Course Syllabus
MSCBIO2030: Introduction to Computational Structural Biology
Fall 2022 (8/29/2022 - 12/15/2022)
Instructors
David Koes
Office Hours: 2pm Friday
748 Murdoch Building
dkoes@pitt.edu
Jim Faeder
Office Hours: 11am Monday
837 Murdoch Building
faeder@pitt.edu
Teaching Assistants
Ian Dunn
Office Hours: 4pm Tuesday
7th Floor Murdoch Building
ian.dunn@pitt.edu
Drew McNutt
Office Hours: 10am Thursday
7th Floor Murdoch Building
and.mcnutt@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 both the joint 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: probability theory, statistical mechanics and thermodynamics, simulation methods, electrostatic phenomena, biochemical kinetics, binding, coarse-grained modeling, enhanced sampling, free energy calculations, protein structure prediction, and structure-based drug design.
Communication
Course material and announcements will be posted to Canvas: https://canvas.pitt.edu/courses/170621/. Whenever possible, questions about the course and assignments should be posted to the discussion boards of Canvas.
Lectures
Lectures will be 2:30pm-3:50pm on Tuesdays and Thursdays in the Murdoch 814 classroom.
Recitations
Recitations will be 12:30pm-2:30pm on Wednesdays in the Murdoch 814 classroom. Most recitations will last no longer than an hour, be reserve extra time for the few cases where we need it. Recitations will be a mix of practical, in-class projects and lectures.
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 10 assignments that will involve a mix of programming (Python) and analytical thinking. At the end of the course there will be a Journal Club where each student critically and clear presents a paper in computational structural biology.
Quizzes
There will be one midterm quiz with both in-class and take home components.
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:
| 80% | Assignments |
| 10% | Exam |
| 10% | Journal Club and Class Participation |
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. Any attempt to `hack' the autograder will result in expulsion from the class and a referral to the dean's office.
COVID19
All University policies will be followed and will take precedence over any course policies. See https://www.coronavirus.pitt.edu/ for more information.