ArticleProteins2015
Nonlinearities in protein space limit the utility of informatics in protein biophysics.
Article in Proteins, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.Proteins · 2024Article
- Design and characterization of a protein fold switching network.Nature communications · 2023Article
- Reduced alphabet of prebiotic amino acids optimally encodes the conformational space of diverse extant protein folds.BMC evolutionary biology · 2019Article
- Homology modeling in a dynamical world.Protein science : a publication of the Protein Society · 2017Article
- Global informatics and physical property selection in protein sequences.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
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1 author.
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Abstract
We examine the utility of informatic-based methods in computational protein biophysics. To do so, we use newly developed metric functions to define completely independent sequence and structure spaces for a large database of proteins. By investigating the relationship between these spaces, we demonstrate quantitatively the limits of knowledge-based correlation between the sequences and structures of proteins. It is shown that there are well-defined, nonlinear regions of protein space in which dissimilar structures map onto similar sequences (the conformational switch), and dissimilar sequences map onto similar structures (remote homology). These nonlinearities are shown to be quite common-almost half the proteins in our database fall into one or the other of these two regions. They are not anomalies, but rather intrinsic properties of structural encoding in amino acid sequences. It follows that extreme care must be exercised in using bioinformatic data as a basis for computational structure prediction. The implications of these results for protein evolution are examined.
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Registered trials
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