European Journal of Medicinal Chemistry 2009-01-01

Scoring function for DNA–drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories

Lázaro G. Pérez-Montoto, Lourdes Santana, Humberto González-Díaz, Lázaro G. Pérez-Montoto, Lourdes Santana, Humberto González-Díaz, Lázaro G. Pérez-Montoto, Lourdes Santana, Humberto González-Díaz

Index: Eur. J. Med. Chem. 44 , 4461-9, (2009)

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Abstract

We introduce here a new class of invariants for MD trajectories based on the spectral moments π k (L) of the Markov matrix associated to lattice network-like (LN) graph representations of Molecular Dynamics (MD) trajectories. The procedure embeds the MD energy profiles on a 2D Cartesian coordinates system using simple heuristic rules. At the same time, we associate the LN with a Markov matrix that describes the probabilities of passing from one state to other in the new 2D space. We construct this type of LNs for 422 MD trajectories obtained in DNA–drug docking experiments of 57 furocoumarins. The combined use of psoralens + ultraviolet light (UVA) radiation is known as PUVA therapy. PUVA is effective in the treatment of skin diseases such as psoriasis and mycosis fungoides. PUVA is also useful to treat human platelet (PTL) concentrates in order to eliminate Leishmania spp. and Trypanosoma cruzi. Both are parasites that cause Leishmaniosis (a dangerous skin and visceral disease) and Chagas disease, respectively; and may circulate in blood products collected from infected donors. We included in this study both lineal (psoralens) and angular (angelicins) furocoumarins. In the study, we grouped the LNs on two sets; set1: DNA–drug complex MD trajectories for active compounds and set2: MD trajectories of non-active compounds or no-optimal MD trajectories of active compounds. We calculated the respective π k (L) values for all these LNs and used them as inputs to train a new classifier that discriminate set1 from set2 cases. In training series the model correctly classifies 79 out of 80 (specificity = 98.75%) set1 and 226 out of 238 (Sensitivity = 94.96%) set2 trajectories. In independent validation series the model correctly classifies 26 out of 26 (specificity = 100%) set1 and 75 out of 78 (sensitivity = 96.15%) set2 trajectories. We propose this new model as a scoring function to guide DNA-docking studies in the drug design of new coumarins for anticancer or antiparasitic PUVA therapy.