A new prediction strategy for long local protein structures using an original description

Abstract

A relevant and accurate description of three-dimensional (3D) protein structures can be achieved by characterizing recurrent local structures. In a previous study, we developed a library of 120 3D structural prototypes encompassing all known 11-residues long local protein structures and ensuring a good quality of structural approximation. A local structure prediction method was also proposed. Here, overlapping properties of local protein structures in global ones are taken into account to characterize frequent local networks. At the same time, we propose a new long local structure prediction strategy which involves the use of evolutionary information coupled with Support Vector Machines (SVMs). Our prediction is evaluated by a stringent geometrical assessment. Every local structure prediction with a Calpha RMSD less than 2.5 A from the true local structure is considered as correct. A global prediction rate of 63.1% is then reached, corresponding to an improvement of 7.7 points compared with the previous strategy. In the same way, the prediction of 88.33% of the 120 structural classes is improved with 8.65% mean gain. 85.33% of proteins have better prediction results with a 9.43% average gain. An analysis of prediction rate per local network also supports the global improvement and gives insights into the potential of our method for predicting super local structures. Moreover, a confidence index for the direct estimation of prediction quality is proposed. Finally, our method is proved to be very competitive with cutting-edge strategies encompassing three categories of local structure predictions.

Analysis of protein contacts into Protein Units

“…In the same way, analysis of PUs in terms of secondary structures did not show any significant tendency in comparison to the entire databank. The Protein Blocks [56,75,85,88,99,[101][102][103][104][105][106][107][108] give a finer description than secondary structures [109], they so give more precise description of protein contacts and even some insights in signature of specific regions of protein loops in the PUs (e.g., PBs h and i).…”

From local structure to a global framework: recognition of protein folds

“…Longer fragments based on PBs were predicted with better accuracy [166,169]. Frequently observed 11 residue fragments described based on the dihedrals were predicted with a significant accuracy of about 63% using SVMs [170]. Prediction of structural flexibility based on local structure preferences has also been successful [171].…”

A short survey on protein blocks

“…Our earlier work on PBs have shown that PBs are effective in describing and predicting conformations of long fragments (Benros, et al, 2006; Benros, et al, 2009; Bornot, et al, 2009; de Brevern, et al, 2007; de Brevern and Hazout, 2001; de Brevern and Hazout, 2003; de Brevern, et al, 2002) and short loops (Fourrier, et al, 2004; Tyagi, et al, 2009; Tyagi, et al, 2009), analyzing protein contacts (Faure, et al, 2008), in building a transmembrane protein (de Brevern, 2005; de Brevern, et al, 2009), and in defining a reduced amino acid alphabet to aid design of mutations (Etchebest, et al, 2007). This reduced amino acid alphabet was recently proved suitable for predicting protein families or sub-families and secretory proteins of P. falciparum (Zuo and Li, 2009; Zuo and Li, 2009).…”

“…This resulted in higher structural variability for the longer fragments through a significant increase in the number of prototypes, e.g ., 100 to 130 prototypes (Benros, et al, 2006; Benros, et al, 2009; de Brevern and Hazout, 2001; de Brevern and Hazout, 2003). These longer fragments were used to perform structural superimposition (de Brevern and Hazout, 2001), methodological optimisation (Benros, et al, 2003; de Brevern and Hazout, 2003), and analysis of sequence – structure relationship (Benros, et al, 2006; Benros, et al, 2009; Bornot, et al, 2009; de Brevern and Hazout, 2001). A modified version of HPM proposed by Pr.…”

“…A global prediction rate of 63.1% was achieved and prediction for 85% of proteins was improved. This method was shown to be among the most efficient of cuttingedge local structure prediction strategies (Bornot, et al, 2009). …”