Biology subjects

Adhikari, B.

Publications and source records attributed to Adhikari, B..

26 records · Page 2Linked to original sources

bioRxiv2020

Epidemiology of Dengue viral infection in Nepal: 2006-2019

BackgroundDengue is one of the newest emerging diseases in Nepal with increasing burden and geographic spread over the last 14 years. The main objective of this study was to explore the spatio-temporal epidemiological patterns of Dengue since its first report (2006) till 2019 in Nepal. MethodsThis study is a retrospective analysis of dengue data available from the Epidemiological Disease Control Division (EDCD) of Government of Nepal. The data in this study cover the last 14 years (2006-2019) of reported dengue cases in Nepal. Epidemiological trend and spatio-temporal analyses were performed. Maps of reported case incidence were created using QGIS version 3.4. ResultsSince the first report of dengue in a foreigner in 2004, Nepal reported a total of 17,992 dengue cases in 68 districts of Nepal in 2019. The incidence was approximately five times higher in 2018 (Incidence Rate Ratio (IRR): 4.8; 95% CI: 1.5 - 15.3) and over 140 times higher in 2019 (IRR: 141.6; CI: 45.8 - 438.4). Population density was not a statistically significant predictor of case incidence. Mean elevation had a negative association with case incidence. A one standard deviation increase in elevation was associated with a 90% decrease in reported case incidence (IRR: 0.10; CI: 0.01 - 0.20). However, the association with mean elevation varied across the years. In comparison to 2016, incidence was greater at higher elevations in 2018 (IRR: 22.7; CI: 6.0 - 86.1) and 2019 (IRR: 9.6; CI: 2.6 - 36.1). ConclusionThere is a high risk of dengue outbreak in the Terai region with increasing spread towards the mid-mountains and beyond as seen over the last 14 years. Urgent measures are required to increase the availability of diagnostics and resources to mitigate future dengue epidemics. Findings from this study can inform the spatio-temporal distribution of dengue and can help in resource allocation and priority setting for future epidemic. Author summaryDengue in humans is caused by four different serotypes (DENV-1, DENV-2, DENV-3 & DENV-4). Globally it is the most pervasive vector borne diseases with increasing number of cases in recent years. Dengue is one of the youngest emerging diseases in Nepal with increasing cases and spread from the tropical lowland to the highland (hilly) regions. We conducted a spatio-temporal analysis of national data to consolidate the information using QGIS to measure the dengue incidence at district levels of Nepal. Spatio-temporal analysis exploring the incidence and distribution of dengue cases aids in identification of high-risk areas which can ultimately enable national dengue programme to mobilize and allocate resources for the control and treatment. This study shows, the persistent high risk of dengue outbreak in lowland Terai region with annual rise in the risk of spread towards the mid-mountains and beyond. Urgent measures are required to increase the diagnostics and resources to mitigate the epidemic burden of dengue in Terai and peripheral regions.

microbiology↗

bioRxiv2020

In Silico Analysis of Secondary Metabolites that Modulates Enzymes of Cholesterol Target

