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Molecular Evolution of Cystine-Stabilized Miniproteins as Stable Proteinaceous Binders

Structure 2009年第4期17卷 620-631页

作者： Chang, Hung-Ju Hsu, Hung-Ju Chang, Chi-Fon Peng, Hung-Pin Sun, Yi-Kun Yu, Hui-Ming Shih, Hsi-Chang Song, Chun-Ying Lin, Yi-Ting Chen, Chu-Chun Wang, Chia-Hung Yang, An-Suei Genomics Research Center Academia Sinica Taipei 115 Taiwan Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Institute of Biochemical Science National Taiwan University Taipei 106 Taiwan Graduate Institute of Life Sciences National Defence Medical Center Taipei 114 Taiwan Institute of BioMedical Informatics National Yang-Ming University Taipei 112 Taiwan Institute of Biochemistry and Molecular Biology National Yang-Ming University Taipei 112 Taiwan

Small cystine-stabilized proteins are desirable scaffolds for therapeutics and diagnostics. Specific folding and binding properties of the proteinaceous binders can be engineered with combinatorial protein libraries in connection with artificial molecular evolution. The combinatorial protein libraries are composed of scaffold variants with random sequence variation, which inevitably produces a portion of the library sequences incompatible with the parent structure. Here, we used artificial molecular evolution to elucidate structure-determining residues in a smallest cystine-stabilized scaffold. The structural determinant information was then applied to designing cystine-stabilized miniproteins binding to human vascular endothelial growth factor. This work demonstrated a general methodology on engineering artificial cystine-stabilized proteins as antibody mimetics with simultaneously enhanced folding and binding properties. © 2009 Elsevier Ltd. All rights reserved.

关键词： PROTEINS

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Identification of microRNA in the protist Trichomonas vaginalis

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Genomics 2009年第5期93卷 487-493页

作者： Lin, Wei-Chen Li, Sung-Chou Lin, Wen-Chang Shin, Jyh-Wei Hu, Song-Nian Yu, Xiao-Min Huang, Ting-Yun Chen, Shih-Chieh Chen, Hua-Chien Chen, Shu-Jen Huang, Po-Jung Ruei-Chi Gan, Richie Chiu, Cheng-Hsun Tang, Petrus Molecular Regulation and Bioinformatics Laboratory Chang Gung University Taoyuan 333 Taiwan Division of Microbiology Graduate Institute of Biomedical Sciences Chang Gung University Taoyuan 333 Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei 112 Taiwan Taiwan International Graduate Program in Bioinformatics Academia Sinica Taipei 115 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei 115 Taiwan Department of Parasitology National Cheng Kung University Tainan 701 Taiwan Key Laboratory of Genome Sciences and Information Beijing Institute of Genomics Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Department of Life Sciences Chang Gung University Taoyuan 333 Taiwan Molecular Medicine Research Center Chang Gung University Taoyuan 333 Taiwan Bioinformatics Center Chang Gung University Tao-Yuan 333 Taiwan Chang Gung Molecular Infectious Diseases Research Center Chang Gung Memorial Hospital Taoyuan 333 Taiwan

MicroRNAs (miRNAs) are a class of small noncoding RNAs that have important regulatory roles in multicellular organisms. However, miRNA has never been identified experimentally in protist. Direct cloning of 438 expressed miRNA tags by microRNA serial analysis of gene expression from the parasitic protist Trichomonas vaginalis identified nine candidate miRNAs. Bioinformatics analysis of the corresponding genomic region revealed that these miRNA candidates contain a classical stem-loop-stem structure of pre-microRNAs. Analysis of the 20 nt long mature tva-miR-001 showed that it is an intergenic miRNA located at the scaffold DS113596. Tva-miR-001 was differentially expressed in the trophozoite, pseudocyst and amoeboid stages. Based on the experimental results of the present study, we provided solid evidence that protist possesses a miRNA regulating network comparable with multicellular organisms for the first time. © 2009 Elsevier Inc. All rights reserved.

