Abstract:The present report introduces a novel module of the QuBiLS-MIDAS software for the distributed computation of the 3D Multi-Linear algebraic molecular indices. The main motivation for developing this module is to deal with the computational complexity experienced during the calculation of the descriptors over large datasets. To accomplish this task, a multi-server computing platform named T-arenal was developed, which is suited for institutions with many workstations interconnected through a local network and wi… Show more
“…Future work includes the development of a version of the MuLiMs-MCoMPAs module to compute biomacromolecular descriptors on a distributed computing system for high-throughput calculation using a T-arenal platform, ,, as well as, a version to employ the graphical processing units (GPUs) available in current personal computers. In addition, a new set of multimetrics based cut-offs, aggregation operators, , contact-based indices, truncation procedures, and user-defined group-based calculations are to be introduced.…”
This
report introduces the MuLiMs-MCoMPAs software
(acronym for Multi-Linear Maps based on N-Metric and Contact Matrices of 3D Protein and Amino-acid weightings), designed to compute tensor-based
3D protein structural descriptors by applying two- and three-linear
algebraic forms. Moreover, these descriptors contemplate generalizing
components such as novel 3D protein structural representations,
(dis)similarity metrics, and multimetrics to extract geometrical related
information between two and three amino acids, weighting schemes based
on amino acid properties, matrix normalization procedures that consider
simple-stochastic and mutual probability transformations, topological
and geometrical cutoffs, amino acid, and group-based MD calculations,
and aggregation operators for merging amino acidic and group MDs.
The MuLiMs-MCoMPAs software, which belongs to the ToMoCoMD-CAMPS suite,
was developed in Java (version 1.8) using the Chemistry Development
Kit (CDK) (version 1.4.19) and the Jmol libraries. This software implemented
a divide-and-conquer strategy to parallelize the computation of the
indices as well as modules for data preprocessing and batch computing
functionalities. Furthermore, it consists of two components: (i) a
desktop-graphical user interface (GUI) and (ii) an API library. The
relevance of this novel approach is demonstrated through two analyses
that considered Shannon’s entropy-based variability and a principal
component analysis. These studies showed that the MuLiMs-MCoMPAs’
three-linear descriptor family contains higher informational entropy
than several other descriptors generated with available computation
tools. Moreover, the MuLiMs-MCoMPAs indices capture additional orthogonal
information to the one codified by the available calculation approaches.
As a result, two sets of suggested theoretical configurations that
contain 13648 two-linear indices and 20263 three-linear indices are available for download at . Furthermore, as a demonstration
of the applicability and easy integration of the MuLiMs library into
a QSAR-based expert system, a software application (ProStAF) was generated to predict SCOP protein structural classes and folding
rate. It can thus be anticipated that the MuLiMs-MCoMPAs framework
will turn into a valuable contribution to the chem- and bioinformatics
research fields.
“…Future work includes the development of a version of the MuLiMs-MCoMPAs module to compute biomacromolecular descriptors on a distributed computing system for high-throughput calculation using a T-arenal platform, ,, as well as, a version to employ the graphical processing units (GPUs) available in current personal computers. In addition, a new set of multimetrics based cut-offs, aggregation operators, , contact-based indices, truncation procedures, and user-defined group-based calculations are to be introduced.…”
This
report introduces the MuLiMs-MCoMPAs software
(acronym for Multi-Linear Maps based on N-Metric and Contact Matrices of 3D Protein and Amino-acid weightings), designed to compute tensor-based
3D protein structural descriptors by applying two- and three-linear
algebraic forms. Moreover, these descriptors contemplate generalizing
components such as novel 3D protein structural representations,
(dis)similarity metrics, and multimetrics to extract geometrical related
information between two and three amino acids, weighting schemes based
on amino acid properties, matrix normalization procedures that consider
simple-stochastic and mutual probability transformations, topological
and geometrical cutoffs, amino acid, and group-based MD calculations,
and aggregation operators for merging amino acidic and group MDs.
The MuLiMs-MCoMPAs software, which belongs to the ToMoCoMD-CAMPS suite,
was developed in Java (version 1.8) using the Chemistry Development
Kit (CDK) (version 1.4.19) and the Jmol libraries. This software implemented
a divide-and-conquer strategy to parallelize the computation of the
indices as well as modules for data preprocessing and batch computing
functionalities. Furthermore, it consists of two components: (i) a
desktop-graphical user interface (GUI) and (ii) an API library. The
relevance of this novel approach is demonstrated through two analyses
that considered Shannon’s entropy-based variability and a principal
component analysis. These studies showed that the MuLiMs-MCoMPAs’
three-linear descriptor family contains higher informational entropy
than several other descriptors generated with available computation
tools. Moreover, the MuLiMs-MCoMPAs indices capture additional orthogonal
information to the one codified by the available calculation approaches.
As a result, two sets of suggested theoretical configurations that
contain 13648 two-linear indices and 20263 three-linear indices are available for download at . Furthermore, as a demonstration
of the applicability and easy integration of the MuLiMs library into
a QSAR-based expert system, a software application (ProStAF) was generated to predict SCOP protein structural classes and folding
rate. It can thus be anticipated that the MuLiMs-MCoMPAs framework
will turn into a valuable contribution to the chem- and bioinformatics
research fields.
