An Approach to Track and Analyze the Trend of Antimicrobial Resistance Using Python: A Pilot Study for Anand, Gujarat, India-May 2022-August 2023.

The present work deals with the analysis and monitoring of bacterial resistance in using Python for the state of Gujarat, India, where occurrences of drug-resistant bacteria are prevalent. This will provide an insight into the portfolio of drug-resistant bacteria reported, which can be used to track resistance behavior and to suggest a treatment regime for the particular bacteria. The present analysis has been done using Python on Jupyter Notebook as the integrated development environment and its data analysis libraries such as Pandas, Seaborn, and Matplotlib. The data have been loaded from excel file using Pandas and cleaned to transform features into required format. Seaborn and Matplotlib have been used to create data visualizations and represent the data inexplicable manner using graphs, plots, and tables. This program can be used to study disaster epidemiology, tracking, analyzing, and surveillance of antimicrobial resistance with a proper system integration approach.

Excavating phytochemicals from plants possessing antiviral activities for identifying SARS-CoV hemagglutinin-esterase inhibitors by diligent computational workflow.

Coronaviruses (CoVs) belong to a group of RNA viruses that cause diseases in vertebrates including. Newer and deadlier than SARS CoV-2 are sought to appear in future for which the scientific community must be prepared with the strategies for their control. Spike protein (S-protein) of all the CoVs require angiotensin-converting enzyme2 (ACE2), while CoVs also require hemagglutinin-acetylesterase (HE) glycoprotein receptor to simultaneously interact with O-acetylated sialic acids on host cells, both these interactions enable viral particle to enter host cell leading to its infection. Target inhibition of viral S-protein and HE glycoprotein receptor can lead to a development of therapy against the SARS CoV-2. The proposition is to recognize molecules from the bundle of phytochemicals of medicinal plants known to possess antiviral potentials as a lead that could interact and mask the active site of, HE glycoprotein which would ideally bind to O-acetylated sialic acids on human host cells. Such molecules can be addressed as 'HE glycoprotein blockers'. A library of 110 phytochemicals from Withania somnifera, Asparagus racemosus, Zinziber officinalis, Allium sativum, Curcuma longa and Adhatoda vasica was constructed and was used under present study. In silico analysis was employed with plant-derived phytochemicals. The molecular docking, molecular dynamics simulations over the scale of 1000 ns (1 µs) and ADMET prediction revealed that the Withania somnifera (ashwagandha) and Asparagus racemosus (shatavari) plants possessed various steroidal saponins and alkaloids which could potentially inhibit the COVID-19 virus and even other CoVs targeted HE glycoprotein receptor.Communicated by Ramaswamy H. Sarma.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints.

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

Development and implementation of optimized chest CT protocol in COVID-19: A clinical audit.

Background: Several studies have justified use of chest computed tomography (CT) in diagnosis, evaluation of severity, treatment response, and complications of coronavirus disease 2019 (COVID-19) pneumonia. Increased utilization of CT in patients with known or suspected COVID-19 pneumonia has resulted in concerns of overuse, lack of protocol optimization, and radiation exposure. Aims: The study was conducted to develop and implement optimized protocol for chest CT for reducing radiation dose in adult patients suspected or diagnosed to have COVID-19 infection. Setting and Design: The study was conducted in the department of radiology of a rural tertiary care teaching hospital in western India. Clinical audit was used as a tool to impart and assess the impact of optimized chest CT protocol. Methods and Material: The pre-intervention audit included radiation dosimetry data, number of phases and length of scan of 50 adult patients, undergoing non-contrast chest CT scans in March 2021. A brief educational intervention outlining the parameters of optimized protocol was conducted on April 1, 2021.The post-intervention audit consisted of two cycles for 109 and 67 chest CT scans in the months April and May 2021. Results: The optimized protocol was found clinically adequate with a good inter-rater reliability. The compliance to the optimized protocol was weak in audit cycle 2, which improved significantly in audit cycle 3 after reinforcement. The mean (SD) per scan Computed Tomography Dose Index-Volume (CTDI-vol) reduced significantly across audit cycles [22.06 (12. 31) Vs. 10.58 (7.58) Vs. 4.51 (2.90) milli Gray, respectively, P < 0.001]. Similar findings were noted for Dose Length Product (DLP). Conclusion: Clinical audit of chest CT protocol and resultant radiation doses provided adequate feedback for dose optimization. A simple educational intervention helped achieve dose optimization.

