Unraveling the regulatory landscape of Parkinson disease: A molecular symphony of miRNAs, transcription factors, and high-risk genes

MicroRNAs (miRNAs) have emerged as critical regulators of post-transcriptional gene expression, impacting various biological processes (development, differentiation, and progression). In medicine, miRNAs are promising diagnostic biomarkers for neurodegenerative diseases, including Parkinson's disease (PD). The current study aims at exploring the role of miRNAs and transcription factors (TFs) in regulating genes-associated with PD. Deploying bioinformatics tools, the study identifies specific miRNAs and TFs involved in PD and their potential connections to the organ-disease junction. Notably, certain miRNAs are found to be highly expressed in brain, than compared to blood. Furthermore, the study explores the expression patterns of PD-related genes in different regions of the brain and attempts to construct complex network of interactions contributing to PD pathogenesis. Additionally, the regulatory relationship of two miRNAs namely hsa-miR-375-3p and hsa-miR-423-3p with TFs are well examined. Overall, the study provides a comprehensive moon-shot view of the molecular aspects of PD and their potential therapeutic targets which could be further used as diagnostic biomarkers in early detection, drug design and development attributing towards precision medicine.

Disaggregation of amyloid-beta fibrils via natural metabolites using long timescale replica exchange molecular dynamics simulation studies

CONTEXT

Amyloid fibrils are self-assembled fibrous protein aggregates that are associated with several presently incurable diseases such as Alzheimer's. disease that is characterized by the accumulation of amyloid fibrils in the brain, which leads to the formation of plaques and the death of brain cells. Disaggregation of amyloid fibrils is considered a promising approach to cure Alzheimer's disease. The mechanism of amyloid fibril formation is complex and not fully understood, making it difficult to develop drugs that can target the process. Diacetonamine and cystathionine are potential lead compounds to induce disaggregation of amyloid fibrils.

METHODS

In the current research, we have used long timescale molecular simulation studies and replica exchange molecular dynamics (REMD) for 1000 ns (1 μs) to examine the mechanisms by which natural metabolites can disaggregate amyloid-beta fibrils. Molecular docking was carried out using Glide and with prior protein minimization and ligand preparation. We focused on a screening a database of natural metabolites, as potential candidates for disaggregating amyloid fibrils. We used Desmond with OPLS 3e as a force field. MM-GBSA calculations were performed. Blood-brain barrier permeability, SASA, and radius of gyration parameters were calculated.

Computational molecular perspectives on novel carbazole derivative as an anti-cancer molecule against CDK1 of breast and colorectal cancers via gene expression studies, novel two-way docking strategies, molecular mechanics and dynamics

With increase in cancer incidences, alternative strategies for disease management are of utmost importance. Carbazole, is a compound that is being studied extensively as an anti-cancer compound. In this work, we aimed to investigate a carbazole derivative against specific cancer types such as breast and colorectal, based on the off-target analyses of carbazole derivative. The present work shortlisted 6 proteins that have an association in both cancer types, and then employed two different molecular docking strategies to examine the binding stability of carbazole derivative: a blind-docking state, where the pockets were undefined and mutation-docking state, where possible mutations were induced within the proteins. The results showed that CDK1 bound best in both states to carbazole derivative, and performed better than an array of positive controls. Molecular dynamic simulations at 100 ns further proved its stability, with carbazole derivative-CDK1-blind and mutated complex having RMSD values between 3.2 and 3.6 Å, and 2.8-3.2 Å respectively. Molecular-mechanics generalized born and surface area solvation disclosed free energy of binding for the complexes as -28.79 ± 3.97 kcal/mol and -31.86 ± 5.09 kcal/mol respectively, with carbazole derivative bound stably within the binding pocket at every 10 ns of the 100 ns trajectory. Radial distribution functions showed that the bell curve was well within 6 Å, thus showing that carbazole derivative and its atoms do not deviate away from the pocket, suggesting its ability to be used as a good anti-cancer compound against breast and colorectal.

BRCA1/TP53 tumor proteins inhibited by novel analogues of curcumin - Insight from computational modelling, dynamic simulation and experimental validation

The current study aimed to design novel curcumin analogue inhibitors with antiproliferative and antitumor activity towards BRCA1 and TP53 tumor proteins and to study their therapeutic potential by computer-aided molecular designing and experimental investigations. Four curcumin analogues were computationally designed and their drug-likeness and pharmacokinetic properties were predicted. The binding of these analogues against six protein targets belonging to BRCA1 and TP53 tumor proteins were modelled by molecular docking and their binding energies were compared with that of curcumin and the standard drug cyclophosphamide and its validated target. The stabilities of selected docked complexes were confirmed by molecular dynamic simulation (MDS) and MMGBSA calculations. The best-docked analogue was chemically synthesized, characterized, and used for in vitro cytotoxic screening using DLA, EAC, and C127I cell lines. In vivo antitumor studies were carried out in Swiss Albino Mice. The study revealed that the designed analogues satisfied drug-likeness and pharmacokinetic properties and demonstrated better binding affinity to the selected targets than curcumin. Among the analogues, NLH demonstrated significant interaction with the BRCA1-BRCT-c domain (TG3; binding energy -8.3 kcal/mol) when compared to the interaction of curcumin (binding energy -6.19 kcal) and cyclophosphamide (binding energy -3.8 kcal/mol) and its usual substrate (TG7). The MDS and MM/GBSA studies revealed that the binding free energy of the NLH-TG3 complex (-61.24 kcal/mol) was better when compared to that of the cyclophosphamide-TG7 complex (-21.67 kcal/mol). In vitro, cytotoxic studies showed that NLH demonstrated significant antiproliferative activities against tumor cell lines. The in vivo study depicted NLH possesses the potential for tumor inhibition. Thus, the newly synthesized curcumin analogue is probably used to develop novel therapeutic agents against breast cancer.

Comprehending interaction mechanism of natural actives of Colchicum autumnale L. for rheumatoid arthritis using integrative chemoinformatic approaches

This research delves into the realm of therapeutic potential within natural compounds derived from Colchicum autumnale L., emphasizing a holistic perspective on medications used in human therapy. Rather than confining the study to their primary actions, the research endeavors to unveil molecular targets for these natural compounds, with a specific focus on their potential applicability in the treatment of rheumatoid arthritis (RA). The study focuses on understanding interactions between specific natural actives that target RA. Fifteen RA target proteins were identified from OMIM, GeneScan and PharmaGKB. Their structures were downloaded from RCSB PDB. Two active components of C. autumnale L. were chosen for mass spectrometry investigation. Ligand characteristics were determined using the ADMETlab and SwissADME software tools. Molecular docking was performed, and the top three complexes were simulated for 200 ns, along with identification of free binding energies. The compounds β-sitosterol-IL-10 (-6.50 kcal/mol), colchicine-IL-10 (-6.01 kcal/mol), linoleic acid-IL-10 (-7.22 kcal/mol) and linoleic acid-IL-10 (-7.22 kcal/mol) exhibited best binding energies. β-Sitosterol and colchicine showed the highest stability in simulations, confirmed by molecular mechanics free energy binding calculations. This work provides insights into the molecular interaction of natural compounds against RA targets, offering potential therapeutic anti-RA medications.Communicated by Ramaswamy H. Sarma.

Investigating the Molecular Interactions of Quinoline Derivatives for Antibacterial Activity Against Bacillus subtilis: Computational Biology and In Vitro Study Interpretations

Bacterial infections are evolving and one of the chief problems is emergence and prevalence of antibacterial resistance. Moreover, certain strains of Bacillus subtilis have become resistant to several antibiotics. To counteract this menace, the present work aimed to comprehend the antibacterial activity of synthesized two quinoline derivatives against Bacillus subtilis. Toxicity predictions via Protox II, SwissADME and T.E.S.T (Toxicity Estimation Software Tool) revealed that these derivatives were non-toxic and had little to no adverse effects. Molecular docking studies carried out in Schrodinger with two quinoline derivatives (referred Q1 and Q2) docked against selected target proteins (PDB IDs: 2VAM and1FSE) of B. subtilis demonstrated ideal binding energies (2VAM-Q1: - 4.63 kcal/mol and 2VAM-Q2: - 4.46 kcal/mol, and 1FSE-Q1: - 3.51 kcal/mol, 1FSE-Q2: - 6.34 kcal/mol). These complexes were simulated at 100 ns and the outcomes revealed their stability with slight conformational changes. Anti-microbial assay via disc diffusion method revealed zones of inhibition showing that B. subtilis was inhibited by both Q1 and Q2, with Q2 performing slightly better than Q1, pointing towards its effectiveness against this organism and necessitating further study on other bacteria in prospective studies. Thus, this study demonstrates that our novel quinoline derivatives exhibit antibacterial properties against Bacillus subtilis and can act as potent anti-bacterials.

