COURSE DESCRIPTION:
An introductory course to Protein Bioinformatics. We provide a systematic introduction to the major techniques, algorithms and tools used in Bioinformatics (for sequence alignments, classifications, secondary and tertiary structure predictions, modeling, sampling of conformations, energy functions, prediction of various functional and structural features of proteins, docking etc.).

We also devote about one third of the lectures to provide an introductory Python programming course with practical applications in bioinformatics.

COURSE OBJECTIVES:

• To learn fundamentals of bioinformatics algorithms and most frequent applications in protein science research
• To learn python programming

PREREQUISITES:
None

SUGGESTED MATERIALS:
Not required, but suggested:

• Computational Biochemistry and Biophysics, Marcel Dekker, New York, NY, ISBN 978-0824704551.
• M. Watanabe, B. Roux, A. MacKerell, and O. Becker; Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids by R. Durbin, S. R. Eddy, A. Krogh, G. Mitchison ISBN 978-0521629713;
• Bioinformatics: The Machine Learning Approach, Second Edition by: P. Baldi ISBN 978-0262025065;
• Protein Structure Prediction: A Practical Approach by MJE Sternberg 978-0199634965;
• From Protein Structure to Function with Bioinformatics. Ed. Daniel John Rigden, Publisher: Springer; 2009 edition ISBN-13: 978-1402090578

SUITABLE FOR 1ST YEAR STUDENTS:
Yes

STUDENT ASSESSMENTS:
25% Midterm exam
25% Python programming exam
25% Final exam
25% Attendance
A pass requires 75%

CREDIT HOURS: 2.5