#Biophysics 205A: Physical Underpinnings of Biological Systems#

##Fall 2014 Syllabus##

Course Title: Physical Underpinnings of Biological Systems (PUBS)

Course Credit: 4 units

Course Format: 12 hours of lab per week

Location: Genentech Hall Teaching Lab - Room 227

Prerequisites: All incoming first year iPQB 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

Instructors: James Fraser; jfraser+bp205a@fraserlab.com

Course Coordinator: David Mavor; David.Mavor@ucsf.edu

Microscopy Coodinator: Laura Deming; biophysics205a@gmail.com


Rosetta Helpers:


James Fraser, Joe DeRisi, Hiten Madhani, Michael Keiser, Sourav Bandyopadhyay, Jessica Lund, Eric Chow, Nadav Ahituv, Ryan Hernandez, Elaine Meng, Bo Huang, David Morgan, Jason Gestwicki, Kurt Thorn, Steven Altschuler, Lani Wu, Tanja Kortemme, Wendell Lim, Adam Abate, Matthew Thomson, Dave Toczyski

Important Dates:


“Precision Medicine” is an emerging theme in biomedical research and patient care, and refers to the use of genome-wide information, such as DNA sequence, expression profiling, metabolic labeling/imaging, and other technologies to better inform ultimately customize therapy. For cancer medicine, discreet genomic changes can be tied directly to particular treatments, such as immunotherapies or small molecules directed against a mutated enzyme. However, the cancer genome is not necessarily a static entity, and may be subjected to intense selective pressures resulting in highly dynamic changes that manifest as relevant phenotypes, such as drug resistance or metastatic potential. Technological revolutions, such as DNA microarrays, followed by ultra-deep sequencing, have allowed high-resolution views of the genome and dynamical views of the expression programs they exhibit.

Despite the promise for personalized care, many challenges remain. The genome, its expression, and its translation into phenotype embody a highly complex and dynamic system, whether it is a cancer cell, a yeast cell, or even a virus. Mutations that drive a phenotype may not be necessarily distinguishable from those that are mere passengers, and the molecular determinants of large-scale alterations remain largely uncharacterized.

Ultimately, the goal is the synthesis of predictive models that can reveal fundamental regulatory principles, and in the case of patients, deliver actionable information for treatment, early detection, and prevention.

Course Description:

The course is a hands-on, project-based course that integrates deep mutational profiling (Fowler and Fields, Nature Methods, 2014), automated microscopy, and computational biology. The model organism, Saccharomyces cerevisiae, will be used as the organismal basis. Our goal is to experimentally determine the fitness of all possible individual point mutants of ubiquitin, an essential protein that is a key cellular integrator of stress, under a variety of experimental perturbations. The library of these point mutants was assembled by Dan Bolon and verified during a summer visit to the Fraser lab. The course is organized around three modules, described below. Each hands-on module will be accompanied by lectures (either “chalk talk” or with slides). The course director and/or each lecturer will assign research papers, literature reviews, or other reading material in advance. In addition students are expected to conduct their own literature reviews during the course of the project. Students will work in small teams, and each team will be assigned a different perturbation for initial analysis. Students are expected to remain in their teams for the duration of the course, although team-team collaboration is highly encouraged. All team members are expected to participate in each activity.

After module 1 and module 3, each team will orally present their findings to the class and faculty, limited to 15 minutes and 15 slides maximum, with 10 minutes for discussion and questions. All members of the team are expected to speak and describe their contributions. These presentations are currently scheduled for Oct 27th and Nov 24th, the final day of class.

Activities and speakers for each week will be announced at the beginning of each module.

Module 1. Sept 29th – Oct 22nd. Ultra-deep sequencing, and chemical genomics.

Module 2. Oct 27th – Nov 5th. Ensemble vs. single observation measurements.

Module 3. Nov 10th – Nov 24th. Computational biology and evolutionary constraints.

Course Goals:

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. 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.

In module 1, each team will be provided an “unknown” chemical perturbation. Using deep mutational profiling, each team will measure the fitness of all possible individual point mutants of ubiquitin, an essential protein that is a key cellular integrator of stress. Upon processing the sequencing data, each team willperform comparisons against a reference dataset.

In module 2, teams will compare their data against the datasets of other teams and perform microscopy experiments to determine whether their stress response elicits a multimodal response in growth rate. This module will reinforce core concepts of ensemble vs. single observation measurements at many levels of biophysics and systems biology.

In module 3, the teams will leverage interactions with Tanja Kortemme and members of her lab to perform protein design protocols to predict sequences optimized for multiple criteria. They will test how well their deep sequencing data matches with protein design profiles generated under different constraints such as: protein stability or maintaining interactions with specific mediators of stress responses. For the final presentations, teams will explain the unique features of their Ubiquitin mutant profile by grounding their analysis in specific protein-protein interactions along branches of the cellular proteostasis network.

