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

Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.6K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.6K

You might also read

Related Articles

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

Sort by
Same author

CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.

Briefings in bioinformatics·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

Integrating protein and DNA embeddings for improving genome-wide transcription factor binding site prediction.

NAR genomics and bioinformatics·2026
Same author

TomoSwin3D: a Swin3D Transformer for the Identification and Classification of Macromolecules in 3D Cryo-ET Tomograms.

bioRxiv : the preprint server for biology·2026
Same author

PreStoi allows accurate prediction of protein complex stoichiometry by integrating AlphaFold3 and template information.

Communications biology·2026
Same author

CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.

Viruses·2026
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

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

16.2K

Improving AlphaFold3 by Engineering MSA and Template Inputs.

Pawan Neupane, Jian Liu, Jianlin Cheng

    Biorxiv : the Preprint Server for Biology
    |May 4, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Enhancing AlphaFold3 predictions involves optimizing multiple sequence alignments (MSAs) and structural templates. Carefully engineered inputs significantly improve the accuracy of protein monomer, multimer, and complex structure predictions.

    More Related Videos

    Design and Development of a Three-Dimensionally Printed Microscope Mask Alignment Adapter for the Fabrication of Multilayer Microfluidic Devices
    06:21

    Design and Development of a Three-Dimensionally Printed Microscope Mask Alignment Adapter for the Fabrication of Multilayer Microfluidic Devices

    Published on: January 25, 2021

    2.5K
    Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
    08:21

    Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

    Published on: April 13, 2022

    2.5K

    Related Experiment Videos

    Last Updated: May 5, 2026

    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

    16.2K
    Design and Development of a Three-Dimensionally Printed Microscope Mask Alignment Adapter for the Fabrication of Multilayer Microfluidic Devices
    06:21

    Design and Development of a Three-Dimensionally Printed Microscope Mask Alignment Adapter for the Fabrication of Multilayer Microfluidic Devices

    Published on: January 25, 2021

    2.5K
    Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
    08:21

    Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

    Published on: April 13, 2022

    2.5K

    Area of Science:

    • Structural biology
    • Computational biology
    • Biophysics

    Background:

    • AlphaFold3 offers a unified framework for predicting biomolecular structures and interactions.
    • Its accuracy is contingent on the quality of multiple sequence alignment (MSA) and structural template inputs.
    • Limited research exists on leveraging customized MSAs and templates to enhance AlphaFold3 performance.

    Purpose of the Study:

    • To systematically investigate the impact of diverse and engineered MSAs and templates on AlphaFold3 predictions.
    • To evaluate the effectiveness of these customized inputs across protein monomers, multimers, and protein-ligand complexes.
    • To compare AlphaFold3 performance with customized inputs against AlphaFold2 using identical inputs.

    Main Methods:

    • Systematic evaluation of AlphaFold3 performance using diverse and carefully engineered MSAs and templates.
    • Benchmarking predictions for protein monomers, multimers, and protein-ligand complexes.
    • Comparative analysis against default AlphaFold3 and AlphaFold2 with identical customized inputs.

    Main Results:

    • Consistent and substantial improvements in structure prediction accuracy were observed across all tested biomolecular types.
    • Specific gains include higher TM-scores for monomers, improved DockQ scores for multimers, and lower ligand RMSD for protein-ligand complexes compared to default AlphaFold3.
    • AlphaFold3 demonstrated significantly superior performance over AlphaFold2 when both utilized the same customized MSA and template inputs.

    Conclusions:

    • The study underscores the critical role of diverse and well-engineered MSAs and templates in enhancing AlphaFold3's predictive power.
    • Customized inputs lead to significant accuracy gains, establishing a new state-of-the-art for AlphaFold3.
    • This work provides a pathway for optimizing protein structure prediction through strategic input engineering.