ArticleThe protein journal2012
Information properties of naturally-occurring proteins: Fourier analysis and complexity phase plots.
Article in The protein journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems.Scientific reports · 2018Article
- A new bioinformatics approach to natural protein collections: permutation structure contrasts of viral and cellular systems.The protein journal · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In previous work from this lab, the information in natural proteins was investigated with Ribonuclease A (RNase A) serving as the source. The signature traits were investigated at three structure levels: primary through tertiary. The present paper travels further by charting the primary structure information of about half a million molecules. This was feasible given abundant sequence archives for both living and viral systems. Notably, a method is presented for evaluating primary structure information, based on Fourier analysis and spectral complexity. Significantly, the results show certain complexity traits to be universal for living sources. Viruses, by contrast, encode protein collections which are case-specific and complexity-divergent. The results have ramifications for discriminating collections on the basis of sequence information. This discrimination offers new strategies for selecting drug targets.
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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.