OlfactionBase: a repository to explore odors, odorants, olfactory receptors and odorant-receptor interactions.

Olfaction is a multi-stage process that initiates with the odorants entering the nose and terminates with the brain recognizing the odor associated with the odorant. In a very intricate way, the process incorporates various components functioning together and in synchronization. OlfactionBase is a free, open-access web server that aims to bring together knowledge about many aspects of the olfaction mechanism in one place. OlfactionBase contains detailed information of components like odors, odorants, and odorless compounds with physicochemical and ADMET properties, olfactory receptors (ORs), odorant- and pheromone binding proteins, OR-odorant interactions in Human and Mus musculus. The dynamic, user-friendly interface of the resource facilitates exploration of different entities: finding chemical compounds having desired odor, finding odorants associated with OR, associating chemical features with odor and OR, finding sequence information of ORs and related proteins. Finally, the data in OlfactionBase on odors, odorants, olfactory receptors, human and mouse OR-odorant pairs, and other associated proteins could aid in the inference and improved understanding of odor perception, which might provide new insights into the mechanism underlying olfaction. The OlfactionBase is available at https://bioserver.iiita.ac.in/olfactionbase/.

SMILES to Smell: Decoding the Structure-Odor Relationship of Chemical Compounds Using the Deep Neural Network Approach.

Finding the relationship between the structure of an odorant molecule and its associated smell has always been an extremely challenging task. The major limitation in establishing the structure-odor relation is the vague and ambiguous nature of the descriptor-labeling, especially when the sources of odorant molecules are different. With the advent of deep networks, data-driven approaches have been substantiated to achieve more accurate linkages between the chemical structure and its smell. In this study, the deep neural network (DNN) with physiochemical properties and molecular fingerprints (PPMF) and the convolution neural network (CNN) with chemical-structure images (IMG) are developed to predict the smells of chemicals using their SMILES notations. A data set of 5185 chemical compounds with 104 smell percepts was used to develop the multilabel prediction models. The accuracies of smell prediction from DNN + PPMF and CNN + IMG (Xception based) were found to be 97.3 and 98.3%, respectively, when applied on an independent test set of chemicals. The deep learning architecture combining both DNN + PPMF and CNN + IMG prediction models is proposed, which classifies smells and may help understand the generic mechanism underlying the relationship between chemical structure and smell perception.

Outbreak of COVID-19: A Detailed Overview and Its Consequences.

INTRODUCTION: After the outbreak from Wuhan City of China, COVID-19, caused by SARS-CoV-2, has become a pandemic worldwide in a very short span of time. The high transmission rate and pathogenicity of this virus have made COVID-19 a major public health concern globally. Basically, the emergence of SARS-CoV-2 is the third introduction of a highly infectious human epidemic coronavirus in the twenty-first century. Various research groups have claimed bats to be the natural host of SARS-CoV-2. However, the intermediate host and mode of transmission from bat to humans are not revealed yet. The COVID-19 cost hundreds and thousands of lives and millions are facing the consequences. The objective of this chapter was to analyze the outbreak of COVID-19 and problems faced globally. METHODS: All published relevant literature from scientific sources and reputed news channels are considered to write the current review. RESULTS: Generally, elder persons and more particularly people with underlying medical conditions are found to be highly vulnerable to severe infection and prone to fatal outcomes. Unfortunately, there is no specific treatment with clinically approved drugs or vaccines to treat this disease. Several research groups have been investigating the efficacies of several antiviral and repurposed drugs. Currently, most of the SARS-COV-2 vaccines are at the preclinical or clinical stage of development. The latest research progress on the epidemiology, clinical characteristics, pathogenesis, diagnosis, and current status of therapeutic intervention indicates that still a specific drug or vaccine needs to come up for the effective treatment of the pandemic COVID-19. It is observed that various aspects of social life, economic status, and healthcare systems are majorly affected by this pandemic. CONCLUSION: It is concluded that the outbreak of COVID-19 has severely affected each and every field, such as social, scientific, industrial, transport, and medical sectors. Irrespective of tremendous efforts globally, few vaccines are now available for the prevention of the disease. Specific drug is not available publicly for the treatment of COVID-19. Prevention of air pollution that can aggravate COVID-19 has been suggested. Therefore, as of now, social distancing and sanitization practices are the only options available for the prevention of the disease for many.

HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network.

AIMS: To develop a tool that can annotate subcellular localization of human proteins. BACKGROUND: With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research. OBJECTIVE: To develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision. METHODS: In this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset. RESULTS: The proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other machine learning-based tools. The result demonstrated promising predictive performance of HumDLoc model based on various machine learning parameters such as accuracy (≥97.00%), precision (≥0.86), recall (≥0.89), MCC score (≥0.86), ROC curve (0.98 square unit), and precision-recall curve (0.93 square unit). CONCLUSION: In conclusion, HumDLoc was able to outperform several alternative tools for correctly predicting subcellular localization of human proteins. The HumDLoc has been hosted as a web-based tool at https://bioserver.iiita.ac.in/HumDLoc/.

Genome-wide analysis of miRNAs and Tasi-RNAs in Zea mays in response to phosphate deficiency.

Globally important cereal crop maize provides important nutritions and starch in dietary foods. Low phosphate (LPi) availability in the soil frequently limits the maize quality and yield across the world. Small non-coding RNAs (Snc-RNAs) play crucial roles in growth and adaptation of plants to the environment. Snc-RNAs like microRNAs (miRs) and trans-acting small interfering RNAs (Tasi-Rs) play important functions in posttranscriptional regulation of gene expression, which controls plant development, reproduction, and biotic/abiotic stress responses. In order to identify the miR and Tasi-R alterations in leaf and root of maize in response to sufficient phosphate and LPi at 3LS and 4LS, the snc-RNA population libraries for 0th, 1st, 2nd, 4th, and 8th day were constructed. These libraries were used for genome-wide alignment and RNA-fold analysis for possible prediction of potential miRs and Tasi-Rs. This study reported 174 known and conserved differentially expressed miRs of 27 miR families of maize plant. In addition, leaf and root specific potential novel miRs representing 155 new families were also discovered. Differentially expressed conserved as well as novel miR functions in root and leaf during early stage of Pi starvation were extensively discussed. Leaf and root specific miRs as well as common miRs with their target genes, participating in different biological, cellular, and metabolic processes were explored. Further, four miR390-directed Tasi-Rs which belong to TAS3 gene family along with other orthologs of Tasi-Rs were also identified. Finally, the study provides an insight into the composite regulatory mechanism of miRs in maize in response to Pi deficiency.

Insights into structure and activity relationship of clinically mutated PER1 and PER2 class A ß-lactamase enzymes.

PER1 and PER2 are among the class A ß-lactamase enzymes, which have evolved clinically to form antibiotic resistance and have significantly expanded their spectrum of activity. Hence, there is a need to study the clinical mutation responsible for such ß-lactamase mediated antibiotic resistance. Alterations in catalytic centre and Ω-loop structure could be the cause of antibiotic resistance in these ß-lactamase enzymes. Structural and functional alterations are caused due to mutations on or near the catalytic centre, which results in active site plasticity and are responsible for its expanded spectrum of activity in these class A ß-lactamase enzymes. Multiple sequence alignment, structure, kinetic, molecular docking, MMGBSA and molecular dynamic simulation comparisons were done on 38 clinically mutated and wild class A ß-lactamase enzymes. This work shows that PER1 and PER2 enzymes contains most unique mutations and have altered Ω-loop structure, which could be responsible for altering the structure-activity relationship and extending the spectrum of activity of these enzymes. Alterations in molecular docking, MMGBSA, kinetic values reveals the modification in the binding and activity of these clinically mutated enzymes with antibiotics. Further, the cause of these alterations can be revealed by active site interactions and H-bonding pattern of these enzymes with antibiotics. Met69Gln, Glu104Thr, Tyr105Trp, Met129His, Pro167Ala, Glu168Gln, Asn170His, Ile173Asp and Asp176Gln mutations were uniquely found in PER1 and PER2 enzymes. These mutations occurs at catalytic important residues and results in altered interactions with ß-lactam antibiotics. Hence, these mutations could be responsible for altering the structure-activity of PER1 and PER2 enzymes.Communicated by Ramaswamy H. Sarma.

