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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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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Chemical Shift: Internal References and Solvent Effects01:17

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In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
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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.
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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Incorporation of Solvent Effect into Multi-Objective Evolutionary Algorithm for Improved Protein Structure

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    Predicting protein 3D structure from its sequence is challenging. A new multi-objective evolutionary algorithm incorporating solvent effects improves accuracy and efficiency in protein structure prediction.

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    Area of Science:

    • Computational Biology
    • Structural Biology
    • Bioinformatics

    Background:

    • Protein structure prediction from sequence is a fundamental challenge in molecular biology.
    • Accurate protein structure prediction is crucial for structural genomics and understanding biological function.
    • Existing computational methods face limitations in accuracy and efficiency.

    Purpose of the Study:

    • To develop a novel multi-objective evolutionary algorithm for protein 3D structure prediction.
    • To enhance protein structure prediction by incorporating solvent effects.
    • To improve the accuracy and efficiency of predicting protein structures from their amino acid sequences.

    Main Methods:

    • Decomposition of the protein energy function (Chemistry at HARvard Macromolecular Mechanics force fields) into bond and non-bond energies as primary objectives.
    • Inclusion of solvent-accessible surface area as a third objective to account for solvent effects.
    • Validation of the proposed multi-objective evolutionary algorithm using 66 benchmark proteins.

    Main Results:

    • The proposed method achieved competitive or superior results compared to existing protein structure prediction techniques.
    • Incorporating solvent-accessible surface area as an objective function demonstrated a positive impact on prediction accuracy.
    • The algorithm showed improved efficiency in predicting the three-dimensional structures of proteins.

    Conclusions:

    • Multi-objective evolutionary algorithms are effective for protein structure prediction.
    • Accounting for solvent effects is essential for improving the accuracy of protein structure prediction.
    • The developed method offers a promising approach for advancing structural genomics and molecular biology research.