QSAR study for macromolecular RNA folded secondary structures of mycobacterial promoters with low sequence

a Department of Organic Chemistry, University of Santiago de Compostela, 15782, Spain. b Chemical Bioactives Center and Department of Veterinary Medicine, Central University of ‘Las Villas’, 54830, Cuba. c Department of Ultrasound Medicine, Calixto, Las Tunas, 77400, Cuba. ______________________________________________________________________________ Abstract. The general belief is that quantitative structure-activity relationships (QSAR) techniques work only for small molecules and, proteins sequences or, more recently, DNA sequences. However, with non-branched graph for proteins and DNA sequences the QSAR often have to be based on powerful non-linear techniques such as support vector machines. In our opinion linear QSAR models based in RNA could be useful to assign biological activity when alignment techniques fail due to low sequence homology. The idea bases in the high level of branching for the RNA graph. This work introduces the so called Markov electrostatic potentials ξM as a new class of RNA 2D-structure descriptors. Subsequently, we validate these molecular descriptors solving a QSAR classification problem for mycobacterial promoter sequences (mps), which constitute a very low sequence homology problem. The model developed (mps = –4.664·ξM + 0.991·ξM – 2.432) was intended to predict whether a naturally occurring sequence is an mps or not on the basis of the calculated ξM value for the corresponding RNA secondary structure. The RNAQSAR approach recognises 115/135 mps (85.2%) and 100% of control sequences. Average predictability and robustness were greater than 95%. A previous non-linear model predicts mps with slightly higher accuracy (97%) but uses a very large parameter space for DNA sequences. Conversely, the ξM-based RNA-QSAR encodes more structural information and needs only two variables. _____________________________________________________________________________