Recent development of multi-targeted inhibitors of human topoisomerase II enzyme as potent cancer therapeutics.

Multi-target therapies have been considered one of the viable options to overcome the challenges to eradicate intrinsic and acquired drug-resistant cancer cells. While to increase the efficacy of therapeutics, the use of a single drug against multiple structurally similar sites, which noncommittedly modulate several vital cellular pathways proposed as a potential alternative to a 'single drug single target'. Besides, it reduces the usage of a number of drugs and their side effects. Topoisomerase II enzyme plays a very significant role in DNA replication and thus served as an important target for numerous anti-cancer agents. However, in spite of promising clinical results, in several cases, it was found that cancer cells have developed resistance against the anti-cancer agents targeting this enzyme. Therefore, multi-target therapies have been proposed as an alternative to overcome different drug resistance mechanisms while topoisomerases II are a primary target site. In this review, we have tried to discuss the characteristics of the binding cavity available for interactions of drugs, and potent inhibitors concurrently modulate the functions of topoisomerases II as well as other structurally related target sites. Additionally, the mechanism of drug resistance by considering molecular and cellular insights by including various types of cancers.

Anti-inflammatory effect of curcumin in an accelerated senescence model of Wistar rat: an in vivo and in-silico study.

Curcumin, a biphenolic substance derived from turmeric (Curcuma longa), offers a number of health-beneficial effects, including anti-inflammatory, cardiovascular protection, anti-cancerous, and anti-angiogenic. By interacting with the PPARγ (Peroxisome Proliferator-Activated Receptor-γ), curcumin inhibits NF-κB. These biological outcomes seem to be the outcome of NF-κB inhibition mediated by curcumin. The current study explores the in vivo impact of curcumin on several inflammatory parameters during aging in Wistar rats. An in-silico docking simulation study using Maestro and Desmond, Schrödinger, was carried out to further validate the experimental findings. According to our observation, rats given curcumin supplementation had a considerably (p ≤ 0.05) reduced level of inflammation. By generating numerous polar and hydrophobic interactions and exhibiting little conformational deviation throughout the simulation, in silico investigations showed that the proposed ligand curcumin had a high affinity for the enzyme COX-2. During simulation, protein-ligand complexes of curcumin with the other targets viz. 5-LOX, TNF-α and IL-6 also demonstrated improved binding and minimal fluctuation. The COX-2 and 5-LOX enzymes and the cytokines (TNF-α and IL-6) implicated in inflammation may have been inhibited by curcumin, highlighting its function as a multi-target inhibitor. Our study provides convincing support for the idea that eating a diet high in curcumin may help to reduce inflammation and help to explain some of its health-beneficial effects.Communicated by Ramaswamy H. Sarma.

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (>99%) and high sensitivity (>99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost-PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship.Communicated by Ramaswamy H. Sarma.

Docking and molecular dynamics simulation for therapeutic repurposing in small cell lung cancer (SCLC) patients infected with COVID-19.

Cancer care has become a challenge with the current COVID-19 pandemic scenario. Specially, cancers like small cell lung cancers (SCLC) are difficult to treat even in the normal situation due to their rapid growth and early metastasis. For such patients, treatment can't be compromised and care must be taken to ensure their minimum exposure to the ongoing spread of COVID-19 infection. For this reason, in-house treatments are being suggested for these patients. Another issue is that symptoms of SCLC match well with that of COVID-19 infection. Hence, the detection of COVID-19 may also get delayed leading to unnecessary complications. Thus, we have tried to investigate if the therapeutics that is currently used in lung cancer treatment can also act against SARS-CoV-2. If it is so, the same treatment protocols can be continued even if the SCLC patient had contracted COVID-19 without compromising the cancer care. For this, RNA dependent RNA polymerase (RdRP) from SARS-CoV-2 has been selected as drug target. Both docking and molecular dynamicssimulation analysis have indicated that Paclitaxel and Dacomitinib may be explored as multi-target drugs for both SCLC and COVID-19.Communicated by Ramaswamy H. Sarma.

