Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Determination01:51

Determination

17.8K
During embryogenesis, cells become progressively committed to different fates through a two-step process: specification followed by determination. Specification is demonstrated by removing a segment of an early embryo, “neutrally” culturing the tissue in vitro—for example, in a petri dish with simple medium—and then observing the derivatives. If the cultured region gives rise to cell types that it would normally generate in the embryo, this means that it is specified. In...
17.8K
Survival Tree01:19

Survival Tree

50
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
50
Experimental Designs01:16

Experimental Designs

11.0K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
11.0K

You might also read

Related Articles

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

Sort by
Same author

Scotty: lattice coincidences for macromolecular crystallographic phasing.

Acta crystallographica. Section D, Structural biology·2026
Same author

Chaperonin recognition of protein dynamics drives drug resistance.

bioRxiv : the preprint server for biology·2026
Same author

Author Correction: Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.

Nature methods·2026
Same author

Inferring structure factors of weakly populated excited states in perturbative crystallography experiments.

bioRxiv : the preprint server for biology·2026
Same author

AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network.

Nature methods·2026
Same author

The Untangle Challenge for accurate ensemble models.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: May 24, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.4K

AlphaFold as a Prior: Experimental Structure Determination Conditioned on a Pretrained Neural Network.

Alisia Fadini1, Minhuan Li2, Airlie J McCoy1

  • 1Cambridge Institute for Medical Research, University of Cambridge.

Biorxiv : the Preprint Server for Biology
|March 3, 2025
PubMed
Summary

We developed ROCKET, a method that enhances protein structure prediction by integrating experimental data with AlphaFold2. ROCKET refines models, capturing crucial biological variations missed by standard methods.

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K

Related Experiment Videos

Last Updated: May 24, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.4K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K

Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Machine learning, particularly AlphaFold2, has revolutionized protein structure prediction from sequence.
  • Challenges remain in modeling sidechain packing, conformational dynamics, and biomolecular interactions due to limited high-quality data.
  • Emerging techniques like cryo-electron tomography (cryo-ET) and high-throughput crystallography generate vast structural data, but model interpretation is a bottleneck.

Purpose of the Study:

  • To improve the efficiency of structural analysis by combining experimental measurements with AlphaFold2.
  • To develop an augmentation of AlphaFold2, named ROCKET, capable of refining predictions using cryo-EM, cryo-ET, and X-ray crystallography data.
  • To demonstrate ROCKET's ability to capture biologically significant structural variations beyond AlphaFold2's scope.

Main Methods:

  • Augmenting AlphaFold2 with ROCKET, which refines predictions using cryo-EM, cryo-ET, and X-ray crystallography data.
  • Performing structure optimization in coevolutionary embedding space, rather than Cartesian coordinates, to automate complex modeling tasks.
  • Utilizing differentiable crystallographic and cryo-EM target functions adaptable to other structure prediction methods.

Main Results:

  • ROCKET successfully refines AlphaFold2 predictions, capturing biologically important structural variations not identified by AlphaFold2 alone.
  • The method automates challenging modeling tasks, including functional loop flips and domain rearrangements, surpassing current state-of-the-art and manual modeling.
  • ROCKET does not require AlphaFold2 retraining and is adaptable to multimers, ligand-cofolding, and other data types.

Conclusions:

  • ROCKET offers a novel framework for integrating experimental data with machine learning for enhanced biomolecular structure prediction.
  • The ability to efficiently sample barrier-crossing rearrangements opens new avenues for scalable and automated model building.
  • ROCKET's extensible framework and adaptable target functions facilitate broader integration of experimental observables with machine learning in structural biology.