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Related Concept Videos

Protein Folding01:22

Protein Folding

Overview
Protein Folding01:25

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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.
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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.
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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.
The primary structure of a protein is its amino acid sequence.

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Updated: May 7, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Heuristic-based tabu search algorithm for folding two-dimensional AB off-lattice model proteins.

Jingfa Liu1, Yuanyuan Sun, Gang Li

  • 1Network Information Center, Nanjing University of Information Science and Technology, Nanjing 210044, PR China; School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, PR China.

Computational Biology and Chemistry
|October 1, 2013
PubMed
Summary
This summary is machine-generated.

A new heuristic-based tabu search (HTS) algorithm improves protein structure prediction. This method effectively finds lower-energy conformations for 2D protein folding in AB off-lattice models, showing promise for identifying ground states.

Keywords:
2D2D AB off-lattice modelACMCCSAELP+HTSHeuristic-based tabu search algorithmLocal searchM-QPSOMPSOPSOProtein structure predictionSASTATSannealing contour Monte Carlo algorithmconformational space annealingheuristic-based tabu searchimproved energy landscape paving methodmulti particle swarm optimizationmultiple-layer quantum-behaved particle swarm optimizationnPERMisnew pruned-enriched-Rosenbluth method with importance samplingparticle swarm optimizationsimulating annealingstatistical temperature annealingtabu searchtwo-dimensional

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Area of Science:

  • Bioinformatics and Computational Biology
  • Biophysics and Structural Biology

Background:

  • Protein structure prediction is an NP-hard problem in bioinformatics, hindered by the lack of effective global optimization methods.
  • The Tabu Search (TS) algorithm is a global optimization technique successfully applied to various complex problems.

Purpose of the Study:

  • To develop an improved Tabu Search algorithm for predicting two-dimensional (2D) protein folding structures.
  • To enhance the Tabu Search algorithm by integrating heuristic mechanisms for better global optimization in protein structure prediction.

Main Methods:

  • An improved Tabu Search (TS) algorithm was developed, defining new neighborhood conformations, tabu objects, and acceptance criteria.
  • A heuristic-based Tabu Search (HTS) algorithm was proposed, incorporating heuristic initialization, conformation updating, and gradient methods.
  • The HTS algorithm was applied to the AB off-lattice protein model, utilizing local search mechanisms near local minima to find lower-energy conformations.

Main Results:

  • The HTS algorithm was tested on Fibonacci and real protein sequences.
  • It successfully identified the lowest-energy conformations for three shorter Fibonacci sequences and improved results for the longest one.
  • The algorithm also achieved new lowest-energy conformations for two real protein sequences.

Conclusions:

  • The heuristic-based Tabu Search (HTS) algorithm demonstrates significant promise for predicting protein structures.
  • HTS is effective in finding ground states for 2D protein folding within the AB off-lattice model.
  • The integration of heuristic strategies enhances the performance of Tabu Search for complex optimization problems in bioinformatics.