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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 Organization01:13

Protein Organization

Overview
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 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...
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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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

PREDICT-2ND: a tool for generalized protein local structure prediction.

Sol Katzman1, Christian Barrett, Grant Thiltgen

  • 1Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA.

Bioinformatics (Oxford, England)
|September 2, 2008
PubMed
Summary
This summary is machine-generated.

Predicting protein local structure using neural networks aids biologists in identifying key amino acid positions. The PREDICT-2ND program, utilizing multilayer neural networks, offers improved predictions with guide sequences.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Protein local structure prediction from sequence data serves as a valuable tool for biologists, especially when experimental structures are unavailable.
  • These predictions are crucial for evaluating amino acid residue importance and can be integrated into various bioinformatics tools, including hidden Markov models (HMMs) for homology searching and genetic algorithms for tertiary structure prediction.

Purpose of the Study:

  • To develop and refine a program (PREDICT-2ND) for predicting protein local structure using multilayer neural networks.
  • To optimize neural network parameters, including layers, units, and window sizes, for enhanced prediction accuracy.
  • To investigate the impact of incorporating guide sequences into the prediction model.

Main Methods:

  • Development of the PREDICT-2ND program utilizing multilayer neural networks.
  • Systematic tuning of network parameters such as the number of layers, units per layer, and window sizes.
  • Implementation of a training protocol involving multiple random starts and iterative refinement of the best-performing networks.
  • Inclusion of an option to incorporate guide sequences into the profile inputs.

Main Results:

  • Four-layer neural networks with gradually increasing window sizes demonstrated the most successful outcomes.
  • The refined training protocol, using multiple random starts and iterative training, mitigates the issue of networks getting trapped in local optima.
  • The addition of a guide sequence to profile inputs consistently yielded a small but significant improvement in prediction accuracy across various local structure alphabets.

Conclusions:

  • The PREDICT-2ND program effectively predicts protein local structure using optimized multilayer neural networks.
  • The use of a refined training strategy and guide sequences enhances prediction reliability and accuracy.
  • The developed methods and tools are accessible online and as downloadable source code for the research community.