Identification of candidate target genes of oral squamous cell carcinoma using high-throughput RNA-Seq data and in silico studies of their interaction with naturally occurring bioactive compounds.

Oral Squamous Cell Carcinoma (OSCC) accounts for more than 90% of all kinds of oral neoplasms that develop in the oral cavity. It is a type of malignancy that shows high morbidity and recurrence rate, but data on the disease's target genes and biomarkers is still insufficient. In this study, in silico studies have been performed to find out the novel target genes and their potential therapeutic inhibitors for the effective and efficient treatment of OSCC. The DESeq2 package of RStudio was used in the current investigation to screen and identify differentially expressed genes for OSCC. As a result of gene expression analysis, the top 10 novel genes were identified using the Cytohubba plugin of Cytoscape, and among them, the ubiquitin-conjugating enzyme (UBE2D1) was found to be upregulated and playing a significant role in the progression of human oral cancers. Following this, naturally occurring compounds were virtually evaluated and simulated against the discovered novel target as prospective drugs utilizing the Maestro, Schrodinger, and Gromacs software. In a simulated screening of naturally occurring potential inhibitors against the novel target UBE2D1, Epigallocatechin 3-gallate, Quercetin, Luteoline, Curcumin, and Baicalein were identified as potent inhibitors. Novel identified gene UBE2D1 has a significant role in the proliferation of human cancers through suppression of 'guardian of genome' p53 via ubiquitination dependent pathway. Therefore, the treatment of OSCC may benefit significantly from targeting this gene and its discovered naturally occurring inhibitors.Communicated by Ramaswamy H. Sarma.

In Silico Study of a Small Bioactive Molecule Targeting Topoisomerase II and P53-MDM2 Complex in Triple-Negative Breast Cancer.

Treatment of triple-negative breast cancer (TNBC) is very challenging as only few therapeutic options are available, including chemotherapy. Thus, a constant search for new and effective approaches of therapy that could potentially fight against TNBC and mitigate side effects is "turn-on". Recently, multitarget therapy has come up with huge possibilities, and it may possibly be useful to overcome several concurrent challenges in cancer therapy. Herein, we proposed the inhibition of both Topoisomerase II enzyme and p53-MDM2 (p53 cavity in MDM2) protein complex by the same bioactive molecules for multitarget therapy. RNA-seq analysis was performed to get a network of essential proteins involved in the apoptosis pathway by considering the triple-negative breast cancer cell line (MDA-MB-231). All of the untreated duplicate sample data were retrieved from NCBI (GSC149768). Further, via in silico screening, potent bioactive molecules were screened out to target both Topo II and the p53-MDM2 complex. The results of ligand-based screening involving docking, MMGBSA, ADME/T, MD simulation, and PCA suggested that resveratrol, a plant bioactive molecule, showed more potential binding in the same cavity of target proteins compared with doxorubicin for Topo IIα (5GWK) and etoposide for the second protein target (p53-MDM2 complex; 4OQ3) as the control drug. This is also evident from the in vitro validation in case of triple-negative breast cancer cell lines (MDA-MB-231) and Western blotting analysis. Thus, it paves the scope of multitargeting against triple-negative breast cancer.

Phytochemical mediated modulation of COX-3 and NFκB for the management and treatment of arthritis.

In this study, we investigated whether zerumbone (ZBN), ellagic acid (ELA) and quercetin (QCT), the plant-derived components, can modulate the role of COX-3 or cytokines liable in arthritic disorder. Initially, the effect of ZBN, ELA, and QCT on inflammatory process was investigated using in-vitro models. In-silico docking and molecular dynamics study of these molecules with respective targets also corroborate with in-vitro studies. Further, the in-vivo anti-arthritic potential of these molecules in Complete Freund's adjuvant (CFA)-induced arthritic rats was confirmed. CFA increases in TNF-α and IL-1ß levels in the arthritic control animals were significantly (***p < 0.001) attenuated in the ZBN- and ELA-treated animals. CFA-induced attenuation in IL-10 levels recovered under treatment. Moreover, ELA attenuated CFA-induced upregulation of COX-3 and ZBN downregulated CFA-triggered NFκB expression in arthritic animals. The bonding patterns of zerumbone in the catalytic sites of targets provide a useful hint in designing and developing suitable derivatives that can be used as a potential drug. To our best knowledge, the first time we are reporting the role of COX-3 in the treatment of arthritic disorders which could provide a novel therapeutic approach for the treatment of inflammatory disorders.

