SARNA-Predict: A Simulated Annealing Algorithm for RNA Secondary Structure Prediction

Ribonucleic acid (RNA) plays fundamental roles in cellular processes and its structure is directly related to its functions. This paper describes and presents a novel algorithm for RNA secondary structure prediction based on simulated annealing (SA). SA is known to be effective in solving many different types of minimization problems and for finding the global minima in the solution space. Based on free energy minimization techniques, this permutation-based SA algorithm heuristically searches for the structure with a free energy value close to the minimum free energy DeltaG for that strand, within given constraints. A detailed study of the convergence behavior of the algorithm is conducted and various cooling schedules are investigated. An evaluation of the performance of the new algorithm in terms of prediction accuracy is made via comparison with the dynamic programming algorithm mfold for eight individual known structures from three RNA classes (5S rRNA, Group I intron 16S rRNA and 16S rRNA). The significant contribution of this algorithm is in showing comparable results with the most common dynamic programming prediction application mfold and surpassing results from an evolutionary algorithm (EA)

[1]  C. Pleij,et al.  An APL-programmed genetic algorithm for the prediction of RNA secondary structure. , 1995, Journal of theoretical biology.

[2]  Huang,et al.  AN EFFICIENT GENERAL COOLING SCHEDULE FOR SIMULATED ANNEALING , 1986 .

[3]  Wilfried Bollweg,et al.  Numerical Prediction of Crystal Structures by Simulated Annealing , 1997 .

[4]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[5]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[6]  Alain Deschenes,et al.  A genetic algorithm for RNA secondary structure prediction using stacking energy thermodynamic models , 2005 .

[7]  Bruce E. Hajek,et al.  Cooling Schedules for Optimal Annealing , 1988, Math. Oper. Res..

[8]  Andrew Hendriks,et al.  Comparison of P-RnaPredict and mfold - algorithms for RNA secondary structure prediction , 2006, Bioinform..

[9]  Nan Yu,et al.  The Comparative RNA Web (CRW) Site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs , 2002, BMC Bioinformatics.

[10]  Christian N. S. Pedersen,et al.  Pseudoknots in RNA secondary structures , 2000, RECOMB '00.

[11]  Michael Zuker,et al.  Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information , 1981, Nucleic Acids Res..

[12]  Kay C. Wiese,et al.  Using stacking-energies (INN and INN-HB) for improving the accuracy of RNA secondary structure prediction with an evolutionary algorithm - a comparison to known structures , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[13]  E Rivas,et al.  A dynamic programming algorithm for RNA structure prediction including pseudoknots. , 1998, Journal of molecular biology.

[14]  F E Cohen,et al.  The origins of protein secondary structure. Effects of packing density and hydrogen bonding studied by a fast conformational search. , 1994, Journal of molecular biology.

[15]  Saeed Zolfaghari,et al.  Adaptive temperature control for simulated annealing: a comparative study , 2004, Comput. Oper. Res..

[16]  T. Steitz,et al.  The structural basis of ribosome activity in peptide bond synthesis. , 2000, Science.

[17]  Emile H. L. Aarts,et al.  Simulated annealing and Boltzmann machines - a stochastic approach to combinatorial optimization and neural computing , 1990, Wiley-Interscience series in discrete mathematics and optimization.

[18]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[19]  Michael Zuker,et al.  Mfold web server for nucleic acid folding and hybridization prediction , 2003, Nucleic Acids Res..

[20]  G. Steger,et al.  Description of RNA folding by "simulated annealing". , 1996, Journal of molecular biology.

[21]  K.C. Wiese,et al.  A parallel evolutionary algorithm for RNA secondary structure prediction using stacking-energies (INN and INN-HB) , 2004, 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

[22]  R. Simons,et al.  RNA structure and function , 1998 .

[23]  Yinghao Li.  Directed annealing search in constraint satisfaction and optimization , 1997 .

[24]  I. Tinoco,et al.  Estimation of Secondary Structure in Ribonucleic Acids , 1971, Nature.

[25]  Pierre Baldi,et al.  Assessing the accuracy of prediction algorithms for classification: an overview , 2000, Bioinform..

[26]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[27]  D. Turner,et al.  Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. , 1998, Biochemistry.

[28]  J. Sabina,et al.  Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. , 1999, Journal of molecular biology.

[29]  Nan Yu,et al.  The Comparative RNA Web (CRW) Site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs: Correction , 2002, BMC Bioinformatics.

[30]  Kathryn A. Dowsland,et al.  General Cooling Schedules for a Simulated Annealing Based Timetabling System , 1995, PATAT.

[31]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[32]  I. Tinoco,et al.  How RNA folds. , 1999, Journal of molecular biology.

[33]  Dimitrios L. Kalpaxis,et al.  Localization of spermine binding sites in 23S rRNA by photoaffinity labeling: parsing the spermine contribution to ribosomal 50S subunit functions , 2005, Nucleic acids research.