ArticleStructure (London, England : 1993)2011
Remote thioredoxin recognition using evolutionary conservation and structural dynamics.
Article in Structure (London, England : 1993), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- A deep learning framework to predict binding preference of RNA constituents on protein surface.Nature communications · 2019Article
- The Role of Conformational Dynamics and Allostery in the Disease Development of Human Ferritin.Biophysical journal · 2015Article
- Knowledge-based fragment binding prediction.PLoS computational biology · 2014Article
- Structural Consequences of Chromophore Formation and Exploration of Conserved Lid Residues amongst Naturally Occurring Fluorescent Proteins.Chemical physics · 2014Article
- Article
- CEP proteins: the knights of centrosome dynasty.Protoplasma · 2013Review
- Sequence evolution correlates with structural dynamics.Molecular biology and evolution · 2012Article
- Bioinformatics and variability in drug response: a protein structural perspective.Journal of the Royal Society, Interface · 2012Review
- Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding.PLoS computational biology · 2011Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.