Identification of naturally occurring compounds as alternatives to radiation therapy for treatment of small cell lung cancer.

Radiation resistance is one of the major problems in the treatment of small cell lung cancer (SCLC). Most of these patients are given radiation as first-line treatment and it was observed that the initial response in these patients is very good. However, they show relapse in a few months which is also associated with resistance to treatment. Thus, targeting the mechanism by which these cells develop resistance could be an important strategy to improve the survival chances of these patients. From the RNA-Seq data analysis, it was identified that CHEK1 gene was overexpressed. Chk1 protein which is encoded by the CHEK1 gene is an important protein that is involved in radiation resistance in SCLC. It is known to favour the cells to deal with replicative stress. CHEK1 is the major cause for developing radiation resistance in SCLC. Thus, natural compounds that could also serve as potential inhibitors for Chk1 were explored. Accordingly; the compounds were screened based on ADME, docking and MM-GBSA scores. MD simulations were performed for the selected protein-ligand complexes and the results were compared to the co-crystallised ligand, 3-(indol-2-yl)indazole. The results showed that compound INC000033832986 could be a natural alternative to the commercial ligand for the prevention of SCLC.Communicated by Ramaswamy H. Sarma.

Screening natural inhibitors against upregulated G-protein coupled receptors as potential therapeutics of Alzheimer's disease.

Computational approaches have been helpful in high throughput screening of drug libraries and designing ligands against receptors. Alzheimer's disease is a complex neurological disorder, which causes dementia. In this disease neurons are damaged due to formation of Amyloid-beta plaques and neurofibrillary tangles, which along with some other factors contributes to disease development and progression. The objective of this study was to predict tertiary structures of five G-protein coulped neurotransmitter receptors; CHRM5, CYSLTR2, DRD5, GALR1 and HTR2C, that are upregulated in Alzheimer's disease, and to screen potential inhibitors for against these receptors. In this study, Comparative modelling, molecular docking, MMGBSA analysis, ADMET screening and molecular dynamics simulation were performed. Tertiary structures of the five GPCRs were predicted and further subjected to molecular docking against natural compounds. Pharmacokinetic studies of natural compounds were also conducted for assessing drug-likeness properties. Molecular dynamics simulations were performed to investigate the structural stability and binding affinities of each complex. Finally, the results suggested that ZINC04098704, ZINC31170017, ZINC05998597, ZINC67911229, and ZINC67910690 had better binding affinity with CHRM5, CYSLTR2, DRD5, GALR1, and HTR2C (5-HT2C) proteins, respectively.Communicated by Ramaswamy H. Sarma.

Pr[m]: An Algorithm for Protein Motif Discovery.

Motifs are the evolutionarily conserved patterns which are reported to serve the crucial structural and functional role. Identification of motif patterns in a set of protein sequences has been a prime concern for researchers in computational biology. The discovery of such a protein motif using existing algorithms is purely based on the parameters derived from sequence composition and length. However, the discovery of variable length motif remains a challenging task, as it is not possible to determine the length of a motif in advance. In current work, a k-mer based motif discovery approach called Pr[m], is proposed for the detection of the statistically significant un-gapped motif patterns, with or without wildcard characters. In order to analyze the performance of the proposed approach, a comparative study was performed with MEME and GLAM2, which are two widely used non-discriminative methods for motif discovery. A set of 7,500 test dataset were used to compare the performance of the proposed tool and the ones mentioned above. Pr[m] outperformed the existing methods in terms of predictive quality and performance. The proposed approach is hosted at https://bioserver.iiita.ac.in/Pr[m].

WITHDRAWN: In-Silico Study of Resveratrol as Multi-Edged Potent Inhibitor against SARSCov- 2 and Variants of Concern Using Molecular Dynamic Simulation and PCA Analysis

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PGC1 alpha coactivates ERG fusion to drive antioxidant target genes under metabolic stress.