Hypercholesterolemia has posed a serious threat of heart diseases and stroke worldwide. Xanthine oxidase (XO), the rate-limiting enzyme in uric acid biosynthesis, is regarded as the root of reactive oxygen species (ROS) that generates atherosclerosis and cholesterol crystals. {beta}-Hydroxy {beta}-methylglutaryl-coenzyme A reductase (HMGR) is a rate-limiting enzyme in cholesterol biosynthesis. Although some commercially available enzyme inhibiting drugs have effectively reduced the cholesterol level, most of them have failed to meet the requirements of being apt drug candidates. Here, we have carried out an in-silico analysis of secondary metabolites that have already shown good inhibitory activity against XO and HMGR. Out of 118 secondary metabolites reviewed, sixteen molecules inhibiting XO and HMGR were taken based on IC50 values reported in vitro assays. Further, receptor-based virtual screening was carried out against secondary metabolites using GOLD Protein-Ligand Docking Software, combined with subsequent post-docking, to study the binding affinities of ligands to the enzymes. In-Silico ADMET analysis was carried out to study their pharmacokinetic properties, followed by toxicity prediction through ProTox-II. The molecular docking of amentoflavone (1) (GOLD score 70.54), and ganomycin I (9) (GOLD score 59.61) evinced that the drug has effectively bind at the competitive site of XO and HMGR, respectively. Besides, 6-paradol (3) and selgin (4) could be potential drug candidates to inhibit XO. Likewise, n-octadecanyl-O--D-glucopyranosyl(6[->]1")-O--D-glucopyranoside (10) could be potential drug candidates to maintain serum cholesterol. In-silico ADMET analysis showed that the sixteen metabolites were optimal within the categorical range in comparison to commercially available XO and HMGR inhibitors, respectively. Toxicity analysis through Protox-II revealed that 6-gingerol (2), ganoleucoin K (11), and ganoleucoin Z (12) are toxic for human use. This computational analysis supports earlier experimental evidence towards the inhibition of XO and HMGR by natural products. Further study is necessary to explore the clinical efficacy of these secondary molecules, which might be alternatives for the treatment of hypercholesterolemia. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=163 HEIGHT=200 SRC="FIGDIR/small/219998v2_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@48d66borg.highwire.dtl.DTLVardef@1f51741org.highwire.dtl.DTLVardef@1c9d87dorg.highwire.dtl.DTLVardef@a41e70_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2020

A fully open-source framework for deep learning protein real-valued distances

As deep learning algorithms drive the progress in protein structure prediction, a lot remains to be studied at this emerging crossway of deep learning and protein structure prediction. Recent findings show that inter-residue distance prediction, a more granular version of the well-known contact prediction problem, is a key to predict accurate models. We believe that deep learning methods that predict these distances are still at infancy. To advance these methods and develop other novel methods, we need a small and representative dataset packaged for fast development and testing. In this work, we introduce Protein Distance Net (PDNET), a dataset derived from the widely used DeepCov dataset and consists of 3456 representative protein chains for training and validation. It is packaged with all the scripts that were used to curate the dataset, generate the input features and distance maps, and scripts with deep learning models to train, validate and test. Deep learning models can also be trained and tested in a web browser using free platforms such as Google Colab. We discuss how this dataset can be used to predict contacts, distance intervals, and real-valued distances (in [A]) by designing regression models. All scripts, training data, deep learning code for training, validation, and testing, and Python notebooks are available at https://github.com/ba-lab/pdnet/.

bioinformatics↗

bioRxiv2019

Forest canopy resists plant invasions: a case study of Chromolaena odorata in sub-tropical Sal (Shorea robusta) forests of Nepal

Invasive Alien Species cause tremendous ecological and economic damage in agriculture, forestry, aquatic ecosystems, and pastoral resources. They are one of the major threats to biodiversity conservation. Understanding the spatial pattern of invasive species and disentangling the biophysical drivers of invasion at forest stand level is essential for managing invasive species in forest ecosystems and the wider landscape. However, forest-level and species-specific information on invasive species abundance and area of extent is largely lacking. In this context, we analysed the cover of one of the worlds worst invasive plant species Chromolaena odorata in Sal (Shorea robusta Gaertn.) forest in central Nepal. Vegetation was sampled in four community-managed forests using 0.01 ha square quadrats, covering forest edge to the interior. Chromolaena cover, floral richness, tree density, forest canopy cover, shrub cover, and tree basal area were measured in each plot. We also estimated the level of disturbance in plots, and calculated distance from the plot to the nearest road. We also explored forest and invasive species management practices in community forests.\n\nChromolaena cover was found to be negatively correlated with forest canopy cover, distance to the nearest road, angle of slope and shrub cover. Canopy cover had the greatest effect on the Chromolaena cover. Chromolaena cover did not show any pattern along native species richness gradients. In conclusion, forest canopy cover is the overriding biotic covariate affecting Chromolaena cover in Sal forests. The practical application of our results in managing Chromolaena in forest ecosystems is discussed.

