Computational investigations into structure and function impact of novel mutations identified in targeted exons from ovarian cancer cell lines.

The lack of sensitive and specific biomarkers for ovarian cancer leads to late stage diagnosis of the disease in a majority of the cases. Mutation accumulation is the basis for cancer progression, thus identifying mutations is an important step in the disease diagnosis. In the present study, a comprehensive analysis of fifteen Next Generation Sequencing samples from thirteen ovarian cancer cell lines was carried out for the identification of new mutations. The study revealed eight clinically significant novel mutations in six ovarian cancer oncogenes, viz. SMARCA4, ARID1A, PPP2R1A, CTNNB1, DICER1 and PIK3CA. In-depth computational analysis revealed that the mutations affected the structure of the proteins in terms of stability, solvent accessible surface area and molecular dynamics. Moreover, the mutations were present in functionally significant domains of the proteins, thereby adversely affecting the protein functionality. PPI network for SMARCA4, CTNNB1, DICER1, PIK3CA, PPP2R1A and ARID1A showed that these genes were involved in certain significant pathways affecting various hallmarks of cancer. For further validation, in vitro studies were performed that revealed hypermutability of the CTNNB1 gene. Through this study we have identified some key mutations and have analysed their structural and functional impact. The study establishes some key mutations, which can be potentially explored as biomarker and drug target.Communicated by Ramaswamy H. Sarma.

Screening of natural product libraries in MCF7 cell line reveals the pro-apoptotic properties of ß tetralone.

Despite the exponential increase in research toward better treatment options for breast cancer patients, developing an effective drug with fewer side effects continues to remain a challenge. Natural compounds have emerged as a viable option and several drugs have been derived or inspired from them. In this study, we screened a library of natural compounds with diverse chemical structures against selected kinase proteins using in silico methods such as molecular docking and dynamics simulation. The best results were obtained between ß tetralone and MDM2 E3 ubiquitin ligase protein. In vitro experiments such as cytotoxicity, scratch assays and flow cytometry analysis using an MCF7 cell line were performed to determine the anti-cancer potential of the compound. As the treatment resulted in cell death and apoptosis, ß tetralone was screened in silico against anti-apoptotic targets where the best results were obtained between Bcl-w and ß tetralone. This comprehensive study suggests that the anti-cancer activity of ß tetralone is probably through the dual targeting of MDM2 E3 ubiquitin kinase and Bcl-w anti-apoptotic protein.Communicated by Ramaswamy H. Sarma.

Computational Analysis of Gastric Canceromics Data to Identify Putative Biomarkers.

BACKGROUND: Gastric cancer develops as a malignant tumor in the mucosa of the stomach, and spreads through further layers. Early-stage diagnosis of gastric cancer is highly challenging because the patients either exhibit symptoms similar to stomach infections or show no signs at all. Biomarkers are active players in the cancer process by acting as indications of aberrant alterations due to malignancy. OBJECTIVE: Though there have been significant advancements in the biomarkers and therapeutic targets, there are still insufficient data to fully eradicate the disease in its early phases. Therefore, it is crucial to identify particular biomarkers for detecting and treating stomach cancer. This review aims to provide a thorough overview of data analysis in gastric cancer. METHODS: Text mining, network analysis, machine learning (ML), deep learning (DL), and structural bioinformatics approaches have been employed in this study. RESULTS: We have built a huge interaction network in the current study to forecast new biomarkers for gastric cancer. The four putatively unique and potential biomarker genes have been identified via a large association network in this study. CONCLUSION: The molecular basis of the illness is well understood by computational approaches, which also provide biomarkers for targeted cancer therapy. These putative biomarkers may be useful in the early detection of disease. This study also shows that in H. pylori infection in early-stage gastric cancer, the top 10 hub genes constitute an essential component of the epithelial cell signaling pathways. These genes can further contribute to the future development of effective biomarkers.

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.

RNA-Seq Analysis of Clinical Samples from TCGA Reveal Molecular Signatures for Ovarian Cancer.

