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 and associated computational methods contribute to the design of drugs, vaccines, proteins, or other important molecules?

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.

Class: Tuesdays and Thursdays, 3:00 PM - 4:20 PM in Shriram 104.

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

Instructor: Ron Dror

• Professor Dror's Office Hours: Tues. & Thurs. 4:20 - 4:45 PM, outside Shriram 104 (i.e., right after each class, outside the classroom)

TA: Armaun Sanayei

TA: Divya Nagaraj

TA: Grace Kim

TA: Hyun Soo Jeon

TA: Tristan Saucedo

Office Hours:

• Some office hours will be held in-person and others will be held virtually over zoom. The same zoom link will be used for all virtual office hours.
• Queuestatus will be used to manage the queue for both virtual and in-person office hours. Please see the Getting Set Up handout for further instructions on QueueStatus.
• The weekly office hour schedule can be viewed through the Google Calendar below. The in-person office hours will indicate the location and the virtual office hours will show the same zoom link as described above.

Contact:
Please use Ed Discussion for questions related to lectures and assignments. If you have issues that cannot be resolved on Ed, please contact us at cs279staff@cs.stanford.edu. For instructions on how to get set up on Ed, please see the Getting Set Up handout.

Announcements:
All announcements will be made on Ed Discussion. For instructions on how to get set up on Ed, please see the Getting Set Up handout.

Handouts

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

Lectures

• Introduction (9/27/22) [slides] [annotated slides]
  • We understand that some students may not be able to make the first class. This is fine. If you cannot make the first class, please read through the annotated slides, which will be posted after class. If you have any questions, either post on Ed or email cs279staff@cs.stanford.edu


• Biomolecular Structure, Including Protein Structure (9/29/22 and 10/4/22) [slides] [annotated slides]

Optional Reading:
• 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 (Monday 10/3/22 from 7-8 PM by Zoom)
• The recording can be found on Canvas here and the slides can be found here.


• [optional] PyMol Tutorial (Tuesday 10/4/22 from 7-8 PM by Zoom) - please note the date change from Wednesday to Tuesday
• The recording can be found on Canvas here.


• Energy Functions & Their Relationship to Molecular Conformation (10/4/22 and 10/6/22) [slides] [annotated slides]

Optional Reading:
• ANI-1: An Extensible Neural Network Potential with DFT Accuracy at Force Field Computational Cost [Public] [Stanford Only]


• Molecular Dynamics Simulation (10/6/22 and 10/11/22) [slides] [annotated slides]

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]


• Predicting Structures of Proteins and Other Biomolecules, Including Machine Learning Methods (10/13/22 and 10/18/22) [slides - Stanford Only] [annotated slides - Stanford Only]

Optional Reading:
• 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]
• Highly Accurate Protein Structure Prediction with AlphaFold [Public] [Stanford Only]
• Geometric Deep Learning of RNA structure [Public] [Stanford Only]


• Protein Design (10/20/22) [slides] [annotated slides]

Optional Reading:
• 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]
• Protein Sequence Design with a Learned Potential [Public] [Stanford Only]


• Fourier Transforms and Convolution (10/25/22) [slides] [annotated slides] [FT notes] [Convolution notes]


• Image Analysis (10/27/22 and 11/1/22) [slides] [annotated slides]


• [optional] Homework 3 Kickstart during Office Hours on Monday 10/31/22 from 3:45-5:45 PM
• The recording can be found on Canvas here and the slides can be found here.


• Microscopy (11/1/22 and 11/3/22) [slides] [annotated slides]
  Optional Reading:
  • Fluorescence Microscopy [Public] [Stanford Only]
  • Super-resolution Fluorescence Imaging with Single Molecules [Public] [Stanford Only]
  • The Diffraction Barrier in Optical Microscopy [Public] [Stanford Only]
  • Deep Learning Advances Super-Resolution Imaging [Public] [Stanford Only]
  • Information-Rich Localization Microscopy through Machine Learning [Public] [Stanford Only]


• Diffusion and Cellular-Level Simulation (11/3/22 and 11/10/22) [slides] [annotated slides]

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)


• Project Topics (11/10/22 and 11/15/22) [slides]


• Ligand Docking and Virtual Screening (11/15/22 and 11/17/22) [slides - Stanford Only] [annotated slides - Stanford Only]

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]
• Free Energy Methods in Drug Design: Prospects of 'Alchemical Perturbation' in Medicinal Chemistry [Public] [Stanford Only]
• Predicting Ligand Binding Potency using Free Energy Calculations [Public] [Stanford Only]
• GNINA 1.0: Molecular Docking with Deep Learning [Public] [Stanford Only]
• Accelerating High-throughput Virtual Screening through Molecular Pool-based Active Learning [Public] [Stanford Only]
• Leveraging Nonstructural Data to Predict Structures and Affinities of Protein–ligand Complexes [Public] [Stanford Only]


• X-Ray Crystallography (11/29/22) [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]
• Improved AlphaFold Modeling with Implicit Experimental Information [Public] [Stanford Only]


• Cryo-Electron Microscopy (12/1/22 and 12/6/22) [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 [Public]
• Iterative Stable Alignment and Clustering of 2D Transmission EM Images [Public] [Stanford Only]
• CryoSPARC: Algorithms for Rapid Unsupervised Cryo-EM Structure Determination [Public] [Stanford Only]
• CryoDRGN: Reconstruction of Heterogeneous Cryo-EM Structures using Neural Networks [Public] [Stanford Only]


• Review (12/6/22 and 12/8/22) [slides] [annotated slides]

Assignments

Please note that the following dates are approximate.

• Assignment 1 – Biomolecular Structure and Visualization [assign1.zip]
  Out by: Thursday, October 6, 2022
  Due: Tuesday, October 18, 2022 at 3:00 PM
  
  
• Assignment 2 – Atomic-Level Molecular Modeling [assign2.zip]
  [assign2_challenge.zip]
  Out by: Thursday, October 13, 2022
  Due: Thursday, October 27, 2022 at 3:00 PM
  
  
• Assignment 3 – Cellular Structure and Dynamics [assign3.zip]
  [assign3_challenge.zip]
  Out by: Thursday, October 27, 2022
  Due: Thursday, November 17, 2022 at 3:00 PM
  
  
• Project [Project Instructions]
  [Sample Projects from Previous Years (only accessible to enrolled students)]
  Out by: Tuesday, November 15, 2022
  Due: Friday, December 9, 2022
  
  

Submission: All assignments and the project writeup should be submitted to Gradescope.

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.