关键词： MicroRNA Protist Trichomonas vaginalis

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A new implementation method of the reverse engineering algorithm to elucidate gene functional dependencies

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2007年第10期127卷 1712-1718+32页

作者： Inaoka, Hidenori Fukuoka, Yutaka Ueno, Akinori Kohane, Isaac S. Institute of Biomaterials and Biomedical Engineering Tokyo Medical and Dental University 2-3-10 Kanda-Surugadai Chiyoda-ku Tokyo 101-0062 Japan School of Biomedical Science Tokyo Medical and Dental University 1-5-45 Yushima Bunkyo-ku Tokyo 113-8510 Japan Graduate School of Science and Engineering Tokyo Denki University Ishizaka Hatoyama-Cho Hiki-Gun Saitama 350-0394 Japan Informatics Program Childrens's Hospital Harvard Medical School Boston MA United States

Various clustering methods are applied to characterizing gene expression data from DNA microarrays. However, to elucidate functional dependencies of genes analysis of their associations is important. The REVerse Engineering ALgorithm (REVEAL) was developed for analyzing the functional dependencies. Although the algorithm has been tested using binary models of genetic networks, it remains unclear how the method or similar technology will operate with systems of continuous variables. In this study, first, the REVEAL was examined using noisy, continuous data and the results suggested that its application to such data required considerable refinement of the algorithm. Then a new implementation method of REVEAL was proposed. This implementation method was tested through numerical experiments. The results of the simulations demonstrated the potential of the proposed method for extracting gene associations.

关键词： Reverse engineering

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CaGEDA: A web application for the integrated analysis of global gene expression patterns in cancer

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Applied Bioinformatics 2004年第1期3卷 49-62页

作者： Patel, Satish Lyons-Weiler, James University of Pittsburgh Cancer Institute University of Pittsburgh Medical School Pittsburgh PA United States Department of Pathology University of Pittsburgh Medical School Pittsburgh PA United States Cancer Biomarkers Laboratory University of Pittsburgh Medical School Pittsburgh PA United States Center for Pathology Informatics University of Pittsburgh Medical School Pittsburgh PA United States Benedum Center for Oncology Informatics University of Pittsburgh Medical School Pittsburgh PA United States Center for Biomedical Informatics University of Pittsburgh Medical School Pittsburgh PA United States Interdisciplinary Biomedical Graduate Program University of Pittsburgh Medical School Pittsburgh PA United States UPCI Benedum Oncology Informatics Center 304 Cancer Pavilion Pittsburgh PA 15232 5150 Centre Avenue United States

The explosion of microarray data from pilot studies, basic research and large-scale clinical trials requires the development of integrative computational tools that can not only analyse gene expression patterns but that can also evaluate the methods of analysis adopted and then provide a boost to post-analysis translational interpretation of those patterns. We have developed a web application called caGEDA (cancer gene expression data analyzer) that can: (1) upload gene expression profiles from cDNA or oligonucleotide microarrays;(2) conduct a diverse range of serial linear normalisations;(3) identify differentially expressed genes using a variety of tests - either threshold or permutation tests;(4) produce tables of literature references to papers reporting that specific genes (identified by accession numbers) are up- or down-regulated in specific cancers;(5) estimate the error of sample class prediction using the significant gene set for features;(6) perform low-bias and accurate validated learning using three computational validation techniques (leave-one-out validation, k-fold validation, random resampling validation);and (7) validate a classifier with a randomly selected or user-defined validation set. Significant genes are reported in a table of links to entries in the following databases: LocusLink, Genome View, UCSC, Ensembl, UniGene, dbSNP, AmiGO and OMIM. caGEDA is seamlessly integrated via embedded forms with UCSD's (University of California at San Diego) 2HAPI server (for medical subject heading (MeSH) term exploration) and EZ-Retrieve (to identify common transcription factors located upstream of sets of genes that exhibit similar modes of differential expression). caGEDA offers a variety of previously described and novel tests for differentially expressed genes, most notably the permutation percentile separability test, which is most appropriate for identifying genes that are significantly differentially expressed in a subset of patients. caGEDA, which

关键词： Cancer Genomics Microarray analysis

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A quantitative analysis of research publications in physical therapy journals - Author response

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PHYSICAL THERAPY 2003年第2期83卷 133-133页

作者： Miller, PA Haynes, RB PA Miller PT MHSc is Assistant Clinical Professor School of Rehabilitation Science McMaster University Hamilton Ontario Canada and Chief of Physiotherapy Practice Hamilton Health Sciences Hamilton Ontario Canada. When this article was written she was a graduate student in the Health Research Methodology Program at McMaster University. KA McKibbon BSc MLS is currently on leave from the Department of Clinical Epidemiology and Biostatistics McMaster University for PhD studies at the Centre for Biomedical Informatics University of Pittsburgh Pittsburgh Pa RB Haynes MD PhD is Professor of Clinical Epidemiology and Medicine McMaster University. He is Editor of ACP Journal Club

