PTML Model for Proteome Mining of B-Cell Epitopes and Theoretical–Experimental Study of Bm86 Protein Sequences from Colima, Mexico

Martínez-Arzate, Saúl; Tenorio-Borroto, Esvieta; Pliego, Alberto Barbabosa; Diaz-Albiter, Hector; Vázquez-Chagoyán, Juan Carlos; González-Dı́az, Humberto

doi:10.1021/acs.jproteome.7b00477

Cited by 49 publications

(71 citation statements)

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“…These models were built by ANN and the topological indices, which can predict the biological activity and toxicity correctly and classify the compounds in experimental conditions. Meanwhile, these models used perturbation models to form structural-activity relationships between the site of infection and the drug, such as the PTML model [85] and the ChEMBL model [86], which has been applied in infectious diseases [71], immunology [85], and cancer [87] widely. Currently, the mtk-QSBER model has been able to carry out the in-silico design and virtual screening of an antibacterial drug efficiently, and these antibacterial drugs have good biosafety.…”

Section: Classical Qsar Methodsmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure-and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.Molecules 2020, 25, 1375 2 of 17 Biomolecular Simulations in Drug Screening and DesignThe Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems, recognizing the important role that theory and computational methods play as a direct and necessary complement to experiments [12]. Further, applications of biomolecular simulations in identifying drug binding sites and elucidating the molecular mechanisms of diseases have been subject to rapid developments [13][14][15].Depending on the biological problems to be studied, multiscale models will be used in biomolecular simulations. The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26]. Generally, MD simulations have been used for the identification of potential drug binding sites on target proteins, the calculation of binding free energy between target proteins and drug molecules, the action mechanism of drug molecules, etc. [27,28]. However, it is worth mentioning that MD simulations are also limited to time and length scales. Currently, AAMD simulations can explore time-dependent phenomena of large systems such as viral capsids in atomic detail as long as microseconds or even milliseconds [21,29,30]. Therefore, different types of ...

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Section: Classical Qsar Methodsmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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“…This meta-structure is modeled in order to elucidate the relationships with the disease agents utilizing perturbation models. 45 These models have been proven to be very versatile when applied to infectious diseases, 46 immunological disorders, 47 neurological pathologies, 48 and cancer. 49 Furthermore, the new approach in drug discovery is known as de novo multiscale approach in which a drug is designed within the chemical subspace where it could be deemed beneficial.…”

Section: Drug Design and Aimentioning

confidence: 99%

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

Álvarez-Machancoses¹,

deAndrés-Galiana²,

Cernea³

et al. 2020

PGPM

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The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.

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“…Moreover, a brand new method for data fusion in nanotechnology, bio-molecular sciences, chemistry and big data analysis has been proposed in different works: it integrates Perturbation Theory (PT) and Machine Learning (ML) [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], using distinct PT operators to analyze changes in the varied non-structural and structural conditions of a test at once (PTML). A few of these PT operators represent the generalization of a classic cheminformatics approach introduced by Corwin Hansch [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

Urista

Carrué

Otero

et al. 2020

Biology

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Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.

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PTML Model for Proteome Mining of B-Cell Epitopes and Theoretical–Experimental Study of Bm86 Protein Sequences from Colima, Mexico

Cited by 49 publications

References 50 publications

A Review on Applications of Computational Methods in Drug Screening and Design

A Review on Applications of Computational Methods in Drug Screening and Design

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

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