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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Ligand Binding and Linkage00:49

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Protein Modifications in the RER01:26

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Modification of secretory and transmembrane proteins entering the rough ER begins in the ER lumen. These modifications aid in protein folding and stabilize the acquired tertiary structure. Protein modifications in the rough ER co-occur at different stages of protein folding.
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Sulfides are the sulfur analog of ethers, just as thiols are the sulfur analog of alcohol. Like ethers, sulfides also consist of two hydrocarbon groups bonded to the central sulfur atom. Depending upon the type of groups present, sulfides can be symmetrical or asymmetrical. Symmetrical sulfides can be prepared via an SN2 reaction between 2 equivalents of an alkyl halide and one equivalent of sodium sulfide.
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Prediction of disulfide bond engineering sites using a machine learning method.

Xiang Gao1,2, Xiaoqun Dong1,2, Xuanxuan Li1,3

  • 1Complex Systems Division, Beijing Computational Science Research Center, 8 E Xibeiwang Rd, Haidian, Beijing, 100193, People's Republic of China.

Scientific Reports
|June 27, 2020
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Summary

This study introduces a neural network to predict engineered disulfide bonds by identifying cysteine pairs for mutation. The method accurately identifies natural disulfide bonds and shows promise for protein engineering applications.

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • Disulfide bonds are crucial for protein folding and stability.
  • Engineered disulfide bonds via cysteine mutation enhance protein structural stability.
  • Predicting sites for engineered disulfide bonds aids experimental design.

Purpose of the Study:

  • To develop a neural network-based method for predicting amino acid pairs for engineered disulfide bonds.
  • To facilitate the design of proteins with enhanced stability through artificial disulfide bonds.

Main Methods:

  • A neural network was designed and trained using high-resolution protein structures from the Protein Data Bank.
  • The method utilizes the full feature space of distance information for prediction.
  • The algorithm was tested on both natural and engineered disulfide bonds.

Main Results:

  • The neural network achieved 99% accuracy in recognizing natural disulfide bonds.
  • For engineered disulfide bonds, the algorithm demonstrated comparable accuracy to state-of-the-art methods on a published dataset.
  • The method achieved 70% accuracy for two comprehensively studied proteins, indicating potential for protein engineering.

Conclusions:

  • The developed neural network method accurately predicts sites for engineered disulfide bonds.
  • This approach offers a valuable tool for protein engineering, enhancing structural stability.
  • The framework's ability to exploit distance features improves prediction accuracy.