Identifying differentially expressed genes and co-expression modules lead to novel biomarkers. GO, pathway enrichment, network, and tumor stage analysis of 318 ovarian cancer samples from TCGA, categorised into primary and recurrent, pre-menopause and post-menopause, and early and late stage tumors was performed. Upregulated and downregulated genes in primary vs recurrent, early stage vs late-stage and pre-menopause vs post-menopause tumors were 84 and 62, 84 and 35, and 88 and 14, respectively. IRAK2 and CXCL8 had higher expression in recurrent tumors while REG1A had higher expression in post-menopause samples. In late stage tumors constant expression of IRAK2 and REG1A was observed, while that of CXCL8 and EGF decreased. These genes may be potential biomarkers for the diagnosis of the disease.

Comprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets.

Drug repurposing is a method to identify novel therapeutic agents from the existing drugs and clinical compounds. In the present comprehensive work, molecular docking, virtual screening and dynamics simulations were carried out for ten cancer types viz breast, colon, central nervous system, leukaemia, melanoma, ovarian, prostate, renal and lung (non-small and small cell) against validated eighteen kinase targets. The study aims to understand the action of chemotherapy drugs mechanism through binding interactions against selected targets via comparative docking simulations with the state-art molecular modelling suits such as MOE, Cresset-Flare, AutoDock Vina, GOLD and GLIDE. Chemotherapeutic drugs (n = 112) were shortlisted from standard drug databases with appropriate chemoinformatic filters. Based on docking studies it was revealed that leucovorin, nilotinib, ellence, thalomid and carfilzomib drugs possessed potential against other cancer targets. A library was built to enumerate novel molecules based on the scaffold and functional groups extracted from known drugs and clinical compounds. Twenty novel molecules were prioritised further based on drug-like attributes. These were cross docked against 1MQ4 Aurora-A Protein Kinase for prostate cancer and 4UYA Mitogen-activated protein kinase for renal cancer. All docking programs yielded similar results but interestingly AutoDock Vina yielded the lowest RMSD with the native ligand. To further validate the final docking results at atomistic level, molecular dynamics simulations were performed to ascertain the stability of the protein-ligand complex. The study enables repurposing of drugs and lead identification by employing a host of structure and ligand based virtual screening tools and techniques.Communicated by Ramaswamy H. Sarma.

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.

Computational studies reveal co-occurrence of two mutations in IL7R gene of high-grade serous carcinoma patients.

Major cause of mortality in ovarian cancer can be attributed to a lack of specific and sensitive biomarkers for diagnosis and prognosis of the disease. Uncovering the mutations in genes involved in crucial oncogenic pathways is a key step in discovery and development of novel biomarkers. Whole exome sequencing (WES) is a powerful method for the detection of cancer driver mutations. The present work focuses on identifying functionally damaging mutations in patients with high-grade serous ovarian carcinoma (HGSC) through computational analysis of WES. In this study, WES data of HGSC patients was retrieved from the genomic literature available in sequence read archive, the variants were identified and comprehensive structural and functional analysis was performed. Interestingly, I66T and V138I mutations were found to be co-occurring in the IL7R gene in four out of five HGSC patient samples investigated in this study. The V138I mutation was located in the fibronectin type-3 domain and computationally assessed to be causing disruptive effects on the structure and dynamics of IL7R protein. This mutation was found to be co-occurring with the neutral I66T mutation in the same domain which compensated the disruptive effects of V138I variant. These comprehensive studies point to a hitherto unexplored significant role of the IL7R gene in ovarian carcinoma. It is envisaged that the work will lay the foundation for the development of a novel biomarker with potential application in molecular profiling and in estimation of the disease prognosis.Communicated by Ramaswamy H. Sarma.

Reversed-phase-HPLC enantioseparation and control of enantiomeric purity of duloxetine using a new chiral reagent and recovery of enantiomers.