Green synthesis, antibacterial and antifungal evaluation of new thiazolidine-2,4-dione derivatives: molecular dynamic simulation, POM study and identification of antitumor pharmacophore sites

In this study, a series of thiazolidine-2,4-dione derivatives 3a-i were synthesized and evaluated for antibacterial activity against Gram-positive and Gram-negative strains of Bacillus licheniformis, Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus. Newly prepared thiazolidine (TZD) derivatives were further screened separately for in vitro antifungal activity against cultures of fungal species, namely, Aspergillus niger, Alternaria brassicicola, Chaetomium murorum, Fusarium oxysporum, Lycopodium sp. and Penicillium notatum. The electron-donating substituents (-OH and -OCH3) and electron-withdrawing substituents (-Cl and -NO2) on the attached arylidene moieties of five-membered heterocyclic ring enhanced the broad spectrum of antimicrobial and antifungal activities. The molecular docking study has revealed that compound 3h strongly interacts with the catalytic residues of the active site of the β-carbonic anhydrase (P. aeruginosa) and has the best docking score. In silico pharmacokinetics studies showed the drug-likeness and non-toxic nature of the synthesized compounds, which indicates the combined antibacterial, antiviral and antitumor pharmacophore sites of the targeted drug. This work demonstrates that potential TZD derivatives bind to different types of bacterial and fungal pathogens for circumventing their activities and opens avenues for the development of newer drug candidates that can target bacterial and fungal pathogens.Communicated by Ramaswamy H. Sarma.

Exploring the Synergistic Mechanism of AP2A2 Transcription Factor Inhibition via Molecular Modeling and Simulations as a Novel Computational Approach for Combating Breast Cancer: In Silico Interpretations

Studies have shown that transcription factor AP2A2 (activator protein-2 alpha-2) is involved in the expression of DLEC1, a tumor suppressor gene, which, when mutated, will cause breast cancer and is thus an excellent target for breast cancer studies. Therefore, in the present research, a synergistic approach toward combating breast cancer is proposed by blocking AP2A2 factor, and allowing the cancer cells to be sensitive to anti-cancer drugs. The effect of AP2A2 on breast cancer was first understood via gene analysis from cBioPortal. AP2A2 was then modeled using RaptorX and its structure was validated from Ramachandran plots. Using all ligands from MolPort database, molecular docking was performed against AP2A2, from which the top three best docked ligands were studied for toxicity in humans using Protox-II. Once the ligands passed these tests, the best complexes were simulated at 200ns in Desmond Maestro, to comprehend their stabilities, followed by the computations of free energies of binding via Molecular mechanics- Generalized Born Solvent Accessibility method (MM-GBSA). The results showed that molecules MolPort-005-945-556 (sachharolipids), MolPort-001-741-124 (flavonoids), and MolPort-005-944-667 (lignan glycosides) with AP2A2 passed toxicity evaluation and belonged to toxicity classes 6, 5, and 5, respectively, with good docking energies. 200 ns simulations revealed stable complexes with slight conformational changes. Stability of ligands was confirmed via snapshots at every 20 ns of the trajectory. Radial distribution of these molecules against the protein revealed very slight deviation from binding pocket. Additionally, the free binding energies for these complexes were found to be - 54.93 ± 12.982 kcal/mol, - 44.39 ± 14.393 kcal/mol, and - 66.51 ± 13.522 kcal/mol, respectively. A preliminary computational validation of the inability of AP2A2 to bind to DLEC1 in the presence of ligands offers beneficial insights into the potential of these ligands. Therefore, this study sheds light on the potential natural molecules that could stably block AP2A2 with least deviation and act in synergy to aid anti-cancer drugs work on breast cancer cells.

Theoretical, antioxidant, antidiabetic and in silico molecular docking and pharmacokinetics studies of heteroleptic oxovanadium(IV) complexes of thiosemicarbazone-based ligands and diclofenac

A series of new heteroleptic oxovanadium(IV) complexes with the general formula [VOL1-6(Dcf)] (1-6), where L1-6 = thiosemicarbazone (TSC)-based ligands and Dcf = diclofenac have been synthesized and characterized. The spectral studies along with the density functional theory calculations evidenced the distorted square-pyramidal geometry around oxovanadium(IV) ion through imine nitrogen and thione sulfur atoms of TSC moiety, and two asymmetric carboxylate oxygen atoms of diclofenac drug. The complexes were evaluated for in vitro antioxidant activity using 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2'-diphenyl-1-picrylhydrazyl (DPPH), hydrogen peroxide (H2O2) and superoxide radical scavenging assays with respect to the standard antioxidant drugs butylated hydroxyanisole (BHA) and rutin. The in vitro antidiabetic activity of the complexes was tested with enzymes such as α-amylase, α-glucosidase and glucose-6-phosphatase. The complexes containing methyl substituent showed higher activity than that containing the nitro substituent due to the electron-donating effect of methyl group. The in silico molecular docking studies of the oxovanadium(IV) complexes with α-amylase and α-glucosidase enzymes showed strong interaction via hydrogen bonding and hydrophobic interactions. The dynamic behavior of the proposed complexes was analyzed by molecular dynamics (MDs) simulations, which revealed the stability of docked structures with α-amylase and α-glucosidase enzymes. The in silico physicochemical and pharmacokinetics parameters, such as Lipinski's 'rule of five', Veber's rule and absorption, distribution, metabolism and excretion (ADME) properties predicted non-toxic, non-carcinogenic and safe oral administration of the synthesized complexes.Communicated by Ramaswamy H. Sarma.

Protocol for the development of coarse-grained structures for macromolecular simulation using GROMACS

Coarse-grained simulations have emerged as a valuable tool in the study of large and complex biomolecular systems. These simulations, which use simplified models to represent complex biomolecules, reduce the computational cost of simulations and enable the study of larger systems for longer periods of time than traditional atomistic simulations. GROMACS is a widely used software package for performing coarse-grained simulations of biomolecules, and several force fields have been developed specifically for this purpose. In this protocol paper, we explore the advantages of using coarse-grained simulations in the study of biomolecular systems, focusing specifically on simulations performed using GROMACS. We discuss the force fields required for these simulations and the types of research questions that can be addressed using coarse-grained simulations. We also highlight the potential benefits of coarse-grained simulations for the development of new force fields and simulation methodologies. We then discuss the expected results from coarse-grained simulations using GROMACS and the various techniques that can be used to analyze these results. We explore the use of trajectory analysis tools, as well as thermodynamic and structural analysis techniques, to gain insight into the behavior of biomolecular systems.

De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein

The drug discovery and research for an anti-COVID-19 drug has been ongoing despite repurposed drugs in the market. Over time, these drugs were discontinued due to side effects. The search for effective drugs is still under process. The role of Machine Learning (ML) is critical in the search for novel drug compounds. In the current work, using the equivariant diffusion model, we built novel compounds targeting the spike protein of SARS-CoV-2. Using the ML models, 196 de novo compounds were generated which had no hits on any major chemical databases. These novel compounds fulfilled all the criteria of ADMET properties to be lead-like and drug-like compounds. Of the 196 compounds, 15 were docked with high confidence in the target. These compounds were further subjected to molecular docking, the best compound having an IUPAC name of (4aS,4bR,8aS,8bS)-4a,8a-dimethylbiphenylene-1,4,5,8(4aH,4bH,8aH,8bH)-tetraone and a binding score of -6.930 kcal/mol. The principal compound is labeled as CoECG-M1. Density Function Theory (DFT) and Quantum optimization was carried out along with the study of ADMET properties. This suggests that the compound has potential drug-like properties. The docked complex was further subjected to MD simulations, GBSA, and metadynamics simulations to gain insights into the stability of binding. The model can be in the future modified to improve the positive docking rate.

