Proteins Markovian 3D-QSAR with spherically-truncated average electrostatic potentials

Saı́z-Urra, Liane; González-Dı́az, Humberto; Uriarte, Eugenio

doi:10.1016/j.bmc.2005.03.041

Cited by 35 publications

(41 citation statements)

References 25 publications

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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%

“…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. This plant enzyme participates in ethylene production from S-adenosylmethionine via ACC, the immediate precursor of ethylene.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Comparison Of Different Molecular Descriptors Using Linear Csupporting

confidence: 53%

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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“…As a consequence, the main aim of the present paper was to introduce in RNA-QSAR studies the Markov electrostatic potentials ( k n M ) previously used for protein QSAR. 39 In this sense, we intend to predict whether a naturally occurring DNA sequence is an mps or not on the basis of the k n M calculated for its putative RNA secondary structure. Consequently, a more specific, but still important, aim of this work is to introduce a novel approach to predict mps.…”

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QSAR study for mycobacterial promoters with low sequence homology

González-Dı́az

Pérez-Bello

Uriarte

et al. 2006

Bioorganic & Medicinal Chemistry Letters

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“…This fact could be due to the differences and similarities between these descriptors concerning the structural information that they are able to encode because of their mathematical definition. 29,68,69 All of these methodologies have different final equations despite the fact that they all consider vdw interactions. Nonetheless, in spite of predicting non-kinases more easily, the Spectral moments have the least separation between the percentage of good classification for these kinds of groups (95.55% (non-K) to 85.40% (K) ¼ 10.15) and the best total percentage (89.71).…”

Section: Comparison Of O B K With Other Stochastic Descriptorsmentioning

confidence: 97%

Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments

et al. 2007

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Three-dimensional (3D) protein structures now frequently lack functional annotations because of the increase in the rate at which chemical structures are solved with respect to experimental knowledge of biological activity. As a result, predicting structure-function relationships for proteins is an active research field in computational chemistry and has implications in medicinal chemistry, biochemistry and proteomics. In previous studies stochastic spectral moments were used to predict protein stability or function (González-Díaz, H. et al. Bioorg Med Chem 2005, 13, 323; Biopolymers 2005, 77, 296). Nevertheless, these moments take into consideration only electrostatic interactions and ignore other important factors such as van der Waals interactions. The present study introduces a new class of 3D structure molecular descriptors for folded proteins named the stochastic van der Waals spectral moments ((o)beta(k)). Among many possible applications, recognition of kinases was selected due to the fact that previous computational chemistry studies in this area have not been reported, despite the widespread distribution of kinases. The best linear model found was Kact = -9.44 degrees beta(0)(c) +10.94 degrees beta(5)(c) -2.40 degrees beta(0)(i) + 2.45 degrees beta(5)(m) + 0.73, where core (c), inner (i) and middle (m) refer to specific spatial protein regions. The model with a high Matthew's regression coefficient (0.79) correctly classified 206 out of 230 proteins (89.6%) including both training and predicting series. An area under the ROC curve of 0.94 differentiates our model from a random classifier. A subsequent principal components analysis of 152 heterogeneous proteins demonstrated that beta(k) codifies information different to other descriptors used in protein computational chemistry studies. Finally, the model recognizes 110 out of 125 kinases (88.0%) in a virtual screening experiment and this can be considered as an additional validation study (these proteins were not used in training or predicting series).

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Proteins Markovian 3D-QSAR with spherically-truncated average electrostatic potentials

Cited by 35 publications

References 25 publications

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

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

QSAR study for mycobacterial promoters with low sequence homology

Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments

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