ecology↗

bioRxiv2019

DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout

BackgroundExciting new opportunities have arisen to solve the protein contact prediction problem from the progress in neural networks and the availability of a large number of homologous sequences through high-throughput sequencing. In this work, we study how deep convolutional neural network methods (ConvNets) may be best designed and developed to solve this long-standing problem.\n\nMethodWith publicly available datasets, we designed and trained various ConvNet architectures. We tested several recent deep learning techniques including wide residual networks, dropouts, and dilated convolutions. We studied the improvements in the precision of medium-range and long-range contacts, and compared the performance of our best architectures with the ones used in existing state-of-the-art methods.\n\nResultsThe proposed ConvNet architectures predict contacts with significantly more precision than the architectures used in several state-of-the-art methods. When trained using the DeepCov dataset consisting of 3,456 proteins and tested on PSICOV dataset of 150 proteins, our architectures achieve up to 15% higher precision when L/2 long-range contacts are evaluated. Similarly, when trained using the DNCON2 dataset consisting of 1,426 proteins and tested on 84 protein domains in the CASP12 dataset, our single network achieves 4.8% higher precision than the ensembled DNCON2 method when top L long-range contacts are evaluated. DEEPCON will be made publicly available at https://github.com/badriadhikari/DEEPCON/.

bioinformatics↗

bioRxiv2019

SAXSDom: Modeling multi-domain protein structures using small-angle X-ray scattering data

Many proteins are composed of several domains that pack together into a complex tertiary structure. Some multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for the domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the assembled protein. Small-angle X-ray scattering (SAXS) reports on the solution structural properties of proteins and has the potential for guiding homology modeling of multidomain proteins. In this work, we describe a novel multi-domain protein assembly modeling method, SAXSDom, that integrates experimental knowledge from SAXS profiles with probabilistic Input-Output Hidden Markov model (IOHMM). Four scoring functions to account for the energetic contribution of SAXS restraints for domain assembly were developed and tested. The method was evaluated on multi-domain proteins from two public datasets. Based on the results, the accuracy of domain assembly was improved for 40 out of 46 CASP multi-domain proteins in terms of RMSD and TM-score when SAXS information was used. Our method also achieved higher accuracy for at least 45 out of 73 multi-domain proteins according to RMSD and TM-score metrics in the AIDA dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at http://github.com/multicom-toolbox/SAXSDom.

bioinformatics↗

bioRxiv2017

CONFOLD2: Improved contact-driven ab initio protein structure modeling

BackgroundContact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support the contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed.\n\nResultsWe develop an improved contact-driven protein modeling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain top five models. CONFOLD2 is benchmarked on various datasets including CASP11 and 12 datasets with publicly available predicted contacts and yields better performance than the popular CONFOLD method.\n\nConclusionCONFOLD2 allows to quickly generate top five structural models for a protein sequence, when its secondary structures and contacts predictions at hand. CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/.

bioinformatics↗

bioRxiv2017

DNCON2: Improved protein contact prediction using two-level deep convolutional neural networks

MotivationSignificant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction.\n\nResultsIn this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks - the first five predict contacts at 6, 7.5, 8, 8.5, and 10 [A] distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps. On the free-modeling datasets in CASP10, 11, and 12 experiments, DNCON2 achieves mean precisions of 35%, 50%, and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset, and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated. We attribute the improved performance of DNCON2 to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length.\n\nAvailabilityThe web server of DNCON2 is at http://sysbio.rnet.missouri.edu/dncon2/ where training and testing datasets as well as the predictions for CASP10, 11, and 12 free-modeling datasets can also be downloaded. Its source code is available at https://github.com/multicom-toolbox/DNCON2/.\n\nContactchengji@missouri.edu\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