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Protein Organization

Protein Organization

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 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...

Protein Folding

Protein Folding

Protein Folding

Protein Folding

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
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

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Related Experiment Video

Updated: Oct 26, 2025

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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A General Framework to Learn Tertiary Structure for Protein Sequence Characterization.

Mu Gao¹, Jeffrey Skolnick¹

¹Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.

Frontiers in Bioinformatics

|July 26, 2021

Summary

This summary is machine-generated.

Deep learning advances protein structure prediction. The SAdLSA algorithm uses structural alignments for sequence comparison, enabling protein fold prediction and relationship detection.

Keywords:

deep-learning protein folding protein structure prediction sequence alignment structural alignment

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Last Updated: Oct 26, 2025

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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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Published on: July 16, 2017

Area of Science:

Computational Biology
Structural Bioinformatics
Machine Learning

Background:

Deep learning has revolutionized tertiary structure prediction from protein sequences.
Established sequence alignment methods have limitations in capturing structural information.

Purpose of the Study:

To present SAdLSA, a novel deep learning framework for protein sequence characterization.
To demonstrate SAdLSA's capability in predicting protein folds and identifying structural relationships.

Main Methods:

Developed SAdLSA, a deep learning algorithm for protein sequence comparison via structural alignments.
Utilized self-alignment of protein sequences to generate fold distograms.
Applied the same neural network for both intra-sequence fold prediction and inter-sequence relationship detection.

Main Results:

SAdLSA significantly improves upon existing sequence alignment methods.
The algorithm generates statistically significant fold distograms for input sequences, including novel folds.
Achieved approximately 70% statistically significant predictions for fold distograms.

Conclusions:

SAdLSA offers a general machine learning framework for structurally characterizing protein sequences.
The method successfully predicts individual protein folds and detects structural relationships between sequences.
SAdLSA's ability to predict structure from pairwise comparisons without explicit fold training is a key innovation.