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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.
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Protein Organization

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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.
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Protein Folding

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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
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Related Experiment Video

Updated: Nov 11, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Protein secondary structure assignment using pc-polyline and convolutional neural network.

Lincong Wang¹, Chen Cao², Shuxue Zuo¹

¹The College of Computer Science and Technology, Jilin University, Changchun, China.

|March 29, 2021

Summary

This summary is machine-generated.

A new convolutional neural network (CNN) method accurately assigns protein secondary structure elements (SSEs) using peptide plane polyline representations. This approach offers a robust alternative for protein structural analysis and prediction.

Keywords:

convolutional neural network hydrogen bond machine learning peptide plane protein secondary structure secondary structure assignment

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Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

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Published on: September 17, 2017

Area of Science:

Structural bioinformatics
Computational biology
Protein structure analysis

Background:

Protein secondary structure element (SSE) assignment is crucial for structural analysis and prediction.
A peptide plane polyline (pc-polyline) representation captures protein backbone geometry.
Pairwise distances within pc-polylines reveal protein secondary structure patterns.

Purpose of the Study:

To investigate the utility of pc-polyline representations for SSE assignment.
To develop and validate a deep learning model for accurate SSE prediction.

Main Methods:

Utilized a convolutional neural network (CNN) model.
Employed peptide plane polyline (pc-polyline) representations of protein backbones.
Trained and tested the model on large protein datasets.

Main Results:

The CNN accurately assigned six types of SSEs: α-helix, β-sheet, β-bulge, 3₁₀-helix, turn, and loop.
The CNN-based p2pSSE program demonstrated high agreement with established tools like DSSP and STRIDE.
Analysis revealed challenges in consistently defining SSEs, suggesting a continuous SSE space.

Conclusions:

Pc-polyline representations contain sufficient information for accurate SSE assignment.
CNNs provide a powerful framework for predicting SSEs from geometric data.
The continuous nature of SSEs complicates precise classification of certain structures.