Global warming is inducing the elevational alpine treeline ecotone (ATE) to migrate to higher elevations in the Himalaya. Prior research on ATE dynamics has been primarily based on field inventory ...and studied at the community level. The potential of using remote sensing and geographic information system for the delineation of the treeline ecotone has been explored. In this study, we used satellite-derived Normalized Difference Vegetation Index (NDVI) data from Landsat-1/2 Multispectral Scanner (MSS), Resourcesat-1/2 Linear Imaging Self Scanning Sensor (LISS-III), and National Oceanographic and Atmospheric Administration-Advanced Very High-Resolution Radiometer (NOAA-AVHRR) to investigate long-term ATE dynamics. Satellite remote sensing of treeline in Arunachal Pradesh Himalaya revealed an upward shift over the past four decades. The ATE has shifted c. 452 m ± 74 m upward in vertical dimension at a rate c. 113 m decade
−1
. Furthermore, the land surface phenology along ATE and forest area has changed significantly over the past 33 years. The significant positive trend in length of the growing season (LOS; p < 0.05) and delay in the end of the growing season (EOS) was observed. The start of the growing season (SOS) had a negative tendency with non-significant linear trend. The treeline upward shift and significant lengthening of the growing season at ATE and forest area indicate changing climatic patterns and processes.
Abstract
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 (M
pro
) 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 M
pro
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 M
pro
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 M
pro
inhibitors.
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
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.
•Natural compounds reduced the IC50 value of PTX and showed anti-cancer effects.•PTX and natural compounds synergistically showed more anti-proliferative effect.•Natural compounds targeted angiogenesis promoting kinase-domain insert receptor.•CAM assay showed increase in anti-angiogenic potency of PTX with natural compounds.
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/.
The cross‐talk between amino acids present in a protein cavity coordinates ligand binding by coupling its residue motions. A new approach termed energetic contributions of amino acid residues and its possible cross‐talk (ECONTACT) is proposed to capture the cross‐talks, which is modeled using joint probability density function. The top two cross‐talks corroborates with the experimental findings and mechanism‐based mode of ligand binding.
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.
Display omitted
•Constructing the library of antiviral compounds from medicinal plants.•Assessing the potency of these compounds for masking S-protein of SARS-CoV-2.•Molecular docking, dynamics simulation and pharmacophore mapping.
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-2
H
-chromen-2-ylbenzo7annulen-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.
Graphic abstract
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.
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.
A machine‐learning scoring function to identify protein‐ligand complexes with desirable pharmacophoric elements with the potential to secure high active enrichments in database screening of small molecules.
Globally, the increase in the climatic variability has led to adverse effects on the treeline species in the high-elevation mountain landscapes. Identifying the geographical space that supports the ...treeline species survival over time is essential for conservation biogeography. Increase in the global warming and snowmelt has made available the treeline species favourable niches in the higher elevations. Random Forest algorithm assuming non-parametric distribution was employed to predict the potential distribution of
Betula utilis
niche in the Hindu-Kush Himalayan (HKH) region. The potential distributions were simulated in the Last Inter-Glaciation (LIG), present (the year 1970–2000) and future (the year 2061–2080) environmental conditions. The actual distribution of the species in the current time was modelled and evaluated. The model sensitivity with reference to independent evaluation dataset for highly suitable
B. utilis
niche was 0.78. The model statistics of the current time was further applied to both the LIG and future (2061–2080) scenarios in order to get a fundamental niche of
B. utilis
. The treeline species,
B. utilis
was projected to become vulnerable to 21st century climate changes. The high suitability of
B. utilis
occurrence in the LIG, current and the future scenario were more likely in the elevation ranges 2601–2800 m, 3801–4000 m, and 4201–4400 m, respectively. The magnitude of advancement was relatively more along elevation and longitude, compared to the latitudinal gradient. The present study provides scientific evidence to conclude that the treeline species potential distribution in HKH is climate driven.
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