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CSSP2: An improved method for predicting contact-dependent secondary structure propensity

2007; Elsevier BV; Volume: 31; Issue: 5-6 Linguagem: Inglês

10.1016/j.compbiolchem.2007.06.002

1476-928X

Sukjoon Yoon, William J. Welsh, Heeyoung Jung, Young Do Yoo,

RNA and protein synthesis mechanisms

The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native beta-strand propensities in the core sequences of amyloidogenic proteins. Here we describe a noble energy-based CSSP method implemented on dual artificial neural networks that rapidly and accurately estimate the potential for the non-native secondary structure formation in local regions of protein sequences. In this method, we attempted to quantify long-range interaction patterns in diverse secondary structures by potential energy calculations and decomposition on a pairwise per-residue basis. The calculated energy parameters and seven-residue sequence information were used as inputs for artificial neural networks (ANNs) to predict sequence potential for secondary structure conversion. The trained single ANN using the >(i, i+/-4) interaction energy parameter exhibited 74% accuracy in predicting the secondary structure of test sequences in their native energy state, while the dual ANN-based predictor using (i, i+/-4) and >(i, i+/-4) interaction energies showed 83% prediction accuracy. The present method provides a simple and accurate tool for predicting sequence potential for secondary structure conversions without using 3D structural information.