A Comparative Chemoinformatics Analysis of compounds extracted from Nyctanthes arbor-tristis.

INTRODUCTION: Natural products are a rich source of diverse chemical compounds with interesting therapeutic properties. There is a need for in-depth investigation of this reservoir with in-silico tools to assert the molecular diversity with respect to clinical significance. Although studies have been reported on plants such as Nyctanthes arbor-tristis(NAT) and its medicinal importance. A comprehensive study on comparative analysis of all phyto-constituents has not been carried out. AIM: In the present work, we have carried out a comparative study of compounds obtained from the ethanolic extracts of various parts such as calyx, corolla, leaf, and bark of the NAT plant. METHODS: The extracted compounds were characterized by LCMS and GCMS studies. This was further corroborated by the network analysis, docking, and dynamic simulation studies with validated anti-arthritic targets. RESULTS: The most significant observation from LCMS and GCMS was that the compounds from calyx and corolla were closer in chemical space to the anti-arthritic compounds. To further expand and explore chemical space, the common scaffolds were seeded to enumerate a virtual library. The virtual molecules were prioritized based on the drug-like, leadlike scores and docked against anti-arthritic targets to reveal identical interactions in the pocket region. CONCLUSION: The comprehensive study will be of immense value to medicinal chemists for the rational synthesis of molecules as well as bioinformatics professionals for getting useful insight into identifying rich diverse molecules from plant sources.

A Review on Computational Analysis of Big Data in Breast Cancer for Predicting Potential Biomarkers.

Breast cancer is the most predominantly occurring cancer in the world. Several genes and proteins have been recently studied to predict biomarkers that enable early disease identification and monitor its recurrence. In the era of high-throughput technology, studies show several applications of big data for identifying potential biomarkers. The review aims to provide a comprehensive overview of big data analysis in breast cancer towards the prediction of biomarkers with emphasis on computational methods like text mining, network analysis, next-generation sequencing technology (NGS), machine learning (ML), deep learning (DL), and precision medicine. Integrating data from various computational approaches enables the stratification of cancer patients and the identification of molecular signatures in cancer and their subtypes. The computational methods and statistical analysis help expedite cancer prognosis and develop precision cancer medicine (PCM). As a part of case study in the present work, we constructed a large gene-drug interaction network to predict new biomarkers genes. The gene-drug network helped us to identify eight genes that could serve as novel potential biomarkers.

In silico binding analysis of lutein and rosmarinic acid against envelope domain III protein of dengue virus.

OBJECTIVE: The study was performed to evaluate in silico binding ability of lutein and rosmarinic acid (RA) with the envelope domain III (EDIII) proteins of the four serotypes of dengue virus (DENV), enlightening potential antiviral activity of the two compounds. MATERIALS AND METHODS: EDIII protein structures for the four DENV serotypes were retrieved from RCSB Protein data bank (PDB) and used as receptors. Four ligands of lutein and four of RA were selected from the ZINC database and used for computational molecular docking and ligand interaction analysis with the four receptors using bioinformatics tools like AutoDock Vina and Molecular Operating Environment (MOE) software. RESULTS: The EDIII of the four serotypes demonstrated significant interaction with ligands of lutein and RA. RA ligand ZINC899870, particularly presented best-binding energy values of 6.4, -7.0, and 6.9 kcal/mol with EDIII of serotype DENV-1, DENV-2, and DENV-4 respectively. Whereas, lutein ligand, ZINC14879959 presented best-binding energy value of 7.9 kcal/mol for EDIII of serotype DENV-3. From the results predicted by MOE, the hydroxyl (OH) of 3, 4-dihydroxyphenyl group of RA ligand ZINC899870 is actively involved in interaction with all four serotypes. CONCLUSION: RA is a competent candidate for further evaluation of potential in vitro antiviral activity that can be effective in conferring protection against the four serotypes of DENV.

Identification of potent chromone embedded [1,2,3]-triazoles as novel anti-tubercular agents.

A series of 20 novel chromone embedded [1,2,3]-triazoles derivatives were synthesized via an easy and convenient synthetic procedure starting from 2-hydroxy acetophenone. The in vitro anti-mycobacterial evaluation studies carried out in this work reveal that seven compounds exhibit significant inhibition against Mycobacterium tuberculosis H37Rv strain with MIC in the range of 1.56-12.5 µg ml-1. Noticeably, compound 6s was the most potent compound in vitro with a MIC value of 1.56 µg ml-1. Molecular docking and chemoinformatics studies revealed that compound 6s displayed drug-like properties against the enoyl-acyl carrier protein reductase of M. tuberculosis further establishing its potential as a potent inhibitor.

Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data.

Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In a case study, a dataset consisting of one hundred and thirty five PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient(CC) of 0.893, low root mean square error (RMSE) and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a)a predictor of the binding energy of cancer related PPI complexes, and (b)a classifier for discriminating PPI complexes related to cancer from those of other diseases.

Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis.

In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein-protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies.

Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R(2)) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science.

Chemoinformatics Approach for Building Molecular Networks from Marine Organisms.

Natural products obtained from marine sources are considered to be a rich and diverse source of potential drugs. In the present work we demonstrate the use of chemoinformatics approach for the design of new molecules inspired by molecules from marine organisms. Accordingly we have assimilated information from two major scientific domains namely chemoinformatics and biodiversity informatics to develop an interactive marine database named MIMMO (Medicinally Important Molecules from Marine Organisms). The database can be queried for species, molecules, scaffolds, drugs, diseases and associated cumulative biological activity spectrum along with links to the literature resources. Molecular informatics analysis of the molecules obtained from MIMMO was performed to study their chemical space. The distinct skeletal features of the biologically active compounds isolated from marine species were identified. Scaffold molecules and species networks were created to identify common scaffolds from marine source and drug space. An analysis of the entire molecular data revealed a unique list of around 2000 molecules from which ten most frequently occurring distinct scaffolds were obtained.

A Study of Applications of Machine Learning Based Classification Methods for Virtual Screening of Lead Molecules.

The ligand-based virtual screening of combinatorial libraries employs a number of statistical modeling and machine learning methods. A comprehensive analysis of the application of these methods for the diversity oriented virtual screening of biological targets/drug classes is presented here. A number of classification models have been built using three types of inputs namely structure based descriptors, molecular fingerprints and therapeutic category for performing virtual screening. The activity and affinity descriptors of a set of inhibitors of four target classes DHFR, COX, LOX and NMDA have been utilized to train a total of six classifiers viz. Artificial Neural Network (ANN), k nearest neighbor (k-NN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree--(DT) and Random Forest--(RF). Among these classifiers, the ANN was found as the best classifier with an AUC of 0.9 irrespective of the target. New molecular fingerprints based on pharmacophore, toxicophore and chemophore (PTC), were used to build the ANN models for each dataset. A good accuracy of 87.27% was obtained using 296 chemophoric binary fingerprints for the COX-LOX inhibitors compared to pharmacophoric (67.82%) and toxicophoric (70.64%). The methodology was validated on the classical Ames mutagenecity dataset of 4337 molecules. To evaluate it further, selectivity and promiscuity of molecules from five drug classes viz. anti-anginal, anti-convulsant, anti-depressant, anti-arrhythmic and anti-diabetic were studied. The TPC fingerprints computed for each category were able to capture the drug-class specific features using the k-NN classifier. These models can be useful for selecting optimal molecules for drug design.