ArticleBiophysical journal2000
Nonlinear methods in the analysis of protein sequences: a case study in rubredoxins.
Article in Biophysical journal, 2000. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 56 citations in OpenAlex.
- Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation.Entropy (Basel, Switzerland) · 2021Article
- Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method.BMC bioinformatics · 2017Article
- Quantiprot - a Python package for quantitative analysis of protein sequences.BMC bioinformatics · 2017Article
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- Response, use and habituation to a mouse house in C57BL/6J and BALB/c mice.Experimental animals · 2015Article
- An interaction-dominant perspective on reading fluency and dyslexia.Annals of dyslexia · 2012Article
- Designing human m1 muscarinic receptor-targeted hydrophobic eigenmode matched peptides as functional modulators.Biophysical journal · 2004Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Two computational methods widely used in time series analysis were applied to protein sequences, and their ability to derive structural information not directly accessible through classical sequence comparisons methods was assessed. The primary structures of 19 rubredoxins of both mesophilic and thermophilic bacteria, coded with hydrophobicity values of amino acid residues, were considered as time series and were analyzed by 1) recurrence quantification analysis and 2) spectral analysis of the sequence major eigenfunctions. The results of the two methods agreed to a large extent and generated a classification consistent with known 3D structural characteristics of the studied proteins. This classification separated in a clearcut manner a thermophilic protein from mesophilic proteins. The classification of primary structures given by the two dynamical methods was demonstrated to be basically different from classification stemming from classical sequence homology metrics. Moreover, on a more detailed scale, the method was able to discriminate between thermophilic and mesophilic proteins from a set of chimeric sequences generated from the mixing of a mesophilic (Rubr Clopa) and a thermophilic (Rubr Pyrfu) protein. Overall, our results point to a new way of looking at protein sequence comparisons.
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