Computer Science

ECS 129 Computational Structural Bioinformatics


Lecture: 3 hours
Discussion: 1 hour

Catalog Description:
Fundamental biological, chemical and algorithmic models underlying computational structural biology; protein structure and nucleic acids structure; comparison of protein structures; protein structure prediction; molecular simulations; databases and online services in computational structural biology.

Prerequisite: (BIS 002A or MCB 010); (ECS 010 or ECS 032A or ECS 036A)

Credit restrictions, cross listings: None

Summary of course contents

  1. Introduction: Top Challenges in Bioinformatics
  2. Bio-molecular Structures
    1. Nucleic acids
    2. Protein
  3. Comparing Sequences and Structures
    1. Sequence alignment
    2. Structure alignment
  4. Protein Structure Databases
    1. Protein domains
    2. Protein structure classification
  5. Stability of Bio-molecules
    1. Semi-empirical energy functions
    2. Statistical potentials
  6. Structure Prediction
    1. Protein: Comparative modeling
    2. Protein: Ab initio prediction
    3. RNA: secondary structure and tertiary structure predictions
  7. Bio-molecular Simulations
  8. Drug Design
  9. Databases and Web Services

Goals:  Students will (1) understand the challenges currently faced by structural bioinformatics; (2) be able to understand and efficiently use the tools currently available in this field; and (3) develop an interest to pursue research in bioinformatics.

Illustrative reading
Selected review papers and technical papers and class notes will be used.

ABET Category Content::
Engineering Science: 1 unit
Engineering Design: 0 unit

GE3: Science & Engineering

Overlap: CS 124 (Theory and Practice of Bioinformatics) and ECS 129 are complementary courses with minimal overlap covering bioinformatics. ECS 124 focuses on sequence analysis, while ECS 129 covers the structural challenges in bioinformatics.

Instructors: P. Koehl

History: Reviewed 2018.9.7 (CSUGA): prerequisites updated to include new lower division ECS series courses. 2012.10.24 (P. Koehl) Reviewed, no changes desired. Prior version by P. Koehl (November 2005).


1 X an ability to apply knowledge of mathematics, science, computing, and engineering
2 an ability to design and conduct experiments, as well as to analyze and interpret data
3 an ability to design, implement, and evaluate a system, process, component, or program to meet desired needs, within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
4 an ability to function on multi-disciplinary teams
5 an ability to identify, formulate, and solve computer science and engineering problems  and define the computing requirements appropriate to their solutions
6 X an understanding of professional, ethical, legal, security and social issues and responsibilities
7 X an ability to communicate effectively with a range of audiences
8 X the broad education necessary to understand the impact of computer science and engineering solutions in a global and societal context
9 a recognition of the need for, and an ability to engage in life-long learning
10 knowledge of contemporary issues
11 X an ability to use current techniques, skills, and tools necessary for computing and engineering practice
12 an ability to apply mathematical foundations, algorithmic principles, and computer science and engineering theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices
13 an ability to apply design and development principles in the construction of software systems or computer systems of varying complexity