Course Syllabus

MSCBIO2030: Introduction to Computational Structural Biology

Spring 2025 

Instructor

David Koes
Office Hours: 3pm Friday
748 Murdoch Building
dkoes@pitt.edu

 

Teaching Assistants

Emma Flynn
Office Hours: 4pm Tuesday
7th Floor Murdoch Building
ELF152@pitt.edu

Alex Goldberg
Office Hours: 1:30pm Thursday

814 Murdoch Building
amg535@pitt.edu

Course Description

This course will introduce students to computational structural biology, primarily relying on physical and chemical principles, as well as associated computational approaches. The course is a core class for the Joint Carnegie Mellon-University of Pittsburgh Ph.D. Program in Computational Biology. The course will cover biomolecular structure, statistical mechanical phenomenon in biophysics, simulation of biomolecular behavior, and key applications of computations in the field of structural biology. Specific topics:statistical mechanics and thermodynamics, simulation methods, electrostatic phenomena, coarse-grained modeling, enhanced sampling, free energy calculations, protein structure prediction, structure-based drug design, and recent advances in generative modeling and applications of deep learning to structural problems.

Communication

Course material and announcements will be posted to Canvas: https://canvas.pitt.edu/courses/301742.  Course communication will be through Slack (https://compstruct.slack.com/). 

Lectures 

Lectures will be 2:30pm-3:50pm on Tuesdays and Thursdays in the Murdoch 814 classroom. Students should bring a laptop or other device to lecture.

Recitations

Recitations will be 2:00pm-2:50pm on Fridays in the Murdoch 814 classroom.   Students should bring a laptop or other device. Recitations are not optional as students will be graded on the work they perform during the recitation. The classroom is reserved for an additional hour to provide extra time and office hours time.  Recitations will consist of practical, in-class projects or problem sets that students will work on in small groups.  In-person attendance is required in order to receive credit for recitation work, unless previous approval is obtained from the instructor. 

Class Recordings

All lectures and recitations will be recorded and available for asynchronous viewing on Panopto, but this is not intended as a substitute for attending class nor will instruction be tailored to ensure usefulness of the recording (e.g., the whiteboard may not be visible in the recording). Students should make every reasonable effort to attend class in real-time as in-class group work and discussion is an important part of lecture.  

Assignments

There will be 7 assignments that will involve a mix of programming (Python) and analytical thinking. Assignments will be turned in using GradeScope.

Journal Club

Towards the end of the course there will be a Journal Club where each student critically and clearly presents a recent or seminal paper in computational structural biology. One of the objectives of the Journal Club is to identify possible project topics. Masters students and undergraduates may present in pairs, but PhD students must present individually.

Project

Students will propose and implement a small research project in computational structural biology. They may propose to answer a biological question using an established technique, perform a comparative assessment of different approaches to the same problem, or suggest and implement an improvement to an existing technique. Projects may be done in groups of 1-3, with the scope of the project scaling with the size of the group.

Quizzes

There will be two in-class quizzes.

Grades

The instructors reserve the right to modify grade distributions and cutoffs to most accurately reflect student performance, but we anticipate that the final grade will be:

55%	Assignments (7)
10%	Recitation
10%	Quizzes
10%	Journal Club
15%	Project

Standard grading scales will applied (Masters and undergraduate students may have slightly more generous cutoffs):

Letter Grade	Percentage
A+	97–100%
A	93–96%
A−	90–92%
B+	87–89%
B	83–86%
B−	80–82%

Lateness

Assignments should be handed in on-time.  When this is not possible, course instructors should be contacted with as much advance notice as possible.  In general, requests for two late days per an assignment for no more than five total late days will be approved.  Requests beyond that will require substantial justification and/or be subject to additional grade penalties.  Late assignments will have a maximum possible score of 95%.

Academic Honesty 

You must do all your own work.  You are encouraged to discuss general concepts, strategies for debugging, and the particulars of a specific software package with other class members.  However, specifics of individual assignments should not be discussed, and you should not show your code to fellow classmates.  You are expected to understand and be able to explain any code you submit. Any attempt to "hack" the autograder will result in expulsion from the class and a referral to the dean's office.