Discovery and development of lead compounds from natural sources using computational approaches

Natural products have a significant role in drug discovery. Their unique chemical structures have led to compounds in clinical use to treat different diseases. Also, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. There are also unique natural products such as peptides and macrocycles that offer sources or starting points to address complex diseases. Computational approaches that used chemoinformatics and molecular modeling methods contribute to assisting and accelerating natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. Herein, we discuss chemoinformatics and molecular modeling applications to uncover bioactive natural products. We also discuss in silico methods to optimize the biological activity and anticipate potential toxicity issues of natural products. As case studies, we discuss the role of natural products for COVID-19 drug discovery and their impact on the identification of compounds with activity against DNA methyltransferase, an epigenetic target with relevance in cancer and other diseases. © 2022 Elsevier Inc. All rights reserved.

Auto-Kla: a novel web server to discriminate lysine lactylation sites using automated machine learning.

Recently, lysine lactylation (Kla), a novel post-translational modification (PTM), which can be stimulated by lactate, has been found to regulate gene expression and life activities. Therefore, it is imperative to accurately identify Kla sites. Currently, mass spectrometry is the fundamental method for identifying PTM sites. However, it is expensive and time-consuming to achieve this through experiments alone. Herein, we proposed a novel computational model, Auto-Kla, to quickly and accurately predict Kla sites in gastric cancer cells based on automated machine learning (AutoML). With stable and reliable performance, our model outperforms the recently published model in the 10-fold cross-validation. To investigate the generalizability and transferability of our approach, we evaluated the performance of our models trained on two other widely studied types of PTM, including phosphorylation sites in host cells infected with SARS-CoV-2 and lysine crotonylation sites in HeLa cells. The results show that our models achieve comparable or better performance than current outstanding models. We believe that this method will become a useful analytical tool for PTM prediction and provide a reference for the future development of related models. The web server and source code are available at http://tubic.org/Kla and https://github.com/tubic/Auto-Kla, respectively.

Evaluation of Mutations in SARS-CoV-2 N and S Genes on the Proteins Stability, Immunogenicity, and Pathogenicity in Iranian Patients from Golestan Province

Background: Natural selection such as mutations is known as a constant process for viral fitness and selective adaptation. Understanding the effects of each mutation, especially on structural proteins in the viral life cycle, is important in tracking the viruses behavior. Here, we evaluated the effects of mutations in SARS-CoV-2 nucleoprotein (N) and spike (S) genes on the protein stability, immunogenicity, and pathogenicity in Iranian COVID-19 patients from Golestan province. Methods: In this study, 8 SARS-CoV-2 RNA samples were enrolled from referral hospitals in Golestan province. These samples were confirmed using a real-time RT-PCR assay targeting the SARS-CoV-2 nucleoprotein (N) and ORF lab genes (Pishtazteb, Iran). Next-generation sequencing (NGS) was done on samples and subsequent sequences were retrieved from Global Initiative on Sharing All Influenza Data (GISAID) EpiCoV database. Structural analysis was performed between wild type (Wuhan accession number: NC_045512.2) and mutant N and S proteins to evaluate their stability, immunogenicity, and pathogenicity via bioinformatics servers such as Dynamut, Prodigy, IEDB, and software's (Mega XI and Pymol II.V.II visualizer). Results: Amino acid codon changes in N and S proteins show that mutations could alter the translation efficiency. Normal Mode Analysis (NMA) by Dynamut server shows that stability and flexibility are changed by the mutations of these proteins. Immunogenicity analysis indicates the potential effects of some mutations such as P681H, Q675R, L699I, and D3L on immune escape. Interaction complex binding energy and affinity are higher in the mutant type compared to the Wuhan wild type, indicating higher pathogenicity. Conclusion: The results indicate that there are some important mutations in N and S genes that affect the virus behavior in the infectivity. Regarding the sample size limitation and various mutations in SARS-CoV-2 variants, other studies using whole-genome sequencing with larger sample sizes will be required. Therefore, continuous monitoring of the SARS-CoV-2 genome seems important.

Mobile Application Based Secured Smart Museum for COVID-19 Pandemic Situation

The spread of COVID-19 disease has reduced the visitors count to various public places like parks, libraries and museums. Even though Indian Government has relaxed the rules for the public to visit these places during the early 2021, people deny visiting those places due to the fear created by the impact of the disease. This leads to huge revenue loss due to lack of visitors. In order to solve this issue, a safer visiting procedure through a Mobile Application based Secured Smart Museum (MSSM) has been provided. This system strictly monitors the entry of the visitors through two way screening process which ensures the safety of the visitors at the museum. Two-way screening process involves the measuring the temperature of the visitors with the support of IR temperature sensor and monitoring the availability of the face mask with the help of Open Computer Vision. The system also facilitates the users to book the ticket through Mobile Application. This system also alerts the museum's housekeeping department to clean and sanitize the museum based upon the visitors count. In addition to this, our system facilitates the visitors to gain the knowledge about the contents available in the museum through mobile application itself in their own preferred language. © 2022 IEEE.

