Protein Homology Detection Through Alignment of Markov Random Fields

1. Front Matter

   Pages i-viii

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2. Introduction

   • Jinbo Xu, Sheng Wang, Jianzhu Ma

   Pages 1-16

3. Method

   • Jinbo Xu, Sheng Wang, Jianzhu Ma

   Pages 17-30

4. Software

   • Jinbo Xu, Sheng Wang, Jianzhu Ma

   Pages 31-36

5. Experiments and Results

   • Jinbo Xu, Sheng Wang, Jianzhu Ma

   Pages 37-48

6. Back Matter

   Pages 49-51

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This work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method.

• Hidden Markov Model
• Long-Range Residue Interaction
• Markov Random Fields
• Position-Specific Scoring Matrix
• Protein Homology Detection

• Toyota Technological Institute, Chicago, USA

  Jinbo Xu, Sheng Wang, Jianzhu Ma

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