title: CSSP2: an improved method for predicting contact-dependent secondary structure propensity.
abstract: 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.
authors.affiliation: Sookmyung Women's University, Department of Biological Sciences, Research Center for Women's Diseases (RCWD), Hyochangwongil 52, Yongsan-gu, Seoul 140-742, Republic of Korea. yoonsj@sookmyung.ac.kr
authors.forename: Sukjoon
authors.initials: S
authors.lastname: Yoon
authors.forename: William J
authors.initials: WJ
authors.lastname: Welsh
authors.forename: Heeyoung
authors.initials: H
authors.lastname: Jung
authors.forename: Young Do
authors.initials: YD
authors.lastname: Yoo
mesh_terms: D000682:Amyloid / Q000737:chemistry; D001185:Artificial Intelligence; D055592:Biophysical Phenomena; D001703:Biophysics; D019748:Carbon-Carbon Double Bond Isomerases / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D045782:Hemiterpenes; D020407:Internet; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D055672:Static Electricity; D014584:User-Computer Interface
chemical_list: D000682:Amyloid; D045782:Hemiterpenes; D011506:Proteins; D019748:Carbon-Carbon Double Bond Isomerases; C021453:isopentenyldiphosphate delta-isomerase