Biophysics 204B: Methods in Macromolecular Structure

Winter 2024 Syllabus

What is the next experiment?

Course Title: Methods in Macromolecular Structure

Course Format: 6 hours of lecture/group work per week in class, substantial group work outside of class hours

Location and Date/Hours: Monday, Tuesday, Wednesday - 9AM-11AM in Genentech Hall 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

Instructors: John Gross, Aashish Manglik, James Fraser, Klim Verba

TAs:

Lecturers/Facilitators:

Klim Verba, John Gross, Yifan Cheng, Aashish Manglik, Robert Stroud, Tom Goddard

Background:

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 “Macromolecular Methods”, a class that places emphasis on playing with data. We have designed Macromolecular Methods to be a team-based class where students develop their own analysis of real data that, in non-pandemic years, they have collected. The goal of this course is to provide students sufficient exposure to major methods in biophysics to enable them to pursue these more fully in their independent research.

Course Description:

This is a team-based class where students work in small groups develop their own analysis of real data. Statistical aspects of rigor and reproducibility in structural biology will be emphasized throughout lectures, journal club presentations, and hands-on activities. The website for the 2017, 2018, 2019, 2020 editions are available online.

Course expectations: This course will likely be different from most other courses you have taken before. Our expectation is that you will maximize your learning by being an active participant - there is significant out of class work required to enable this. Many learning opportunities will arise if you are willing to take advantage of the hands on experience and from those taking time to show you interesting aspects of the various methods. Data analysis will not follow a linear path - our expectation is that you start to become confortable with unknowns and messy results. It is likely that you will encounter issues using the various scientific software required for the course - while we will help troubleshoot, navigating these challenges effectively is a core expectation that we want to see students build towrads during the course.

Ethics: This course is more than a training experience; data analysis is part of ongoing active research projects, the results of which 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.

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.

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.

Commitment to Diversity, Equity and Inclusion: The course instructors and teaching assistants value the contributions, ideas and perspective of all students. It is our intent that students from diverse backgrounds be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit. It is our intent to present materials and activities that are respectful of diversity: gender identity, sexuality, disability, age, socioeconomic status, ethnicity, race, nationality, religion, and culture. However, we also acknowledge that many of the literature examples used in this course were authored in an environment that marginalized many groups. Integrating a diverse set of experiences is important for a more comprehensive understanding of science and we strive towards that goal. Although the instructors are committed to continuous improvement of our practices and our learning environment, we value input from students and your suggestions are encouraged and appreciated. Please let the course director or program leadership know ways to improve the effectiveness of the course for you personally, or for other students or student groups. (modeled after CCB and Brown University’s Diversity & Inclusion Syllabus Statements)

2023 schedule

TEAM ASSIGNMENTS

Journal Club assignments

Jan 8-10 - Class intro

Monday January 8

Tuesday January 9

Wednesday January 10

Jan 16-24 - CryoEM - Lectures Yifan Cheng, Tutorials Klim Verba

Monday January 15

MLK DAY - HOLIDAY

Tuesday January 16

9-11: Lecture 1 from Yifan Cheng

Wednesday January 17

Hands on practical day with David Bulkley on cryo-EM grid freezing

Monday January 22

Hands on practical day with Klim and David on negative stain

Tuesday January 23

9-11: Lecture 2 from Yifan Cheng

Wednesday January 24

KV talk about resolution metrics/validation 9-10:30, catch up on processing 10:30-11.

Reading on rigor and reproducibility in EM:

Jan 29-Feb 7 X-ray Crystallography - Lectures Bob Stroud, Tutorials Aashish Manglik

Monday January 29th

9-1030: Lecture 1 from Bob Stroud

10:30-11: Tutorial 1: What’s the deal with the spots

Tuesday January 30th

Beamline trip/X-ray facility tour

Wednesday January 31st

Beamline trip/X-ray facility tour

Monday February 5

9-10:30: Lecture 2 from Bob Stroud

10:30-11: Tutorial 2: Data Reduction

Tuesday February 6

9-10:30: Lecture 3 from Bob Stroud

Tutorial 3: Molecular Replacement

Tutorial 4: Model Refinement

Wednesday February 7

9-10:30: Lecture 4 from Bob Stroud

Continue Model Refinement and Tutorial 5: Model Analysis

Reading on rigor and reproducibility in Crystallography:

Feb 12-Feb 21 NMR - Lectures John Gross, Tutorials Amy Guo and Catherine Kuhn

Monday February 12

Lecture 1 from John Gross, Introduction to Multidimensional NMR

Tuesday February 13

Lecture 2 from John Gross, Detecting Ligand and Protein Interactions by NMR

Wednesday February 14

Lecture 3 from John Gross, Dynamic NMR -Hydrogen Deuterium Exchange (HDX) and intro to ms-usec dynamics

Monday February 19

Presidents Day - HOLIDAY

Tuesday February 20

Lecture 4 from John Gross, Methods to quantify slow dynamics, ZZ-exchange and CPMG

Wednesday February 21

Lecture 5 from John Gross, Measuring ns-ps dynamics in proteins

How to setup and process HSQCs on the 800 MHz spectrometer

Materials for TA Office Hours

Reading on rigor and reproducibility in NMR:

Final write up due: one per team - Feb 26

Supplemental material and tutorial videos