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Accurate and automated classification of protein secondary structure with PsiCSI

Computational Genomics, Department of Microbiology, University of Washington, Seattle, Washington 98109, USA

Reprint requests to Ram Samudrala, Computational Genomics, Department of Microbiology, University of Washington, Box 357242, Rosen Building, 960 Republican St., Seattle, WA 98109, USA; e-mail: ram{at}compbio.washington.edu; fax: (206) 732-6055.

PsiCSI is a highly accurate and automated method of assigning secondary structure from NMR data, which is a useful intermediate step in the determination of tertiary structures. The method combines information from chemical shifts and protein sequence using three layers of neural networks. Training and testing was performed on a suite of 92 proteins (9437 residues) with known secondary and tertiary structure. Using a stringent cross-validation procedure in which the target and homologous proteins were removed from the databases used for training the neural networks, an average 89% Q3 accuracy (per residue) was observed. This is an increase of 6.2% and 5.5% (representing 36% and 33% fewer errors) over methods that use chemical shifts (CSI) or sequence information (Psipred) alone. In addition, PsiCSI improves upon the translation of chemical shift information to secondary structure (Q3 = 87.4%) and is able to use sequence information as an effective substitute for sparse NMR data (Q3 = 86.9% without ¹³C shifts and Q3 = 86.8% with only H shifts available). Finally, errors made by PsiCSI almost exclusively involve the interchange of helix or strand with coil and not helix with strand (<2.5 occurrences per 10000 residues). The automation, increased accuracy, absence of gross errors, and robustness with regards to sparse data make PsiCSI ideal for high-throughput applications, and should improve the effectiveness of hybrid NMR/de novo structure determination methods. A Web server is available for users to submit data and have the assignment returned.

Keywords: NMR; chemical shifts; secondary structure; neural networks

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