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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...

Protein Folding

Protein Folding

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...

Molecular Chaperones and Protein Folding

Molecular Chaperones and Protein Folding

The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
The...

Molecular Chaperones and Protein Folding

Molecular Chaperones and Protein Folding

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Related Experiment Video

Updated: May 4, 2026

Optical Tweezers to Study RNA-Protein Interactions in Translation Regulation

Optical Tweezers to Study RNA-Protein Interactions in Translation Regulation

Published on: February 12, 2022

Multicore and GPU Algorithms for Nussinov RNA Folding.

Junjie Li¹, Sanjay Ranka¹, Sartaj Sahni¹

¹Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611.

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [Proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences

|January 4, 2014

Summary

This summary is machine-generated.

We developed faster algorithms for RNA folding using Nussinov

Keywords:

CUDA GPU Nussinov’s Algorithm RNA Folding cache multicore

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Last Updated: May 4, 2026

Optical Tweezers to Study RNA-Protein Interactions in Translation Regulation

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Published on: February 12, 2022

Microfluidic Mixers for Studying Protein Folding

Microfluidic Mixers for Studying Protein Folding

Published on: April 10, 2012

Nanomanipulation of Single RNA Molecules by Optical Tweezers

Nanomanipulation of Single RNA Molecules by Optical Tweezers

Published on: August 20, 2014

Area of Science:

Computational Biology
Bioinformatics
Algorithm Development

Background:

RNA folding is crucial for understanding gene regulation and function.
Efficient algorithms are needed to handle the computational complexity of RNA folding predictions.

Purpose of the Study:

To develop optimized algorithms for RNA folding using Nussinov's equations.
To improve computational efficiency on modern hardware architectures.

Main Methods:

Implemented cache-efficient algorithms for single-core processors.
Developed multicore algorithms leveraging parallel processing capabilities.
Designed GPU-accelerated algorithms for NVIDIA C2050.

Main Results:

Cache-efficient algorithm achieved 1.6-3.0x speedup over naive code.
Multicore algorithm yielded 7.5-14.0x speedup on a 6-core CPU.
GPU algorithm was up to 1582x faster than naive code and 5.1-11.2x faster than prior GPU methods.

Conclusions:

Optimized algorithms significantly enhance RNA folding computation speed.
Cache-efficient, multicore, and GPU approaches offer substantial performance gains.
These advancements facilitate larger-scale RNA structure predictions.