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

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...

Predicting Products: SN1 vs. SN2

Predicting Products: S_N1 vs. S_N2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...

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...

You might also read

Related Articles

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

Sort by

Same author

Diagnostic Performance of Serum AKR1B10 in Early-Stage and AFP-Negative Hepatocellular Carcinoma: A Multicentre Study.

Liver international : official journal of the International Association for the Study of the Liver·2026

Same author

Interfacial Enhancement of Polyethylene Fiber-Reinforced ECC via Multi-Walled Carbon Nanotubes Functionalization.

Nanomaterials (Basel, Switzerland)·2026

Same author

Fusion prediction model for post-ERCP pancreatitis under NSAIDs prophylaxis.

Surgical endoscopy·2026

Same author

<i>Akkermansia muciniphila</i>-derived L-norleucine modulates FABP1-dependent fatty acid transport.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Unraveling GhMYB102: dual roles of Gh_A01G069800 in inhibiting anthocyanin biosynthesis and inducing drought tolerance in cotton.

BMC genomics·2026

Same author

Smart-responsive lentinan-DMXAA conjugate for synergistic STING-mediated pancreatic cancer immunotherapy.

International journal of pharmaceutics·2026

Search research articles

Related Experiment Video

Updated: Oct 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network.

Huiqing Wang¹, Hong Zhao¹, Zhiliang Yan¹

¹College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.

|July 2, 2021

Summary

This study introduces MDCAN-Lys, a deep learning network for identifying lysine succinylation sites. This method improves the accuracy of recognizing these important post-translational modifications, aiding disease research and drug development.

Keywords:

convolutional block attention module deep learning dense convolutional block feature combination lysine succinylation

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Oct 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Biochemistry
Computational Biology
Genomics

Background:

Lysine succinylation is a critical post-translational modification implicated in various diseases.
Accurate identification of succinylation sites is vital for disease treatment and drug discovery.
Existing computational prediction methods primarily rely on traditional machine learning, necessitating advanced approaches.

Purpose of the Study:

To develop an effective deep learning model for predicting lysine succinylation sites.
To enhance the accuracy and efficiency of succinylation site identification compared to existing methods.

Main Methods:

Proposed a novel multilane dense convolutional attention network (MDCAN-Lys).
MDCAN-Lys integrates sequence, physicochemical, and structural protein properties through a three-way network architecture.
Employed a cascading model of dense convolutional blocks and attention modules for feature extraction and abstraction.

Main Results:

MDCAN-Lys demonstrated superior performance in recognizing succinylation sites.
Experimental validation using 10-fold cross-validation and independent testing confirmed the model's effectiveness.
Case studies corroborated the model's ability to identify a greater number of succinylation sites.

Conclusions:

Deep learning-based methods offer a promising avenue for accurate succinylation site recognition.
MDCAN-Lys provides a robust computational tool for advancing research in lysine succinylation and related diseases.