CSE 307/407 Structural Bioinformatics(3)


Brian Chen (Fall 2017)

Course Description:

Computational techniques and principles of structural biology used to examine molecular structure, function, and evolution. Topics include: protein structure alignment and prediction; molecular surface analysis; statistical modeling; QSAR; computational drug design; influences on binding specificity; protein-ligand, -protein, and -DNA interactions; molecular simulation, electrostatics. Tutorials on UNIX systems and research software support an interdisciplinary collaborative project in computational structural biology. Credit will not be given for both CSE 307 and 407. Must have junior standing or higher. Prerequisites as noted below or consent of instructor. Prerequisite:BIOS 120 or CSE 109 or CHM 113 or MATH 231.


Jenny Gu and Philip E. Bourne, "Structural Bioinformatics", 2nd Edition, Wiley-Blackwell, 2009, ISBN 978-0470181058


Students will have

1. Understand the basic design and purpose of several major computational technologies in the field of structural bioinformatics

2. Be aware of how biological, algorithmic, and statistical concepts can be integrated to draw meaningful conclusions from multi-faceted biological data

3. Have experience in the implementation challenges relating to these major technologies

4. Have experience in technical communication with collaborators with technical expertise outside of their own field


CSE 307 substantially supports the following student enabled characteristics

A. An ability to apply knowledge of computing and mathematics appropriate to the discipline

B. An ability to analyze a problem and identify and define the computing requirements appropriate to its solution

C. An ability to design, implement, and evaluate a computer-based systems process, component, or program to meet desired needs

G. An ability to analyze the local and global impact of computing on individual organizations, and society

I. An ability to use current techniques, skills, and tools necessary for computing practices

J. An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices

K. An ability to apply design and development principles in the construction of software systems of varying complexity

Major Topics Covered in the Course

  • Introduction to structural bioinformatics
  • Introduction to proteins
  • Volumetric comparison and statistical modeling
  • Molecular Simulation and date set construction
  • Geometric matching and match scoring
  • Motif Refinement
  • Protein structure alignment
  • Multiple structure alignment
  • Analyzing molecular surfaces
  • Protein-Protein interactions
  • Protein-DNA interactions
  • Computational drug design
  • Protein structure prediction
  • Quantative structure-activity relationships
  • Protein electrostatics

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