Bioinformatics I
/ PHAR 201
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Overview
Bioinformatics is driven by the need to understand complex biological systems for which data are accumulating at exponential or near exponential rates. Such an understanding relies of the effective representation of these data and the ability to analyze these data. This is a broad topic and we focus on macromolecular structure data, which is suitably complex, to introduce the principles of formal data representation, reductionism, comparison, classification, visualization and biological inference. As such the course also serves as an introduction to Structural Bioinformatics. Assessment
Weekly assignments which will make you think: On Friday of each week students will receive a question paper on that weeks work which will be due 5pm the following Wed. in class. (50%). Final Exam: Students will be assigned one or more papers which cover a significant amount of the material covered in the course. They will be expected to critique that paper based on what they have learned and propose the next set of experiments (50%). Course Text
Jenny Gu & Philip.Bourne (Eds.) Structural Bioinformatics Second Edition. Wiley 2009 [from Google Books] The text is available for use in our Lab. Library 2011 Skaggs. Lectures go on-line typically the morning of the lecture, until then the previous years lecture and podcast, where available, is on-line. Schedule
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Topic & Date |
Content |
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Workflow Overview of Course [slides] |
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Lecture 1: 10/03
Know Your Data - Principles of Protein Structure [slides] |
To model and analyze biological data it must first be
understood from a biological perspective. Goal: Refresh or achieve a better understanding of primary,
secondary, tertiary and quaternary protein structure. Reading:
Chapters 1 and 2. |
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Lecture 2: 10/05
Know Your Data -Principles of DNA and RNA structure [slides] |
To model and analyze biological data it must first be understood from a biological perspective. Goal: Refresh or achieve a better understanding of the features of DNA and RNA structure and its interaction with proteins. Reading: Chapters 3. [assignment 1] |
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Lecture 3:10/10
Know the Limits of Your Data & How to Validate |
To effectively utilize biological data it is necessary to
understand the limitations of the experiments used to determine that data. Structure
determination is a relatively quantitative science and good statistical
measures exist. Goal: Explore the
quantitative and qualitative measures of data quality with respect to data
from X-ray crystallography, NMR and electron microscopy and how to validate
that data. Reading: Chapters 4, 5, 6.
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Lecture 4: 10/12 Data Representation: The Role of the Gene
Ontology
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The Gene Ontology (GO) has a profound impact on bioinformatics research. Goal: To understand the structure and use of GO. Reading: Creating the Gene Ontology Resource: Design and Implementation Genome Research (2001) 11:1425-1433 [ Podcast] [slides] [assignment 2] |
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Lecture 5: 10/17
Algorithms: Secondary Structure Assignment |
The secondary structure of a protein is now routinely assigned by algorithms. Goal: To explore the Kabsch-Sander algorithm and the impact it has had on the community. Also to explore other methods of secondary structure assignment. Reading: Chapter 19. [slides] [ Podcast] |
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Lecture 6: 10/19
Algorithms: Tertiary Structure Comparison & Alignment |
3D structure comparison and alignment is a difficult problem
when trying to achieve biologically meaning results. Goal: Understand the problem and the methods used to address it
and explore the use of the profiles arising from structure alignment. Reading: Chapter 16. [Previous Podcast] [slides] |
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Lecture 7: 10/24 Reductionism & Classification: Sequence-Structure-Function Relationships |
With so much data available it is necessary to produce non-redundant sets for many bioinformatics tasks. However, given the complex relationship between sequence, structure and function, non-redundancy means different things in each case. Goal: Understand this complex relationship and the associated meaning of reductionism. Reading: Chapter 21 [Previous Podcast][slides] |
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Lecture 8: 10/26
Applications of Structure: Evolution
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Traditionally protein structure has been studied through looking at evolution. Most recently evolution has been studied through looking at protein structure. Goal: Provide an appreciation of what protein structure brings to the study of evolution. Reading: Chapter 23 [Podcast ] [slides] [Assignment 4] |
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Lecture 9: 10/31
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Predicting the function of a protein from either a sequence or a structure is not trivial. We will explore algorithms used for this task. Guest Lecturer: Prof Adam Godzik [slides]
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Lecture 10: 11/02 Algorithms: Disorder
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Disorder is a frequent phenomenon in proteins, more common in eukaryotes than prokaryotes. Goal; To understand how disorder is treated, predicted from sequence based on structure and the functional role of disorder will be discussed Guest Lecturer: Prof. Lilia Iakoucheva Reading: Chapter 38. [slides] [Assignment 5] |
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Lecture 11: 11/07
Applications of Structure: Protein-ligand and
Protein- Protein Interactions |
Many pdb structures are co-crystallized with small molecule modulators. We will discuss the issues related to understanding the nature of molecular interactions between ligands and their pockets, including ligand identity, density placement, ambiguities of His, Asn, Gln rotations, protonation or protein residues and ligand, tautomerization. We will also discuss the pocket flexibility and docking of other ligands to the same pocket. Guest Lecturer: Prof Ruben Abagyan [slides] |
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Lecture 12: 11/09 Algorithms: Protein Ligand
Interactions
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Examination of one approach to ligand binding site prediction in some detail and a discussion of the implications for drug discovery. [slides] |
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Lecture 13: 11/14 Algorithms: Protein Protein Interactions
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Goal: Understand the importance of the study of protein-protein interactions at the structural level. Consider one method in detail. [ Podcast] Chapter 26 [slides] |
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Lecture 14: 11/16
Applications: Chemical Data & Computer Aided
Drug Discovery |
Structure and ligand based computational methods for drug
discovery. Guest Lecturer: Prof.
Michael Gilson |
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Lecture 15: 11/21
Algorithms: Domain Determination |
A domain is a crucial component of a protein structure, however, reliably determining the number of domains and their boundaries is not a solved problem. Goal: To understand the algorithms in common use and the value of a consensus approach. Chapter 20 [slides][podcast] |
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Lecture 16: 11/28
Applications of Structure: Immunology
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Structural has played an important role in understanding
the immune response and in epitope prediction. Goal: To understand the role of structure in the study of
immunology. Guest Lecturer: Dr. Julia Ponomarenko
[Previous Podcast] Reading: Chapter 35 |
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Lecture 17: 11/30
Applications: Systems Biology |
In some ways structure is the devil in the details of systems biology. Goal: To appreciate what structure is bringing to systems biology now and into the future. Guest Lecturer: Roger Chang. [slides] [podcast] |
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Lecture 18: 12/05 Applications: Systems Biology
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Recent work in systems biology using structure from the Godzik laboratory. Guest Lecturer: Prof Adam Godzik. |
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Lecture 19: 12/07 Futures
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A discussion of the future of structural bioinformatics and of biological data [slides] [podcast] |
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Finals 12/10-15
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