A pentapeptide-based method for protein secondary structure prediction

Abstract

We present a new method for protein secondary structure prediction, based on the recognition of well-defined pentapeptides, in a large databank. Using a databank of 635 protein chains, we obtained a success rate of 68.6%. We show that progress is achieved when the databank is enlarged, when the 20 amino acids are adequately grouped in 10 sets and when more pentapeptides are attributed one of the defined conformations, alpha-helices or beta-strands. The analysis of the model indicates that the essential variable is the number of pentapeptides of well-defined structure in the database. Our model is simple, does not rely on arbitrary parameters and allows the analysis in detail of the results of each chosen hypothesis.

Conformation biases of amino acids based on tripeptide microenvironment from PDB database

“…In this set, equivalent amino acids have roughly similar physico-chemical properties and secondary structure bias, although their algorithm does not take these into account. Furthermore, we found that our sub-groups are similar to other sets as follows (Figureau et al, 2003;Johnson and Overington, 1993) …”

“…At the same time, the conformation angles ðj; cÞ of the second residue in a peptide triplet unit were calculated by coordinates of dihedral angel using the sliding window method (Yang et al) along protein sequences from PDB database. The type of the triplet is defined as a, b, or others based on its conformation angles ðj; cÞ, if they fit Ramachandran's and Frishman's chart, as compared with the previous works (Figureau et al, 1999(Figureau et al, , 2003 …”

Characterization of protein secondary structure from NMR chemical shifts

“…As the secondary structure definitions are based on the coordinates of a model derived by X-ray crystallography or NMR, any algorithm will be affected by the quality of the underlying data. The best estimation rate varies widely depending on the choice of algorithm [86], [87], [88]. However, of the many different methods of defining secondary structure proposed, DSSP has most successfully stood the test of time, and is widely used in the field of structural biology.…”

A Frequency-Based Linguistic Approach to Protein Decoding and Design: Simple Concepts, Diverse Applications, and the SCS Package

“…Additionally, secondary structure characterization is one of the important applications of the frequency-based word analysis [18, 19, 31, 32, 35]. Through the construction and analysis of secondary-structure-specific databases, we have shown that some SCSs are favored in α-helices and others in β-strands [31].…”