Using entropy of drug and protein graphs to predict FDA drug-target network: Theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica

Prado-Prado, Francisco J.; Garcı́a-Mera, Xerardo; Abeijón, Paula; Alonso, Nerea; Caamaño, Olga; Yáñez, Matilde; Gárate, Teresa; Mezo, Mercedes; González-Warleta, Marta; Muiño, Laura; Ubeira, Florencio M.; González-Dı́az, Humberto

doi:10.1016/j.ejmech.2011.01.023

Cited by 49 publications

(21 citation statements)

References 88 publications

(70 reference statements)

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“…Then, the incremental selection method based on SVM was used to select the optimal feature subset. The k-fold cross validation test is executed to examine the accuracy of various predictors [12]. Fig.…”

Section: Results Of the Incremental Selection Methodsmentioning

confidence: 99%

“…Unfortunately, these approaches are not always effective when the query proteins don't match any homologous proteins. As biological features are highly able to represent and distinguish proteins of different structural, functional and interaction profiles, which have been used in several bioinformatics research fields [12,27]. Thus, the process of the construction of the target space can be introduced as following steps: (i) the calculation of the original features.…”

Section: Feature Selection Of the Genomic Spacementioning

confidence: 99%

“…MARCH-INSIDE, introduced by González-Díaz et al [11], is another more recent method which has been applied to construct drug-target networks. MARCH-INSIDE incorporates both structural parameters of drugs and 3D parameters of protein structure [12][13][14][15]. The method uses the theory of Markov Chains to calculate structural parameters of both drug molecular graphs and protein 3D structural networks [16].…”

Section: Introductionmentioning

confidence: 99%

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Using Feature Selection Technique for Drug-Target Interaction Networks Prediction

Jiang²,

Wang³

et al. 2011

CMC

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Elucidating the interaction relationship between target proteins and all drugs is critical for the discovery of new drug targets. However, it is a big challenge to integrate and optimize different feature information into one single "knowledge view" for drug-target interaction prediction. In this article, a feature selection method was proposed to rank the original feature sets. Then, an improved bipartite learning graph method was used to predict four types of drug-target datasets based on the optimized feature subsets. The cross-validation results demonstrate that the proposed method can provide superior performance than previous method on four classes of drug target families.

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Section: Results Of the Incremental Selection Methodsmentioning

confidence: 99%

Section: Feature Selection Of the Genomic Spacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Feature Selection Technique for Drug-Target Interaction Networks Prediction

Jiang²,

Wang³

et al. 2011

CMC

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“…47 Thus, in numerous fields of research in drug discovery, various works have reported the development of advanced chemoinformatic models inspired by the idea regarding the calculation of Box-Jenkins moving averages. [33][34][35][36][48][49][50][51][52][53][54][55][56][57][58] In the first step, the following equation is employed: In Eq. 3, n(c r ) represents the number of peptides assayed by considering the same element of the experimental condition (ontology) c r , which have also been annotated as positive.…”

Section: Box-jenkins Moving Averages and Generation Of The Mtk-computmentioning

confidence: 99%

Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity

et al. 2016

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Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs.

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“…Previous works suggest that natural protein sequences can be differentiated from random sequences based on structural features (De Lucrezia et al, 2012;Munteanu et al, 2008b;Szoniec and Ogorzalek, 2013). Moreover, complexity evaluation of the entire sequence have been used in different classification tasks (AguiarPulido et al, 2012;Giuliani et al, 2000;Munteanu et al, 2008a), also associated with secondary and tertiary structure information as well as kinetic properties (Concu et al, 2009;Gonzalez-Diaz et al, 2004;Gonzalez-Diaz et al, 2007;Liao et al, 2005;Tejera et al, 2014) as well as Shannon entropy prediction of drug-protein interaction networks (Prado-Prado et al, 2011) and other biological networks (Riera-Fernandez et al, 2012). Interestingly, no previous research on the relationship between entire sequence entropy and LCRs complexity has been found.…”

Section: Introductionmentioning

confidence: 99%

Unravelling the relationship between protein sequence and low-complexity regions entropies: Interactome implications

Martins

Gonçalves

Oliveira

et al. 2015

Journal of Theoretical Biology

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Using entropy of drug and protein graphs to predict FDA drug-target network: Theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica

Cited by 49 publications

References 88 publications

Using Feature Selection Technique for Drug-Target Interaction Networks Prediction

Using Feature Selection Technique for Drug-Target Interaction Networks Prediction

Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity

Unravelling the relationship between protein sequence and low-complexity regions entropies: Interactome implications

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