QSAR study for mycobacterial promoters with low sequence homology

González-Dı́az, Humberto; Pérez-Bello, Alcides; Uriarte, Eugenio; González-Díaz, Yenny

doi:10.1016/j.bmcl.2005.10.057

Cited by 33 publications

(14 citation statements)

References 63 publications

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“…In addition, the model based on sto-TIs of Spectral moment type ( k values) classified correctly 83.7% of 132 mps and 98.89% of 274 control sequences in training [43]. Similar results were obtained in four cross validation experiments using a re-substitution technique that showed the model to have an average 93.9% of robustness and 94.1% of predictability for the 407 sequences used.…”

Section: Prediction Of Micobacterial Promoters With March-inside Descsupporting

confidence: 81%

Predicting Antimicrobial Drugs and Targets with the MARCH-INSIDE Approach

González-Dı́az¹,

Prado-Prado²,

Ubeira

2008

CTMC

114

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The method MARCH-INSIDE (MARkovian CHemicals IN SIlico DEsign) is a simple but efficient computational approach to the study of Quantitative Structure-Activity Relationships (QSAR) in Medicinal Chemistry. The method uses the theory of Markov Chains to generate parameters that numerically describe the chemical structure of drugs and drug targets. This approach generates two principal types of parameters Stochastic Topological Indices (sto-TIs) and stochastic 3D-Topographic Indices (sto-TPGIs). The use of these parameters allows the rapid collection, annotation, retrieval, comparison and mining of molecular and macromolecular chemical structures within large databases. In the work described here, we review and comment on the several applications of MARCH-INSIDE to the Medicinal Chemistry of Antimicrobial agents as well as their molecular targets. First we revised the use of classic sto-TIs to predict antiparasite compounds for the treatment of Fascioliasis. Next, we revised the use of chiral sto-TIs (sto-CTIs) to predict new antibacterial, antiviral and anti-coccidial compounds. After that, we review multi-target sto-TIs (mt-sto-TIs), which unifying QSAR models predicting antifungal, antibacterial, or anti-parasite drugs with multiple targets (microbial species). We also discussed the uses of mt-sto-TIs to assemble drug-drug similarity Complex Networks of antimicrobial compounds based on molecular structure. Last, we review the use of MARCH-INSIDE to generate macromolecular TIs and TPGIs for proteins or RNA targets for antimicrobial drugs.

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Section: Prediction Of Micobacterial Promoters With March-inside Descsupporting

confidence: 81%

Predicting Antimicrobial Drugs and Targets with the MARCH-INSIDE Approach

González-Dı́az¹,

Prado-Prado²,

Ubeira

2008

CTMC

114

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“…Consequently, unlike 1 (2D-RNA), which only measures connectivity, the 1 index is also more complicated to calculate. 28,52,[64][65][66] In any case, we can explain the similar results for both indices base on the fact that both are calculated for a large RNA secondary structure. This fact determines that the effect of the weight of each particular nucleotide is less important than the topology of the RNA as a whole.…”

Section: Comparison Of Different Molecular Descriptors Using Linear Csupporting

confidence: 53%

“…50 Later, one may calculate molecular descriptors of the RNA secondary structure and use it for QSAR studies. 51 One of this indices called the electrostatic potential has been shown to correlated to biological function of biopolymers. 52,53 In the present study, we selected as example QSAR study of the RNAs of the family of 1-aminocyclopropane-1-carboxylic acid (ACC) oxidases known as ACO.…”

