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

Tagging and Fusion Proteins

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Ligand Binding Sites

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

Protein and Protein Structure

Protein and Protein Structure

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...

You might also read

Related Articles

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

Sort by

Same author

Procyanidin C1 modulates microglia cellular iron homeostasis via targeting HuR/ACSL4 pathway and alleviates high-glucose-induced ferroptosis in diabetic retinopathy.

Experimental eye research·2026

Same author

Autism Spectrum Disorders in China: An Analysis of Temporal Trends in Prevalence, Incidence, and DALYs (1990-2021) Based on GBD 2021 Data.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih·2026

Same author

Global comprehensive transcriptomic and proteomic analyses of murine terminal erythroid differentiation.

Global medical genetics·2025

Same author

Global Profiling of Lysine Ubiquitylation in the Human Hypothalamus.

Proteomics. Clinical applications·2025

Same author

Artificial Intelligence Applications in Fangcang Shelter Hospitals: Opportunities and Challenges.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih·2025

Same author

Untargeted Metabolomics of Plasma From Coronavirus Disease 2019 Patients One Year After Recovery.

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae·2025

Search research articles

Related Experiment Video

Updated: Jul 2, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Lactylation prediction models based on protein sequence and structural feature fusion.

Ye-Hong Yang¹, Jun-Tao Yang^1,2, Jiang-Feng Liu^1,2

¹State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, No.5, Dongdan 3, Dongcheng District Municipality of Beijing, Beijing 100005, China.

Briefings in Bioinformatics

|February 22, 2024

Summary

Lysine lactylation (Kla) prediction is improved by combining sequence and 3D structural features. New models, ABFF-Kla and EBFF-Kla, offer better performance than sequence-only methods for Kla site identification.

Keywords:

automatic feature extraction deep learning feature fusion lysine lactylation residue contact map

More Related Videos

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

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

Published on: January 26, 2024

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Jul 2, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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

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

Published on: January 26, 2024

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Area of Science:

Biochemistry
Computational Biology
Genomics

Background:

Lysine lactylation (Kla) is a crucial posttranslational modification impacting cellular functions like glycolysis and macrophage polarization.
Kla is increasingly recognized for its role in tumor cell metabolism, particularly the Warburg effect.

Purpose of the Study:

To develop an automated method for extracting 3D structural features of Kla sites, mitigating manual bias.
To establish robust Kla prediction frameworks integrating sequence and structural data.

Main Methods:

A natural language processing approach was used for automatic 3D structural feature extraction of Kla sites.
Two frameworks, Attention-based feature fusion Kla model (ABFF-Kla) and Embedding-based feature fusion Kla model (EBFF-Kla), were developed.
These models integrate protein sequence and spatial structure features using attention and embedding layers.

Main Results:

Models integrating both sequence and spatial structure features (ABFF-Kla and EBFF-Kla) demonstrated superior predictive performance.
The proposed methods outperformed models relying solely on sequence features for Kla prediction.
The study provides a novel approach for automated protein structural feature extraction.

Conclusions:

Feature fusion of protein sequence and spatial structure significantly enhances Kla prediction accuracy.
The developed ABFF-Kla and EBFF-Kla frameworks offer a flexible and effective approach for Kla site identification.
This work contributes automated methods for structural feature extraction and Kla prediction, with code publicly available.