Insights on Novel Effectors and Characterization of Metacaspase (RS107_6) as a Potential Cell Death-Inducing Protein in Rhizoctonia solani

Effectors play an important role in host–pathogen interactions. Though an economically significant disease in rice, knowledge regarding the infection strategy of Rhizoctonia solani is obscure. In this study, we performed a genome-wide identification of the effectors in R. solani based on the characteristics of previously reported effector proteins. A total of seven novel effectors (designated as RS107_1 to RS107_7) in the disease mechanism of R. solani were identified and were predicted to be non-classically secreted proteins with functionally conserved domains. The function, reactivity, and stability of these proteins were evaluated through physiochemical characterization. The target proteins involved in the regulation of rice defense mechanisms were identified. Furthermore, the effector genes were cloned and RS107_6 (metacaspase) was heterologously expressed in Escherichia coli to obtain a purified protein of ~36.5 kDa. The MALD-TOF characterization confirmed that the protein belonged to a metacaspase of the Peptidase_C14 protein family, 906 bp in size, and encoded a polypeptide of 301 amino acids. These findings suggest that the identified effectors can potentially serve as a virulence factor and can be targeted for the management of sheath blight in rice.

Provision of smoking cessation support for patients following a diagnosis of cancer in Ireland

There is growing evidence that smoking cessation (SC) improves outcomes following diagnosis of cancer. Notwithstanding adverse outcomes, a significant number of those diagnosed with cancer continue to smoke. Our objective was to document the SC services provided for patients with cancer by specialist adult cancer hospitals across Ireland, a country with a stated tobacco endgame goal. A cross-sectional survey based on recent national clinical guidelines was used to determine SC care delivery across eight adult cancer specialist hospitals, and one specialist radiotherapy centre. Qualtrics was used. The response rate was 88.9% with data reported from seven cancer hospitals and one specialist radiotherapy centre, all indicating they had some SC related provision (100%). Stop smoking medications were provided to cancer inpatients in two hospitals, at outpatients and attending day ward services in one hospital. Smokers with cancer were referred automatically to the SC service in two hospitals at diagnosis. While stop smoking medications were available 24 h a day in five hospitals, most did not stock all three (Nicotine Replacement Therapy, Bupropion, Varenicline). One hospital advised they had data on uptake of SC services for smokers with cancer but were unable to provide detail. There is considerable variation in SC information and services provided to cancer patients across adult cancer specialist centres in Ireland, reflecting the suboptimal practice of smoking cessation for patients with cancer found in the limited international audits. Such audits are essential to demonstrate service gaps and provide a baseline for service improvement.

Carboxymuconolactone decarboxylase is a prospective molecular target for multi-drug resistant Acinetobacter baumannii-computational modeling, molecular docking and dynamic simulation studies

Multidrug-resistant Acinetobacter baumannii (MDRAb), a priority-I pathogen declared by the World Health Organization, became a potential healthcare concern worldwide with a high mortality rate. Thus, the identification of putative molecular targets and potential lead molecules is an important concern in healthcare. The present study aimed to screen a prospective molecular target and effectual binders for the drug discovery of MDRAb by computational virtual screening approach. Based on the functional role, γ-carboxymuconolactone decarboxylase (CMD) was prioritized as the target and its three-dimensional (3D) structure was computationally modeled. Based on the availability of the 3D structure, twenty-five herbal molecules were selected by database search, and their drug-likeliness, pharmacokinetic, and toxicity features were predicted. The effectual binding of the selected molecules towards CMD was predicted by molecular docking. The stability of the best-docked complexes was predicted by molecular dynamics (MD) simulation for 100 ns and binding energy calculations were carried out by molecular mechanics generalized Born and surface area solvation (MM/GBSA) method. Out of twenty-five molecules screened, hirsutine (an indole alkaloid of Uncaria rhynchophylla) and thymoquinone (a phytochemical of Nigella sativa) were qualified for drug likeliness, pharmacokinetic, and toxicity features and demonstrated significant effectual binding to CMD when compared with the binding of co-crystallized inhibitor and CMD (control). The docked complexes of hirsutine and thymoquinone, and CMD were stabilized by the binding energies of -8. 30 and -8. 46 kcal/mol respectively. These molecules were qualified in terms of ideal drug likeliness, ADME, and toxicity properties. MD simulation studies showed that the ligand-protein complexes were stable throughout the simulation. The binding free energies of the complexes by MMGBSA were estimated to be -42.08157745 kcal/mol and -36.58618242 kcal/mol for hirsutine and thymoquinone respectively when compared with the calculated binding free energy of the control (-28.75032666 kcal/mol). This study concluded that hirsutine and thymoquinone can act as potential lead molecules against CMD and the present hypothesis can be scaled up to develop potential inhibitors against MDRAb.

Coumarin derivative as a potent drug candidate against triple negative breast cancer targeting the frizzled receptor of wingless-related integration site signaling pathway

Triple negative breast cancer constitutes to about 21.8 percent of the total breast cancer related cases. Its ability to affect young ladies and in pre-menstrual stage makes this a disease of concern worldwide. The current treatment regimens involve chemotherapy which are used for treatment of other cancer types. In this regard, there is a need for specific and targeted drug candidate for its effective treatment. In the current study, assessment of coumarin derivative 2-(2-(6- Methyl-2-Oxo-2H-chromen-4-yl) acetamido)-3-phenylpropanoic acid is carried out both In-silico and In-vitro methods. Frizzled transmembrane proteins of Wingless-related integration site signaling pathway was targeted in which Frizzled-7 proved to a prospective target and showed a binding energy of -6.78 kcal/mol. The complex was subjected to molecular dynamics simulation for 200 ns and showed stable interaction with cysteine rich domain of the receptor. Cell proliferation, viability and apoptosis assay were performed on MDA-MB-231 and MDA-MB-468 cell lines with an IC₅₀ value of 81.23 and 84.68 µM, respectively. The results provide a drug candidate which is derivative of a natural compound with targeted TNBC inhibitory effect. Communicated by Ramaswamy H. Sarma.

Repurposing of known drugs for COVID-19 using molecular docking and simulation analysis

We selected fifty one drugs already known for their potential disease treatment roles in various studies and subjected to docking and molecular docking simulation (MDS) analyses. Five of them showed promising features that are discussed and suggested as potential candidates for repurposing for COVID-19. These top five compounds were boswellic acid, pimecrolimus, GYY-4137, BMS-345541 and triamcinolone hexacetonide that interacted with the chosen receptors 1R42, 4G3D, 6VW1, 6VXX and 7MEQ, respectively with binding energies of -9.2 kcal/mol, -9.1 kcal/mol, -10.3 kcal/mol, -10.1 kcal/mol and -8.7 kcal/mol, respectively. The MDS studies for the top 5 best complexes revealed binding features for the chosen receptor, human NF-kappa B transcription factor as an important drug target in COVID-19-based drug development strategies.

Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications

Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.

Deciphering the interaction mechanism of natural actives against larval proteins of Aedes aegypti to identify potential larvicides: a computational biology analysis

Aedes aegypti is the target for repellents to curb incidences of vector-borne diseases. Stopping breeding of this mosquito species at its larval stages helps in controlling spread of insect-borne diseases. Therefore, the present study focused on deciphering the mechanism of interaction of selected natural actives against larval proteins of A. aegypti to identify potential natural alternative larvicides. 65 larval proteins were identified from literature, whose structures were modelled and validated using RaptorX and ProCheck. 11 natural actives were selected for predicting their ligand properties and toxicities via Toxicity Estimation Software Tool and ProTox-II. Molecular docking studies were carried out using POAP followed by 100 ns molecular dynamic simulation studies for top three best docked complexes to better understand the robustness of docking, complex stabilities and molecular mechanisms of interactions. Toxicity predictions revealed that 6 molecules belonged to toxicity class 4, and five to toxicity class 5, implying that they were all safe to use. Complexes goniothalamin-translation elongation factor (-10 kcal/mol), andrographolide-acetyl-CoA C-myristoyltransferase (-9.2 kcal/mol) and capillin-translation elongation factor (-8.4 kcal/mol) showed best binding energies. When simulated, capillin-translation elongation factor showed most stability, while the remaining two also evidenced robust docking. Evolutionary studies for top two larval proteins disclosed 100 other insect species in which these proteins can be targeted using various larvicides. Protein-protein interaction network analysis revealed several protein pathways that might be affected due to aforesaid naturals. Therefore, this study provides computational insights into the molecular interaction of naturals against larval proteins, acting as potential natural larvicides.Communicated by Ramaswamy H. Sarma.

