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Related Concept Videos

Protein Folding01:22

Protein Folding

Overview
Protein Folding01:25

Protein Folding

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.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
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Protein Families02:47

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...
Protein Families02:47

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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...
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.
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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

Learning generative models for protein fold families.

Sivaraman Balakrishnan1, Hetunandan Kamisetty, Jaime G Carbonell

  • 1Language Technologies Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

Proteins
|January 27, 2011
PubMed
Summary
This summary is machine-generated.

We developed GREMLIN, a new method for learning protein statistical models from multiple sequence alignments. This generative approach accurately captures residue correlations, outperforming existing methods in predictive accuracy.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Protein Sequence Analysis

Background:

  • Learning statistical models from protein multiple sequence alignments (MSA) is crucial for understanding protein function and evolution.
  • Existing methods often rely on strong assumptions or suboptimal algorithms, limiting their accuracy and applicability.

Purpose of the Study:

  • Introduce GREMLIN (Generative REgularized ModeLs of proteINs), a novel approach for learning undirected probabilistic graphical models from MSA.
  • To develop a method that makes no a priori assumptions about conditional independencies within MSA and guarantees a globally optimal model.

Main Methods:

  • GREMLIN learns an undirected probabilistic graphical model of amino acid composition within an MSA.
  • It encodes position-specific conservation and correlated mutation statistics between residue pairs.
  • The method formulates and solves a convex optimization problem for guaranteed global optimality.

Main Results:

  • GREMLIN accurately captures both position-specific conservation and correlated mutation statistics.
  • The model is generative, enabling the design of new protein sequences with similar statistical properties.
  • GREMLIN significantly outperforms Hidden Markov Models (HMMs) in predictive accuracy on PFAM families.

Conclusions:

  • GREMLIN offers a powerful and flexible new approach to statistical modeling of protein sequences from MSA.
  • Its ability to capture complex residue dependencies and its generative nature make it valuable for protein design and analysis.
  • The method demonstrates superior performance compared to existing techniques, including HMMs.