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Protein Structure Prediction, Design, and Computed Structure Models

the PDB archive made protein structure prediction and protein design possible

Researchers have long used the wealth of experimentally-determined structures available from the open-access PDB archive to understand basic principles of protein architecture. More recently, PDB structures have been used as training data for Artificial Intelligence/Machine Learning software tools (e.g., AlphFold, RosettaFold, OpenFold) that are able to predict three-dimensional structures of proteins and design proteins with novel shapes and biochemical functions.

Molecule of the Month Articles (3)

Designed Proteins and Citizen Science

Designer Proteins

ZAR1 Resistosome

Learning Resources (4)

Computed Structure Models

(Guide)

PDB Data, RCSB PDB, and KBase

(Other Resource)

Structural Biology and Nobel Prizes

(Other Resource)

Exploring Structural Biology with Computed Structure Models (CSMs)

(Article)

	Computed Structure Models Guide New machine-learning methods for predicting protein structures build on and complement the PDB archive of experimentally-determined structures.
	PDB Data, RCSB PDB, and KBase Other Resource
	Structural Biology and Nobel Prizes Other Resource Many Nobel Prizes have recognized achievements made in molecular biology, structural biology, and related research.
	Exploring Structural Biology with Computed Structure Models (CSMs) Article Computational methods like AlphaFold2 and RoseTTAFold2 use structures in the PDB archive to predict the folding of proteins.

	Designed Proteins and Citizen Science What if people with no formal experience in science could help to improve or even rewrite nature, simply by playing a game?
	Designer Proteins Researchers have successfully designed entirely new proteins based on biological principles
	ZAR1 Resistosome Plants protect themselves from infection with immune system machines such as the resistosome