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PASSML: combining evolutionary inference and protein secondary structure prediction

P Liò1, N Goldman, J L Thorne

  • 1Department of Genetics, University of Cambridge, Cambridge, UK. University of Warwick, UK.

Bioinformatics (Oxford, England)
|October 28, 1998
PubMed
Summary

A new program, PASSML, integrates protein structure and evolutionary data to improve phylogenetic reconstruction and secondary structure prediction. This tool enhances evolutionary models by considering eight structural environments for amino acid sequences.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Evolutionary models of amino acid sequences can be enhanced by incorporating structural information.
  • Phylogenetic relationships among species can improve protein structure prediction accuracy.

Purpose of the Study:

  • To develop and describe PASSML (Phylogeny and Secondary Structure using Maximum Likelihood), a software tool that implements an evolutionary model combining protein secondary structure and amino acid replacement.
  • To demonstrate PASSML's capability in reconstructing phylogenies and predicting secondary structure from aligned amino acid sequences.

Main Methods:

  • Developed an evolutionary model distinguishing eight categories of structural environments (alpha helix, beta sheet, turn, coil, classified by solvent accessibility: buried or exposed).

Related Experiment Videos

  • Modeled sequence evolution for each category as a Markov process with discrete states in continuous time.
  • Utilized a hidden Markov model to describe the organization of structure along protein sequences.

    • Main Results:

    • Implemented the described evolutionary model in the PASSML software.
    • Demonstrated the software's ability to perform both phylogenetic reconstruction and secondary structure prediction.
    • The model refines existing evolutionary models by incorporating detailed structural environment information.

    Conclusions:

    • PASSML provides a novel approach to integrate evolutionary and structural data in protein sequence analysis.
    • The software facilitates improved accuracy in phylogenetic analysis and secondary structure prediction.
    • This work highlights the utility of combining evolutionary modeling with structural biology insights.