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

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

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native β-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.

Original languageEnglish
Pages (from-to)373-377
Number of pages5
JournalComputational Biology and Chemistry
Issue number5-6
Publication statusPublished - 2007 Oct

Bibliographical note

Funding Information:
This work was supported by Korea Research Foundation Grant funded by Korea Government (MOEHRD, Basic Research Promotion Fund) (KRF-2005-003-C00158). This work was also supported by the SRC/ERC program of MOST/KOSEF (R11-2005-017-01003-0) and by grant No.R01-2006-000-10515-0 from the Basic Research Program of the Korea Science & Engineering Foundation. This research was also supported in part by NIH Integrated Advanced Information Management Systems (IAIMS) Grant # 2G08LM06230-03A1 from the National Library of Medicine (to W.J.W.).


  • Amyloid fibril formation
  • Artificial neural network
  • Energy decomposition
  • Machine learning
  • Secondary structure prediction

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Organic Chemistry
  • Computational Mathematics


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