Course Information

Description: This course will focus on computational techniques used to study the structure and dynamics of biomolecules, cells, and everything in between. For example, what is the structure of proteins, DNA, and RNA? How do their motions contribute to their function? How do they bind to other molecules? How are molecules distributed and compartmentalized within a cell, and how do they move around? How might one modify the behavior of these systems using drugs or other therapeutics? How can structural information contribute to the design of drugs, proteins, or perhaps even cells?

Computation can contribute to addressing such questions in at least two distinct ways. First, computational analysis is required to extract useful information from experimental measurements. Second, one can use computational techniques to predict structures, dynamics, and important biochemical properties.

The course will cover (1) atomic-level molecular modeling methods for proteins and other biomolecules, including structure prediction, molecular dynamics simulation, docking, and protein design, (2) computational methods involved in solving molecular structures by x-ray crystallography and cryo-electron microscopy, and (3) computational methods for studying spatial organization of cells, including computational analysis of optical microscopy images and video, and simulations at the cellular scale. The course will cover both foundational material and cutting-edge research in each of these areas, including recent advances in machine learning for structural biology.

Coursework: Students will be expected to complete three assignments, each of which will involve a combination of theoretical questions and computer work. Additionally, students will be expected to complete a project. The project will involve about as much work as an assignment, but it will be more open-ended and will allow students to delve into a topic of their choosing in more depth. Finally, students will be expected to complete an exam at the end of the quarter. More details regarding content of the exam will be released toward the second half of the quarter.

Prerequisites: Elementary programming background (at the level of CS 106A) and introductory course in biology.

Instructor: Ron Dror

• Professor Dror's Office Hours: Tues. & Thurs. 4:35 - 5:00 PM, outside Shriram 104 (i.e., right after each class, outside the classroom)

TA: Ben Parks

TA: Hikaru Hotta

TA: Ayushi Tandel

TA: Jacklyn Luu

Office Hours:

Please use Queuestatus to sign up for a spot in office hours.

Our default Zoom link for all TA and instructor office hours will be listed here in Ed Discussions.

Remote and in-person office hours and relevant course dates are noted in the Google Calendar below.

Please use Ed Discussions for questions related to lectures and assignments.

Class: Tuesdays and Thursdays, 3:15 PM - 4:35 PM in Shriram 104.

Announcements: All announcements will be made on Ed Discussions.

Materials: There is no required textbook. We will suggest a variety of optional reading material throughout the course.

Handouts

• Course Policies and Evaluation Criteria
• Software & Using Terminal
• Working Remotely on LTS Machines

Lectures

• Introduction (9/21/21) [slides] [annotated slides]


• Biomolecular structure (including protein structure) (9/23/21 and 9/28/21) [slides] [annotated slides]

Optional Reading:
• Michael Levitt's Introductory Lecture from former course SB228 [slides]
• Michael Levitt's Molecular Architecture Lecture from former course SB228 [slides]
• RNA Structure: Advances and Assessment of 3D Structure Prediction [Public] [Stanford Only]


• [optional] Python and Terminal Tutorial (9/27/21 from 4-5 PM by Zoom)
• Recording link (Requires Canvas login)


• Energy Functions & Their Relationship to Molecular Conformation (9/28/21 and 9/30/21) [slides] [annotated slides] [Canvas recording]

Optional Reading:
• ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost [Public] [Stanford Only]


• [optional] PyMOL Tutorial (9/29/21 from 4-5 PM by Zoom)
• Recording link (Requires Canvas login)


• Molecular Dynamics Simulation (10/5/21) [slides] [annotated slides] [Canvas recording]

Optional Reading:
• Molecular Dynamics Simulation for All [Public] [Stanford Only]
• Molecular mechanism of biased signaling in a prototypical G protein–coupled receptor [Public] [Stanford Only]
• Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning [Public] [Stanford Only]


• Protein Structure Prediction (10/7/21) [slides] [annotated slides] [Canvas recording]

Optional Reading:
• The Protein-Folding Problem, 50 Years On [Public] [Stanford Only]
• The Phyre2 web portal for protein modeling, prediction and analysis [Public] [Stanford Only]
• Improved protein structure prediction using potentials from deep learning [Public] [Stanford Only]


• Protein Design (10/12/21) [slides] [annotated slides] [Canvas recording]

Optional Reading:
• Computer-Based Design of Novel Protein Structures [Public] [Stanford Only]
• The coming of age of de novo protein design [Public] [Stanford Only]
• Designed proteins assemble antibodies into modular nanocages [Public] [Stanford Only]
• A Brief History of De Novo Protein Design: Minimal, Rational, and Computational [Public] [Stanford Only]


• Fourier Transforms and Convolution (10/14/21) [slides] [annotated slides] [Canvas recording] [FT notes] [Convolution notes]


• Image Analysis (10/19/21) [slides] [annotated slides] [Canvas recording] [notes]


• Microscopy (10/21/21) [slides] [annotated slides] [Canvas recording]
  
  Optional Reading:
  • Fluorescence Microscopy [Public] [Stanford Only]
  • Super-resolution fluorescence imaging with single molecules [Public] [Stanford Only]
  • The Diffraction Barrier in Optical Microscopy


• Diffusion and Cellular-Level Simulation (10/21/21 and 10/26/21) [slides] [annotated slides]
  • Note: Today's lecture and onwards will be in-person in Shriram 104.