Unveiling the nature's fruit basket to computationally identify Citrus sinensis csi-mir169-3p as a probable plant miRNA against Reference and Omicron SARS-CoV-2 genome.

The fundamental role of microRNAs (miRNAs) has long been associated with regulation of gene expression during transcription and post transcription of mRNA's 3'UTR by the RNA interference mechanism. Also, the process of how miRNAs tend to induce mRNA degradation has been predominantly studied in many infectious diseases. In this article, we would like to discuss the interaction of dietary plant miRNAs derived from fresh fruits against the viral genome of the causative agent of COVID-19, specifically targeting the 3'UTR of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) genome. Expanding the analysis, we have also identified plant miRNAs that interact against the Omicron (B.1.1.529) variant of SARS-CoV-2 across 37 countries/territories throughout the world. This cross-species virus-plant interaction led us to identify the alignment of dietary plant miRNAs found in fruits like Citrus sinensis (Orange), Prunus persica (Peaches), Vitis vinifera (Grapes) and Malus domestica (Apple) onto the viral genomes. In particular, the interaction of C. sinensis miRNA - csi-miR169-3p and SARS-CoV-2 is noteworthy, as the targeted 3'UTR region "CTGCCT" is found conserved amongst all curated 772 Omicron variants across the globe. Hence this site "CTGCCT" and miRNA csi-miR169-3p may become promising therapeutic candidates to induce viral genome silencing. Thereby, this study reveals the mechanistic way of how fruits tend to enact a fight against viruses like SARS-CoV-2 and aid in maintaining a strong immune system of an individual.

PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments.

Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.

Repurposing of anticancer phytochemicals for identifying potential fusion inhibitor for SARS-CoV-2 using molecular docking and molecular dynamics (MD) simulations.

The viral particle, SARS-CoV-2 is responsible for causing the epidemic of Coronavirus disease 2019 (COVID-19). To combat this situation, numerous strategies are being thought for either creating its antidote, vaccine, or agents that can prevent its infection. For enabling research on these strategies, several target proteins are identified where, Spike (S) protein is of great potential. S-protein interacts with human angiotensin-converting-enzyme-2 (ACE2) for entering the cell. S-protein is a large protein and a portion of it designated as a receptor-binding domain (RBD) is the key region that interacts with ACE2, following to which the viral membrane fuses with the alveolar membrane to enter the human cell. The hypothesis is to identify molecules from the pool of anticancer phytochemicals as a lead possessing the ability to interact and mask the amino acids of RBD, making them unavailable to form associations with ACE2. Such a molecule is termed as 'fusion inhibitor'. We hypothesized to identify fusion inhibitors from the NPACT library of anticancer phytochemicals. For this, all the molecules from the NPACT were screened using molecular docking, the five top hits (Theaflavin, Ginkgetin, Ursolic acid, Silymarin and Spirosolane) were analyzed for essential Pharmacophore features and their ADMET profiles were studied following to which the best two hits were further analyzed for their interaction with RBD using Molecular Dynamics (MD) simulation. Binding free energy calculations were performed using MM/GBSA, proving these phytochemicals containing anticancer properties to serve as fusion inhibitors.Communicated by Ramaswamy H. Sarma.

Scoping review of published research on medical education in India during the Covid-19 pandemic.

Background Medical educators in India made rapid adjustments to maintain continuity and integrity of medical education in the midst of disruption caused by the Covid-19 pandemic. However, there are concerns regarding achievement of competence by undergraduate medical students due to inadequate clinical exposure. We explored the focus of initiatives from medical educators in India by a scoping review of published articles on developments in medical education during the pandemic to map concepts, main sources and the literature available in PubMed. Methods We did this scoping review of published articles in PubMed database in four steps: (i) identification of research questions; (ii) identification of relevant studies; (iii) selection of studies meeting inclusion and exclusion criteria, and charting of data; and (iv) collating the summary and reporting of results. Manual content analysis was done to derive frequencies of variables. Results Of the 52 articles identified, 22 met the requirements. Most studies (68.2%) were published in 2020. Half of the studies were conducted among undergraduate students and the remaining among postgraduates (27.3%), faculty (18.2%) and interns (4.5%). All the studies were evaluations at Kirkpatrick level-1 (18; 81.8%) and level-2 (4; 18.2%). Most of the studies (9, 41%) focused on exploration of perspectives about online learning among students and faculty, 9 (27.3%) on teaching- learning, 4 (18.2%) on formative assessment and 3 (13.6%) on summative assessment. Conclusions Most studies were evaluations at Kirkpatrick level-1 and level-2 among undergraduate medical students with a focus on conceptual understanding.

