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Localizing Frustration in Proteins Using All-Atom Energy Functions.

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    Researchers developed new methods to measure protein frustration, a key factor in protein folding and design. Optimizing these measures could improve the design of novel proteins with specific structures and functions.

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    Area of Science:

    • Protein biochemistry
    • Computational biology
    • Structural biology

    Background:

    • Protein folding and design are inverse problems: folding explores sequence space for a structure, while design searches sequence space for a structure.
    • Efficient protein folding requires energy landscapes biased towards the native state, free of kinetic traps.
    • Natural protein sequences are evolutionarily optimized for minimal folding frustration.

    Purpose of the Study:

    • To develop and apply localized frustration measures for evaluating protein energy landscapes.
    • To assess frustration in both naturally evolved (mutated WW domain) and computationally designed (three-helix bundle) proteins.
    • To explore the relationship between energy, frustration, and successful protein design.

    Main Methods:

    • Utilized the Rosetta all-atom energy function for calculations.
    • Developed and calculated several localized frustration measures.
    • Applied these measures to a mutated WW domain (FiP35) and a de novo designed protein (Alpha3D).

    Main Results:

    • The WW domain (FiP35) exhibited less localized frustration than the designed protein (Alpha3D).
    • A specific mutation in FiP35 disrupted its hydrophobic core, increasing localized frustration and unexpectedly hindering folding.
    • Redesign efforts on Alpha3D indicated that energy minimization does not always correlate with reduced frustration.

    Conclusions:

    • Localized frustration measures can detect residual frustration in protein structures.
    • Optimizing these frustration measures offers a potential automated approach to balance positive and negative design in protein engineering.
    • This work provides new tools for understanding and improving the process of protein design.