Optional Reading:
• Random Walks and 1-D Diffusion [Stanford Only] (notes from Chris Burge from his class at UCSF)
• Continuum Diffusion Equations [Stanford Only] (notes from Chris Burge from his class at UCSF)


• Ligand Docking and Virtual Screening (10/28/21) [slides] [annotated slides] [docking discussion notes]

Optional Reading:
• Ultra-large library docking for discovering new chemotypes [Public] [Stanford Only]
• Glide: A New Approach for Rapid, Accurate Docking and Scoring [Public] [Stanford Only]
• Prediction of Protein — Ligand Interactions. Docking and Scoring: Successes and Gaps [Public] [Stanford Only]
• Alchemical free energy methods for drug discovery: progress and challenges. [Public] [Stanford Only]
• Predicting ligand binding potency using free energy calculations [Public] [Stanford Only]
• Note: The last two articles cover molecular-dynamics-based methods for computing a ligand's binding affinity. These methods are distinct from standard docking methods.


• Project Topics (11/4/21) [slides]

Sample Projects (only accessible to enrolled students):
• Download here


• X-Ray Crystallography (11/9/21) [slides] [annotated slides] [notes]

Optional Reading:
• Super-resolution biomolecular crystallography with low-resolution data [Public] [Stanford Only]
• Improved low-resolution crystallographic refinement with Phenix and Rosetta [Public] [Stanford Only]
• Machine learning applications in macromolecular X-ray crystallography [Public] [Stanford Only]


• Cryo-Electron Microscopy (11/11/21, 11/16/21, and 11/18/21) [slides] [annotated slides]

Optional Reading:
• A Primer to Single-Particle Cryo-Electron Microscopy [Public] [Stanford Only]
  Supplemental Information: [Public] [Stanford Only]
• Three-Dimensional Electron Microscopy of Macromolecular Assemblies
• Iterative Stable Alignment and Clustering of 2D Transmission EM Images


• Macromolecular structure prediction by machine learning (11/18/21 and 11/30/21) [slides] [annotated slides]

Optional Reading:
• Highly accurate protein structure prediction with AlphaFold [Public] [Stanford Only]
• Geometric deep learning of RNA structure [Public] [Stanford Only]


• Review (12/2/21) [slides]
  
  Optional Reading:
  • The Coronavirus in a Tiny Drop - The New York Times [Stanford Only]

Assignments

Submission: Assignment 1-3 should be submitted to Gradescope. Final project and writeup should be submitted to Canvas.

• Assignment 1 – Biomolecular Structure and Visualization [assign1.zip]
  Out by: Friday, September 24, 2021
  Due: Tuesday, October 5, 2021 at 3:00 PM
  
  
• Assignment 2 – Atomic-Level Molecular Modeling [assign2.zip] [assignment 2 setup]
  Out by: Tuesday, October 5, 2021
  Due: Thursday, October 21, 2021 at 3:00 PM
  
  
• Assignment 3 – Cellular Structure and Dynamics [assign3.zip] [assignment 3 setup] [assignment 3 challenge problem instructions] [assignment 3 challenge problem image files]
  Out by: Thursday, October 21, 2021
  Due: Thursday, November 4, 2021 at 3:00 PM
  
  
• Project [final project instructions]
  Sample Projects from Previous Years (only accessible to enrolled students):
  • Download here
  Out by: Thursday, November 4, 2021
  Due: Friday, December 3, 2021 at 11:59 PM
  
  

Exam

• Practice Exam Questions
  
• Practice Exam Questions and Solutions
  

Python Resources

• This class's "Introduction to Python" tutorial will give a brief introductions to Python programming.
• Codecademy is a site that does a good job of introducing the basics of Python, organized by topic. If you're just getting started with Python or if you want to brush up on specific issues, this may be helpful.
• Check out CME 193: Introduction to Scientific Python! It's a 1-unit course that runs for four weeks, beginning in the second week of the quarter. It is recommended for students who want to use Python in math, science, or engineering courses and for students who want to learn the basics of Python programming.