Nucleic Acid Quadratic Indices of the “Macromolecular Graph’s Nucleotides Adjacency Matrix”. Modeling of Footprints after the Interaction of Paromomycin with the HIV-1 Ψ-RNA Packaging Region

Marrero-Ponce, Yovani; Nodarse, Delvin; González-Dı́az, Humberto; Armas, Ronal Ramos de; Zaldívar, Vicente Romero; Torrens, Francisco; Castro, Eduardo A.

doi:10.3390/i5110276

Cited by 44 publications

(3 citation statements)

References 25 publications

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“…Graphical-numerical approaches have been applied to encode the structure of DNA, RNA, and proteins for functional annotation with no alignments [101][102][103], for the prediction of the stability of Arc repressors [104,105], the interaction between drug receptor [106,107], and detection of protein biomarkers in human diseases [108]. However, the potentialities of such approaches to overcome some bioinformatics' handicaps like the detection of remote homologs in the twilight zone have not been fully explored and that is probably the reason why they are not considered among the AF methods reviewed in recent reviews [16,17].…”

Section: Brief Background Of Graphical-numerical Approachesmentioning

confidence: 99%

Graph Theory-Based Sequence Descriptors as Remote Homology Predictors

et al. 2019

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Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies. The most popular alignment-free methodologies, as well as their applications to classification problems, have been described in previous reviews. Despite a new set of graph theory-derived sequence/structural descriptors that have been gaining relevance in the detection of remote homology, they have been omitted as AF predictors when the topic is addressed. Here, we first go over the most popular AF approaches used for detecting homology signals within the twilight zone and then bring out the state-of-the-art tools encoding graph theory-derived sequence/structure descriptors and their success for identifying remote homologs. We also highlight the tendency of integrating AF features/measures with the AB ones, either into the same prediction model or by assembling the predictions from different algorithms using voting/weighting strategies, for improving the detection of remote signals. Lastly, we briefly discuss the efforts made to scale up AB and AF features/measures for the comparison of multiple genomes and proteomes. Alongside the achieved experiences in remote homology detection by both the most popular AF tools and other less known ones, we provide our own using the graphical–numerical methodologies, MARCH-INSIDE, TI2BioP, and ProtDCal. We also present a new Python-based tool (SeqDivA) with a friendly graphical user interface (GUI) for delimiting the twilight zone by using several similar criteria.

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Section: Brief Background Of Graphical-numerical Approachesmentioning

confidence: 99%

Graph Theory-Based Sequence Descriptors as Remote Homology Predictors

et al. 2019

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“…In this report, the CARDD module was selected for drawing all structures and for the codification of non-stochastic and stochastic atom-based linear indices for this large series of active/inactive compounds. The approach has been successfully employed in QSPR/QSAR studies (Marrero-Ponce, 2003, 2004Marrero-Ponce, Pérez, Zaldivar, Ofori, & Montero, 2003;Marrero-Ponce, Nodarse, González, Ramos de Armas, Romero-Zaldivar, Torrens, & Castro, 2004), including studies related to nucleic acid-drug interactions (Marrero-Ponce, Castillo-Garit et al, 2004). The TOMOCOMD-CARDD strategy is very useful for the selection of novel subsystems of compounds having a desired property/activity, which can be further optimized by using some of the many molecular modelling methods at the disposition of the medicinal chemists.…”

Section: Computational Approachmentioning

confidence: 99%

Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices

Casañola-Martín

Khan

Le-Thi-Thu

et al. 2012

International Journal of Chemoinformatics and Chemical Engineering

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In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 for C. All individual models were validated and fulfilled by OECD principles. A brief analysis of AD for the training set of 478 compounds and the new active compounds included in the re-training was carried out. Various assembled multiclassifier systems contained eighteen models using different selection criterions were obtained, which provide possibility of select the best strategy for particular problem. The various assembled multiclassifier systems also estimated the potency of active identified compounds. Eighteen validated potency models by OECD principles were used.

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“…Among them the linear multiple regression (LMR)13, 14 continues being one of the most used techniques in QSAR analysis, especially in the 2D QSAR 15. New improvements of LMR in QSAR and QSPR analysis have recently appeared in the chemical literature 16–22. This fact is not only concerned to tradition, which plays an important role in human activities, but is also due to the facilities in the interpretation of the results that these simple linear models give.…”

Section: Introductionmentioning

confidence: 99%

Quantitative structure‐antibacterial activity relationship modeling using a combination of piecewise linear regression‐discriminant analysis (I): Quantum chemical, topographic, and topological descriptors

Molina

Estrada

Nodarse

et al. 2008

Int J of Quantum Chemistry

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Time-dependent antibacterial activity of 2-furylethylenes using quantum chemical, topographic, and topological indices is described as inhibition of respiration in E. coli. A QSAR strategy based on the combination of the linear piecewise regression and the discriminant analysis is used to predict the biological activity values of strong and moderates antibacterial furylethylenes. The breakpoint in the values of the biological activity was detected. The biological activities of the compounds are described by two linear regression equations. A discriminant analysis is carried out to classify the compounds in one of the biological activity two groups. The results showed using different kind of descriptors were compared. In all cases the piecewise linear regression - discriminant analysis (PLR-DA) method produced significantly better QSAR models than the linear regression analysis. The QSAR models were validated using an external validation previously extracted from the original data. A prediction of reported antibacterial activity analysis was carried out showing dependence between the probability of a good classification and the experimental antibacterial activity. Statistical parameters showed the quality of quantum-chemical descriptors based models prediction in LDA having an accuracy of 0.9 and a C of 0.9. The best PLR-DA model explains more than 92% of the variance of experimental activity. Models with best prediction results were those based on quantum-chemical descriptors. An interpretation of quantum-chemical descriptors entered in models was carried ou

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Nucleic Acid Quadratic Indices of the “Macromolecular Graph’s Nucleotides Adjacency Matrix”. Modeling of Footprints after the Interaction of Paromomycin with the HIV-1 Ψ-RNA Packaging Region

Cited by 44 publications

References 25 publications

Graph Theory-Based Sequence Descriptors as Remote Homology Predictors

Graph Theory-Based Sequence Descriptors as Remote Homology Predictors

Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices

Quantitative structure‐antibacterial activity relationship modeling using a combination of piecewise linear regression‐discriminant analysis (I): Quantum chemical, topographic, and topological descriptors

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