MCC 0.74 –0.01 1.00 0.95 0.85 0.74 0.89 0.91 0.94 0.91 0.87 0.93 0.81 0.80 0.70 0.89 0.86 0.95 0.48 0.88 1.00 0.86 0.89 0.79 1.00 0.94 0.83 0.87 0.81 CF SNS 0.77 0.00 1.00 1.00 1.00 0.77 1.00 0.87 1.00 0.83 0.87 1.00 1.00 0.76 0.67 0.80 0.78 0.91 0.46 1.00 1.00 1.00 0.85 0.63 1.00 1.00 0.85 0.85 0.84 PPV 0.747 0.756 0.684 0.692 0.71 0.00 1.00 0.91 0.73 0.71 0.80 0.95 0.89 1.00 0.87 0.88 0.67 0.85 0.74 1.00 0.95 1.00 0.50 0.78 1.00 0.75 0.93 1.00 1.00 0.89 0.83 0.89 0.80 Table 1. Prediction results corresponding to 26 RNA structures that are available in the Protein Dank Bank PDB Description 1A60 1CX0 1E95 1HVU 1KAJ 1KH6 1KPY 1KXK 1L2X 1Q9A 1U8D 2A43 2G1W 2GIS 2HOO 2K95 2OIU 2QUS 2QWY 2RP0 2TPK 361D 3DIG 3FU2 3PHP 437D Mean Mean Mean TYMV tRNA-like structure HDV ribozyme SRV-1 pseudoknot HIV RT bind. pseudoknot MMTV RNA pseudoknot HCV IRES domain PEMV-1 P1P2 pseudoknot GroupII self-splic. intron Viral RNA pseudoknot 23S rRNA sarcin/ricin Guanine riboswitch Luteoviral pseudoknot tmRNA pseudoknot SAM- riboswitch thi-box riboswitch P2B-P3 telo-merase RNA L1 Ribozyme Ligase adduct Hammerhead Ribozyme SAM-II riboswitch PEMV1 mRNA pseudoknot T2 gene 32 mRNA p.k. Domain E of 5S rRNA Lysine Riboswitch class-I preQ1 riboswitch TYMV p.k. hairpin rib. frame-shifting p.k. All No pseudoknots Pseudoknots 0.96 –0.01 1.00 0.95 0.85 0.55 0.94 0.81 0.94 0.77 0.88 0.93 0.86 0.80 0.58 0.89 0.98 0.95 0.48 0.88 1.00 0.86 0.74 0.79 1.00 0.94 0.82 0.81 0.82 PK SNS 1.00 0.00 1.00 1.00 1.00 0.54 1.00 0.83 1.00 0.83 0.78 1.00 1.00 0.76 0.62 0.80 0.96 0.91 0.46 1.00 1.00 1.00 0.72 0.63 1.00 1.00 0.84 0.84 0.84 Nucleic Acids Research, 2010, Vol. 38, Web Server issue W371 PPV MCC PPV MCC PPV MCC HK SNS 0.77 0.00 1.00 0.40 1.00 0.54 0.62 0.83 0.63 1.00 0.78 0.57 0.67 0.86 0.62 0.8 0.96 0.91 0.31 0.71 0.58 0.83 0.72 0.63 1.00 0.63 0.71 0.84 0.65 0.91 0.00 1.00 0.80 0.73 0.54 1.00 0.79 1.00 0.75 1.00 1.00 1.00 0.86 0.54 0.71 1.00 1.00 0.4 1.00 0.88 0.83 0.76 1.00 1.00 1.00 0.83 0.81 0.84 0.83 –0.01 0.70 0.56 0.53 0.92 0.79 0.96 0.79 0.77 0.88 0.75 0.81 0.55 0.58 0.54 0.98 0.95 0.35 0.84 0.63 0.83 0.74 0.79 1.00 0.79 0.73 0.87 0.66 L 44 72 36 30 32 42 27 70 27 27 67 26 22 94 83 48 71 68 52 26 36 19 173 32 23 27 PKF 13.6 22.2 33.3 26.6 25.0 0.0 22.2 0.0 22.2 0.0 11.9 23.0 18.1 8.5 0.0 37.5 0.0 2.9 26.9 15.3 27.7 0.0 30.1 18.8 0.0 22.2 <5.0 >5.0 SNS 0.763 0.753 0.565 0.532 UF SNS 0.77 0.00 0.50 0.40 0.50 0.93 0.63 0.96 0.63 0.83 0.78 0.57 0.67 0.55 0.62 0.40 1.00 1.00 0.31 0.71 0.58 0.83 0.72 0.63 1.00 0.63 0.65 0.90 0.55 PPV 0.91 0.00 1.00 0.80 0.57 0.93 1.00 0.96 1.00 0.71 1.00 1.00 1.00 0.55 0.54 0.75 1.00 1.00 0.40 1.00 0.70 0.83 0.76 1.00 1.00 1.00 0.83 0.87 0.80 0.93 0.00 1.00 0.91 0.73 0.58 0.89 0.79 0.89 