This study reports a rapid and low-cost LC method for control of enantiomeric purity of duloxetine. Though duloxetine, as marketed and administered, is expected to be a single (S)-enantiomer, the analysis of a few commercial branded samples by the method developed and presented here showed that they contain a relatively high percentage of (R)-enantiomer (e.g., 2.71-5.42%, which is undesirable in drug formulations). A new chiral derivatizing reagent [isatinyl-(S)-naproxen amide] was synthesized on (S)-naproxen platform. Diastereomeric derivatives were synthesized under microwave irradiation and were separated using reversed-phase-HPLC on a C18 column. A combination of acetonitrile and triethylammonium phosphate buffer (9 mM, pH 4) as the mobile phase and detection at 273 nm were found successful. The diastereomeric derivatives at preparative scale were separated using open column chromatography, and the native enantiomers were obtained and characterized. The HPLC separation method was validated for detection limit, linearity, accuracy, and precision. The limits of detection of (S,R)-diastereomer and (S,S)-diastereomer were found to be 12 and 16 pg/mL, respectively, for the 20-µL injected volume. The method so developed has a practical significance and greater societal impact in establishing the control of enantiomeric purity and in ensuring the enantiomeric purity of the drug meant for human consumption.

Molecular docking and simulation studies on SARS-CoV-2 Mpro reveals Mitoxantrone, Leucovorin, Birinapant, and Dynasore as potent drugs against COVID-19.

The outbreak of novel coronavirus (COVID-19), which began from Wuhan City, Hubei, China, and declared as a Public Health Emergency of International Concern by World Health Organization (WHO) on 30th January 2020. The present study describes how the available drug candidates can be used as a potential SARS-CoV-2 Mpro inhibitor by molecular docking and molecular dynamic simulation studies. Drug repurposing strategy is applied by using the library of antiviral and FDA approved drugs retrieved from the Selleckchem Inc. (Houston, TX, http://www.selleckchem.com) and DrugBank database respectively. Computational methods like molecular docking and molecular dynamics simulation were used. The molecular docking calculations were performed using LeadIT FlexX software. The molecular dynamics simulations of 100 ns were performed to study conformational stability for all complex systems. Mitoxantrone and Leucovorin from FDA approved drug library and Birinapant and Dynasore from anti-viral drug libraries interact with SARS-CoV-2 Mpro at higher efficiency as a result of the improved steric and hydrophobic environment in the binding cavity to make stable complex. Also, the molecular dynamics simulations of 100 ns revealed the mean RMSD value of 2.25 Å for all the complex systems. This shows that lead compounds bound tightly within the Mpro cavity and thus having conformational stability. Glutamic acid (Glu166) of Mpro is a key residue to hold and form a stable complex of reported lead compounds by forming hydrogen bonds and salt bridge. Our findings suggest that Mitoxantrone, Leucovorin, Birinapant, and Dynasore represents potential inhibitors of SARS-CoV-2 Mpro.

Computational Studies of Hydroxychloroquine and Chloroquine Metabolites as Possible Candidates for Coronavirus (COVID-19) Treatment.

The coronavirus disease 2019 or COVID-19 pandemic is claiming many lives, impacting the health and livelihoods of billions of people worldwide and causing global economic havoc. As a novel disease with protean manifestations, it has pushed the scientific community into a frenzy to find a cure. The chloroquine class of compounds, used for decades for their antimalarial activity, have been well characterized. Hydroxychloroquine (HCQ), a less toxic metabolite of chloroquine, is used to treat rheumatic diseases such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), juvenile idiopathic arthritis (JIA), and Sjögren's syndrome. Preliminary studies in non-randomized clinical trials point to the possible use of chloroquine and its derivatives in the treatment of coronavirus. However, more robust clinical studies carried out in the United States, Italy, Australia, and China have shown mixed and inconclusive results and indicate the need for additional research. Cardiac, neurological, and retinal toxicity as well as increasing parasite resistance to these drugs is a major hindrance for their use in a world that is already dealing with antimicrobial resistance (AMR). In this context, we chose to study the monoquinoline analogs of 4-aminoquinoline as well as their metabolites which have the same mechanism of action albeit with lower toxicity. All the compounds were extensively studied computationally using docking, cheminformatics, and toxicity prediction tools. Based on the docking scores against ACE (angiotensin-converting enzyme) receptors and the toxicity data computed by employing the chemical analyzer module by ViridisChem™ Inc., the work reveals significant findings that can help in the process of use of these metabolites against coronavirus.

Design of Novel Drug-like Molecules Using Informatics Rich Secondary Metabolites Analysis of Indian Medicinal and Aromatic Plants.

