Recognition and architecture of the framework structure of protein

Protein fold recognition based on functional domain composition

Recognition of protein fold types is an important step in protein structure and function predictions and is also an important method in protein sequence-structure research. Protein fold type reflects the topological pattern of the structure's core. Now there are three methods of protein structure prediction, comparative modeling, fold recognition and de novo prediction. Since comparative modeling is limited by sequence similarity and there is too much workload in de novo prediction, fold recognition has the greatest potential. In order to improve recognition accuracy, a recognition method based on functional domain composition is proposed in this paper. This article focuses on the 124 fold types which have more than 2 samples in LIFCA database. We apply the functional domain composition to predict the fold types of a protein or a domain. In order to evaluate our method and its sensibility to the samples involving SCOP family divided, we tested our results from different aspects. The average sensitivity, specificity and Matthew's correlation coefficient (MCC) of the 124 fold types were found to be 94.58%, 99.96% and 0.91, respectively. Our results indicate that the functional domain composition method is a very promising method for protein fold recognition. And though based on simple classification rules, LIFCA database can grasp the functional features of different proteins, reflecting the corresponding relation between protein structure and function.

High-accuracy prediction of protein structural classes using PseAA structural properties and secondary structural patterns

Since introduction of PseAAs and functional domains, promising results have been achieved in protein structural class predication, but some challenges still exist in the representation of the PseAA structural correlation and structural domains. This paper proposed a high-accuracy prediction method using novel PseAA structural properties and secondary structural patterns, reflecting the long-range and local structural properties of the PseAAs and certain compact structural domains. The proposed prediction method was tested against the competing prediction methods with four experiments. The experiment results indicate that the proposed method achieved the best performance. Its overall accuracies for datasets 25 PDB, D640, FC699 and 1189 are 88.8%, 90.9%, 96.4% and 87.4%, which are 4.5%, 7.6%, 2% and 3.9% higher than the existing best-performing method. This understanding can be used to guide development of more powerful methods for protein structural class prediction. The software and supplement material are freely available at http://bioinfo.zstu.edu.cn/PseAA-SSP.

A folding "framework structure" of Tetrahymena group I intron

We have published the dynamic extended folding (DEF) method, which is a RNA secondary structure prediction approach-to simulate the in vivo RNA co-transcriptional folding process. In order to verify the reliability of the method, we selected the X-ray-determined Tetrahymena group I intron as a sample to construct the framework of its folding secondary structure. Our prediction coincides well with the secondary structure predicted by T.R. Cech and the X-ray diffraction crystal structure determined by Lehnert V. Our results show that the DEF framework structure of Tetrahymena group I intron reflects its function sites in a concise and straightforward manner, and the scope of the simulation was expanded.

The prediction of the structural class of protein: application of the measure of diversity

Based on the concept that the structural class of a protein is mainly determined by its secondary structure sequence, a new algorithm for prediction of the structural class of a protein is proposed. By use of the number of alpha -helices, beta -strands, and betaalphabeta fragments, the structural class of a protein can be predicted by an algorithm based on the increment of diversity (ID), in which the sole prediction parameter-the increment of diversity is used as the index of prediction of structural class of a protein. The results indicate that the high rates of correct prediction are obtained for complete set (standard set) from Brookhaven Protein Data Bank-CD ROM (PDB) published in October 1995 and the test set newly released from Brookhaven Protein Data Bank-CD ROM (PDB) before July 1998, respectively.

Elucidating protein secondary structures using alpha-carbon recurrence quantifications

Secondary structures of proteins were studied by recurrence quantification analysis (RQA). High-resolution, 3-dimensional coordinates of alpha-carbon atoms comprising a set of 68 proteins were downloaded from the Protein Data Bank. By fine-tuning four recurrence parameters (radius, line, residue, separation), it was possible to establish excellent agreement between percent contribution of alpha-helix and beta-sheet structures determined independently by RQA and that of the DSSP algorithm (Define Secondary Structure of Proteins). These results indicate that there is an equivalency between these two techniques, which are based upon totally different pattern recognition strategies. RQA enhances qualitative contact maps by quantifying the arrangements of recurrent points of alpha carbons close in 3-dimensional space. For example, the radius was systematically increased, moving the analysis beyond local alpha-carbon neighborhoods in order to capture super-secondary and tertiary structures. However, differences between proteins could only be detected within distances up to about 6-11 A, but not higher. This result underscores the complexity of alpha-carbon spacing when super-secondary structures appear at larger distances. Finally, RQA-defined secondary structures were found to be robust against random displacement of alpha carbons upwards of 1 A. This finding has potential import for the dynamic functions of proteins in motion.