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PHD--an automatic mail server for protein secondary structure prediction

B Rost, C Sander, R Schneider

    Computer Applications in the Biosciences : CABIOS
    |February 1, 1994
    PubMed
    Summary

    Protein structure prediction is advancing, with secondary structure prediction methods like PHD offering improved accuracy for protein sequences. This aids in understanding protein function and evolution.

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    Area of Science:

    • Structural Biology
    • Bioinformatics
    • Computational Biology

    Background:

    • Over 30,000 protein sequences were available by mid-1993.
    • Experimental determination of tertiary structures was limited to 1000 proteins.
    • Homology modeling could address an additional 7000 sequences.

    Purpose of the Study:

    • To assess the utility of secondary structure prediction for the remaining 21,000 protein sequences.
    • To evaluate the accuracy of secondary structure prediction methods.
    • To introduce and validate the PHD (Protein His-structure Determination) server for automated prediction.

    Main Methods:

    • Secondary structure prediction was applied to estimate structural features.
    • The PHD automatic e-mail server was utilized, incorporating evolutionary conservation from multiple sequence alignments.
    • Prediction accuracy was evaluated based on three states (e.g., helix, sheet, coil).

    Main Results:

    • Secondary structure prediction accuracy ranged from 35% (random) to 88% (homology modeling).
    • The PHD server predicted secondary structures for 4700 protein sequences.
    • For proteins with known homologues, PHD achieved an expected overall three-state accuracy of 71.4% on 126 unique protein chains.

    Conclusions:

    • Secondary structure prediction offers a valuable approach for characterizing protein structural features when experimental data is unavailable.
    • The PHD server demonstrates significant accuracy, particularly for proteins with evolutionary relatives.
    • Automated prediction tools like PHD enhance the ability to analyze large protein sequence datasets.

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