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ADPDB: A Comprehensive Knowledgebase of Manually Curated Peptides Against Dengue Virus
Dengue, a significant mosquito-borne disease, presents a global health challenge with limited treatment options. Recently, there have been estimates of 390 million dengue infections annually worldwide. Thus, Dengue viruses (DENV) continue to result in a severe burden on human health all over the world. Here, we are introducing the Anti-Dengue Peptide Database (ADPDB) as a comprehensive knowledgebase dedicated to anti-dengue peptides, aiming to aid research and development efforts against the dengue virus. ADPDB consolidates information on antimicrobial peptides (AMPs) exhibiting anti-dengue activity, sourced from extensive literature curation. The database provides a user-friendly interface offering functionalities such as simple and advanced search options, data retrieval, and customizable reports. Currently housing 606 peptide entries, ADPDB encompasses peptides from various sources, including natural and synthetic origins. Name, sequence, source, target, mode of action (MoA), length, IC50, toxicity, hemolytic activity of peptides are meticulously curated, facilitating insights into their therapeutic potential. Notably, ADPDB addresses the gap in specialized databases focusing on anti-DENV peptides, aligning with the growing interest in peptide-based therapeutics. The database enables researchers, pharmaceutical industries, and clinicians to explore peptide candidates, study structure-activity relationships, and accelerate drug discovery processes. By leveraging bioinformatics-driven approaches, ADPDB aims to advance the understanding and development of peptide-based interventions against dengue. This resource is accessible via any web browser at URL:
https://bblserver.org.in/adpdb/
.
Graphical abstract
Graphical abstract of ADPDB (for the creation of graphical abstract, we have used the image of dengue virus (PDB ID: 1K4R by Kuhn et al in Structure of dengue virus: implications for flavivirus organization, maturation, and fusion, Cell, 108(5):717–725, 2002), image of arenicin-1 AMP (PDB ID: 2JSB by Andrä et al in Structure and mode of action of the antimicrobial peptide arenicin, Biochem J, 410(1):113–122, 2008), and image of an PubMed article (PMID: 29200948 by Chew MiawFang et al in Peptides as therapeutic agents for dengue virus, Int J Med Sci 14(13):1342–1359, 2017). The remaining images are generated and incorporated by us).
Journal Article
Bioactive Molecule Prediction Using Extreme Gradient Boosting
Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today’s drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound’s molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets.
Journal Article
Collecting experiments : making Big Data biology
Databases have revolutionized nearly every aspect of our lives. Information of all sorts is being collected on a massive scale, from Google to Facebook and well beyond. But as the amount of information in databases explodes, we are forced to reassess our ideas about what knowledge is, how it is produced, to whom it belongs, and who can be credited for producing it. Every scientist working today draws on databases to produce scientific knowledge. Databases have become more common than microscopes, voltmeters, and test tubes, and the increasing amount of data has led to major changes in research practices and profound reflections on the proper professional roles of data producers, collectors, curators, and analysts. Collecting Experiments traces the development and use of data collections, especially in the experimental life sciences, from the early twentieth century to the present. It shows that the current revolution is best understood as the coming together of two older ways of knowing--collecting and experimenting, the museum and the laboratory. Ultimately, Bruno J. Strasser argues that by serving as knowledge repositories, as well as indispensable tools for producing new knowledge, these databases function as digital museums for the twenty-first century.
Book
Including Digital Sequence Data in the Nagoya Protocol Can Promote Data Sharing
The Nagoya Protocol (NP), a legal framework under the Convention on Biological Diversity (CBD), formalises fair and equitable sharing of benefits arising from biological diversity. It encompasses biological samples and associated indigenous knowledge, with equitable return of benefits to those providing samples. Recent proposals that the use of digital sequence information (DSI) derived from samples should also require benefit-sharing under the NP have raised concerns that this might hamper research progress. Here, we propose that formalised benefit-sharing for biological data use can increase willingness to participate in research and share data, by ensuring equitable collaboration between sample providers and researchers, and preventing exploitative practices. Three case studies demonstrate how equitable benefit-sharing agreements might build long-term collaborations, furthering research for global benefits.
The volume and type of digital sequence data are rapidly growing, driven by the ongoing development of new technologies to generate ‘omics data.Historical and ongoing exploitative practices, as well as biopiracy, mean trust relationships cannot be relied on for equitable sharing from low- to high-resourced stakeholders.Detractors of benefit sharing for digital sequence information (DSI) often promulgate the perspective of those who have historically benefitted from inequitable practices.Extending the Nagoya Protocol (NP) to include all DSI could ensure that all stakeholders agree with the terms of data sharing, which does not preclude Open Research where acceptable to all parties.Extending the NP to include all digital biological data could facilitate research and resource sharing by implementing and enforcing equitable benefit sharing.
Journal Article
scMC learns biological variation through the alignment of multiple single-cell genomics datasets
Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.
Journal Article
Scaling the propulsive performance of heaving and pitching foils
Scaling laws for the propulsive performance of rigid foils undergoing oscillatory heaving and pitching motions are presented. Water tunnel experiments on a nominally two-dimensional flow validate the scaling laws, with the scaled data for thrust, power and efficiency all showing excellent collapse. The analysis indicates that the behaviour of the foils depends on both Strouhal number and reduced frequency, but for motions where the viscous drag is small the thrust closely follows a linear dependence on reduced frequency. The scaling laws are also shown to be consistent with biological data on swimming aquatic animals.
Journal Article
Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review
Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called ‘COVID-19’, as a ‘public health emergency of international concern’. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.
Journal Article
Vortex dynamics and hydrodynamic performance enhancement mechanism in batoid fish oscillatory swimming
The effects of chordwise deformation and the half-amplitude asymmetry on the hydrodynamic performance and vortex dynamics of batoid fish have been numerically investigated, in which the two parameters were represented by the wavenumber ($W$) and the ratio of the half-amplitude above the longitudinal axis to that below ($HAR$). Fin kinematics were prescribed based on biological data. Simulations were conducted using the immersed boundary method. It was found that moderate chordwise deformation enhances the thrust, saves the power and increases the efficiency. A large $HAR$ can also increase thrust performance. By using the derivative-moment transformation theory at several subdomains to capture the local vortical structures and a force decomposition, it was shown that, at high Strouhal numbers ($St$), the tip vortex is the main source of thrust, whereas the leading-edge vortex (LEV) and trailing-edge vortex weaken the thrust generation. However, at lower $St$, the LEV would enhance the thrust. The least deformation ($W=0$) leads to the largest effective angle of attack, and thus the strongest vortices. However, moderate deformation ($W=0.4$) has an optimal balance between the performance enhancement and the opposite effect of different local structures. The performance enhancement of $HAR$ was also due to the increase of the vortical contributions. This work provides a new insight into the role of vortices and the force enhancement mechanism in aquatic swimming.
Journal Article