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Protein Organization

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....

Protein Organization

Protein Organization

Protein Complex Assembly

Protein Complex Assembly

Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types. Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...

Protein Complex Assembly

Protein Complex Assembly

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...

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Related Experiment Video

Updated: Jan 10, 2026

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

High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.

Minghua Hou¹, Yuhao Xia², Pengcheng Wang¹

¹College of Information Engineering, Zhejiang University of Technology, HangZhou, China.

Nature Communications

|November 19, 2025

Summary

This summary is machine-generated.

DeepSCFold enhances protein complex structure prediction accuracy using sequence-based deep learning. This novel pipeline outperforms current state-of-the-art methods for modeling protein-protein interactions.

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Last Updated: Jan 10, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Published on: October 15, 2018

Area of Science:

Structural biology
Computational biology
Bioinformatics

Background:

Protein complexes are essential for life activities.
Accurate modeling of protein complex structures is challenging despite advances in monomer prediction.
Existing methods struggle with inter-chain interaction signals.

Purpose of the Study:

To introduce DeepSCFold, a novel pipeline for improving protein complex structure modeling.
To enhance the accuracy of predicting protein-protein interactions and complex structures.
To leverage sequence-based deep learning for structure prediction.

Main Methods:

DeepSCFold utilizes sequence-based deep learning models.
Predicts protein-protein structural similarity and interaction probability.
Constructs deep paired multiple-sequence alignments (MSAs) for complex structure prediction.

Main Results:

DeepSCFold significantly increases the accuracy of protein complex structure prediction.
Achieves superior TM-score improvements over AlphaFold-Multimer and AlphaFold3 on CASP15 multimer targets.
Enhances prediction success rates for antibody-antigen binding interfaces on SAbDab complexes.

Conclusions:

DeepSCFold effectively captures protein-protein interaction patterns using sequence-derived structure-aware information.
Outperforms state-of-the-art methods in protein complex structure modeling.
Demonstrates a powerful approach beyond traditional co-evolutionary signals.