Student Learning Objectives

Class Policies

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: Please see the instructor as soon as possible if you need particular accommodations, and we will work out the necessary arrangements.

##Lab work and recommended reading schedule

Week 1 – Theme: Ubiquitin and Deep Mutational Profiling

Lab work: Measure doubling times as a function of small molecule perturbation concentrations

Lecturers: James Fraser (9/29,9/30), Joe DeRisi (9/29), Hiten Madhani (10/1)

Recommended reading:

Files for Computation:

Other Class Material:

Week 2 – Theme: Chemical Genetics

Lab work: Performing selection experiments under chemical stresses

Lecturers: Michael Keiser (10/6), Sourav Bandyopadhyay (10/7), Eric Chow (10/8)

Recommended reading:

Other Class Material:

Week 3 – Theme: Massive Functional Profiling

Lab work: Deep sequencing library preparation

Lecturers: Nadav Ahituv (10/13), Journal Club (10/14), Ryan Hernandez (10/15)

Journal Club Assignments:

Recommended reading:

Other Class Material:

Week 4 – Theme: Sequence Conservation and Statistical Mechanics

Lab Work: Computational analysis of sequencing data

Lecturers: Joe DeRisi (10/20), Elaine Meng (10/21), Bo Huang (10/22)

Recommended reading:

Other Class Material:

Week 5 – Theme: Stress Response Networks

Lab Work: Comparisons of perturbations between teams

Lecturers: Student Presentations (10/27), David Morgan (10/28), Jason Gestwicki (10/29)

Recommended reading:

Other Class Material:


Week 6 – Theme: Single Cell/Molecule vs. Bulk Measurements

Lab Work: Growth rates via microscopy and bulk measurements

Lecturers: Kurt Thorn (11/3), Steven Altschuler and Lani Wu (11/4), Tanja Kortemme (11/5), Wendell Lim Kilobot Demo (11/5)

Recommended reading:

Other Class Material:

Week 7– Theme: Constraints on Stability and Interaction Specificity

Lab Work: Computational protein design

Lecturers: Kyle Barlow (11/10), Samuel Thompson (11/12)

Recommended reading:

Other Class Material:

Week 8– Theme: Constraints in the Context of Networks

Lab Work: Comparison of computational design and selection experiments

Lecturers: Adam Abate (11/17), Peter Turnbaugh (11/18), Dave Toczyski (11/19)

Other Class Material:

Recommended reading:


Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., Bork, P., Kondrashov, A.S., and Sunyaev, S.R. (2010). A method and server for predicting damaging missense mutations. Nat Meth 7, 248–249.

Aghajan, M., Jonai, N., Flick, K., Fu, F., Luo, M., Cai, X., Ouni, I., Pierce, N., Tang, X., Lomenick, B., et al. (2010). Chemical genetics screen for enhancers of rapamycin identifies a specific inhibitor of an SCF family E3 ubiquitin ligase. Nat. Biotechnol. 28, 738–742.

Agresti, J.J., Antipov, E., Abate, A.R., Ahn, K., Rowat, A.C., Baret, J.C., Marquez, M., Klibanov, A.M., Griffiths, A.D., and Weitz, D.A. (2010). Ultrahigh-throughput screening in drop-based microfluidics for directed evolution. Proceedings of the National Academy of Sciences 107, 4004–4009.

Araya, C.L., Fowler, D.M., Chen, W., Muniez, I., Kelly, J.W., and Fields, S. (2012). A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function. Proc. Natl. Acad. Sci. U.S.a. 109, 16858–16863.

Bandyopadhyay, S., Mehta, M., Kuo, D., Sung, M.K., Chuang, R., Jaehnig, E.J., Bodenmiller, B., Licon, K., Copeland, W., Shales, M., et al. (2010). Rewiring of Genetic Networks in Response to DNA Damage. Science 330, 1385–1389.

Bystrykh LV (2012) Generalized DNA Barcode Design Based on Hamming Codes. PLoS ONE 7(5): e36852. doi:10.1371/journal.pone.0036852

David, L.A., Maurice, C.M., Carmody, R.N., Gootenberg, D.B., Button, J.E., Wolfe, B.E., Ling, A.V., Devlin, A.S., Varma, Y., Fischbach, M.A., Biddinger, S.B., Dutton, R.J., and P.J. Turnbaugh. Diet rapidly and reproducibly alters the human gut microbiome. Nature, Epub ahead of print, Nov 2013. doi:10.1038/nature12820.

Finley, D., Ulrich, H.D., Sommer, T., and Kaiser, P. (2012). The Ubiquitin-Proteasome System of Saccharomyces cerevisiae. Genetics 192, 319–360.

Fowler, D.M., and Fields, S. (2014). Deep mutational scanning: a new style of protein science. Nat Meth 11, 801–807.

