Remote Thioredoxin Recognition Using Evolutionary Conservation and Structural Dynamics

The thioredoxin family of oxidoreductases plays an important role in redox signaling and control of protein function. Not only are thioredoxins linked to a variety of disorders, but their stable structure has also seen application in protein engineering. Both sequence-based and structure-based tools...

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Bibliographic Details
Published in:Structure (London) 2011-04, Vol.19 (4), p.461-470
Main Authors: Tang, Grace W., Altman, Russ B.
Format: Article
Language:English
Subjects:
Amino Acid Sequence
Animals
Catalytic Domain
Computational Biology - methods
Databases, Protein
Disorders
Dynamics
Evolution, Molecular
Evolutionary
Humans
Kinetics
Mathematical models
Models, Molecular
Molecular dynamics
Molecular Sequence Data
Protein Conformation
Protein Structure, Secondary
Protein Structure, Tertiary
Proteins
Recall
Recognition
Reproducibility of Results
Sequence Homology, Amino Acid
Software
Thioredoxins - chemistry
Thioredoxins - genetics
Thioredoxins - metabolism
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Summary:The thioredoxin family of oxidoreductases plays an important role in redox signaling and control of protein function. Not only are thioredoxins linked to a variety of disorders, but their stable structure has also seen application in protein engineering. Both sequence-based and structure-based tools exist for thioredoxin identification, but remote homolog detection remains a challenge. We developed a thioredoxin predictor using the approach of integrating sequence with structural information. We combined a sequence-based Hidden Markov Model (HMM) with a molecular dynamics enhanced structure-based recognition method (dynamic FEATURE, DF). This hybrid method (HMMDF) has high precision and recall (0.90 and 0.95, respectively) compared with HMM (0.92 and 0.87, respectively) and DF (0.82 and 0.97, respectively). Dynamic FEATURE is sensitive but struggles to resolve closely related protein families, while HMM identifies these evolutionary differences by compromising sensitivity. Our method applied to structural genomics targets makes a strong prediction of a novel thioredoxin. [Display omitted] ► New structure-based thioredoxin model allows more exact active site representation ► MD simulation improves recall of structure-based function prediction methods ► Implicit solvent MD simulation is sufficiently precise for simulating thioredoxins ► Sequence and structural dynamics data jointly improve remote thioredoxin detection
ISSN:0969-2126
1878-4186
DOI:10.1016/j.str.2011.02.007