“…Several default configurations, determined according to their results in cheminformatics studies, are also provided. These fuzzy 3D-MDs can also be obtained using the distributed computation module coupled to the heterogeneous and non-dedicated T-arenal platform (http://tomocomd.com/t-arenal) [74]. Fig.…”
BackgroundSeveral topological (2D) and geometric (3D) molecular descriptors (MDs) are calculated from local vertex/edge invariants (LOVIs/LOEIs) by performing an aggregation process. To this end, norm-, mean- and statistic-based (non-fuzzy) operators are used, under the assumption that LOVIs/LOEIs are independent (orthogonal) values of one another. These operators are based on additive and/or linear measures and, consequently, they cannot be used to encode information from interrelated criteria. Thus, as LOVIs/LOEIs are not orthogonal values, then non-additive (fuzzy) measures can be used to encode the interrelation among them.ResultsGeneral approaches to compute fuzzy 2D/3D-MDs from the contribution of each atom (LOVIs) or covalent bond (LOEIs) within a molecule are proposed, by using the Choquet integral as fuzzy aggregation operator. The Choquet integral-based operator is rather different from the other operators often used for the 2D/3D-MDs calculation. It performs a reordering step to fuse the LOVIs/LOEIs according to their magnitudes and, in addition, it considers the interrelation among them through a fuzzy measure. With this operator, fuzzy definitions can be derived from traditional or recent MDs; for instance, fuzzy Randic-like connectivity indices, fuzzy Balaban-like indices, fuzzy Kier–Hall connectivity indices, among others. To demonstrate the feasibility of using this operator, the QuBiLS-MIDAS 3D-MDs were used as study case and, as a result, a module was built into the corresponding software to compute them (http://tomocomd.com/qubils-midas). Thus, it is the only software reported in the literature that can be employed to determine Choquet integral-based fuzzy MDs. Moreover, regression models were created on eight chemical datasets. In this way, a comparison between the results achieved by the models based on the non-fuzzy QuBiLS-MIDAS 3D-MDs with regard to the ones achieved by the models based on the fuzzy QuBiLS-MIDAS 3D-MDs was made. As a result, the models built with the fuzzy QuBiLS-MIDAS 3D-MDs achieved the best performance, which was statistically corroborated through the Wilcoxon signed-rank test.ConclusionsAll in all, it can be concluded that the Choquet integral constitutes a prominent alternative to compute fuzzy 2D/3D-MDs from LOVIs/LOEIs. In this way, better characterizations of the compounds can be obtained, which will be ultimately useful in enhancing the modelling ability of existing traditional 2D/3D-MDs.Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0306-7) contains supplementary material, which is available to authorized users.
“…In order to automatize the calculation of the 3D N-linear algebraic indices used in the present manuscript the QuBiLS-MIDAS software has been developed [ 41 ]. This software has as one of its main features the multi-core processing of the MDs, as well as the option to carry out the distributed calculation of the indices by using the Multi-Server Distributed Computing Platform known as T-arenal [ 42 ]. The latter is particularly useful for high-throughput calculation tasks.…”
Section: Mathematical Overview Of the 3d N-linear Algebraic Molecularmentioning
BackgroundRecently, novel 3D alignment-free molecular descriptors (also known as QuBiLS-MIDAS) based on two-linear, three-linear and four-linear algebraic forms have been introduced. These descriptors codify chemical information for relations between two, three and four atoms by using several (dis-)similarity metrics and multi-metrics. Several studies aimed at assessing the quality of these novel descriptors have been performed. However, a deeper analysis of their performance is necessary. Therefore, in the present manuscript an assessment and statistical validation of the performance of these novel descriptors in QSAR studies is performed.ResultsTo this end, eight molecular datasets (angiotensin converting enzyme, acetylcholinesterase inhibitors, benzodiazepine receptor, cyclooxygenase-2 inhibitors, dihydrofolate reductase inhibitors, glycogen phosphorylase b, thermolysin inhibitors, thrombin inhibitors) widely used as benchmarks in the evaluation of several procedures are utilized. Three to nine variable QSAR models based on Multiple Linear Regression are built for each chemical dataset
according to the original division into training/test sets. Comparisons with respect to leave-one-out cross-validation correlation coefficients reveal that the models based on QuBiLS-MIDAS indices possess superior predictive ability in 7 of the 8 datasets analyzed, outperforming methodologies based on similar or more complex techniques such as: Partial Least Square, Neural Networks, Support Vector Machine and others. On the other hand, superior external correlation coefficients are attained in 6 of the 8 test sets considered, confirming the good predictive power of the obtained models. For the values non-parametric statistic tests were performed, which demonstrated that the models based on QuBiLS-MIDAS indices have the best global performance and yield significantly better predictions in 11 of the 12 QSAR procedures used in the comparison. Lastly, a study concerning to the performance of the indices according to several conformer generation methods was performed. This demonstrated that the quality of predictions of the QSAR models based on QuBiLS-MIDAS indices depend on 3D structure generation method considered, although in this preliminary study the results achieved do not present significant statistical differences among them.ConclusionsAs conclusions it can be stated that the QuBiLS-MIDAS indices are suitable for extracting structural information of the molecules and thus, constitute a promissory alternative to build models that contribute to the prediction of pharmacokinetic, pharmacodynamics and toxicological properties on novel compounds.Graphical abstractComparative graphical representation of the performance of the novel QuBiLS-MIDAS 3D-MDs with respect to other methodologies in QSAR modeling of eight chemical datasetsElectronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0122-x) contains supplementary material, which is available to authorized users.
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