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Related Concept Videos

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-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...
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...
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...
Protein and Protein Structure02:15

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme can...

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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

Conditional graphical models for protein structural motif recognition.

Yan Liu1, Jaime Carbonell, Vanathi Gopalakrishnan

  • 1IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA. liuya@us.ibm.com

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 13, 2009
PubMed
Summary
This summary is machine-generated.

Predicting protein structures aids infection research and drug design. This study introduces a novel conditional graphical model for protein structural motif prediction, improving accuracy over existing methods.

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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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Area of Science:

  • Structural biology
  • Computational biology
  • Bioinformatics

Background:

  • Protein structure determination is vital for understanding disease mechanisms and developing therapeutics.
  • Crystallographic experiments, while powerful, can be a time-consuming bottleneck in protein structure elucidation.
  • Accurate prediction of protein structural motifs is essential for accelerating drug discovery and biological mechanism studies.

Purpose of the Study:

  • To present a novel probabilistic graphical model framework, conditional graphical models (CGMs), for predicting protein structural motifs.
  • To develop efficient approximate inference algorithms for handling complex graphical models in protein structure prediction.
  • To evaluate the performance of the proposed method against state-of-the-art algorithms for motif recognition.

Main Methods:

  • Representing protein structural motifs as graphs with nodes for secondary structures and edges for side-chain interactions.
  • Utilizing a discriminative training approach by maximizing the conditional probability for optimal sequence segmentation.
  • Implementing reversible jump Markov Chain Monte Carlo (MCMC) algorithms for efficient approximate inference in complex graphical models.

Main Results:

  • The proposed conditional graphical model framework demonstrates superior performance in recognizing four important structural motifs compared to existing algorithms.
  • The method successfully predicts protein structural motifs by effectively modeling secondary structure elements and their interactions.
  • Hypothesized protein memberships for target folds from Swiss-Prot provide further support for evolutionary hypotheses regarding viral folds.

Conclusions:

  • Conditional graphical models offer a powerful and accurate approach for predicting protein structural motifs.
  • The developed MCMC-based inference algorithms efficiently handle complex graphical models, enabling practical application.
  • This work advances computational methods for protein structure prediction, potentially accelerating drug discovery and understanding of viral evolution.