Background and *** physical therapists depend on their professional journals for high-quality evidence. The purpose of this study was to evaluate the rigor of research and review articles in 4 national physical therapy *** and *** articles in 6 consecutive issues of theAustralian Journal of Physiotherapy,Physical Therapy,Physiotherapy, andPhysiotherapy Canada, published between January 2000 and June 2001 (N=179), were reviewed. One trained reviewer identified the type and purpose of each article and assessed the rigor of treatment and review articles according to explicit *** majority of articles reviewed were original studies (56%). The majority of the research articles that dealt with human health care (66%) addressed topics that were not directly applicable to the provision of patient care such as measurement topics and studies on subjects without identified pathologies or impairments. Of the 179 journal articles, 19 met the standards for rigor (11%). The majority of these articles dealt with treatment. The pass rate per journal was as follows:Australian Journal of Physiotherapy, 10% (4/42);Physical Therapy, 15% (7/47);Physiotherapy, 12% (4/34); andPhysiotherapy Canada, 7% (4/56).*** such a small percentage of articles in these professional journals were identified as having direct application to patient care, physical therapists should attempt to access other sources of information to find additional high-quality evidence. A larger sample with a greater number of issues per journal may have yielded different results and indicated different trends, and further research appears to be warranted.

关键词： Clinical research Evidence-based practice Publications and audiovisual materials

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Contributory presentations/posters

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Journal of Biosciences 1999年第1期24卷 33-198页

作者： N. Manoj V. R. Srinivas A. Surolia M. Vijayan K. Suguna R. Ravishankar R. Schwarzenbacher K. Zeth Diederichs G. M. Kostner A. Gries P. Laggner R. Prassl Madhusudan Pearl Akamine Nguyen-huu Xuong Susan S. Taylor M. Bidva Sagar K. Saikrishnan S. Roy K. Purnapatre P. Handa U. Varshney B. K. Biswal N. Sukumar J. K. Mohana Rao A. Johnson Vasantha Pattabhi S. Sri Krishna Mira Sastri H. S. Savithri M. R. N. Murthy Bindu Pillai Kannan M. V. Hosur Mukesh Kumar Swati Patwardhan K. K. Kannan B. Padmanabhaa S. Sasaki-Sugio M. Nukaga T. Matsuzaki S. Karthikevan S. Sharma A. K. Sharma M. Paramasivam P. Kumar J. A. Khan S. Yadav A. Srinivasan T. P. Singh S. Gourinath Neelima Alam A. Srintvasan Vikas Chandra Punit Kaur Ch. Betzel S. Ghosh A. K. Bera S. Bhattacharya S. Chakraborty A. K. Pal B. P. Mukhopadhyay I. Dey U. Haldar Asok Baneriee Jozef Sevcik Adriana Solovicova K. Sekar M. Sundaralingam N. Genov Dong-cai Liang Tao Jiang Ji-ping Zhang Wen-rui Chang Wolfgang Jahnke Marcel Blommers S. C. Panchal R. V. Hosur Bindu Pillay Puniti Mathur S. Srivatsun Ratan Mani Joshi N. R. Jaganathan V. S. Chauhan H. S. Atreya S. C. Sahu K. V. R. Chary Girjesh Govil Elisabeth Adjadj Éric Quinjou Nadia Izadi-Pruneyre Yves Blouquit Joël Mispelter Bernadette Heyd Guilhem Lerat Philippe Milnard Michel Desmadreil Y. Lin B. D. Nageswara Rao Vidva Raghunathan Mei H. Chau Prashant Pesais Sudha Srivastava Evans Coutinho Anil Saran Leizl F. Sapico Jayson Gesme Herbert Lijima Raymond Paxton Thamarapu Srikrishnan C. R. Grace G. Nagenagowda A. M. Lynn Sudha M. Cowsik Sarata C. Sahu S. Chauhan A. Bhattacharya G. Govil Anil Kumar Maurizio Pellecchia Erik R. P. Zuiderweg Keiichi Kawano Tomoyasu Aizawa Naoki Fujitani Yoichi Hayakawa Atsushi Ohnishi Tadayasu Ohkubo Yasuhiro Kumaki Kunio Hikichi Katsutoshi Nitta V. Rani Parvathy R. M. Kini Takumi Koshiba Yoshihiro Kobashigawa Min Yao Makoto Demura Astushi Nakagawa Isao Tanaka Kunihiro Kuwajima Jens Linge Seán O. Donoghue Michael Nilges G. Chakshusmathi Girish S. Ratnaparkhi P. K. Madhu R. Varadarajan C. Tetreau Molecular Biophysics Unit Indian Institute of Science Bangalore India Austrian Academy of Sciences Institute of Biophysics and X-ray Structure Research Graz Austria Faculty of Biology University of Konstanz Germany Institute of Medical Biochemistry University of Graz Austria Howard Huges Medical Institute Department of Chemistry & Biochemistry University of California La Jolla USA Department of Chemistry & Biochemistry University of California San Diego La Jolla USA Molecular Biophysics Unit India Department of Microbiology &Cell Biology Indian Institute of Science Bangalore India Macromolecular Structure Laboratory NCI - Frederick Cancer Research and Development Center ABL - Basic Research Program Frederick USA Department of Crystallography and Biophysics University of Madras Chennai Biochemistry Department Indian Institute of Science Bangalore India Condensed Matter Physics Division B.A.R.C. Trombay Mumbai Condensed Matter Physics Division Bhabha Atomic Research Centre Trombay Mumbai IMCB The University of Tokyo Tokyo Japan Mitsubishi Chemical Corporation Yokohama Research Center Yokohama Japan Faculty of Pharmaceutical Sciences Chiba University Chiba Japan Department of Biophysics All India Institute of Medical Sciences New Delhi India Department of Biophysics All India Institute of Medical sciences New Delhi India Institüt Physiologische Chemie Hamburg Germany Biophysics Department Bose Institute Calcutta India Slovak Academy of Sciences Institute of Molecular Biology Bratislava Slovakia Indian Institute of Science Bioinformatics Centre Bangalore Departments of Chemistry and Biochemistry Biological Macromolecular Structure Center USA Bulgarian Academy of Sciences Institute of Organic Chemistry Sofia Bulgaria Chinese Academy of Sciences Institute of Biophysics Beijing China Novartis Pharma AG Preclinical Research Basel Switzerland Department of Chemical Sciences Tata Institute of Fundamental Research Mumbai India Solid State Physics Div