Computational design of prospective molecular targets for Burkholderia cepacia complex by molecular docking and dynamic simulation studies

The study aimed to screen prospective molecular targets of BCC and potential natural lead candidates as effective binders by computational modeling, molecular docking, and dynamic (MD) simulation studies. Based on the virulent functions, tRNA 5-methylaminomethyl-2-thiouridine biosynthesis bifunctional protein (mnmC) and pyrimidine/purine nucleoside phosphorylase (ppnP) were selected as the prospective molecular targets. In the absence of experimental data, the three-dimensional (3D) structures of these targets were computationally predicted. After a thorough literature survey and database search, the drug-likeness, and pharmacokinetic properties of 70 natural molecules were computationally predicted and the effectual binding of the best lead molecules against both the targets was predicted by molecular docking. The stabilities of the best-docked complexes were validated by MD simulation and the binding energy calculations were carried out by MM-GBSA approaches. The present study revealed that the hypothetical models of mnmC and ppnP showed stereochemical accuracy. The study also showed that among 70 natural compounds subjected to computational screening, Honokiol (3',5-Di(prop-2-en-1-yl) [1,1'-biphenyl]-2,4'-diol) present in Magnolia showed ideal drug-likeness, pharmacokinetic features and showed effectual binding with mnmC and ppnP (binding energies -7.3 kcal/mol and -6.6 kcal/mol, respectively). The MD simulation and GBSA calculation studies showed that the ligand-protein complexes stabilized throughout tMD simulation. The present study suggests that Honokiol can be used as a potential lead molecule against mnmC and ppnP targets of BCC and this study provides insight into further experimental validation for alternative lead development against drug resistant BCC.

Design of Novel Imidazopyrazine Derivative against Breast Cancer via Targeted NPY1R Antagonist

INTRODUCTION

Breast cancer is the most frequent malignancy in women with more than one in ten new cancer diagnoses each year. Synthetic products are a key source for the identification of new anticancer medicines and drug leads.

OBJECTIVES

Imidazopyrazine is a highly favored skeleton for the design of new anticancer drugs. In silico designed derivatives were screened using computer aided drug design techniques and validated using MTT assay.

METHODS

A template-based methodology was used in the current work to create novel Imidazopyrazine derivatives, targeting the NPY1R protein. Molecular docking, Diffusion docking, MD simulation, MM-GBSA and meta-dynamics techniques were followed. MTT assay was performed to validate the activity of principal compound.

RESULTS

A docking score of -6.660 and MMGBSA value of -108.008 (+/-) 9.14 kcal/mol was obtained from the investigations conducted. In addition, molecular dynamics simulation was carried out for 500 ns, yielding a stable RMSD and value of 5.6 Å, thus providing insights on the stability of the protein conformation on interaction with the principal compound. Furthermore, the in vivo validation studies conducted via MTT assay showed an IC50 value of 73.45 (+/-) 0.45 μg /mL.

CONCLUSION

The research has produced encouraging findings and can be applied as a model for precise enumerations in the future. It also encourages the study of novel synthetic compounds with potential anti-cancer properties.

Novel Biomarker Prediction for Lung Cancer Using Random Forest Classifiers

Lung cancer is considered the most common and the deadliest cancer type. Lung cancer could be mainly of 2 types: small cell lung cancer and non-small cell lung cancer. Non-small cell lung cancer is affected by about 85% while small cell lung cancer is only about 14%. Over the last decade, functional genomics has arisen as a revolutionary tool for studying genetics and uncovering changes in gene expression. RNA-Seq has been applied to investigate the rare and novel transcripts that aid in discovering genetic changes that occur in tumours due to different lung cancers. Although RNA-Seq helps to understand and characterise the gene expression involved in lung cancer diagnostics, discovering the biomarkers remains a challenge. Usage of classification models helps uncover and classify the biomarkers based on gene expression levels over the different lung cancers. The current research concentrates on computing transcript statistics from gene transcript files with a normalised fold change of genes and identifying quantifiable differences in gene expression levels between the reference genome and lung cancer samples. The collected data is analysed, and machine learning models were developed to classify genes as causing NSCLC, causing SCLC, causing both or neither. An exploratory data analysis was performed to identify the probability distribution and principal features. Due to the limited number of features available, all of them were used in predicting the class. To address the imbalance in the dataset, an under-sampling algorithm Near Miss was carried out on the dataset. For classification, the research primarily focused on 4 supervised machine learning algorithms: Logistic Regression, KNN classifier, SVM classifier and Random Forest classifier and additionally, 2 ensemble algorithms were considered: XGboost and AdaBoost. Out of these, based on the weighted metrics considered, the Random Forest classifier showing 87% accuracy was considered to be the best performing algorithm and thus was used to predict the biomarkers causing NSCLC and SCLC. The imbalance and limited features in the dataset restrict any further improvement in the model's accuracy or precision. In our present study using the gene expression values (LogFC, P Value) as the feature sets in the Random Forest Classifier BRAF, KRAS, NRAS, EGFR is predicted to be the possible biomarkers causing NSCLC and ATF6, ATF3, PGDFA, PGDFD, PGDFC and PIP5K1C is predicted to be the possible biomarkers causing SCLC from the transcriptome analysis. It gave a precision of 91.3% and 91% recall after fine tuning. Some of the common biomarkers predicted for NSCLC and SCLC were CDK4, CDK6, BAK1, CDKN1A, DDB2.

A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways

The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained.

Novel 1,2,5-Trisubstituted Benzimidazoles Potentiate Apoptosis by Mitochondrial Dysfunction in Panel of Cancer Cells

Synthetic small molecules have been very effective in decimating cancer cells by targeting various aberrantly overexpressed oncogenic proteins. These small molecules target proteins involved in cell cycle regulation, cell division, migration, invasion, angiogenesis, and other regulatory proteins to induce apoptosis in cancer cells. In this study, we have synthesized a novel 1,2,5-trisubstituted benzimidazole chemical library of small molecules and unveiled their anticancer potential against a panel of cancer cell lines such as Jurkat, K-562, MOLT-4, HeLa, HCT116, and MIA PaCa-2 cancer cells. The MTT assay and Trypan blue dye exclusion assay clearly unveiled the cytotoxic effect of methyl 1-benzyl-2-(4-fluoro-3-nitrophenyl)-1H-benzo[d]imidazole-5-carboxylate (TJ08) and its potential to induce apoptosis with effective IC₅₀ of 1.88 ± 0.51, 1.89 ± 0.55, 2.05 ± 0.72, 2.11 ± 0.62, 3.04 ± 0.8, and 3.82 ± 0.25 μM against Jurkat, K562, MOLT-4, HeLa, HCT116, and MIA PaCa-2 cancer cell lines, respectively. Altered mitochondrial membrane potential was observed in HeLa, HCT116, and Jurkat cells due to TJ08 treatment, which was unveiled by JC10 staining. Induction of early and late apoptosis by TJ08 treatment was also unveiled by apoptotic analysis and immunofluorescence imaging. Cell cycle analysis distribution confirms the accumulation of cells in the S-phase in a dose-dependent manner.

Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers

Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.

