ABSTRACT
Recurrence quantification analysis (RQA) was used to characterize the folding properties of 22 chimeric sequences derived from two parent proteins of similar length but different three-dimensional arrangement. A non-linear relation between sequence data and their RQA representation was revealed, which points to new information carried by this method as compared with classical best-alignment methods. This new information is significantly correlated with the folding properties of the hybrid polypeptide chains, as substantiated by careful statistical analysis of the recurrence plots' numerical descriptors, thus encouraging their systematic use to complement sequence data in both proteomics and protein engineering tasks. Even the direct visual screening of the qualitative graphical features of recurrence plots is shown to provide useful hints to discriminate between different recurrence structures of protein sequences.
Subject(s)
Protein Folding , Recombinant Fusion Proteins/chemistry , Amino Acid Sequence , Linear Models , Models, Molecular , Nerve Tissue Proteins/chemistry , Protein Engineering/methods , Protein Structure, Tertiary , Spectrin/chemistry , src Homology DomainsABSTRACT
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.
Subject(s)
Rubredoxins/chemistry , Sequence Analysis, Protein/methods , Amino Acid Sequence , Bacterial Proteins/chemistry , Models, Molecular , Molecular Sequence Data , Protein Structure, Tertiary , Sequence Alignment , Sequence Homology, Amino Acid , SoftwareABSTRACT
The dynamics of learning in the Active Avoidance test was analyzed at the trials level as well as at the level of daily sessions, each comprising numerous trials. The two scales (large scale for the sessions and small scale for the trials) were demonstrated to be mutually independent. The intermediate derived scales (blocks of trials) were found consistent among themselves and with small scale but independent of the large one. Moreover, the two extreme scales were kinetically discriminable. These results point to the existence of two independent mechanisms for large and small scale learning together with the need to postulate a consolidation process during the rest period.