Identification of antiviral phytochemicals as a potential SARS-CoV-2 main protease (Mpro) inhibitor using docking and molecular dynamics simulations.

Novel SARS-CoV-2, an etiological factor of Coronavirus disease 2019 (COVID-19), poses a great challenge to the public health care system. Among other druggable targets of SARS-Cov-2, the main protease (Mpro) is regarded as a prominent enzyme target for drug developments owing to its crucial role in virus replication and transcription. We pursued a computational investigation to identify Mpro inhibitors from a compiled library of natural compounds with proven antiviral activities using a hierarchical workflow of molecular docking, ADMET assessment, dynamic simulations and binding free-energy calculations. Five natural compounds, Withanosides V and VI, Racemosides A and B, and Shatavarin IX, obtained better binding affinity and attained stable interactions with Mpro key pocket residues. These intermolecular key interactions were also retained profoundly in the simulation trajectory of 100 ns time scale indicating tight receptor binding. Free energy calculations prioritized Withanosides V and VI as the top candidates that can act as effective SARS-CoV-2 Mpro inhibitors.

Identifying potential human and medicinal plant microRNAs against SARS-CoV-2 3'UTR region: A computational genomics assessment.

The coronavirus disease of 2019 (COVID-19) began as an outbreak and has taken a toll on human lives. The current pandemic requires scientific attention; hence we designed a systematic computational workflow to identify the cellular microRNAs (miRNAs) from human host possessing the capability to target and silence 3'UTR of SARS-CoV-2 genome. Based on this viewpoint, we extended our miRNA search to medicinal plants like Ocimum tenuiflorum, Zingiber officinale and Piper nigrum, which are well-known to possess antiviral properties, and are often consumed raw or as herbal decoctions. Such an approach, that makes use of miRNA of one species to interact and silence genes of another species including viruses is broadly categorized as cross-kingdom interactions. As a part of our genomics study on host-virus-plant interaction, we identified one unique 3'UTR conserved site 'GGAAGAG' amongst 5024 globally submitted SARS-CoV-2 complete genomes, which can be targeted by the human miRNA 'hsa-miR-1236-3p' and by Z. officinale miRNA 'zof-miR2673b'. Additionally, we also predicted that the members of miR477 family commonly found in these three plant genomes possess an inherent potential to silence viral genome RNA and facilitate antiviral defense against SARS-CoV-2 infection. In conclusion, this study reveals a universal site in the SARS-CoV-2 genome that may be crucial for targeted therapeutics to cure COVID-19.

Network pharmacology-based evaluation of natural compounds with paclitaxel for the treatment of metastatic breast cancer.

Metastatic breast cancer is a prevalent life-threatening disease. Paclitaxel (PTX) is widely used in metastatic breast cancer therapy, but the side effects limit its chemotherapeutic application. Multidrug strategies have recently been used to maximize potency and decrease the toxicity of a particular drug by reducing its dosage. Therefore, we have evaluated the combined anti-cancerous effect of PTX with tested natural compounds (andrographolide (AND), silibinin (SIL), mimosine (MIM) and trans-anethole (TA)) using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, trypan blue dye exclusion assay, proliferating cell nuclear antigen (PCNA) staining, network pharmacology, molecular docking, molecular dynamics (MD) and in vivo chick chorioallantoic membrane (CAM) angiogenesis assay. We observed a reduction in the IC50 value of PTX with tested natural compounds. Further, the network pharmacology-based analysis of compound-disease-target (C-D-T) network showed that PTX, AND, SIL, MIM and TA targeted 55, 61, 56, 31 and 18 proteins of metastatic breast cancer, respectively. Molecular docking results indicated that AND and SIL inhibited the C-D-T network's core target kinase insert domain receptor (KDR) protein more effectively than others. While MD showed that the binding of AND with KDR was stronger and more stable than others. In trypan blue dye exclusion assay and PCNA staining, AND and SIL along with PTX were found to be more effective than PTX alone. CAM assay results suggested that AND, SIL and TA increase the anti-angiogenic potential of PTX. Thus, natural compounds can be used to improve the anti-cancer potential of PTX.

Pinpointing the potential hits for hindering interaction of SARS-CoV-2 S-protein with ACE2 from the pool of antiviral phytochemicals utilizing molecular docking and molecular dynamics (MD) simulations.

