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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
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Remote homology search with hidden Potts models.
Grey W Wilburn1, Sean R Eddy2,3
1Department of Physics, Harvard University, Cambridge, Massachusetts, United States of America.
Plos Computational Biology
|November 30, 2020
Summary
This study introduces a hidden Potts model (HPM) for biological sequence alignment and homology search, incorporating higher-order correlations. HPMs show promise in RNA structure homology searches, advancing sequence analysis beyond primary sequence data.
Area of Science:
- Computational Biology
- Bioinformatics
- Statistical Physics
Background:
- Current homology search methods primarily use primary sequence data, overlooking complex correlations.
- Potts models have successfully inferred pairwise correlations from sequence alignments, enhancing 3D structure predictions.
Purpose of the Study:
- To extend Potts models for biological sequence alignment and homology search.
- To develop a hidden Potts model (HPM) integrating Potts emissions with insertion/deletion models.
Main Methods:
- Developed a hidden Potts model (HPM) merging Potts emission and generative insertion/deletion models.
- Created an approximate importance sampling algorithm for HPM alignment due to dynamic programming incompatibility.
- Tested HPM on RNA structure homology search benchmarks against stochastic context-free grammars.
Main Results:
- HPMs demonstrate promising performance in RNA structure homology search tasks.
- The developed approximate algorithm enables HPM application in sequence alignment.
Conclusions:
- Hidden Potts models offer a novel approach to biological sequence alignment and homology search.
- HPMs effectively capture higher-order correlations, improving upon primary sequence-only methods.
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