Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

X-ray Crystallography

X-ray Crystallography

The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Farewell and thank you note.

Structural dynamics (Melville, N.Y.)·2026

Same author

Sequence-based calculation of local energetic frustration in proteins.

Structural dynamics (Melville, N.Y.)·2025

Same author

Artificial intelligence in structural biology: Preface.

Structural dynamics (Melville, N.Y.)·2025

Same author

Integrative machine learning predicts activating kinase mutations for precision oncology.

bioRxiv : the preprint server for biology·2025

Same author

Completion of partial structures using Patterson maps with the CrysFormer machine-learning model.

Acta crystallographica. Section D, Structural biology·2025

Same author

Happy 100th, structural biology.

Structural dynamics (Melville, N.Y.)·2025

Same journal

Towards light-coupled sample preparation for time-resolved cryoEM studies.

IUCrJ·2026

Same journal

Cryo-EM analysis of cooperative conformational changes in the SARS-CoV-2 spike protein trimer.

IUCrJ·2026

Same journal

Towards time-resolved MicroED grid preparation using mix-and-inject gas dynamic virtual nozzles.

IUCrJ·2026

Same journal

How cryoEM has advanced our understanding of bacteriophages and bacteriocins targeting Clostridioides difficile.

IUCrJ·2026

Same journal

CryoEM structures reveal allosteric regulation of the catalytic activity of the multi-protein human MAT enzyme complexes.

IUCrJ·2026

Same journal

Cryo-EM-guided subtractive optimization of a novel VCP/p97 inhibitor.

IUCrJ·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 24, 2025

Sample Preparation and Transfer Protocol for In-Vacuum Long-Wavelength Crystallography on Beamline I23 at Diamond Light Source

Sample Preparation and Transfer Protocol for In-Vacuum Long-Wavelength Crystallography on Beamline I23 at Diamond Light Source

Published on: April 23, 2021

A deep learning solution for crystallographic structure determination.

Tom Pan¹, Shikai Jin², Mitchell D Miller²

¹Department of Computer Science, Rice University, Houston, Texas, USA.

|July 6, 2023

Summary

This summary is machine-generated.

Solving the protein crystallography phase problem is challenging. This study introduces a deep learning neural network approach using synthetic data to estimate electron density directly from Patterson maps, offering a new pathway for crystallographic phase determination.

Keywords:

X-ray crystallography deep learning structure determination structure prediction

More Related Videos

$Microcrystallography of Protein Crystals and In Cellulo Diffraction$

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Published on: January 16, 2021

Related Experiment Videos

Last Updated: Jul 24, 2025

Sample Preparation and Transfer Protocol for In-Vacuum Long-Wavelength Crystallography on Beamline I23 at Diamond Light Source

Sample Preparation and Transfer Protocol for In-Vacuum Long-Wavelength Crystallography on Beamline I23 at Diamond Light Source

Published on: April 23, 2021

$Microcrystallography of Protein Crystals and In Cellulo Diffraction$

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Published on: January 16, 2021

Area of Science:

Crystallography
Structural Biology
Artificial Intelligence

Background:

The crystallographic phase problem remains a significant hurdle in determining protein structures.
Current de novo solutions are often limited to specific conditions, necessitating alternative approaches.

Purpose of the Study:

To explore a deep learning neural network as a novel method for solving the phase problem in protein crystallography.
To develop an initial pathway for directly estimating electron density from Patterson maps.

Main Methods:

A synthetic dataset of small molecular fragments was generated from curated Protein Data Bank (PDB) structures.
A convolutional neural network (CNN) architecture was employed to learn the relationship between Patterson maps and electron density.
The CNN was trained to produce electron-density estimates from synthetic Patterson data.

Main Results:

The study demonstrates a proof-of-concept for a deep learning approach to electron-density estimation.
The developed neural network successfully generated electron-density estimates from Patterson maps for artificial systems.
This indicates the potential of AI in addressing the crystallographic phase problem.

Conclusions:

Deep learning, specifically CNNs, presents a promising avenue for tackling the crystallographic phase problem.
This work lays the foundation for future AI-driven methods in protein structure determination.
Further development could lead to more efficient and generalizable solutions for phase determination in crystallography.