SARS-CoV-2, the viral particle, is responsible for triggering the 2019 Coronavirus disease outbreak (COVID-19). To tackle this situation, a number of strategies are being devised to either create an antidote, a vaccine, or agents capable of preventing its infection. To enable research on these strategies, numerous target proteins are identified where Spike (S) protein is presumed to be of immense potential. S-protein interacts with human angiotensin-converting-enzyme-2 (ACE2) for cell entry. The key region of S-protein that interacts with ACE2 is a portion of it designated as a receptor-binding domain (RBD), following whereby the viral membrane fuses with the alveolar membrane to enter the human cell. The proposition is to recognize molecules from the bundle of phytochemicals of medicinal plants known to possess antiviral potentials as a lead that could interact and mask RBD, rendering them unavailable to form ACE2 interactions. Such a molecule is called the 'S-protein blocker'. A total of 110 phytochemicals from Withania somnifera, Asparagus racemosus, Zinziber officinalis, Allium sativum, Curcuma longa and Adhatoda vasica were used in the study, of which Racemoside A, Ashwagandhanolide, Withanoside VI, Withanoside IV and Racemoside C were identified as top five hits using molecular docking. Further, essential Pharmacophore features and their ADMET profiles of these compounds were studied following to which the best three hits were analyzed for their interaction with RBD using Molecular Dynamics (MD) simulation. Binding free energy calculations were performed using MM/GBSA, proving these phytochemicals can serve as S-protein blocker.

Regulatory system to promote the culture of quality at Indian medical schools: Are we doing enough?

The replacement of the Medical Council of India (MCI) with the National Medical Commission (NMC) was an important change in regulatory oversight to bring about transparency in regulatory procedures for improving quality of medical education and meeting the needs of healthcare in India. Similarly, due to globalization of medicine including migration of health workforce and desire to raise standards of medical education and healthcare, efforts have progressed well towards transnational regulation and establishment of an overarching body, which recognizes regulatory agencies for their adherence to good practices. We describe the global collaborative efforts to improve the quality of medical education by the promotion of accreditation through the recognition programme of the World Federation of Medical Education (WFME), the publication of the expert consensus standards across the continuum of medical education and the Guidelines for Accreditation of Basic Medical Education. We also highlight that many medical schools across the world have adopted the WFME standards and many regulatory and accrediting agencies have achieved recognition status. Based on appraisal of the NMC Act and notification on minimum standard requirements (MSRs) for medical colleges, we point out the gaps between the intent stated in the preamble of the NMC Act and the notification on MSRs. We recommend a way forward to develop a regulatory model and approaches that match NMC's stated intent and meet the requirement for medical schools in India to gain international recognition.

A Foundational Guide to Understanding Radiology Department Business Operations for Trainees.

The financial success of a radiology department is crucial to the well-being of both the hospital and the community it serves. Radiology trainees should therefore be conscious of how the department maintains its value within the health system. The purpose of this review is to provide a concise foundational resource for contemporary radiology residents and fellows to understand the basic financial operations of a hospital-based radiology department and to demonstrate its importance in supporting clinical activities. The radiology report is at the heart of reimbursement. Coders use this tool to assign International Classification of Diseases and Current Procedural Terminology codes to file reimbursement claims. Medicare, commanding the highest market share for third-party payers, sets algorithmic standards for compensation practices. Private insurers contract with hospitals, and providers use these systems or create their own contractual framework. Radiology leaders strategically balance these revenue streams with various departmental costs utilizing tools such as budgets and forecasts to ensure long-term organizational viability. Notably, payment practices in the United States are transforming from fee-for-service to value-based care. The roles of the radiologist and the radiology report are evolving with it. Examples of value-based payment models are accountable care organizations and bundled payments. Radiologists participating in these models are increasingly expected to be stewards of imaging utilization and effectively manage health care resources. Within this context of a globally changing incentive structure, trainees must reconceptualize their educational experience to equip themselves for both current and future types of clinical practice.

Identification of potential inhibitors of coronavirus hemagglutinin-esterase using molecular docking, molecular dynamics simulation and binding free energy calculation.

The pandemic outbreak of the Corona viral infection has become a critical global health issue. Biophysical and structural evidence shows that spike protein possesses a high binding affinity towards host angiotensin-converting enzyme 2 and viral hemagglutinin-acetylesterase (HE) glycoprotein receptor. We selected HE as a target in this study to identify potential inhibitors using a combination of various computational approaches such as molecular docking, ADMET analysis, dynamics simulations and binding free energy calculations. Virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin as potential HE inhibitors with better binding energy. Furthermore, molecular dynamics simulations for 100 ns time scale revealed that most of the key HE contacts were retained throughout the simulations trajectories. Binding free energy calculations using MM/PBSA approach ranked the top-five potential NPACT compounds which can act as effective HE inhibitors.