The presence of ERG gene fusion; from developing prostatic intraepithelial neoplasia (PIN) lesions to hormone resistant high grade prostate cancer (PCa) dictates disease progression, altered androgen metabolism, proliferation and metastasis1-3. ERG driven transcriptional landscape may provide pro-tumorigenic cues in overcoming various strains like hypoxia, nutrient deprivation, inflammation and oxidative stress. However, insights on the androgen independent regulation and function of ERG during stress are limited. Here, we identify PGC1α as a coactivator of ERG fusion under various metabolic stress. Deacetylase SIRT1 is necessary for PGC1α-ERG interaction and function. We reveal that ERG drives the expression of antioxidant genes; SOD1 and TXN, benefitting PCa growth. We observe increased expression of these antioxidant genes in patients with high ERG expression correlates with poor survival. Inhibition of PGC1α-ERG axis driven transcriptional program results in apoptosis and reduction in PCa xenografts. Here we report a function of ERG under metabolic stress which warrants further studies as a therapeutic target for ERG fusion positive PCa.

In silico study indicates antimalarials as direct inhibitors of SARS-CoV-2-RNA dependent RNA polymerase.

Coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused a global pandemic. RNA-dependent RNA polymerase (RdRp) is the key component of the replication or transcription machinery of coronavirus. Therefore SARS-CoV-2-RdRp has been chosen as an important target for the development of antiviral drug(s). During the early pandemic of the COVID-19, chloroquine and hydroxychloroquine were suggested by the researchers for the prevention or treatment of SARS-CoV-2. In our study, the antimalarial compounds have been screened and docked against SARS-CoV-2-RdRp (PDB ID: 7BTF), and it was observed that the antimalarials chloroquine, hydroxychloroquine, and amodiaquine exhibit good affinity. Since the crystal structure of SARS-CoV-2-RdRp with its substrate is not available, poliovirus-RdRp crystal structure co-crystallized with its substrate ATP (PDB ID: 2ILY) was used as a reference structure. The superimposition of SARS-CoV-2-RdRp and poliovirus-RdRp structures showed that the active sites of both of the RdRps superimposed very well. The amino acid residues involved in the binding of ATP in the case of poliovirus-RdRp and residues involved in binding with the antimalarial compounds with SARS-CoV-2-RdRp were compared. In both cases, the conserved residues were found to be involved in establishing the interactions. The MMGBSA and molecular dynamic simulation studies were performed to strengthen our docking results. Further residues involved in binding of antimalarials with SARS-CoV-2-RdRp were compared with the residues involved in the SARS-CoV-2-RdRp complexed with remdesivir [PDB ID: 7BV2]. It was observed that co-crystallized remdesivir and docked antimalarials bind in the same pocket of SARS-CoV-2 -RdRp.Communicated by Ramaswamy H. Sarma.

Bacterial biofilm infections, their resistance to antibiotics therapy and current treatment strategies.

Nearly 80% of human chronic infections are caused due to bacterial biofilm formation. This is the most leading cause for failure of medical implants resulting in high morbidity and mortality. In addition, biofilms are also known to cause serious problems in food industry. Biofilm impart enhanced antibiotic resistance and become recalcitrant to host immune responses leading to persistent and recurrent infections. It makes the clinical treatment for biofilm infections very difficult. Reduced penetration of antibiotic molecules through EPS, mutation of the target site, accumulation of antibiotic degrading enzymes, enhanced expression of efflux pump genes are the probable causes for antibiotics resistance. Accordingly, strategies like administration of topical antibiotics and combined therapy of antibiotics with antimicrobial peptides are considered for alternate options to overcome the antibiotics resistance. A number of other remediation strategies for both biofilm inhibition and dispersion of established biofilm have been developed. The metallic nanoparticles (NPs) and their oxides have recently gained a tremendous thrust as antibiofilm therapy for their unique features. This present comprehensive review gives the understanding of antibiotic resistance mechanisms of biofilm and provides an overview of various currently available biofilm remediation strategies, focusing primarily on the applications of metallic NPs and their oxides.