PwCOV in cluster-based web server

In this work, we are introducing a novel assessment methodology for evaluating a prototype web service-based system for COVID-19 disease processing system by using cluster-based web server. We call it as PwCOV. The service generates clinical instructions and process respective information for distributed disease data sets. It follows the business processes and principles of service-oriented computing for each end user request. The assessment methodology illustrates different aspects of service deployment for massive growth of service users. In this study, the PwCOV is observed to be stable up to the stress level of 1700 simultaneous users. The response time of 14.35s, throughput of 8592 bytes/s, and central processing unit (CPU) utilization of 22.16% with a strong reliability of service execution is observed. However, the reliability of PwCOV execution degrades beyond that execution limit. For 1800 simultaneous users of the service, the response time, throughput and CPU utilization is recorded to be 25.28 s, 15, 729 bytes/s, and 39.13%, respectively. During this stress, the service failure rate of 35% is observed. A moderate reliability of 70% of service period is observed for 1800 users. The propose study also discusses the impact of system metric, reliability, and their correlation over the service execution. The statistical analysis is carried out to study the viability, acceptability, applicability of such deployment for COVID-19 disease processing system. The limitation of PwCOV for processing geographically scattered data sets is also discussed. © 2021 Elsevier Inc. All rights reserved.

A Web-based Method for Designing and Validating Primer-probe Sets for SARS-CoV-2

Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread around the world with high mortality rates. Since this virus constantly mutating rapidly, it causes different kinds of variants such as the Delta Plus variant, and this variant even dominated the original Delta variant. Thus, designing a new primer-probe set is required to cope with the new variant effectively. However, the existing primer-probe set design tools are also effective in designing for a given sequence, but it is difficult to consider all variants or off-target. Here, we introduce a web-based method for designing and validating primer-probe sets for SARS-CoV-2. Through the web server we built, a user could get results of the binding site matching, homology tests, and variant coverage calculation of the primer-probe set they designed. Moreover, a user also could find the primer-probe sets which bind as many SARS-CoV-2 variants as possible by using the user-input FASTA sequence. We believe that our web service would help researchers by reducing the difficulty of designing primers and probes of SARS-CoV-2. © 2021 IEEE.

GeneTrail: A Framework for the Analysis of High-Throughput Profiles.

Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles. Our framework enables the analysis of standard bulk, time-series, and single-cell measurements and includes various state-of-the-art methods to identify potentially deregulated biological processes and to detect driving factors within those deregulated processes. We highlight the capabilities of our web service with an analysis of a single-cell COVID-19 data set that demonstrates its potential for uncovering complex molecular mechanisms. GeneTrail can be accessed freely and without login requirements at http://genetrail.bioinf.uni-sb.de.

DrugRepV: a compendium of repurposed drugs and chemicals targeting epidemic and pandemic viruses.

Viruses are responsible for causing various epidemics and pandemics with a high mortality rate e.g. ongoing SARS-CoronaVirus-2 crisis. The discovery of novel antivirals remains a challenge but drug repurposing is emerging as a potential solution to develop antivirals in a cost-effective manner. In this regard, we collated the information of repurposed drugs tested for antiviral activity from literature and presented it in the form of a user-friendly web server named 'DrugRepV'. The database contains 8485 entries (3448 unique) with biological, chemical, clinical and structural information of 23 viruses responsible to cause epidemics/pandemics. The database harbors browse and search options to explore the repurposed drug entries. The data can be explored by some important fields like drugs, viruses, drug targets, clinical trials, assays, etc. For summarizing the data, we provide overall statistics of the repurposed candidates. To make the database more informative, it is hyperlinked to various external repositories like DrugBank, PubChem, NCBI-Taxonomy, Clinicaltrials.gov, World Health Organization and many more. 'DrugRepV' database (https://bioinfo.imtech.res.in/manojk/drugrepv/) would be highly useful to the research community working to develop antivirals.

COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database.

In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing ROBOKOP biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to test new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19. COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.

CoV-Seq, a New Tool for SARS-CoV-2 Genome Analysis and Visualization: Development and Usability Study.

BACKGROUND: COVID-19 became a global pandemic not long after its identification in late 2019. The genomes of SARS-CoV-2 are being rapidly sequenced and shared on public repositories. To keep up with these updates, scientists need to frequently refresh and reclean data sets, which is an ad hoc and labor-intensive process. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes. OBJECTIVE: To address these challenges, we developed CoV-Seq, an integrated web server that enables simple and rapid analysis of SARS-CoV-2 genomes. METHODS: CoV-Seq is implemented in Python and JavaScript. The web server and source code URLs are provided in this article. RESULTS: Given a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are displayed in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is available for high-throughput processing. In addition, we aggregated all publicly available SARS-CoV-2 sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID), National Center for Biotechnology Information (NCBI), European Nucleotide Archive (ENA), and China National GeneBank (CNGB), and extracted genetic variants from these sequences for download and downstream analysis. The CoV-Seq database is updated weekly. CONCLUSIONS: We have developed CoV-Seq, an integrated web service for fast and easy analysis of custom SARS-CoV-2 sequences. The web server provides an interactive module for the analysis of custom sequences and a weekly updated database of genetic variants of all publicly accessible SARS-CoV-2 sequences. We believe CoV-Seq will help improve our understanding of the genetic underpinnings of COVID-19.