Keywords: antidepressants, artif icial neural networks, major depressive disorder, pharmacogenomics, single nucleotide polymorphisms

Frédéric Guyon1, Pierre Tufféry1,21MTi, INSERM UMR-S973, Université Paris Diderot-Paris 7, Paris, France; 2RPBS, Université Paris Diderot-Paris 7, Paris, FranceAbstract: Quantifying the 3D similarity between two proteins is a difficult task that has motivated the assessment of several 3D scores. New developments in protein modeling and analysis have led to the emergence of new interest towards mining structures at the local level. We assess in the context of fragment mining several dissimilarity scores. We revisit the concept of mirror conformation previously introduced at the level of complete structures and extend it to the more local level. We also consider an explicit criterion measuring the fragment boundary discrepancies. Whereas classical criteria such as the root mean square deviation (RMSd) fail to identify similar shapes in a consistent way, we show that local mirror and boundary mismatch filtering greatly supplements classical scores to select significant matches. The geometrical conditions defined by such criteria can be considered as signatures of fragment similarity. Furthermore, it is possible to tune the degree of similarity depending on the size of the mirrors accepted. This results in a more intuitive perception of the concept of similarity, and opens new perspectives for the rapid mining of large collections of structures.Keywords: protein fragments, similarity, distance, mining

Jun Lu1, Liaofu Luo2, Lirong Zhang2, Wei Chen2, Ying Zhang1,21Department of Physics, Inner Mongolia University of Technology, Hohhot 010051, China; 2Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, ChinaAbstract: Accompanying the rapid development of genome research, how to correctly ­recognize functional sites and structural modes of DNA and protein sequences has become a great challenge to bioinformatics. Therefore, a great number of algorithms and tools have been proposed and applied to sequence pattern recognition. Increment of Diversity with Quadratic Discriminant analysis (IDQD) is one of the efficient computational tools. In this article we shall introduce the main points of IDQD method and review its application in DNA and protein sequence pattern recognition, and finally give some discussions on the prospects of the approach.Keywords: Increment of Diversity, Quadratic Discriminant analysis, sequence pattern recognition

Waqas Amin1, S Joseph Sirintrapun3, Anil V Parwani1,21Department of Biomedical Informatics, 2Department of Pathology University of Pittsburgh, Pittsburgh, PA, USA; 3Department of Pathology, Emory University, Atlanta, GA, USABackground: Synoptic reports in routine pathology practice provide composite documents that include information from morphology and molecular technologies. It is clear and accurate structured information and developed by incorporating standardized data elements in the form of checklist for pathology reporting. This facilitates pathologists to document their findings and ultimately improve the overall quality of pathology reports.Objectives: The goal of this review article is to discuss (1) the importance of synoptic reporting in pathology, (2) utility and applications, (3) its impact on pathology reporting and patient care, and (4) the challenges and barriers of implementing synoptic reporting. Pertinent literature will also be reviewed.Design: The synoptic reporting system provides a complete set of data elements in the form of synoptic templates or “worksheets” for pathology tumor reporting based on the World Health Organization (WHO) Classification and the College of American Pathologists (CAP) Cancer Checklists. These standards provide most updated and supplemented classification scheme, specimen details, and staging as well as prognostic information. Data from synoptic reporting tool can be imported to a relational database where they are organized and efficiently searched and retrieved. Since search and retrieval are streamlined, synoptic databases enhance basic ­science, clinical, and translational cancer research.Conclusion: Synoptic reporting facilitates a standard based structured method for entering the diagnostic and prognostic information in accurate and consistent fashion for a particular ­pathology specimen, thus reducing transcription services, specimen turnaround time, and typographical and transcription errors. The structured data can be imported into the Laboratory Information Service (LIS) database, which facilitates swift data access and improved communication for cancer management. Finally, these synoptic templates act as a robust medium of high-quality data from the various biospecimens, which can be shared across multiple on-going research projects to enhance basic and translational research.Keywords: synoptic reporting, pathology

Mahnaz Habibi1, Changiz Eslahchi1, Mehdi Sadeghi2, Hamid Pezashk3,4,51Faculty of Mathematics, Shahid-Beheshti University, GC, Tehran, Iran; 2National Institute of Genetic Engineering and Biotechnology, Tehran, Iran; 3School of Mathematics, Statistics and Computer Sciences, College of Science, University of Tehran, Tehran, Iran; 4Center of Excellence in Biomathematics, College of Science, University of Tehran, Tehran, Iran; 5Bioinformatics Group, School of Computer Science, IPM, Tehran, IranPurpose: The analysis of a protein’s structure allowing detailed exploration of the protein’s biological function is one of the most challenging problems in bioinformatics. There are efficient algorithms to calculate main properties of a protein structure, such as packing density, buried or surface residues, and accessible surface area. But these algorithms need the three-dimensional (3D) coordinates of the proteins.Methods: We used the contact map of a protein to construct a graph. By considering several features of the corresponding graph, we proposed some algorithms to discuss the above-mentioned properties of a protein. We also introduced a new measure for the hydrophobicity of an amino acid by defining an average degree for the amino acid as a vertex on the graph.Results: We compared our results with those obtained by some other existing algorithms. We found strong correlations between the popular methods, which use 3D coordinates, and our methods, which only use a predicted contact map.Conclusion: Many features of a protein can be predicted without having 3D coordinates, based on the contact map of the protein. The programs are freely available from http://www.bioinf.cs.ipm.ir/softwares/asa/asa.rar.Keywords: accessible surface area, buried residue, surface residue, packing density, hydrophobic

Viren C Patel1*, Kajari Mondal1*, Amol Carl Shetty1*, Vanessa L Horner1, Jirair K Bedoyan2, Donna Martin2,3, Tamara Caspary1, David J Cutler1, Michael E Zwick11Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA; 2Department of Pediatrics, 3Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA; *These authors contributed equally to this work.Abstract: Methods of genomic selection that combine high-density oligonucleotide microarrays with next-generation DNA sequencing allow investigators to characterize genomic variation in selected portions of complex eukaryotic genomes. However, choosing the specific oligonucleotides to be used can pose a major technical challenge. To address this issue, we have developed a software package called MOPeD (microarray oligonucleotide probe designer), which automates the process of designing genomic selection microarrays. This Web-based software allows individual investigators to design custom genomic selection microarrays optimized for synthesis with Roche NimbleGen’s maskless photolithography. Design parameters include uniqueness of the probe sequences, melting temperature, hairpin formation, and the presence of single-nucleotide polymorphisms. We generated probe databases for the human, mouse, and rhesus macaque genomes and conducted experimental validation of MOPeDdesigned microarrays in human samples by sequencing the human X chromosome exome, where relevant sequence metrics indicated superior performance relative to a microarray designed by the Roche NimbleGen proprietary algorithm. We also performed validation in the mouse to identify known mutations contained within a 487-kb region from mouse chromosome 16, the mouse chromosome 16 exome (1.7 Mb), and the mouse chromosome 12 exome (3.3 Mb). Our results suggest that the open source MOPeD software package and Web site (http://moped.genetics.emory.edu/) will make a valuable resource for investigators in their sequence-based studies of complex eukaryotic genomes.Keywords: genomic selection, oligonucleotide, microarray, next-generation sequencing, software