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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
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 Organization01:24

Protein Organization

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.
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,...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Distance matrix-based approach to protein structure prediction.

Andrzej Kloczkowski1, Robert L Jernigan, Zhijun Wu

  • 1Laurence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, 112 Office and Lab Bldg, Ames, IA 50011-3020, USA. kloczkow@iastate.edu

Journal of Structural and Functional Genomics
|February 19, 2009
PubMed
Summary

This study introduces a novel distance matrix approach for protein structure prediction and refinement. By analyzing spectral decomposition of distance matrices, researchers can predict protein structure and dynamics with improved accuracy.

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Protein structure and dynamics are crucial for biological function.
  • Traditional methods like contact matrices offer limited information compared to distance matrices.
  • Predicting protein structure and dynamics from sequence remains a significant challenge.

Purpose of the Study:

  • To develop and validate a distance matrix-based approach for protein structure prediction and refinement.
  • To explore the relationship between sequence information and structural properties encoded in distance matrices.
  • To investigate protein dynamics using spectral decomposition of distance matrices and compare with experimental and simulation data.

Main Methods:

  • Spectral decomposition of protein square distance matrices (D).
  • Prediction of dominant eigenvectors (e.g., square distance from center of mass, principal components) from amino acid sequence.
  • Principal Component Analysis (PCA) applied to ensembles of known protein structures to analyze conformational changes and dynamics.
  • Utilizing distance constraints from known protein structures for refinement.

Main Results:

  • Predicting the dominant eigenvector (r^2) from sequence yields an RMSD of ~7.3 Å; combining with the first principal component improves accuracy to ~4.0 Å.
  • Eigenvectors correlate with hydrophobic profiles, contact numbers, residue-wise contact order (RWCO), and mean square fluctuations, aiding structure prediction.
  • PCA of HIV-1 protease structures reveals motions similar to low-frequency normal modes from Elastic Network Models (ENM), suggesting global motions drive conformational changes.
  • Distance constraints derived from known structures improve the plausibility of refined structural models.

Conclusions:

  • Distance matrix spectral decomposition provides a powerful framework for protein structure and dynamics prediction.
  • Sequence-derived structural and dynamic information can be effectively captured through eigenvector analysis.
  • Experimental structure ensembles and computational methods like ENM can reveal similar protein dynamics.
  • Distance-based refinement using known structure distributions enhances the accuracy of protein models.