Fast evaluation of internal loops in RNA secondary structure prediction

Motivation: Though not as abundant in known biological processes as proteins, RNA molecules serve as mol-e than mere intermediaries between DNA and proteins. Research in the last 15 years demonstrates that RNA molecules serve in many roles, including catalysis. Furthermore, RNA secondary structure prediction based on fi ee energy rules for stacking and loop formation remains one of the few major breakthroughs in the field of structure prediction, as minimum free energy structures and related quantities can be computed with full mathematical rigor. However, with the curl-ent energy parameters, the algorithms used hither to suffer the disadvantage of either employing heuristics that risk (though highly unlikely) missing the optimal structure or becoming prohibitively time consuming for model ate to large sequences. Results: We present a new method to evaluate internal loops utilizing currently used energy rules. This method reduces the time complexity of this part of the structure prediction from O(n(4)) to O(n(3)), thus reducing the overall complexity to O(n(3)). Even when the size of evaluated internal loops is bounded by k (a commonly used heuristic), the method presented has a competitive edge by reducing the time complexity of internal loop evaluation fi-om O(k(2)n(2)) to O(kn(2)). The method also applies to the calculation of the equilibrium partition function.