Machine Learning Methods in Biomolecular Structure Prediction

Biomolecular Structure

Protein Structure

RNA Structure

Energy Functions

Molecular Conformation

Molecular Dynamics Simulation

Predicting Structures of Proteins and Other Biomolecules

Protein Design

Fourier Transform

Convolution

Image Analysis

Microscopy

Fluorescence Microscopy

Super-resolution Fluorescence Imaging

Diffusion

Cellular-Level Simulation

Ligand Docking

Virtual Screening

X-Ray Crystallography

Cryo-Electron Microscopy

CS 279 Computational Biology: Structure and Organization of Biomolecules and Cells Stanford University Fall 2022 This course focuses on computational techniques to study biomolecule and cell structures. Topics include molecular modeling methods, structural prediction, dynamics simulation, protein design, and computational analysis of optical microscopy images. Requires basic programming skills and biology knowledge. 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.  Elementary programming background (at the level of CS 106A) and introductory course in biology. There is no required textbook. We will suggest a variety of optional reading material throughout the course.

CS 279 Computational Biology: Structure and Organization of Biomolecules and Cells

Interdisciplinary

Machine Learning Methods in Biomolecular Structure Prediction

1 courses cover this concept

CS 279 Computational Biology: Structure and Organization of Biomolecules and Cells