Impact of community outreach programme on improving cancer related preventive health behaviour

 

Cancer services were negatively affected due to the Covid-19 pandemic and will have likely delayed early detection and diagnosis. The Irish Cancer Society (ICS) planned and delivered a number of cancer roadshow events in 4 deprived regions in Ireland to address improvements in awareness of cancer signs, importance of screening, positive lifestyle changes and encourage medical care-seeking behaviour. Health checks (blood pressure, BMI & CO2 monitoring) and motivational interviews were carried out by ICS nurses and trained ICS volunteers. Data was collected via anonymous questionnaire from participants and volunteers. SPSS was used for analysis. 98 people (54 male, 44 female) participated; 87.7% found the information provided useful, 84.7% approved of the health check and 72.5% stated that they would likely make changes to their current lifestyle to reduce their cancer risk. Moderate/high understanding of cancer signs and symptoms and moderate/high awareness of cancer risk factors both rose post event (from 62.2% to 81.6% (p < 0.001) and from 49% to 61.2% (p < 0.001) respectively). If symptomatic for cancer 77.6% of participants would likely visit their GP and 73.5% would likely contact ICS. Younger people (aged ≤40 years) were more likely to consider consulting a healthcare professional if symptomatic (p = 0.027) and to contact the ICS (p = 0.007) for more information. High numbers of participants (98.9%) and volunteers (95.2%) recommended a nationwide roll out. Volunteers reported moderate/high levels of public engagement; the most common topics discussed were own treatment experience, cancer screening, information on making lifestyle changes and ICS services. Volunteers reported the need for more training and improvements to some organisational aspects. More rigorous cancer health promotion programmes are required to counter disrupted cancer services. Our results suggest such cancer roadshow events are both feasible and beneficial at this time post pandemic.

Key messages

• Health literacy is continuous process to achieve positive health outcomes.

• Reaching out to people in different setting is acceptable and potentially effective.

Smoking cessation for cancer patients through the lens of cancer specialists: challenges & solutions

Background

The benefits of smoking cessation (SC) for cancer patients are widely recognised. However, there has been a limited emphasis on SC in this context and it continues to be a challenge for cancer patients. As part of a larger feasibility study aiming to develop a structured SC pathway for cancer patients in Ireland, this qualitative study explored the SC practices, experiences and opinions of oncology healthcare professionals (HCPs).

Methods

Semi-structured interviews were conducted with 18 HCPs from lung, breast, cervical, head and neck and general oncology, across 4 specialist adult cancer hospitals in Ireland. Interview transcripts were analysed using thematic analysis.

Results

Four key themes emerged:

(1) Frequently ask and advise but infrequently assist: most HCPs ask about smoking and many advise about available supports, but few refer patients to SC services. Where offered, referrals were to hospital SC services and/or nicotine replacement therapy was prescribed; no HCP prescribed varenicline or bupropion. Barriers included lack of time, ill-defined referral pathways and lack of knowledge.

(2) Increased willingness but differing ability to quit: most patients were interested in quitting post diagnosis and had varying support needs, linked to cancer stage, social circumstances and stress levels.

(3) Need for an integrated or parallel service: all HCPs suggested that a structured and defined referral pathway will facilitate SC.

(4) Motivational counselling and pharmacotherapy combination: many HCPs suggested face to face as the best mode of intervention initially, with regular follow ups and ongoing support virtually, started pre-treatment, with an empathetic and empowering approach with provision of both motivational counselling and SC pharmacotherapy.

Conclusions

Smoking post cancer diagnosis has serious implications for cancer treatment and prognosis but is frequently overlooked. These findings will inform the design of a SC pathway for cancer patients.

Key messages

• Despite increased willingness to quit, there is inadequate and inconsistent SC support provision for cancer patients.

• Tailored SC support should be an integral part of comprehensive cancer care.

Rapid systematic review of smoking cessation interventions for people who smoke and have cancer

Background

Higher rates of cancer are reported in smokers compared to non-smokers, and continued smoking following a cancer diagnosis is associated with reduced health outcomes and survival. Despite international evidence of increased risks, a substantial percentage of people with a cancer diagnosis continue to smoke. Patients may be unaware of the additional risks associated with continued smoking, and health care professionals may not engage with quit supports. As part of a larger feasibility study to develop a smoking cessation pathway in cancer services in Ireland, a rapid review of the evidence was completed.

Methods

Systematic searches of PubMed, Embase, and CINAHL 2015 to December 2020 were conducted; with studies restricted to adults with a cancer diagnosis [lung, breast, cervical, head and neck] and published in English. No restriction was placed on study designs. 6404 studies were identified and uploaded into COVIDENCE platform, Cochrane's systematic review methods were adopted throughout, PRISMA reporting guidelines were used, and narrative data synthesis was completed (CRD 42020214204).

Results

The twenty-three-studies report evidence from USA, Canada, England, Lebanon, and Australia. The setting for all interventions was hospitals and cancer clinics. Evidence identifies high dropout rates, inconsistencies in approaches and duration of smoking cessation interventions with varied outcomes. A wide-ranging number of critical components emerged associated with optimal quit support- including the timing of and frequency of quit conversations, use of electronic records, in-person support meetings, provision of nicotine replacement therapy and extended use of Varenicline, smoking cessation services embedded in oncology depts, and engaging with families wanting to quit at the same time.

Conclusions

Developing tailored smoking cessation interventions are needed for smokers diagnosed with cancer to enable engagement.

Key messages

• Continued smoking following a cancer diagnosis is associated with reduced health outcomes.

• Smoking cessation programmes for cancer patient should be tailored to meet needs.

Comprehensive Molecular Interaction Studies to Construe the Repellent/Kill Activity of Geraniol During Binding Event Against Aedes aegypti Proteins

Aedes aegypti is an etiological agent for dengue, chikungunya, zika, and yellow fever viruses. With the advent of the use of natural alternatives as repellents, their precise mode of action during the event of binding is still unclear. Geraniol is one such bioactive natural that has been previously shown to have some insecticide properties. Thus, the present study aimed to understand the mechanism of the binding event of geraniol with the whole proteome of A. aegypti. Twenty protein target categories were shortlisted for the mosquito, wherein the proteins were downloaded with respect to the reference proteome. Conserved domain analysis was performed for the same using the CDD search tool to find the proteins that have common domains. 309 proteins were modeled using RaptorX standalone tool, and validated using Ramachandran plots from SAVES v6.0 from ProCheck. These modeled and validated proteins were then docked against geraniol, using POAP software, for understanding the binding energies. The top 3 best-docked complexes were then analyzed for their stabilities and event of binding via 100 ns simulation studies using DESMOND's Maestro environment. The docking results showed that the geraniol-voltage-gated sodium channel had the best energy of - 7.1 kcal/mol, followed by geraniol-glutathione-S-transferase (- 6.8 kcal/mol) and geraniol-alpha esterase (- 6.8 kcal/mol). The simulations for these 3 complexes revealed that several residues of the proteins interacted well with geraniol at a molecular level, and all three docked complexes were found to be stable when simulated (RMSD: 16-18 Å, 3.6-4.8 Å, 4.8-5.6 Å, respectively). Thus, the present study provides insights into the mechanism of the binding event of geraniol with the major A. aegypti targets, thereby, assisting the use of geraniol as a natural repellent.

Transcriptomic responses under combined bacterial blight and drought stress in rice reveal potential genes to improve multi-stress tolerance

BACKGROUND

The unprecedented drought and frequent occurrence of pathogen infection in rice is becoming more due to climate change. Simultaneous occurrence of stresses lead to more crop loss. To cope up multiple stresses, the durable resistant cultivars needs to be developed, by identifying relevant genes from combined biotic and abiotic stress exposed plants.

RESULTS

We studied the effect of drought stress, bacterial leaf blight disease causing Xanthomonas oryzae pv. oryzae (Xoo) pathogen infection and combined stress in contrasting BPT5204 and TN1 rice genotypes. Mild drought stress increased Xoo infection irrespective of the genotype. To identify relevant genes that could be used to develop multi-stress tolerant rice, RNA sequencing from individual drought, pathogen and combined stresses in contrasting genotypes has been developed. Many important genes are identified from resistant genotype and diverse group of genes are differentially expressed in contrasting genotypes under combined stress. Further, a meta-analysis from individual drought and Xoo pathogen stress from public domain data sets narrowed- down candidate differentially expressed genes. Many translation associated genes are differentially expressed suggesting their extra-ribosomal function in multi-stress adaptation. Overexpression of many of these genes showed their relevance in improving stress tolerance in rice by different scientific groups. In combined stress, many downregulated genes also showed their relevance in stress adaptation when they were over-expressed.

