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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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...
Protein and Protein Structures02:15

Protein and Protein Structures

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

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Related Experiment Video

Updated: May 23, 2026

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

Boosting Protein Threading Accuracy.

Jian Peng, Jinbo Xu

    Research in Computational Molecular Biology : ... Annual International Conference, RECOMB ... : Proceedings. RECOMB (Conference : 2005- )
    |April 17, 2012
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel nonlinear scoring function for protein threading, improving alignment accuracy and fold recognition. This method better models feature interactions than traditional linear approaches.

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    Published on: July 14, 2015

    Area of Science:

    • Computational biology
    • Structural bioinformatics
    • Machine learning in bioinformatics

    Background:

    • Protein threading is a key method for protein structure prediction.
    • Current methods often use linear scoring functions, limiting accuracy due to unexploited feature interdependencies.

    Purpose of the Study:

    • To develop a nonlinear scoring function for protein threading.
    • To improve alignment accuracy and fold recognition rates in protein structure prediction.

    Main Methods:

    • Modeling the threading problem using Conditional Random Fields (CRF), a probabilistic graphical model.
    • Training the CRF model with the gradient tree boosting algorithm.
    • Developing a nonlinear scoring function composed of regression trees capturing feature interactions.

    Main Results:

    • The nonlinear scoring function effectively models interactions among sequence and structure features.
    • The new threading model significantly improves alignment accuracy.
    • Enhanced fold recognition rates were observed compared to linear models.

    Conclusions:

    • Nonlinear scoring functions, like the one developed, offer advantages over linear models in protein threading.
    • This approach successfully leverages weak biological signals for improved protein structure prediction.
    • The CRF and gradient tree boosting framework provides an efficient and powerful tool for protein threading.