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Profile conditional random fields for modeling protein families with structural information.

Akira R Kinjo1

  • 1Institute for Protein Research, Osaka University, Suita, Osaka, 565-0871, Japan.

Biophysics (Nagoya-Shi, Japan)
|November 19, 2016
PubMed
Summary
This summary is machine-generated.

A new statistical model, profile conditional random fields (CRFs), integrates profile hidden Markov models (HMMs) and protein folding theory. This model captures sequence correlations and long-range interactions for improved protein family analysis.

Keywords:
dynamic programmingfold recognitionmean field approximationsequence analysisstructure prediction

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Statistical modeling

Background:

  • Profile hidden Markov models (HMMs) are widely used for protein family analysis.
  • Protein folding theories, such as Finkelstein-Reva (FR), provide insights into protein structure.
  • Integrating sequence information and folding principles can enhance protein modeling.

Purpose of the Study:

  • To propose a novel statistical model, profile conditional random fields (CRFs), for protein family analysis.
  • To integrate the strengths of profile HMMs and FR theory into a unified framework.
  • To develop methods for parameter learning and alignment optimization within the proposed model.

Main Methods:

  • Formulation of the profile CRF model, analogous to profile HMMs but incorporating arbitrary correlations.
  • Application of mean-field-like approximations to handle long-range pair-wise interactions between model states, inspired by FR theory.
  • Development of algorithms for computing partition functions and marginal probabilities.
  • Outline of global optimization methods for model parameters and a Bayesian framework for parameter learning and alignment selection.

Main Results:

  • The profile CRF model allows for the incorporation of arbitrary correlations in sequences.
  • Long-range pair-wise interactions between model states can be modeled using approximations.
  • Algorithms for key computations and optimization strategies are presented.
  • A Bayesian framework is proposed for robust parameter learning and alignment selection.

Conclusions:

  • Profile CRFs offer a powerful statistical framework for protein family modeling by combining sequence and structural information.
  • The model's ability to capture complex correlations and interactions advances protein sequence analysis.
  • The proposed methods provide a foundation for improved protein family classification and understanding folding principles.