When global and local molecular descriptors are more than the sum of its parts: Simple, But Not Simpler?

Martínez-López, Yoan; Marrero-Ponce, Yovani; Barigye, Stephen J.; Terán, Enrique; Martínez-Santiago, Oscar; Zambrano, Cesar H.; Torres, Javier

doi:10.1007/s11030-019-10002-3

Cited by 12 publications

(6 citation statements)

References 56 publications

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“…Unlike the legacy descriptors, new descriptors (those in Table SI1-2) are implemented by www.nature.com/scientificreports/ applying statistical and aggregation operators on amino acid property vectors, e.g., measures of central tendency, statistical dispersion, OWA operators 54,55 , and fuzzy Choquet integral operators 56,57 . Further, it should be noted that the reasons for using these operators in the calculation of MDs have been demonstrated elsewhere [58][59][60][61][62] . Let D = [x ij ] n×m be a descriptor matrix whose rows and columns represent peptide instances and calculated features, respectively, i.e., x ij encodes the numerical value for the jth descriptor of the ith peptide sequence.…”

Section: Methodsmentioning

confidence: 96%

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach

Aguilera‐Mendoza

Marrero-Ponce

García-Jacas

et al. 2020

Sci Rep

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The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases. Indeed, our main contribution is a novel workflow for the automatic construction of such similarity networks, enabling visual graph mining techniques to uncover new insights from the “ocean” of known bioactive peptides. The workflow presented here relies on the following sequential steps: (i) calculation of molecular descriptors by applying statistical and aggregation operators on amino acid property vectors; (ii) a two-stage unsupervised feature selection method to identify an optimized subset of descriptors using the concepts of entropy and mutual information; (iii) generation of sparse networks where nodes represent bioactive peptides, and edges between two nodes denote their pairwise similarity/distance relationships in the defined descriptor space; and (iv) exploratory analysis using visual inspection in combination with clustering and network science techniques. For practical purposes, the proposed workflow has been implemented in our visual analytics software tool (http://mobiosd-hub.com/starpep/), to assist researchers in extracting useful information from an integrated collection of 45120 bioactive peptides, which is one of the largest and most diverse data in its field. Finally, we illustrate the applicability of the proposed workflow for discovering central nodes in molecular similarity networks that may represent a biologically relevant chemical space known to date.

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Section: Methodsmentioning

confidence: 96%

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach

Aguilera‐Mendoza

Marrero-Ponce

García-Jacas

et al. 2020

Sci Rep

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“…In the same spirit, we apply different aggregation operators (AOs) that generalize the linear combination of amino acid and/or pairwise contributions. These AOs have been applied in the definition of descriptors for small molecules and comparative studies demonstrated that these generally performed better in predicting physicochemical and biological properties of organic molecules, when compared to the summation operator 14‐19,31‐33 . These AOs are stratified in three groups: (a) Norms which are comprised of: Minkowski's norms N1, N2, and N3 (note that N1 is equivalent to the summation operator).…”

Section: Methodsmentioning

confidence: 99%

PeptiDesCalculator: Software for computation of peptide descriptors. Definition, implementation and case studies for 9 bioactivity endpoints

Barigye¹,

Gómez-Ganau²,

Serrano‐Candelas³

et al. 2020

Proteins

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We present a novel Java‐based program denominated PeptiDesCalculator for computing peptide descriptors. These descriptors include: redefinitions of known protein parameters to suite the peptide domain, generalization schemes for the global descriptions of peptide characteristics, as well as empirical descriptors based on experimental evidence on peptide stability and interaction propensity. The PeptiDesCalculator software provides a user‐friendly Graphical User Interface (GUI) and is parallelized to maximize the use of computational resources available in current work stations. The PeptiDesCalculator indices are employed in modeling 8 peptide bioactivity endpoints demonstrating satisfactory behavior. Moreover, we compare the performance of a support vector machine (SVM) classifier built using 15 PeptiDesCalculator indices with that of a recently reported deep neural network (DNN) antimicrobial activity classifier, demonstrating comparable test set performance notwithstanding the remarkably lower degree of freedom for the former. This software will facilitate the development of in silico models for the prediction of peptide properties.

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“…Graph theory has been (and is being) applied successfully in chemistry—see, for example, the recent papers [1–3]. The multidisciplinary field where graph theory and chemistry meet is known as the “chemical graph theory.” The present paper is concerned with the part of the chemical graph theory that involves topological indices [4,5].…”

Section: Introductionmentioning

confidence: 99%

On the second maximum Wiener polarity index of chemical trees of a fixed order

Ali

Zaineb

et al. 2021

Int J of Quantum Chemistry

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The Wiener polarity index Wp of a graph is defined as the number of unordered pairs of its vertices at distance 3. The problem of finding trees attaining the maximum Wp value, among all chemical trees of a fixed order n, was solved in the paper (Mol. Inf. 2019, 38, 1800076) for n ≥ 8. Motivated by the usage of Wp in a recent publication (J. Chem. Inf. Model. 2020, 60, 1224–1234), in this article we extend the work done in the aforementioned paper by giving a further ordering of chemical trees with respect to the maximum value of Wp. More precisely, we characterize the trees having the second maximum Wp value (which is 3n − 16) from the class of all chemical trees of a fixed order n, for n ≥ 9.

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When global and local molecular descriptors are more than the sum of its parts: Simple, But Not Simpler?

Cited by 12 publications

References 56 publications

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach

PeptiDesCalculator: Software for computation of peptide descriptors. Definition, implementation and case studies for 9 bioactivity endpoints

On the second maximum Wiener polarity index of chemical trees of a fixed order

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