Combining prediction of secondary structure and solvent accessibility in proteins

Abstract

Owing to the use of evolutionary information and advanced machine learning protocols, secondary structures of amino acid residues in proteins can be predicted from the primary sequence with more than 75% per-residue accuracy for the 3-state (i.e., helix, beta-strand, and coil) classification problem. In this work we investigate whether further progress may be achieved by incorporating the relative solvent accessibility (RSA) of an amino acid residue as a fingerprint of the overall topology of the protein. Toward that goal, we developed a novel method for secondary structure prediction that uses predicted RSA in addition to attributes derived from evolutionary profiles. Our general approach follows the 2-stage protocol of Rost and Sander, with a number of Elman-type recurrent neural networks (NNs) combined into a consensus predictor. The RSA is predicted using our recently developed regression-based method that provides real-valued RSA, with the overall correlation coefficients between the actual and predicted RSA of about 0.66 in rigorous tests on independent control sets. Using the predicted RSA, we were able to improve the performance of our secondary structure prediction by up to 1.4% and achieved the overall per-residue accuracy between 77.0% and 78.4% for the 3-state classification problem on different control sets comprising, together, 603 proteins without homology to proteins included in the training. The effects of including solvent accessibility depend on the quality of RSA prediction. In the limit of perfect prediction (i.e., when using the actual RSA values derived from known protein structures), the accuracy of secondary structure prediction increases by up to 4%. We also observed that projecting real-valued RSA into 2 discrete classes with the commonly used threshold of 25% RSA decreases the classification accuracy for secondary structure prediction. While the level of improvement of secondary structure prediction may be different for prediction protocols that implicitly account for RSA in other ways, we conclude that an increase in the 3-state classification accuracy may be achieved when combining RSA with a state-of-the-art protocol utilizing evolutionary profiles. The new method is available through a Web server at http://sable.cchmc.org.

Large-scale global identification of protein lysine methylation in vivo

“…48 SABLE was used for prediction of the protein secondary structure. 49 Cytoscape plugin BiNGO was used to assess overrepresentation of Gene Ontology (GO) categories. 50 The hypergeometric model and the Benjamini Hochberg false discovery rate correction were applied.…”

Inferring Protein-Protein Interactions (PPIs) Based on Computational Methods

“…The variables were obtained by adding two features, which are derived from the amino acid pairs of the central amino acid with the other amino acids in the window. In total, 18 features were defined, and they were divided into four groups, designated as physicochemical, mobility, secondary structure (predicted by psipred (Jones, 1999) or PHD (Rost, 1996)), and ASA (predicted by sable (Adamczak et al, 2005) or RVPnet (Ahmad et al, 2003)). The profile-based predictors (psipred and sable) have higher prediction accuracy than the amino-acid propensity-based predictors (PHD and RVPnet).…”

Computational and Experimental Analyses of Mammalian ArylamineN-Acetyltransferase Structure and Function

“…N-Acetyltransferase protein sequences from S. typhimurium, P. aeruginosa, M. loti, and human NAT2 were evaluated using 12 secondary structure prediction programs: APSSP2, PROF king, PROF sec, PORTER, JPRED, JUFO, SCRATCH, SABLE1, SABLE2, SAM-T99, PSIpred, and YASPIN (McClelland and Rumelhart, 1988;Kneller et al, 1990;Rost, 1996;Karplus et al, 1998;Cuff and Barton, 2000;McGuffin et al, 2000;Ouali and King, 2000;Meiler et al, 2001;Raghava, 2002;Adamczak et al, 2005;Cheng et al, 2005;Lin et al, 2005;Pollastri and McLysaght, 2005). Secondary structure predictions were then aligned based on Modeler or 3DCoffee protein sequence alignments.…”