CONCLUSIONS

Our study identifies many important genes, which can be used as molecular markers and targets for genetic manipulation to develop durable resistant rice cultivars. Strategies should be developed to activate downregulated genes, to improve multi-stress tolerance in plants.

MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets

Advancements in the field of cancer research have enabled researchers and clinicians to access a massive amount of data to aid cancer patients and to add to the existing knowledge of research. However, despite the existence of reliable sources for extricating this data, it remains a challenge to accurately comprehend and draw conclusions based on the entirety of available information. Therefore, the current study aimed to design and develop a database for the identified variants of 5 different cancer types using 20 different cancer exomes. The exome data were retrieved from NCBI SRA and an NGS data clean-up protocol was implemented to obtain the best quality reads. The reads which passed the quality checks were then used for calling the variants which were then processed and filtered. This data was used to normalize and the normalized data generated was used for developing the database. MutaXome, which stands for mutations in cancer exome was designed in SQL, with the front end in bootstrap and HTML, and backend in PHP. The normalized data containing the variants inclusive of Single Nucleotide Polymorphisms (SNPs), were added into MutaXome, which contains detailed information regarding each type of identified variant. This database, available online via http://www.vidyalab.rf.gd/, serves as a knowledge base for cancer exome variations and holds much potential for enriching it by linking it to a decision support system as prospective studies.

Novel small molecules targeting bZIP23 TF improve stomatal conductance and photosynthesis under mild drought stress by regulating ABA

Drought-induced abscisic acid (ABA) accumulation plays a key role in plant water relations by regulating stomatal movements. Although ABA helps in the survival of the plants, reduced carbon gain affects plant productivity. To improve crop productivity under mild drought stress conditions, it is necessary to manipulate ABA responses. Other research groups have used forward chemical genomics for the identification of ABA agonists and antagonists aiming to manipulate ABA biosynthesis and signalling. In the present study, we identified indolyl-ethyl amine and serotonin small molecules using a reverse chemical genomics approach, with these acting as potent inhibitors of ABA biosynthesis through transient regulation of bZIP23 transcription factor activity. In rice, wheat and soybean, each of the small molecules enhanced the germination of seeds, even in the presence of ABA. These molecules nullified the effect of ABA on intact and detached leaves, resulting in higher photosynthesis. Furthermore, these small molecules effectively reduced the transcription levels of bZIP23 targeting NCED4, PP2C49 and CO3 genes. Rice plants treated with the small molecules were found to have improved stomatal conductance, spikelet fertility and yield compared to untreated plants under mild drought stress conditions. Our results suggest that indolyl-ethyl amine and serotonin small molecules could be utilized to improve yield under mild drought conditions.

Targeting non-structural proteins and 3CLpro in SARS-CoV-2 virus using phytochemicals from medicinal plants - In-silico approach

In the present study, the main protease 3CL^pro and non-structural protein (NSP-12 with co-factors 7 and 8) trimer complex are used to study the protein-drug interactions with the phytochemicals from Ocimum Sanctum, Tinospora Cordifolia, Glycyrrhiza Glabra, and Azadirachta Indica. Which can give insight to be used as potent antiviral drugs against SARS-CoV-2. Twenty phytochemicals, five from each plant species, known for their wide range of biological activities were chosen from the literature. The in-silico study was carried out using virtual screening tools and the top five, which showed the least binding energies, were selected. Molecular docking tools revealed that gedunin and epoxy azadiradione proved to be excellent inhibitors for 3CL^pro and so did Tinosporide for non-structural-protein complex. Further, the best-hit phytochemicals with respect to structure similarities with FDA drugs and investigatory drugs, were considered for comparative study. Molecular docking was done to check the drug-protein interactions and to check the inhibitory responses of these drugs against the viral protein. The analyses showed that the phytochemicals had similar responses on the protein complex but with exceptionally higher inhibitory responses hence which may be taken for further clinical study.

Molecular dynamics simulation and docking analysis of NF-κB protein binding with sulindac acid

It is of interest to document the Molecular Dynamics Simulation and docking analysis of NF-κB target with sulindac sodium in combating COVID-19 for further consideration. Sulindac is a nonsteroidal anti-inflammatory drug (NSAID) of the arylalkanoic acid class that is marketed by Merck under the brand name Clinoril. We show the binding features of sulindac sodium with NF-κB that can be useful in drug repurposing in COVID-19 therapy.

Molecular Docking and Interaction Studies of Identified Abscisic Acid Receptors in Oryza sativa: An In-Silico Perspective on Comprehending Stress Tolerance Mechanisms

Background

Abiotic stresses affect plants in several ways and as such, phytohormones such as abscisic acid (ABA) play an important role in conferring tolerance towards these stresses. Hence, to comprehend the role of ABA and its interaction with receptors of the plants, a thorough investigation is essential.

Aim

The current study aimed to identify the ABA receptors in Oryza sativa, to find the receptor that binds best with ABA and to examine the mutations present to help predict better binding of the receptors with ABA.

Methods

Protein sequences of twelve PYL (Pyrabactin resistance 1) and seven PP2C (type 2C protein phosphatase) receptors were retrieved from the Rice Annotation Project database and their 3D structures were predicted using RaptorX. Protein-ligand molecular docking studies between PYL and ABA were performed using AutoDock 1.5.6, followed by 100ns molecular dynamic simulation studies using Desmond to determine the acceptable conformational changes after docking via root mean square deviation RMSD plot analysis. Protein-protein docking was then carried out in three sets: PYL-PP2Cs, PYL-ABA-PP2C and PYL(mut)-ABA-PP2C to scrutinize changes in structural conformations and binding energies between complexes. The amino acids of interest were mapped at their respective genomic coordinates using SNP-seek database to ascertain if there were any naturally occurring single nucleotide polymorphisms (SNPs) responsible for triggering rice PYLs mutations.

Results

Initial protein-ligand docking studies revealed good binding between the complexes, wherein PYL6-ABA complex showed the best energy of -8.15 kcal/mol. The 100ns simulation studies revealed changes in the RMSD values after docking, indicating acceptable conformational changes. Furthermore, mutagenesis study performed at specific PYL-ABA interacting residues followed by downstream PYL(mut)-ABA-PP2C protein-protein docking results after induction of mutations demonstrated binding energy of -8.17 kcal/mol for PP2C79-PYL11-ABA complex. No naturally occurring SNPs that were responsible for triggering rice PYL mutations were identified when specific amino acid coordinates were mapped at respective genomic coordinates.

Conclusion

Thus, the present study provides valuable insights on the interactions of ABA receptors in rice and induced mutations in PYL11 that can enhance the downstream interaction with PP2C.

Understanding the Xylooligosaccharides Utilization Mechanism of Lactobacillus brevis and Bifidobacterium adolescentis: Proteins Involved and Their Conformational Stabilities for Effectual Binding

Xylooligosaccharides having various degrees of polymerization such as xylobiose, xylotriose, and xylotetraose positively affect human health by interacting with gut proteins. The present study aimed to identify proteins present in gut microflora, such as xylosidase, xylulokinase, etc., with the help of retrieved whole-genome annotations and find out the mechanistic interactions of those with the above substrates. The 3D structures of proteins, namely Endo-1,4-beta-xylanase B (XynB) from Lactobacillus brevis and beta-D-xylosidase (Xyl3) from Bifidobacterium adolescentis, were computationally predicted and validated with the help of various bioinformatics tools. Molecular docking studies identified the effectual binding of these proteins to the xylooligosaccharides, and the stabilities of the best-docked complexes were analyzed by molecular dynamic simulation. The present study demonstrated that XynB and Xyl3 showed better effectual binding toward Xylobiose with the binding energies of - 5.96 kcal/mol and - 4.2 kcal/mol, respectively. The interactions were stabilized by several hydrogen bonding having desolvation energy (- 6.59 and - 7.91). The conformational stabilities of the docked complexes were observed in the four selected complexes of XynB-xylotriose, XynB-xylotetraose, Xyl3-xylobiose, and Xyn3-xylotriose by MD simulations. This study showed that the interactions of these four complexes are stable, which means they have complex metabolic activities among each other. Extending these studies of understanding, the interaction between specific probiotics enzymes and their ligands can explore the detailed design of synbiotics in the future.

