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Protein Folding

Protein Folding

Allosteric Proteins-ATCase

Allosteric Proteins-ATCase

Binding sites linkages can regulate a protein's function. For example, enzyme activity is often regulated through a feedback mechanism where the end product of the biochemical process serves as an inhibitor.
Aspartate transcarbamoylase (ATCase) is a cytosolic enzyme that catalyzes the condensation of L-aspartate and carbamoyl phosphate to N-carbamoyl-L-aspartate. This reaction is the first step in pyrimidine biosynthesis. UTP and CTP, the end products of the pyrimidine synthesis...

Protein Folding Quality Check in the RER

Protein Folding Quality Check in the RER

ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...

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Related Experiment Video

Updated: Oct 18, 2025

Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

Published on: April 2, 2021

Applying and improving AlphaFold at CASP14.

John Jumper¹, Richard Evans¹, Alexander Pritzel¹

¹DeepMind, London, UK.

|October 2, 2021

Summary

This summary is machine-generated.

AlphaFold2, a novel deep learning system, achieved unprecedented accuracy in protein structure prediction at CASP14. This breakthrough in computational biology enables highly accurate, large-scale protein structure determination.

Keywords:

AlphaFold CASP deep learning machine learning protein structure prediction

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Area of Science:

Computational Biology
Structural Biology
Artificial Intelligence in Science

Background:

Protein structure prediction is crucial for understanding biological function.
Previous methods had limitations in accuracy, especially for complex protein targets.
The Critical Assessment of Protein Structure Prediction (CASP) benchmarks prediction methods.

Purpose of the Study:

To describe the operation and advancements of the AlphaFold system.
To report the performance of AlphaFold in the CASP14 competition.
To highlight the system's capability for automated, large-scale protein structure prediction.

Main Methods:

Development of a novel end-to-end deep neural network.
Training the network using amino acid sequences, multiple sequence alignments, and homologous proteins.
Evaluation using CASP14 assessment metrics, including summed z scores and GDT_TS.

Main Results:

AlphaFold achieved a summed z score of 244.0, significantly outperforming other methods.
The median domain GDT_TS score was 92.4, a record for CASP.
High accuracy was demonstrated even on challenging Free Modeling targets.

Conclusions:

AlphaFold represents a significant leap forward in the state of the art of protein structure prediction.
The system's ability to operate autonomously opens possibilities for widespread, accurate structure determination.
This advancement has profound implications for biological research and drug discovery.