Machine learning for the advancement of genome-scale metabolic modeling.

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health.

Metabolic activities of the microbial population are important to maintain the balance of almost all the ecosystems on earth. In the human gut environment, these microbial communities play essential roles in digestion and help to maintain biochemical homeostasis by synthesizing several vital metabolic compounds. Imbalance in the microbial abundance and community structure in the human gut microbiota leads to different diseases and metabolic disorders. Studying the metabolic interplay between the microbial consortia within the host environment is the key to exploring the cause behind the development of various diseases condition. However, mapping the entire biochemical characteristic of human gut microbiota may not be feasible only through experimental approaches. Therefore, the advanced systems biology approach, i.e., metagenome-scale community metabolic modelling, is introduced for understanding the metabolic role and interaction pattern of the entire microbiome. This in silico method directly uses the metagenomic information to model the microbial communities, which mimic the metabolic behavior of the human gut microbiome. This review discusses the recent development of metagenome-scale community metabolic model reconstruction tools and their application in studying the inter-link between the human gut microbiome and health. The application of the community metabolic models to study the metabolic profile of the human gut microbiome has also been investigated. Alteration of the metabolic fluxes associated with different biochemical activities in type 1 diabetics, type 2 diabetics, inflammatory bowel diseases (IBD), gouty arthritis, colorectal cancer (CRC), etc., has also been assessed with the metagenome-scale models. Thus, modelling the microbial communities combined with advanced experimental design may lead to novel therapeutic approaches like personalized microbiome modelling for treating human disease.

In-silico strategies for probing chloroquine based inhibitors against SARS-CoV-2.

The global health emergency of novel COVID-19 is due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Currently there are no approved drugs for the treatment of coronaviral disease (COVID-19), although some of the drugs have been tried. Chloroquine is being widely used in treatment of SARS-CoV-2 infection. Hydroxychloroquine, the derivative of Chloroquine shows better inhibition than Chloroquine and has in vitro activity against SARS-CoV-2 also used to treat COVID-19. To study the interactions of Chloroquine and derivatives of Chloroquine with SARS-CoV-2, series of computational approaches like pharmacophore model, molecular docking, MM_GBSA study and ADME property analysis are explored. The pharmacophore model and molecular docking study are used to explore the structural properties of the compounds and the ligand-receptor (PDB_ID: 6LU7) interactions respectively. MM_GBSA study gives the binding free energy of the protein-ligand complex and ADME property analysis explains the pharmacological property of the compounds. The resultant best molecule (CQD15) further subjected to molecular dynamics (MD) simulation study which explains the protein stability (RMSD), ligand properties as well as protein-ligand contacts. Outcomes of the present study conclude with the molecule CQD15 which shows better interactions for the inhibition of SARS-CoV-2 in comparison to Chloroquine and Hydroxychloroquine.Communicated by Ramaswamy H. Sarma.

Diacetoxy iodobenzene mediated regioselective synthesis and characterization of novel [1,2,4]triazolo[4,3-a]pyrimidines: apoptosis inducer, antiproliferative activities and molecular docking studies.

Prompt and regioselective synthesis of eleven novel [1,2,4]triazolo[4,3-a]pyrimidines 2a-2k, via intramolecular oxidative-cyclization of 2-(2-arylidenehydrazinyl)-4-methyl-6-phenylpyrimidine derivatives 1a-1k has been demonstrated here using diacetoxy iodobenzene (DIB) as inexpensive and ecofriendly hypervalent iodine(III) reagent in CH2Cl2 at room temperature. Regiochemistry of final product has been established by developing single crystal and studied X-ray crystallographic data for two derivatives 2c and 2h without any ambiguity. These prominent [1,2,4]triazolo[4,3-a]pyrimidines were evaluated for human osteosarcoma bone cancer (MG-63) and breast cancer (MCF-7) cell lines using MTT assay to find potent antiproliferative agent and also on testicular germ cells to find potent apoptotic inducing activities. All compounds show significant cytotoxicity, particularly 3-(2,4-dichlorophenyl)-5-methyl-7-phenyl-[1,2,4]triazolo[4,3-a]pyrimidine (2g) was found significant apoptotic inducing molecule, as well as the most potent cytotoxic agent against bone cancer (MG-63) and breast cancer (MCF-7) cell lines with GI50 value 148.96 µM and 114.3 µM respectively. Molecular docking studies has been carried out to see the molecular interactions of synthesized compounds with the protein thymidylate synthase (PBD ID: 2G8D).Communicated by Ramaswamy H. Sarma.