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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 Organization

Protein Organization

Protein Organization

Protein Organization

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...

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...

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Related Experiment Video

Updated: Mar 25, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Robust classification of protein variation using structural modelling and large-scale data integration.

Evan H Baugh¹, Riley Simmons-Edler¹, Christian L Müller²

¹Department of Biology, New York University, New York, NY 10003, USA New York University Center for Genomics and Systems Biology, New York, NY 10003, USA.

Nucleic Acids Research

|March 2, 2016

Summary

This summary is machine-generated.

VIPUR is a new computational tool that integrates sequence and structural data to interpret protein variants. It accurately predicts variant deleteriousness, aiding in understanding disease-associated mutations.

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

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

Published on: July 16, 2017

Area of Science:

Computational biology
Protein structure and function
Genomics and bioinformatics

Background:

Current protein variation interpretation methods often focus on pathogenicity and lack detailed deleteriousness analysis.
Existing approaches frequently rely solely on sequence or structure-based information, limiting comprehensive interpretation.
There is a need for integrated computational frameworks to accurately identify and interpret deleterious protein variants.

Purpose of the Study:

To develop and validate VIPUR, a computational framework for integrated sequence and structural analysis of protein variants.
To improve the accuracy and interpretability of deleterious variant prediction compared to existing methods.
To demonstrate VIPUR's utility in interpreting disease-associated mutations and identifying candidate variants for human diseases.

Main Methods:

Developed VIPUR, a computational framework integrating sequence analysis and structural modeling (Rosetta).
Trained VIPUR on 9477 experimentally validated protein variants across multiple organisms.
Curated structural models from crystal structures and homology models for variant training and analysis.

Main Results:

VIPUR achieved high generalized accuracy (AUROC .83) and interpretability (AUPR .87) in predicting variant deleteriousness.
Predictions correlated with biological phenotypes in ClinVar, providing confidence rankings.
VIPUR successfully interpreted mutations linked to inflammation and diabetes, revealing disrupted functional sites.

Conclusions:

VIPUR offers an accurate and interpretable method for identifying and interpreting deleterious protein variants by integrating sequence and structural data.
The framework enhances understanding of mutation impacts on protein function and disease association.
VIPUR shows promise in highlighting candidate variants for complex human diseases like autism spectrum disorders.