COVID-19 Pandemic Impact on Decreased Imaging Utilization: A Single Institutional Experience.

RATIONALE AND OBJECTIVES: Predictive models and anecdotal articles suggest radiology practices were losing 50%-70% of their normal imaging volume during the COVID-19 pandemic. Using actual institutional data, we investigated the change in imaging utilization and revenue during this public health crisis. MATERIALS AND METHODS: Imaging performed within the 8-week span between March 8 and April 30, 2020 was categorized into the COVID-19 healthcare crisis timeframe. The first week of this date range and the 10 weeks prior were used to derive the normal practice expected volume. A rolling 7-day total value was used for volume tracking and comparison. Total imaging utilization was derived and organized by patient setting (outpatient, inpatient, emergency) and imaging modality (X-ray, CT, Mammography, MRI, Nuclear Medicine/PET, US). The three highest volume hospitals were analyzed. Revenue information was collected from the hospital billing system. RESULTS: System-wide imaging volume decreased by 55% between April 7 and 13, 2020. Outpatient exams decreased by 68% relative to normal practice. Emergency exams decreased by 48% and inpatient exams declined by 31%. Mammograms and nuclear medicine scans were the most affected modalities, decreasing by 93% and 61%, respectively. The main campus hospital experienced less relative imaging volume loss compared to the other smaller and outpatient-driven hospitals. At its lowest point, the technical component revenue from main campus imaging services demonstrated a 49% negative variance from normal practice. CONCLUSION: The trends and magnitude of the actual imaging utilization data presented will help inform evidence-based decisions for more accurate volume predictions, policy changes, and institutional preparedness for current and future pandemics.

miRDetect: A combinatorial approach for automated detection of novel miRNA precursors from plant EST data using homology and Random Forest classification.

Identification of microRNAs from plants is a crucial step for understanding the mechanisms of pathways and regulation of genes. A number of tools have been developed for the detection of microRNAs from small RNA-seq data. However, there is a lack of pipeline for detection of miRNA from EST dataset even when a huge resource is publicly available and the method is known. Here we present miRDetect, a python implementation to detect novel miRNA precursors from plant EST data using homology and machine learning approach. 10-fold cross validation was applied to choose best classifier based on ROC, accuracy, MCC and F1-scores using 112 features. miRDetect achieved a classification accuracy of 93.35% on a Random Forest classifier and outperformed other precursor detection tools in terms of performance. The miRDetect pipeline aids in identifying novel plant precursors using a mixed approach and will be helpful to researchers with less informatics background.

Energetic contributions of amino acid residues and its cross-talk to delineate ligand-binding mechanism.

Receptor-based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein-ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the Energetic CONTributions of Amino acid residues and its possible Cross-Talk (ECONTACT) to study ligand binding using per-residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross-talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein-ligand interaction energies deduced from multiple structures are decomposed into per-residue energy terms, which are employed as variables to principal component analysis and generated cross-terms. Out of 16 cross-talks derived from eight datasets of protein-ligand systems, the ECONTACT approach is able to associate 10 potential cross-talks with site-directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross-talks in protein structures. The top two cross-talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross-talks in any protein-ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact/.

Transcriptome-wide miRNA identification of Bacopa monnieri: a cross-kingdom approach.

Bacopa monnieri known as 'Brahmi' is a well-known medicinal plant belonging to Scrophulariaceae family for its nootropic properties. To the best of our knowledge, no characterization data is available on the potential role of micro RNAs (miRNAs) from this plant till date. We present here the first report of computational characterizations of miRNAs from B. monnieri. Owing to the high conservation of miRNAs in nature, new and potential miRNAs can be identified in plants using in silico techniques. Using the plant miRNA sequences present in the miRBase repository, a total of 12 miRNAs were identified from B. monnieri which pertained to 11 miRNA families from the shoot and root transcriptome data. Furthermore, gene ontology analysis of the identified 68 human target genes exhibited significance in various biological processes. These human target genes were associated with signaling pathways like NF-kB and MAPK with TRAF2, CBX1, IL1B, ITGA4 and ITGB1BP1 as the top five hub nodes. This cross-kingdom study provides initial insights about the potential of miRNA-mediated cross-kingdom regulation and unravels the essential target genes of human with implications in numerous human diseases including cancer.