MutVis: Automated framework for analysis and visualization of mutational signatures in pathogenic bacterial strains

In recent years, mutational signature analysis has become a routine practice in cancer genomics for classification and diagnosis. Characterizing mutational signatures across species or within genomes of a bacteria helps in understanding their evolution and adaptation. However, an integrated framework for analysis and visualization of mutational signatures in bacterial genome is lacking. Hence, we aim to develop an integrated, automated, open-source and user-friendly framework called MutVis to analyze mutational signatures from bacterial whole genome next generation sequencing data. The current framework integrates various publicly available packages using Snakemake workflow management software, Python and R scripting. MutVis supports variant calling, transition (Ti) and transversion (Tv) graphical representation, generation of mutational count matrix, graphical visualization of base-pair substitution spectrum (BPSs) and mutation signatures extraction. TvTi plots provide the 6 base substitution classification for both genome and gene level. Further resolution of base pair substitution classification is provided as 96-profile BPSs plot. Mutation signatures is derived based on the characteristic pattern observed in BPSs using non-negative matrix factorization. Relative contribution of signatures is given as hierarchically clustered heatmap. This provides information on active signatures in the individual given sample and classify samples according to signature contributions. We demonstrated the MutVis framework using geographically different strains of Mycobacterium tuberculosis, downloaded from PATRIC TB-ARC Antibiotic Resistance Catalog (n = 963). The current framework can be used to study mutation biases and characteristic mutational signatures in bacterial genomes and is freely available at https://github.com/AkshathaPrasanna/MutVis.

Structural insights on the interaction potential of natural leads against major protein targets of SARS-CoV-2: Molecular modelling, docking and dynamic simulation studies

Though significant efforts are in progress for developing drugs and vaccines against COVID-19, limited therapeutic agents are available currently. Thus, it is essential to undertake COVID-19 research and to identify therapeutic interventions in which computational modeling and virtual screening of lead molecules provide significant insights. The present study aimed to predict the interaction potential of natural lead molecules against prospective protein targets of SARS-CoV-2 by molecular modeling, docking, and dynamic simulation. Based on the literature survey and database search, fourteen molecular targets were selected and the three targets which lack the native structures were computationally modeled. The drug-likeliness and pharmacokinetic features of ninety-two natural molecules were predicted. Four lead molecules with ideal drug-likeliness and pharmacokinetic properties were selected and docked against fourteen targets, and their binding energies were compared with the binding energy of the interaction between Chloroquine and Hydroxychloroquine to their usual targets. The stabilities of selected docked complexes were confirmed by MD simulation and energy calculations. Four natural molecules demonstrated profound binding to most of the prioritized targets, especially, Hyoscyamine and Tamaridone to spike glycoprotein and Rotiorinol-C and Scutifoliamide-A to replicase polyprotein-1ab main protease of SARS-CoV-2 showed better binding energy, conformational and dynamic stabilities compared to the binding energy of Chloroquine and its usual target glutathione-S-transferase. The aforementioned lead molecules can be used to develop novel therapeutic agents towards the protein targets of SARS-CoV-2, and the study provides significant insight for structure-based drug development against COVID-19.

A study of road rage in India

Introduction

Road rage is a term used to describe driving usually extreme in nature. There seems to be a multifactorial relationship between the situational characteristics of an anger provoking road situation and the feelings of anger and road behaviour.

Objectives

To examine driver anger with regards to various sociodemographic parameter.

Methods

282 participants completed an internet-based survey including sociodemographic profile, anger assessment while driving using the Deffenbacher Driver Anger Scale, details of the driving. Participants were recruited through networks of authors, institution. The survey was disseminated through social media applications and email by snowball sampling method.

Results

Mean age of the sample was 26.1 years with age group 24-29 years making half of the population. Majority sample were males (62.1%), graduates (53.2%), professionals (45.7%), urban locality based, nuclear family type. People experienced greater anger on Defenbacher likert scale for the following situations, when Someone is driving very close to your rear bumper (mean= 3.09), Someone cuts in right in front of you on the motorway(mean= 3.44), Someone cuts in and takes the parking spot(mean= 3.19), Someone coming towards you does not dim headlights at night(mean= 3.26), driving behind a vehicle smoking badly or giving off fumes(mean= 3.38).

Conclusions

The results revealed a prevalence of high anger scores amongst Indian drivers. The rage didn’t vary significantly within gender, locality, type of vehicle, however the anger scores were significantly higher in younger population. Strategies targeting at driving safety and reducing road rage should be implemented by authorities with sensitization of the drivers.

Disclosure

No significant relationships.

A report of use of baclofen in intractable hiccups

Introduction

Hiccups are an involuntarily powerful spasm of the diaphragm, followed by a sudden inspiration with a closure of the glottis. Hiccups lasting longer than one month is termed intractable hiccups. Intractable hiccups can be caused by structural or functional disturbances of the medulla, afferent or efferent nerves to the respiratory muscles or metabolic and endocrine disorders, drugs, general anaesthesia and emotional problems.

Objectives

Authors present a case report about curing a patient of intractable hiccups using baclofen along with literature review.

Methods

A case report along with literature review forms the basis of discussion.

Results

A 30-year female diagnosed with schizophrenia stable on 2mg risperidone for 3 years presented to the outpatient department with complain of intractable hiccups for 6 months. Frequency of hiccups was around 10-12 times per minute and continued throughout the day leading to significant socio-occupational distress. patient had been receiving medical treatment for last 4 months for the same including Metoclopramide, chlorpromazine along with trying breath holding and drinking cold water but symptoms persisted. Her ECG, chest X-ray, complete blood counts were unremarkable, CT scan of brain was normal. Patient was started on baclofen 10mg thrice daily. Within 1-week patient had dramatic response and complete remission was achieved in 2 weeks.

Conclusions

Beclofen is effective in hiccups because it is an analogue of GABA, that decreases excitability and inhibits the hiccup reflex, which reduces synaptic transmission. Baclofen is used to treat hiccups, and can be used either as a first-line treatment or if patient does not respond to other medications.

Disclosure

No significant relationships.

Stress-Induced Detoxification Enzymes in Rice Have Broad Substrate Affinity

Reactive carbonyl compounds (RCCs) such as hydroxynonenol, malondialdehyde, acrolein, crotonaldehyde, methylglyoxal, and glyoxal accumulate at higher levels under stress in plants and damage the cell metabolic activities. Plants have evolved several detoxifying enzymes such as aldo-keto reductases (AKRs), aldehyde/alcohol dehydrogenases (ALDH/ADH), and glyoxalases. We report the phylogenetic relationship of these proteins and in silico analysis of rice-detoxifying protein structures and their substrate affinity with cofactors using docking and molecular simulation studies. Molecular simulations with nicotinamide adenine dinucleotide phosphate or glutathione cofactor docking with commonly known reactive substrates suggests that the AKRs, ALDH, and ADH proteins attain maximum conformational changes, whereas glyoxalase has fewer conformational changes with cofactor binding. Several AKRs showed a significant binding affinity with many RCCs. The rice microarray studies showed enhanced expression of many AKRs in resistant genotypes, which also showed higher affinity to RCCs, signifying their importance in managing carbonyl stress. The higher expression of AKRs is regulated by stress-responsive transcription factors (TFs) as we identified stress-specific cis-elements in their promoters. The study reports the stress-responsive nature of AKRs, their regulatory TFs, and their best RCC targets, which may be used for crop improvement programs.

Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

Background:

Since bacteria are the earliest known organisms, there has been significant interest in their variety and biology, most certainly concerning human health. Recent advances in Metagenomics sequencing (mNGS), a culture-independent sequencing technology, have facilitated an accelerated development in clinical microbiology and our understanding of pathogens.

Objective:

For the implementation of mNGS in routine clinical practice to become feasible, a practical and scalable strategy for the study of mNGS data is essential. This study presents a robust automated pipeline to analyze clinical metagenomic data for pathogen identification and classification.