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Non-rigid image alignment for layered cell representation and motion tracking

Non-rigid image alignment for layered cell representation an...

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Annual Northeast Bioengineering Conference

作者： S. Shemlon S. Hawkins Graduate Program in Biomedical Informatics SHRP Newark NJ USA Department of Biomedical Engineering Rutgers University Piscataway NJ USA

Mathematical modeling of the dynamic behavior of physical systems, gives computers that mimic the capabilities to intelligently monitor and predict their evolutionary characteristics. In this paper, we present a system for tracking the time-varying features of non-rigid objects in images of evolving scenes, using the elastic string model of planar contours, which permits the inference and prediction of the quantitative parameters that characterize evolutionary behavior. The goal of our work is to dynamically track non-rigid objects in video sequences, using object alignment techniques based on the properties of the elastic string. We present experimental results of growth cone and neurite tracking in cell growth and motion studies.

关键词： Tracking Deformable models Predictive models Mathematical model biomedical computing Layout Image motion analysis Motion analysis Equations Neurons

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Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

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biomedical Data Science 1000年第1期5卷 293-320页

作者： Ying Wang Kristin Tsuo Masahiro Kanai Benjamin M. Neale Alicia R. Martin 2Stanley Center for Psychiatric Research and Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge Massachusetts USA 1Analytic and Translational Genetics Unit Massachusetts General Hospital Boston Massachusetts USA email: armartin@*** 3Biological and Biomedical Sciences Harvard Medical School Boston Massachusetts USA 4Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA 5Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan

Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.

关键词： polygenic risk scores (PRS) genetic risk prediction diverse ancestry populations PRS generalizability clinical translation of PRS

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Computational Methods for Single-Cell Proteomics

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biomedical Data Science 1000年第1期6卷 47-71页

作者： Sophia M. Guldberg Trine Line Hauge Okholm Elizabeth E. McCarthy Matthew H. Spitzer 3Gladstone-UCSF Institute for Genomic Immunology San Francisco California USA 2Biomedical Sciences Graduate Program University of California San Francisco California USA 1Department of Otolaryngology–Head and Neck Surgery and Department of Microbiology and Immunology University of California San Francisco California USA email: matthew.spitzer@ucsf.edu 4Helen Diller Family Comprehensive Cancer Center University of California San Francisco California USA 5Institute for Human Genetics Division of Rheumatology Department of Medicine Medical Scientist Training Program and Biological and Medical Informatics Graduate Program University of California San Francisco California USA 6Parker Institute for Cancer Immunotherapy San Francisco California USA 7Chan Zuckerberg Biohub San Francisco California USA

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

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Erratum: Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

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biomedical Data Science 1000年第1期

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