BACKGROUND: Several medicinal plants are being used in Indian medicine systems from ancient times. However, in most cases, the specific molecules or the active ingredients responsible for the medicinal or therapeutic properties are not yet known. OBJECTIVE: This study aimed to report a computational protocol as well as a tool for generating novel potential drug candidates from the bioactive molecules of Indian medicinal and aromatic plants through the chemoinformatics approach. METHODS: We built a database of the Indian medicinal and aromatic plants coupled with associated information (plant families, plant parts used for the medicinal purpose, structural information, therapeutic properties, etc.) We also developed a Java-based chemoinformatics open-source tool called DoMINE (Database of Medicinally Important Natural products from plantaE) for the generation of virtual library and screening of novel molecules from known medicinal plant molecules. We employed chemoinformatics approaches to in-silico screened metabolites from 104 Indian medicinal and aromatic plants and designed novel drug-like bioactive molecules. For this purpose, 1665 ring containing molecules were identified by text mining of literature related to the medicinal plant species, which were later used to extract 209 molecular scaffolds. Different scaffolds were further used to build a focused virtual library. Virtual screening was performed with cluster analysis to predict drug-like and lead-like molecules from these plant molecules in the context of drug discovery. The predicted drug-like and lead-like molecules were evaluated using chemoinformatics approaches and statistical parameters, and only the most significant molecules were proposed as the candidate molecules to develop new drugs. RESULTS AND CONCLUSION: The supra network of molecules and scaffolds identifies the relationship between the plant molecules and drugs. Cluster analysis of virtual library molecules showed that novel molecules had more pharmacophoric properties than toxicophoric and chemophoric properties. We also developed the DoMINE toolkit for the advancement of natural product-based drug discovery through chemoinformatics approaches. This study will be useful in developing new drug molecules from the known medicinal plant molecules. Hence, this work will encourage experimental organic chemists to synthesize these molecules based on the predicted values. These synthesized molecules need to be subjected to biological screening to identify potential molecules for drug discovery research.

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.

ChemEngine: harvesting 3D chemical structures of supplementary data from PDF files.

Digital access to chemical journals resulted in a vast array of molecular information that is now available in the supplementary material files in PDF format. However, extracting this molecular information, generally from a PDF document format is a daunting task. Here we present an approach to harvest 3D molecular data from the supporting information of scientific research articles that are normally available from publisher's resources. In order to demonstrate the feasibility of extracting truly computable molecules from PDF file formats in a fast and efficient manner, we have developed a Java based application, namely ChemEngine. This program recognizes textual patterns from the supplementary data and generates standard molecular structure data (bond matrix, atomic coordinates) that can be subjected to a multitude of computational processes automatically. The methodology has been demonstrated via several case studies on different formats of coordinates data stored in supplementary information files, wherein ChemEngine selectively harvested the atomic coordinates and interpreted them as molecules with high accuracy. The reusability of extracted molecular coordinate data was demonstrated by computing Single Point Energies that were in close agreement with the original computed data provided with the articles. It is envisaged that the methodology will enable large scale conversion of molecular information from supplementary files available in the PDF format into a collection of ready- to- compute molecular data to create an automated workflow for advanced computational processes. Software along with source codes and instructions available at https://sourceforge.net/projects/chemengine/files/?source=navbar.Graphical abstract.

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.

Design and Development of ChemInfoCloud: An Integrated Cloud Enabled Platform for Virtual Screening.

The power of cloud computing and distributed computing has been harnessed to handle vast and heterogeneous data required to be processed in any virtual screening protocol. A cloud computing platorm ChemInfoCloud was built and integrated with several chemoinformatics and bioinformatics tools. The robust engine performs the core chemoinformatics tasks of lead generation, lead optimisation and property prediction in a fast and efficient manner. It has also been provided with some of the bioinformatics functionalities including sequence alignment, active site pose prediction and protein ligand docking. Text mining, NMR chemical shift (1H, 13C) prediction and reaction fingerprint generation modules for efficient lead discovery are also implemented in this platform. We have developed an integrated problem solving cloud environment for virtual screening studies that also provides workflow management, better usability and interaction with end users using container based virtualization, OpenVz.