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

Improving Translational Accuracy

Improving Translational Accuracy

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...

Improving Translational Accuracy

Improving Translational Accuracy

Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

You might also read

Related Articles

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

Sort by

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

AlphaFold Protein Structure Database 2025: a redesigned interface and updated structural coverage.

Nucleic acids research·2025

Same author

Exploring structural diversity across the protein universe with The Encyclopedia of Domains.

Science (New York, N.Y.)·2024

Same author

Merizo: a rapid and accurate protein domain segmentation method using invariant point attention.

Nature communications·2023

Same author

Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins.

Proceedings of the National Academy of Sciences of the United States of America·2022

Same author

The impact of AlphaFold2 one year on.

Nature methods·2022

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026

Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026

Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026

Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 30, 2025

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

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

Published on: September 25, 2021

Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework.

Lewis Moffat^1,2, David T Jones^1,2

¹Department of Computer Science, University College London, London WC1E 6BT, UK.

Bioinformatics (Oxford, England)

|July 2, 2021

Summary

This summary is machine-generated.

We introduce Profile Augmentation of Single Sequences (PASS), a novel machine learning framework for accurately modeling single orphan protein sequences. Our method, S4PRED, significantly advances secondary structure prediction accuracy.

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Related Experiment Videos

Last Updated: Oct 30, 2025

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

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

Published on: September 25, 2021

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Area of Science:

Computational biology
Bioinformatics
Machine learning

Background:

Protein sequence modeling has advanced significantly, but single orphan sequences remain challenging.
Existing deep learning methods struggle with modeling proteins lacking evolutionary information.

Purpose of the Study:

To develop a novel framework for accurate single-sequence protein modeling.
To improve secondary structure prediction for orphan proteins.

Main Methods:

Developed Profile Augmentation of Single Sequences (PASS), a semi-supervised machine learning approach.
Applied PASS to secondary structure prediction, creating the S4PRED model.

Main Results:

S4PRED achieved an unprecedented Q3 score of 75.3% on the CB513 dataset.
The PASS framework offers a blueprint for future predictive methods for individual protein sequences.

Conclusions:

PASS is a powerful framework for building accurate single-sequence predictive models.
S4PRED represents a significant advancement in secondary structure prediction, particularly for orphan proteins.