Gestwicki, J.E., and Garza, D. (2012). Protein Quality Control in Neurodegenerative Disease. In Molecular Biology of Neurodegenerative Diseases, (Elsevier), pp. 327–353.

Haiser, H.J., Gootenberg, D.B., Chatman, K., Sirasani, G., Balskus, E.P., and P.J. Turnbaugh. (2013) Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295-298.

Humphris EL, Kortemme T. (2007). Design of multi-specificity in protein interfaces. PLoS Comput Biol. 2007 3(8):e164.

Kellogg EH, Leaver-Fay A, Baker D. (2011). Role of conformational sampling in computing mutation-induced changes in protein structure and stability. 79(3):830-8.

Leaver-Fay, A., Tyka, M., Lewis, S.M., Lange, O.F., Thompson, J., Jacak, R., Kaufman, K.W., Renfrew, P.D., Smith, C.A., Sheffler, W., et al. (2011). Rosetta3. In Computer Methods, Part C, (Elsevier), pp. 545–574.

Lemieux, G.A., Keiser, M.J., Sassano, M.F., Laggner, C., Mayer, F., Bainton, R.J., Werb, Z., Roth, B.L., Shoichet, B.K., and Ashrafi, K. (2013). In Silico Molecular Comparisons of C. elegans and Mammalian Pharmacology Identify Distinct Targets That Regulate Feeding. PLoS Biol 11, e1001712.

Mandell D.J., and Kortemme, T. Computer-aided design of functional protein interactions. (2009). Nat Chem Biol. _5_797-807.

McLaughlin, R.N., Jr, Poelwijk, F.J., Raman, A., Gosal, W.S., and Ranganathan, R. (2012). The spatial architecture of protein function and adaptation. Nature 491, 138–142.

Ollikainen, N., Smith, C.A., Fraser, J.S., and Kortemme, T. (2013). Flexible Backbone Sampling Methods to Model and Design Protein Alternative Conformations. In Methods in Protein Design, (Elsevier), pp. 61–85.

Pei, J., and Grishin, N.V. (2001). AL2CO: calculation of positional conservation in a protein sequence alignment. Bioinformatics 17, 700–712.

Phillips, A.H., Zhang, Y., Cunningham, C.N., Zhou, L., Forrest, W.F., Liu, P.S., Steffek, M., Lee, J., Tam, C., Helgason, E., et al. (2013). Conformational dynamics control ubiquitin-deubiquitinase interactions and influence in vivo signaling. Proceedings of the National Academy of Sciences 110, 11379–11384.

Pollock, D.D., Thiltgen, G., and Goldstein, R.A. (2012). Amino acid coevolution induces an evolutionary Stokes shift. Proceedings of the National Academy of Sciences 109, E1352–E1359.

Rajaram, S., Pavie, B., Wu, L.F., and Altschuler, S.J. (2012). PhenoRipper: software for rapidly profiling microscopy images. Nat Meth 9, 635–637.

Rodrigo-Brenni, M.C., Foster, S.A., and Morgan, D.O. (2010). Catalysis of Lysine 48-Specific Ubiquitin Chain Assembly by Residues in E2 and Ubiquitin. Mol. Cell 39, 548–559.

Roscoe, B.P., Thayer, K.M., Zeldovich, K.B., Fushman, D., and Bolon, D.N.A. (2013). Analyses of the Effects of All Ubiquitin Point Mutants on Yeast Growth Rate. J. Mol. Biol. 425, 1363–1377.

Smith, R.P., Taher, L., Patwardhan, R.P., Kim, M.J., Inoue, F., Shendure, J., Ovcharenko, I., and Ahituv, N. (2013). Massively parallel decoding of mammalian regulatory sequences supports a flexible organizational model. Nature Genetics 1–10.

Sowa, M.E., Bennett, E.J., Gygi, S.P., and Harper, J.W. (2009). Defining the Human Deubiquitinating Enzyme Interaction Landscape. Cell 138, 389–403.

Stelter, P., and Ulrich, H.D. (2003). Control of spontaneous and damage-induced mutagenesis by SUMO and ubiquitin conjugation. Nature 425, 188–191.

Tinoco, I., and Gonzalez, R.L. (2011). Biological mechanisms, one molecule at a time. Genes Dev. 25, 1205–1231.

van Wijk SJ, Fiskin E, Putyrski M, Pampaloni F, Hou J, Wild P, Kensche T, Grecco HE, Bastiaens P, and Dikic I. (2012) Fluorescence-based sensors to monitor localization and functions of linear and K63-linked ubiquitin chains in cells. Molecular Cell. 14, 797-809.

Ye Y, Blaser G, Horrocks MH, Ruedas-Rama MJ, Ibrahim S, Zhukov AA, Orte A, Klenerman D, Jackson SE, and Komander D. (2012). Ubiquitin chain conformation regulates recognition and activity of interacting proteins. Nature 492, 266-70