'Secondary structure prediction' presentation slideshows

Protein structure prediction: The holy grail of bioinformatics

Protein structure prediction: The holy grail of bioinformatics. Proteins: Four levels of structural organization: Primary structure Secondary structure Tertiary structure Quaternary structure. Primary structure = the linear amino acid sequence.

1.3k views • 97 slides

RNA Secondary Structure Prediction

RNA Secondary Structure Prediction. Lecture 11: June 13, 2006 Algorithms of Molecular Biology. Introduction to RNA Sequence/Structure Analysis. RNAs have many structural and functional uses Translation Transcription RNA splicing RNA processing and editing cellular localization catalysis.

1.14k views • 84 slides

Protein Structure

Protein Structure. IST 444. Protein Chemistry Basics. Proteins are polymers consisting of amino acids linked by peptide bonds Each amino acid consists of: a central carbon atom an amino group NH 2 a carboxyl group COOH a side chain (R group)

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MicroRNA

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Homology Modeling via Protein Threading

Homology Modeling via Protein Threading. Kristen Huber ECE 697S Topics in Computational Biology April 19, 2006. Fundamentals of Protein Threading. Protein Modeling Homology Modeling Protein Threading Generalized Overview of a Threading Score

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Computational Prediction of RNA and DNA Secondary Structure

710 views • 46 slides

Globular Proteins

Globular Proteins. Figure 8-35 X-Ray diffraction photograph of a single crystal of sperm whale myoglobin. Page 240. Figure 8-39a Representations of the X-ray structure of sperm whale myoglobin. ( a ) The protein and its bound heme are drawn in stick form. Page 244.

625 views • 39 slides

Protein Secondary Structure Prediction

Protein Secondary Structure Prediction. G P S Raghava. Protein Structure Prediction. Importance CASP Competition What is secondary structure Assignment of secondary structure (SS) Type of SS prediction methods Description of various methods Role of multiple sequence alignment/profiles

1.2k views • 35 slides

Protein Sequence Analysis - Overview

Protein Sequence Analysis - Overview. Raja Mazumder Senior Protein Scientist, PIR Assistant Professor, Department of Biochemistry and Molecular Biology Georgetown University Medical Center. NIH Proteomics Workshop 2004. Overview.

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11/4/05 Protein Structure & Function

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ICML 2001 Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML 2001 Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. John Lafferty, Andrew McCallum, Fernando Pereira Presentation by Rongkun Shen Nov. 20, 2003. Sequence Segmenting and Labeling. Goal: mark up sequences with content tags

568 views • 28 slides

Microbiology of arsenic redox transformations

Environment. Molecular Genetics. Arsenic. Public Health. Microbiology of arsenic redox transformations. UCSC. Chad Saltikov and collaborators. Arsenic Contamination. Arsenite Oxidizer. As(III). Arsenate Reducing Bacterium. Microbial redox transformations of arsenic. Aqueous.

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Introduction to SVM ( Support V ector M achine ) and CRF (C onditional R andom F ield )

Introduction to SVM ( Support V ector M achine ) and CRF (C onditional R andom F ield ). MIS510 Spring 2009. Outline. SVM What is SVM? How does SVM Work? SVM Applications SVM Software/Tools CRF What is SVM? CRF Applications CRF Software/Tools.

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Protein structure Visualization Molecular Story

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Protein structure Visualization Molecular Story

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Project presentation using TWiki

Project presentation using TWiki . Lim Yun Ping National University of Singapore. What is a wiki ?. Definitions of Wiki on the Web:

367 views • 18 slides

Protein Structure Prediction

Protein Structure Prediction. Mason Bially. Types of Structure. Primary Structure The linear amino acid sequence. Secondary Structure The local three-dimensional structure. Defined by hydrogen bonding patterns. Tertiary Structure The global three-dimensional structure.

408 views • 17 slides

RNA Secondary Structure Prediction

315 views • 16 slides

AgeWa: An integrated approach for Antisense Experiment design

AgeWa: An integrated approach for Antisense Experiment design. P.Arrigo (1) , P.Romano (2) , P.Scartezzini (3) CNR IIET, sezione di Genova e-mail: arrigo@ice.ge.cnr.it Natl. Cancer Res.Institute, Genova,Italy (3) Dept of Neonatology, E.O Ospedali Galliera, Genova,Italy.

172 views • 9 slides