Course Title: Methods in Macromolecular Structure
Course Credit: 4 units
Course Format: 12 hours of lab per week
Location: Genentech Hall Teaching Lab - Room 227
Prerequisites: All incoming first year BP and CCB graduate students are required to enroll in this course.
Grading: Letter grade
Textbook: None. Lab protocols and course materials will be available in class or online
Course Days/Hours: Monday, Tuesday, Wednesday 1pm-5pm
EM Coordinator: David Bulkley
HSP90 Preparer/NMR guru: Ryan Tibble (Gross lab)
HSP90 Crystallizer: Kazu Ito (Fraser lab)
X-ray guru: Michael Thompson (Fraser lab)
EM Computational Experts: Eugene Palovcak, Daniel Asarnow (Cheng lab)
James Fraser, John Gross, Dan Southworth, David Bulkley, James Holton, Yifan Cheng, David Agard, Aashish Manglik, Andrej Sali, Robert Stroud
Fluency in multiple biophysical methods is often critical for answering mechanistic questions. Traditionally, students are exposed to the fundamentals of multiple techniques through lectures that cover the theory prior to exposure, for some, in analysis or data collection during lab rotations. However, this structure means that only students that rotate in specific labs gain hands-on-exposure, which could limit adventurous experiments in future years. To train the next generation of biophysicists at UCSF, we have decided to alter this traditional structure by creating a new 6 week “Macromolecular Methods” class that places data collection at the beginning of the course. Based on our experiences designing the project-based class Physical Underpinnings of Biological Systems, aka PUBS!, which used deep sequencing to assay the function of a comprehensive set of point mutants to introduce principles of high-throughput interrogation of biological functions, we have designed Macromolecular Methods to be a team-based class where students develop their own analysis of real data that they have collected.
This is a team-based class where students work in small groups develop their own analysis of real data that they have collected. Statistical aspects of rigor and reproducibility in structural biology will be emphasized throughout lectures, journal club presentations, and hands-on activities. The course will function in three modules. In module 1 “data collection” students collect either NMR, negative stain EM, and X-ray crystallographic data. In module 2 “fundamentals of analysis”, students will are mixed into new groups for lectures and hands-on computational tutorials. These lessons emphasize connections to both the molecular interpretations and the fundamental physical principles that generated the data. In module 3 “integrative structural biology”, the students will finalize their analysis and lectures will emphasize rigorous theory of individual techniques and computational frameworks for integrative structural modeling. Finally, each group will present to their findings to the class and course faculty.
The goal of the course is to provide an immersive, hands-on experience in the context of genuine research questions. As articulated by Vale and colleagues, there are tremendous advantages when graduate students work “pursuing a research question with unknown answers and uncertain outcomes, students and faculty combine their wits and skills to design experiments, evaluate progress, and troubleshoot along the way”. These advantages are likely to be common accross all learning levels. In our course, teams may use whatever literature, software, and resources that are available publicly, and are encouraged to write their own scripts and software where necessary.
This course will introduce students to approaches and methodologies for interrogating macromolecular structure and dynamics, which will require the integration of experiment and computation. In addition to fundamental techniques in X-ray crystallography, NMR and EM, students will learn to interpret datasets, draw original conclusions, and present findings in written and oral formats.
The “official” language of the class is python - beginners should try Learn Python The Hard Way, people with a background in other languages should try Google’s python course. The QB3 Berkeley intensive python course provides many biological examples. Students should be comfortable with basic syntax and scripting prior to the start of instruction. Here is a spreadsheet with a listing of multiple Python resources
Journal club presentations will be limited to 6 minutes. You should use 3 slides: two of which can contain a figure from the paper and the other should be a self-drawn (or created) schematic of the key concept behind the paper. Many of these papers are quite technical, so please engage the instructors and other course personnel as you prepare your presentation.
Student Learning Objectives
Ethics: This course is more than a training experience; it is an active research project whose results will be published to the broader scientific community. The community must be able to understand our work, replicate it, and have confidence in its findings. We must therefore ensure the integrity of the information we disseminate. To do so, it is essential that students perform and document their experiments and analyses as faithfully as possible. Mistakes and oversights are normal and to be expected, but they must not be ignored, concealed, or disguised. In addition, to merit authorship, students must contribute to three aspects of the project: intellectual conception or interpretation of the methods or data, technical execution of the experiments and/or analyses, and documentation or dissemination of the results. We fully expect that by actively participating in the course and working toward the course objectives, all students will merit authorship.
Respect: This course is built around an open research project performed in teams. Successful completion of the course objectives will require that students work together effectively, so please respect the time and effort of your classmates and instructors. Moreover, as part of the research process, we will consider and debate a variety of ideas and approaches; however, we must not allow our position on a particular idea or argument to compromise our respect for its author. We therefore expect course participants to give all instructors and students, regardless of academic or personal background, their complete professional respect; anything less will not be tolerated.
Absences: The instructor must be notified by the second week of classes for any planned absences, or in advance of class due to illness. Active participation in the laboratory is essential and students are required to attend normal class hours. Attendance during all of the three required presentations is absolutely mandatory, except in cases of doctor-excused medical illness. Any class material or lecture that is missed will be the responsibility of the student. Written evaluations of each team and its members will be provided to the Graduate Tracking System for inclusion into the graduate record, and provided to oral committee members and thesis committee members.
Accommodations for students with disabilities: The Graduate Division embraces all students, including students with documented disabilities. UCSF is committed to providing all students equal access to all of its programs, services, and activities. Student Disability Services (SDS) is the campus office that works with students who have disabilities to determine and coordinate reasonable accommodations. Students who have, or think they may have, a disability are invited to contact SDS (StudentDisability@ucsf.edu); or 415-476-6595) for a confidential discussion and to review the process for requesting accommodations in classroom and clinical settings. More information is available online at http://sds.ucsf.edu. Accommodations are never retroactive; therefore students are encouraged to register with Student Disability Services (http://sds.ucsf.edu/) as soon as they begin their programs. UCSF encourages students to engage in support seeking behavior via all of the resources available through Student Life, for consistent support and access to their programs.
Week 1 – Welcome
Tues Oct 31
Weds Nov 1
Week 2 – Working in Method Teams
Week 3 – Working in Compound Groups
Mon Nov 13 Presentations on Methods Week: Two students from each Method Team will present for 10 minutes and summarize what occured in Week 2. Take pictures and try to give the students a feel for not only the theory of what you learned but also the practical aspects!
NMR: Adam Catching and Neha Prasad
Tues Nov 14
Weds Nov 15
Week 4 - NMR
Mon Nov 27
Tues Nov 28
Weds Nov 29
Week 5 - X-ray
Compound Data Processing Logs, MTZs, CIFs, PDBs:
Mon Dec 4
Tues Dec 5
Weds Dec 6
Week 6 - EM
Mon Dec 11
Tues Dec 12
Weds Dec 13
FINAL PRESENTATIONS: Mon Dec 18
Please be on time and wait outside the teaching lab before your presentation. The presentations will be stopped after 15 min and questions will be for 5 minutes. Please email your presentations (use a filename that includes your team number!) to James Fraser by 12:30PM on Monday Dec 18th.