Bioinformatics I / PHAR 201
Structural Bioinformatics with an Emphasis on Biological Data Representation and Analysis

Lecturer: Prof. Philip E. Bourne mail | page Office Hours 2-3 daily 2111 SSPPS

TAs: Cory White [MAIL] & Yu Chen Chen  [MAIL]

Office Hours: YCC Mon 3-5pm SSPPS 2252; CW Tue 3:30-5:30pm SSPPS 2109

Format: 2-one hour lectures and six hours of practicum per week

Time/Location: Wed. 12:30-2:00pm ; Friday 12:30-2:00pm Skaggs School of Pharmacy & Pharm Sci. Education Center (EC) 1 (in the basement).

Last Update Nov. 19, 2012

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

Topic & Date

Content

Workflow Overview of Course [slides]

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.

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]

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.

[Podcast] [slides]

Lecture 4: 10/12

Data Representation: The Role of the Gene Ontology

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]

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]

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] [assignment 3]

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]

Lecture 8: 10/26 Applications of Structure: Evolution

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]

Lecture 9: 10/31 Protein Functional Prediction

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]

 

Lecture 10: 11/02 Algorithms: Disorder

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]

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]

Lecture 12: 11/09 Algorithms: Protein Ligand Interactions

Examination of one approach to ligand binding site prediction in some detail and a discussion of the implications for drug discovery. [slides]

Lecture 13: 11/14 Algorithms: Protein Protein Interactions

Goal: Understand the importance of the study of protein-protein interactions at the structural level. Consider one method in detail. [ Podcast] Chapter 26

[slides]

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 [slides] [Podcast] [Assignment 6]

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]

Lecture 16: 11/28 Applications of Structure: Immunology

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 [slides]

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]

Lecture 18: 12/05 Applications: Systems Biology

Recent work in systems biology using structure from the Godzik laboratory. Guest Lecturer: Prof Adam Godzik.

Lecture 19: 12/07 Futures

A discussion of the future of structural bioinformatics and of biological data [slides]

[podcast]

Finals 12/10-15