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

Protein Folding01:22

Protein Folding

118.0K
Overview
118.0K
Protein Organization01:13

Protein Organization

137.5K
Overview
137.5K
Protein and Protein Structure02:15

Protein and Protein Structure

79.5K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
79.5K
Molecular Models02:00

Molecular Models

38.3K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.3K
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

47.0K
The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
47.0K

You might also read

Related Articles

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

Sort by
Same author

Replication-coupled hemimethylation in <i>Escherichia coli</i> K-12: mechanisms, dynamics, and emerging opportunities for direct observation.

Frontiers in microbiology·2026
Same author

Paradox of Reversible Vasoconstriction and Stroke-Like Lesions in MELAS: A Multimodal Imaging Sequence.

Stroke·2026
Same author

Multiple waves of westward dry-land agriculture expansions along the East Silk Road during the Neolithic age.

Fundamental research·2026
Same author

Wnt5b/FZD1/LRP6 signaling drives renal fibrosis by triggering cytoplasmic stabilization and nuclear translocation of β-catenin under hypoxia.

iScience·2026
Same author

Non-invasive Computational Techniques for Diagnosing Myocardial Ischemia: Challenges and Future of FFR<sub>CT</sub>/iFR<sub>CT</sub>.

Annals of biomedical engineering·2026
Same author

Hydrological position shapes the resilience of Silk Road oasis civilizations to megadroughts.

Science bulletin·2026

Related Experiment Video

Updated: Jun 28, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model.

Benzhi Dong1, Zheng Liu1, Dali Xu1

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.

Computational and Structural Biotechnology Journal
|April 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning model for protein secondary structure prediction (PSSP). The method enhances accuracy by focusing on amino acid properties and using a hybrid feature extraction approach.

Keywords:
Hybrid deep feature extractionMulti-factor featuresProtein secondary structureSecondary structure propensity scores

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.8K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

31.5K

Related Experiment Videos

Last Updated: Jun 28, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.8K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

31.5K

Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Protein secondary structure prediction (PSSP) is vital for understanding protein function.
  • Current PSSP methods heavily rely on deep learning and multi-factor features.

Purpose of the Study:

  • To develop a novel PSSP method emphasizing amino acid properties and propensity scores.
  • To create an effective hybrid deep learning model for enhanced feature extraction.

Main Methods:

  • Utilized dilated convolution (D-Conv) and channel attention network (SENet) for local feature extraction.
  • Employed BiGRU, BiLSTM, and a transformer module for global bidirectional information processing.
  • Integrated sequence and property features through a differential feature-selection strategy.

Main Results:

  • Achieved 84.9% accuracy and an Sov score of 85.1% in PSSP.
  • The hybrid model demonstrated superior performance compared to existing methods.
  • Successfully explored intricate residue associations in protein sequences.

Conclusions:

  • The proposed method offers a novel and efficient approach to PSSP.
  • This advancement deepens the understanding of protein molecular structure applications.
  • Highlights the importance of amino acid properties in PSSP accuracy.