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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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 polypeptide...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Inferring biomolecular interaction networks based on convex optimization.

Soohee Han1, Yeoin Yoon, Kwang-Hyun Cho

  • 1Bio-MAX Institute, Seoul National University, Seoul 151-818, Republic of Korea.

Computational Biology and Chemistry
|September 25, 2007
PubMed
Summary

This study introduces a novel optimization method to map cellular regulatory networks by analyzing component perturbations. The approach effectively infers functional interactions, revealing the cell

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Cellular regulatory networks govern complex biological functions.
  • Understanding these networks is crucial for deciphering cellular mechanisms and disease.
  • Inferring network structure from experimental data remains a significant challenge.

Purpose of the Study:

  • To develop an optimization-based inference scheme for unraveling the functional interaction structure of biomolecular components within a cell.
  • To infer cellular regulatory networks using data from perturbations of adjustable parameters or initial concentrations.
  • To address the challenge of network sparsity and incorporate prior information.

Main Methods:

  • An optimization-based inference scheme is proposed.
  • The inference procedure is formulated as a convex optimization problem with regularization to ensure network sparsity and incorporate prior information.
  • Cubic spline fitting is integrated to estimate time derivatives from discrete-time samples.

Main Results:

  • The proposed scheme effectively uncovers the functional interaction structure of biomolecular regulatory networks.
  • Simulation studies demonstrate the accuracy and efficacy of the convex optimization approach.
  • The method successfully balances sparsity requirements with the incorporation of existing network knowledge.

Conclusions:

  • The developed optimization-based inference scheme provides an effective computational tool for reconstructing cellular regulatory networks.
  • This approach offers a robust method for analyzing complex biological systems and advancing systems biology.
  • The integration of convex optimization and cubic spline fitting enhances the accuracy of network inference from perturbation data.