Vibrational Markovian modelling of footprints after the interaction of antibiotics with the packaging region of HIV type 1

The design of novel anti-HIV compounds has now become a crucial area for scientists working in numerous interrelated fields of science such as molecular biology, medicinal chemistry, mathematical biology, molecular modelling and bioinformatics. In this context, the development of simple but physically meaningful mathematical models to represent the interaction between anti-HIV drugs and their biological targets is of major interest. One such area currently under investigation involves the targets in the HIV-RNA-packaging region. In the work described here, we applied Markov chain theory in an attempt to describe the interaction between the antibiotic paromomycin and the packaging region of the RNA in Type-1 HIV. In this model, a nucleic acid squeezed graph is used. The vertices of the graph represent the nucleotides while the edges are the phosphodiester bonds. A stochastic (Markovian) matrix was subsequently defined on this graph, an operation that codifies the probabilities of interaction between specific nucleotides of HIV-RNA and the antibiotic. The strength of these local interactions can be calculated through an inelastic vibrational model. The successive power of this matrix codifies the probabilities with which the vibrations after drug-RNA interactions vanish along the polynucleotide main chain. The sums of self-return probabilities in the k-vicinity of each nucleotide represent physically meaningful descriptors. A linear discriminant function was developed and gave rise to excellent discrimination in 80.8% of interacting and footprinted nucleotides. The Jackknife method was employed to assess the stability and predictability of the model. On the other hand, a linear regression model predicted the local binding affinity constants between a specific nucleotide and the antibiotic (R2 = 0.91, Q2 = 0.86). These kinds of models could play an important role either in the discovery of new anti-HIV compounds or the study of their mode of action.

[1]  Jean Garnier,et al.  FORESST: fold recognition from secondary structure predictions of proteins , 1999, Bioinform..

[2]  Iraj Daizadeh,et al.  EID: the Exon?Intron Database?an exhaustive database of protein-coding intron-containing genes , 2000, Nucleic Acids Res..

[3]  W D Wilson,et al.  Targeting RNA with small molecules. , 2000, Current medicinal chemistry.

[4]  Nicholas J. Schisler,et al.  The IDB and IEDB: intron sequence and evolution databases , 2000, Nucleic Acids Res..

[5]  K. Chou.  Prediction and classification of α‐turn types , 1997 .

[6]  J. Francis.  Statistica for Windows , 1995 .

[7]  D. Haussler,et al.  Hidden Markov models in computational biology. Applications to protein modeling. , 1993, Journal of molecular biology.

[8]  P. Argos,et al.  Seventy‐five percent accuracy in protein secondary structure prediction , 1997, Proteins.

[9]  Han van de Waterbeemd,et al.  Chemometric methods in molecular design , 1995 .

[10]  D. Goodsell,et al.  Automated docking to multiple target structures: Incorporation of protein mobility and structural water heterogeneity in AutoDock , 2002, Proteins.

[11]  Ramón García-Domenech,et al.  Antimicrobial Activity Characterization in a Heterogeneous Group of Compounds , 1998, J. Chem. Inf. Comput. Sci..

[12]  T. Pöschel,et al.  Stochastic Processes in Physics, Chemistry, and Biology , 2000 .

[13]  Jerry Goodisman,et al.  Absorption studies on aminoglycoside binding to the packaging region of human immunodeficiency virus type-1. , 2002, Bioorganic & medicinal chemistry letters.

[14]  A. Bernstein,et al.  RNA tumor viruses , 1982 .

[15]  T. Hubbard,et al.  Fold recognition and ab initio structure predictions using hidden markov models and β‐strand pair potentials , 1995, Proteins.

[16]  Ernest Frederick Gale,et al.  The Molecular basis of antibiotic action , 1972 .

[17]  Humberto González Díaz,et al.  Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer aided molecular design II: experimental and theoretical assessment of a novel method for virtual screening of fasciolicides , 2002, Journal of molecular modeling.

[18]  Zheng Yuan.  Prediction of protein subcellular locations using Markov chain models , 1999, FEBS letters.

[19]  J D Puglisi,et al.  Biochemical and nuclear magnetic resonance studies of aminoglycoside-RNA complexes. , 2000, Methods in enzymology.

[20]  E V Koonin,et al.  New genes in old sequence: a strategy for finding genes in the bacterial genome. , 1994, Trends in biochemical sciences.

[21]  Jerry Goodisman,et al.  Footprinting and circular dichroism studies on paromomycin binding to the packaging region of human immunodeficiency virus type-1. , 2002, Bioorganic & medicinal chemistry.

[22]  Christophe Geourjon,et al.  Improved performance in protein secondary structure prediction by inhomogeneous score combination , 1999, Bioinform..

[23]  M. Borodovsky,et al.  Detection of new genes in a bacterial genome using Markov models for three gene classes. , 1995, Nucleic acids research.