Section: Introductionmentioning

confidence: 99%

2D‐RNA‐coupling numbers: A new computational chemistry approach to link secondary structure topology with biological function

et al. 2007

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Methods for prediction of proteins, DNA, or RNA function and mapping it onto sequence often rely on bioinformatics alignment approach instead of chemical structure. Consequently, it is interesting to develop computational chemistry approaches based on molecular descriptors. In this sense, many researchers used sequence-coupling numbers and our group extended them to 2D proteins representations. However, no coupling numbers have been reported for 2D-RNA topology graphs, which are highly branched and contain useful information. Here, we use a computational chemistry scheme: (a) transforming sequences into RNA secondary structures, (b) defining and calculating new 2D-RNA-coupling numbers, (c) seek a structure-function model, and (d) map biological function onto the folded RNA. We studied as example 1-aminocyclopropane-1-carboxylic acid (ACC) oxidases known as ACO, which control fruit ripening having importance for biotechnology industry. First, we calculated tau(k)(2D-RNA) values to a set of 90-folded RNAs, including 28 transcripts of ACO and control sequences. Afterwards, we compared the classification performance of 10 different classifiers implemented in the software WEKA. In particular, the logistic equation ACO = 23.8 . tau(1)(2D-RNA) + 41.4 predicts ACOs with 98.9%, 98.0%, and 97.8% of accuracy in training, leave-one-out and 10-fold cross-validation, respectively. Afterwards, with this equation we predict ACO function to a sequence isolated in this work from Coffea arabica (GenBank accession DQ218452). The tau(1)(2D-RNA) also favorably compare with other descriptors. This equation allows us to map the codification of ACO activity on different mRNA topology features. The present computational-chemistry approach is general and could be extended to connect RNA secondary structure topology to other functions.

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“…In a series of recent publications we described the definition and calculation of new RNA TIs for Markov models defined for NAGs. We can identify these TIs as RNA secondary structure stochastic spectral moments ( SR k ) [101], entropies ( k ) and potentials ( k ) [102] in analogy to those defined for small molecules. The NAG, the stochastic matrix and the first SR k for a miRNA are given in Table 2.…”

Section: Tis For Rna Secondary Structurementioning

confidence: 99%

Medicinal Chemistry and Bioinformatics - Current Trends in Drugs Discovery with Networks Topological Indices

González-Dı́az

Vilar²,

Santana³

et al. 2007

CTMC

Self Cite

280

170

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The numerical encoding of chemical structure with Topological Indices (TIs) is currently growing in importance in Medicinal Chemistry and Bioinformatics. This approach allows the rapid collection, annotation, retrieval, comparison and mining of chemical structures within large databases. TIs can subsequently be used to seek quantitative structure-activity relationships (QSAR), which are models connecting chemical structure with biological activity. In the early 1990's, there was an explosion in the introduction and definition of new TIs. The Handbook of Molecular Descriptors by Todeschini and Consonni lists more than 1500 of these indices. At the end of the last century, researchers produced a large number of TIs with essentially the same advantages and/or disadvantages. Consequently, many researchers abandoned the definition of TIs for a time. In our opinion, one of the problems associated with TIs is that researchers aimed their efforts only at the codification of chemical connectivity for small-sized drugs. As a consequence, recently it seems that we have arrived at "Fukuyama's End of History in TIs definition". In the work described here, we review and comment on the "quo vadis" and challenges in the definition of TIs as we enter the new century. Emphasis is placed on new chiral TIs (CTIs), flexible TIs for unifying QSAR models with multiple targets, topographic indices (TPGIs), TIs for DNA and protein sequences, TIs for 2D RNA structures, TPGIs and drug-protein or drug-RNA quantitative structure-binding relationship (QSBR) studies, TIs to encode protein surface information and TIs for protein interaction networks (PINs).

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QSAR study for mycobacterial promoters with low sequence homology

Cited by 33 publications

References 63 publications

Predicting Antimicrobial Drugs and Targets with the MARCH-INSIDE Approach

Predicting Antimicrobial Drugs and Targets with the MARCH-INSIDE Approach

2D‐RNA‐coupling numbers: A new computational chemistry approach to link secondary structure topology with biological function

Medicinal Chemistry and Bioinformatics - Current Trends in Drugs Discovery with Networks Topological Indices

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