3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif

González-Dı́az, Humberto; González, Angeles Sánchez; González-Díaz, Yenny

doi:10.1016/j.jinorgbio.2006.02.019

Cited by 60 publications

(33 citation statements)

References 40 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%

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%

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

et al. 2007

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“…15 Applications to macromolecules have been restricted to the field of RNA without applications to proteins. [16][17][18][19] In three recent reviews, we discussed the multiple applications of MARCH-INISDE to classic QSAR, macromolecular QSAR, and specially mt-QSAR. 20,21 However, we have never used before stochastic spectral moments to develop an mt-QSAR for antiparasitic drugs.…”

Section: Introductionmentioning

confidence: 99%

Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species

Prado-Prado

Garcı́a-Mera

González-Dı́az

2010

Bioorganic & Medicinal Chemistry

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105

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“…Once the information of 3D structures is obtained, there are varieties of approaches for studying structure-related function and mechanisms from dynamic and other points of view, such as the quasi-continuum model for investigating the low-frequency (or Terahertz frequency) internal collective motion of biomacromolecules and its biological functions (see, e.g., [21][22][23][24][25][26] and the review articles [27,28]) as well as its medical applications [29,30], molecu- lar dynamics for simulating the local motion of protein atoms [31,32], molecular docking for investigating the binding interactions of proteins and their ligands [33][34][35], QSAR for quantitatively studying structure-activity relationship [36][37][38], and pharmacophore for defining the essential features of biological activities of drugs [39,40], among other approaches [41].…”

Section: ) How Can Be Done?mentioning

confidence: 99%

Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology

Chou¹

2009

445

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With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop automated methods for efficiently identifying various attributes of uncharacterized proteins. This is one of the most important tasks facing us today in bioinformatics, and the information thus obtained will have important impacts on the development of proteomics and system biology. To realize that, one of the keys is to find an effective model to represent the sample of a protein. The most straightforward model in this regard is its entire amino acid sequence; however, the entire sequence model would fail to work when the query protein did not have significant homology to proteins of known characteristics. Thus, various non-sequential models or discrete models were proposed. The simplest discrete model is the amino acid (AA) composition. Using it to represent a protein, however, all the sequence-order information would be completely lost. To cope with such a dilemma, the concept of pseudo amino acid (PseAA) composition was introduced. Its essence is to keep using a discrete model to represent a protein yet without completely losing its sequence-order information. Therefore, in a broad sense, the PseAA composition of a protein is actually a set of discrete numbers that is derived from its amino acid sequence and that is different from the classical AA composition and able to harbour some sort of sequence order or pattern information. Ever since the first PseAA composition was formulated to predict protein subcellular localization and membrane protein types, it has stimulated many different modes of PseAA composition for studying various kinds of problems in proteins and proteins-related systems. In this review, we shall give a brief and systematic introduction of various modes of PseAA composition and their applications. Meanwhile, the challenges for finding the optimal PseAA composition are also briefly discussed.

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3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif

Cited by 60 publications

References 40 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

Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species

Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology

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