0.71 1.00 0.88 0.75 0.85 0.54 1.00 1.00 1.00 0.5 0.78 1.00 0.75 0.76 1.00 1.00 0.89 0.81 0.80 0.82 0.83 –0.01 1.00 0.56 0.85 0.53 0.79 0.81 0.79 0.86 0.88 0.75 0.81 0.86 0.58 0.75 0.98 0.95 0.34 0.84 0.71 0.83 0.74 0.79 1.00 0.79 0.75 0.82 0.73 very simple (24). In that sense it is surprising how well the method performs, and it should be an encouragement to continue to develop RNA folding algorithms that are sub- stantially diﬀerent from established approaches. CONCLUSION CyloFold is a new method for RNA secondary structure prediction. We show using two diﬀerent data sets that the prediction accuracy (MCC) is comparable to the RNA secondary structure prediction program pknotsRG. The search algorithm has no restriction in terms of pseudoknot complexity. Another novel aspect is that at each step during the simulated folding process, the steric feasibility of the predicted structures is checked for steric feasibility using a highly coarse-grained 3D representation. The method is made available in the form of a user-friendly web server. ACKNOWLEDGEMENTS We wish to thank the Advanced Biomedical Computing Center (ABCC) at the NCI for their computing support. The content of this publication does not necessarily reﬂect the views or policies of the Department of Health and Human Services, nor does mention of trade names, com- mercial products, or organizations imply endorsement by L, Sequence length; PKF, fraction of pseudoknot interactions; For each of the four diﬀerent prediction methods (CF, Cylofold; PK, pknotsRG; HK, HotKnots 2.0; UF, UNAFold) we report three diﬀerent measures of prediction quality (SNS, sensitivity; PPV, positive predictive value). Table 2. Prediction results for a set of 241 RNA sequences that are part of PseudoBase for the programs CyloFold, pknotsRG (7), HotKnots 2.0 (8) and UNAFold (23) MCC 0.752 0.748 0.611 0.597 CyloFold pknotsRG HotKnots 2.0 UNAFold SNS, sensitivity of predicted base pairs; PPV, positive predictive value. reference secondary structure), all programs obtain averages between 0.68 and 0.76 for data set 2 with pknotsRG leading with a value of 0.756. It should be noted that the MCC is often used as an overall measure of prediction quality, while sensitivity, speciﬁcity and positive predictive value capture certain other aspects of the prediction quality. These results indicate that the prediction accuracy of CyloFold compared to pknotsRG is similar. The key ad- vantage of CyloFold is that there is no restriction in terms of the classes of pseudoknots that are being considered. Also, it should be noted that the employed model of simulated RNA folding by placing helices with a probabil- ity according to their free energy contribution is in essence