Method:

The proposed Clin-mNGS pipeline is an integrated, open-source, scalable, reproducible, and user-friendly framework scripted using the Snakemake workflow management software. The implementation avoids the hassle of manual installation and configuration of the multiple commandline tools and dependencies. The approach directly screens pathogens from clinical raw reads and generates consolidated reports for each sample.

Results:

The pipeline is demonstrated using publicly available data and is tested on a desktop Linux system and a High-performance cluster. The study compares variability in results from different tools and versions. The versions of the tools are made user modifiable. The pipeline results in quality check, filtered reads, host subtraction, assembled contigs, assembly metrics, relative abundances of bacterial species, antimicrobial resistance genes, plasmid finding, and virulence factors identification. The results obtained from the pipeline are evaluated based on sensitivity and positive predictive value.

Conclusion:

Clin-mNGS is an automated Snakemake pipeline validated for the analysis of microbial clinical metagenomics reads to perform taxonomic classification and antimicrobial resistance prediction.

Physician Health in the Times of COVID-19

BACKGROUND AND OBJECTIVES

A virtual registry study evaluating real world evidence on physicians' use of prophylactic regimens for protection against SARS-CoV-2. This paper summarizes the interim results.

METHODS

Asymptomatic physicians at risk of acquiring SARS-CoV-2 responded to online questions at baseline and 7 weeks post-baseline. Baseline data included demographics, prophylaxis regimen (including "no prophylaxis") and start date. Participants who provided complete week-7 data (information on type of health facility [COVID/Non-COVID], number of presumed/confirmed cases exposed to, PPE use, SARS-CoV-2 testing and symptoms, regimen adherence and intercurrent illness) comprised the Completer population. Limited data (regimen adherence, SARS-CoV-2 testing) was collected for participants who failed to provide complete week7 data. Those providing limited/complete information comprised the Evaluable population.

RESULTS

Of 369 enrolled participants, 182 (mean age 42±11.05 years) comprised the Evaluable population. They showed a male preponderance (67.6%). Practitioners from Maharashtra (59.9%) and specialties of Pediatrics, Internal Medicine, Anesthesiology and Critical Care (63.2%) accounted for the majority. ICMR's HCQ prophylaxis regimen was initiated by 125 (68.7%) participants with 31 (17%) initiating 'No prophylaxis'. The highest adherence was for the ICMRregimen (87.2%). In the Completer population comprising 150 participants, 87 were exposed to presumed (81) and/or confirmed cases (60). Most exposures to confirmed cases (49, 81.7%) were high-risk. PPE use was generally high (75-100%). Most participants (94.7%) did not report an AE. The proportions with an AE was similar with ICMR regimen (5.9%) and no prophylaxis (6.5%).

INTERPRETATION AND CONCLUSIONS

Physicians in India preferred ICMR's HCQ regimen. The regimen appears to be safe and associated with a high level of adherence.

Proteomic variations of esophageal squamous cell carcinoma revealed by combining RNA-seq proteogenomics and G-PTM search strategy

Background

Cancer that arises from epithelial cells of the esophagus is called esophagus squamous cell carcinoma (ESCC) and is mostly observed in developing nations. Evaluation of cancer genomes and its regulation into proteins plays a predominant role in understanding the cancer progressions. Mass-spectrometry-based proteomics is a consequential tool to estimate proteomic variation and posttranslational modifications (PTMs) from standard protein databases. Post-translational modifications play a crucial role in protein folding and PTMs can be accounted for as a biological signal to interpret the structural changes and transition order of proteins. Functional validation of cancer-related mutations can explain the effects of mutations on genes and the identification of Oncogenes and tumor suppressor genes. Therefore, we present a study on protein variations to interpret the structural changes and transition order of proteins in ESCC carcinogenesis.

Methodology

We are using a bottom-up proteomics approach with Galaxy-P framework and RNA sequence data analysis to generate the sample-specific databases containing details of RNA splicing and variant peptides. Once the database generated with information on variable modification, only the curated PTMs at specific positions are considered to perform spectral matching. Proteogenomics mapping was performed to identify protein variations in ESCC.

Results

RNA-sequence proteogenomics with G-PTM (Global Post-Translational Modification) searching strategy has revealed proteomic events including several peptides that contain single amino acid variations, novel splice junction peptides and posttranslationally modified peptides. Proteogenomic mapping exhibited the splice junction peptides mapped predominantly for Malic enzyme exon type (ME-3) and MCM7 protein-coding genes that promote cancer progression, found to be exhibited in ESCC samples. Approximately 25 ± types of PTM modifications were recorded, and Protein Phosphorylation was largely noted.

Conclusion

ESCC cancer prognosis at the molecular level enables a better understanding of cancer carcinogenesis and protein modifications can be used as potential biomarkers.

Deciphering effectual binding potential of xylo-substrates towards xylose isomerase and xylokinase through molecular docking and molecular dynamic simulation

Xylooligosaccharides (XOS) such as xylobiose and xylotriose are prebiotics with important functions and relevance and the study of interaction mechanism between these substrate and their respective enzymes has scope and applications. Thus, the present study aimed to decipher the interaction mechanisms of xylose isomerase (XylA) and xylokinase (XylB) towards their xylo-substrates namely xylobiose and xylotriose by computational modeling and molecular dynamic simulation studies. The three-dimensional structures of XylA and XylB, not available in their native forms, were predicted, energy minimized and validated by various computational biology tools and software. The binding mechanisms of xylobiose and xylotriose towards XylA and XylB were modeled by molecular docking and the stability of the docked complexes was confirmed by molecular dynamic (MD) simulation. The current study suggested that the theoretical models of XylA and XylB possessed good stereo-chemical validity, structural stabilities and minimum energy conformers. The molecular docking studied showed that xylotriose showed better binding interactions to XylA than xylobiose and xylobiose showed better binding interaction to XylB than xylotriose with ideal root mean square deviation (RMS), minimum binding energy (kcal/mol), hydrogen bonding and weak interactions. The MD simulation confirmed the stabilities of the docked complexes predicted by docking studies. The study suggested that interactions between the probiotics and prebiotics and provides the novel insights in exploring synbiotics as functional foods towards their futuristic applications. [Formula: see text]HighlightsThis study deciphers the interactions of xylosubstrates to XylA and XylB.The XylA and XylB possessed ideal structural stability and stereochemistryXylotriose and Xylobiose showed significant interactionsThe interactions of Xylotriose-XylA and Xylobiose-XylB were found stable in MD studies.Communicated by Ramaswamy H. Sarma.

Natural epiestriol-16 act as potential lead molecule against prospective molecular targets of multidrug resistant Acinetobacter baumannii-Insight from in silico modelling and in vitro investigations

The current study aimed to identify putative drug targets of multidrug resistant Acinetobacter baumannii (MDRAb) and study the therapeutic potential of natural epiestriol-16 by computer aided virtual screening and in vitro studies. The clinical isolates (n = 5) showed extreme dug resistance to carbapenems and colistins (p ≤ .05). Computational screening suggested that out of 236 natural molecules selected, 06 leads were qualified for drug likeliness, pharmacokinetic features and one potential molecule namely natural epiestriol-16 (16b-Hydroxy-17a-estradiol) exhibited significant binding potential towards four prioritised drug targets in comparison with the binding of faropenem to their usual target. Natural epiestriol demonstrated profound binding to the outer membrane protein (Omp38), protein RecA (RecA), orotate phosphoribosyltransferase (PyrE) and orotidine 5'-phosphate decarboxylase (PyrF) with binding energy of -6.0, -7.3, -7.3 and -8.0 kcal/mol respectively. MD simulations suggested that 16-epiestriol-receptor complexes demonstrated stability throughout the simulation. The growth curve and time kill assays revealed that MDRAb showed resistance to faropenem and polymyxin-B and the pure epiestriol-16 showed significant inhibitory properties at a concentration of 200 μg/mL (p ≤ .5). Thus, natural epiestriol-16 can be used as potential inhibitor against the prioritised targets of MDRAb and this study provide insight for drug development against carbapenem and colistin resistant A. baumannii.