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Showing 2071 to 2074 of 5186 entries
Published in 2020
Small angle X-ray scattering and molecular dynamic simulations provide molecular insight for stability of recombinant human transferrin.
Abstract: Transferrin is an attractive candidate for drug delivery due to its ability to cross the blood brain barrier. However, in order to be able to use it for therapeutic purposes, it is important to investigate how its stability depends on different formulation conditions. Combining high-throughput thermal and chemical denaturation studies with small angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations, it was possible to connect the stability of transferrin with its conformational changes. Lowering pH induces opening of the transferrin N-lobe, which results in a negative effect on the stability. Presence of NaCl or arginine at low pH enhances the opening and has a negative impact on the overall protein stability. Statement of Significance: Protein-based therapeutics have become an essential part of medical treatment. They are highly specific, have high affinity and fewer off-target effects. However, stabilization of proteins is critical, time-consuming, and expensive, and it is not yet possible to predict the behavior of proteins under different conditions. The current work is focused on a molecular understanding of the stability of human serum transferrin; a protein which is abundant in blood serum, may pass the blood brain barrier and therefore with high potential in drug delivery. Combination of high throughput unfolding techniques and structural studies, using small angle X-ray scattering and molecular dynamic simulations, allows us to understand the behavior of transferrin on a molecular level.
Published in 2020
Computational search for potential COVID-19 drugs from FDAapproved drugs and small molecules of natural origin identifies several anti-virals and plant products.
Abstract: The world is currently facing the COVID-19 pandemic, for which mild symptoms include fever and dry cough. In severe cases, it could lead to pneumonia and ultimately death in some instances. Moreover, the causative pathogen is highly contagious and there are no drugs or vaccines for it yet. The pathogen, SARS-CoV-2, is one of the human coronaviruses which was identified to infect humans first in December 2019. SARS-CoV-2 shares evolutionary relationship to other highly pathogenic viruses such as Severe Acute Respiratory Syndrome (SARS) and Middle East respiratory syndrome (MERS). We have exploited this similarity to model a target non-structural protein, NSP1, since it is implicated in the regulation of host gene expression by the virus and hijacking of host machinery. We next interrogated the capacity to repurpose around 2300 FDA-approved drugs and more than 3,00,000 small molecules of natural origin towards drug identification through virtual screening and molecular dynamics. Interestingly, we observed simple molecules like lactose, previously known anti-virals and few secondary metabolites of plants as promising hits. These herbal plants are already practiced in Ayurveda over centuries to treat respiratory problems and inflammation. Disclaimer: we would not like to recommend uptake of these small molecules for suspect COVID patients until it is approved by competent national or international authorities.
Published in 2020
Identification and Functional Analysis of Long Non-coding RNAs in Autism Spectrum Disorders.
Abstract: Genetic and environmental factors, alone or in combination, contribute to the pathogenesis of autism spectrum disorder (ASD). Although many protein-coding genes have now been identified as disease risk genes for ASD, a detailed illustration of long non-coding RNAs (lncRNAs) associated with ASD remains elusive. In this study, we first identified ASD-related lncRNAs based on genomic variant data of individuals with ASD from a twin study. In total, 532 ASD-related lncRNAs were identified, and 86.7% of these ASD-related lncRNAs were further validated by an independent copy number variant (CNV) dataset. Then, the functions and associated biological pathways of ASD-related lncRNAs were explored by enrichment analysis of their three different types of functional neighbor genes (i.e., genomic neighbors, competing endogenous RNA (ceRNA) neighbors, and gene co-expression neighbors in the cortex). The results have shown that most of the functional neighbor genes of ASD-related lncRNAs were enriched in nervous system development, inflammatory responses, and transcriptional regulation. Moreover, we explored the differential functions of ASD-related lncRNAs in distinct brain regions by using gene co-expression network analysis based on tissue-specific gene expression profiles. As a set, ASD-related lncRNAs were mainly associated with nervous system development and dopaminergic synapse in the cortex, but associated with transcriptional regulation in the cerebellum. In addition, a functional network analysis was conducted for the highly reliable functional neighbor genes of ASD-related lncRNAs. We found that all the highly reliable functional neighbor genes were connected in a single functional network, which provided additional clues for the action mechanisms of ASD-related lncRNAs. Finally, we predicted several potential drugs based on the enrichment of drug-induced pathway sets in the ASD-altered biological pathway list. Among these drugs, several (e.g., amoxapine, piperine, and diflunisal) were partly supported by the previous reports. In conclusion, ASD-related lncRNAs participated in the pathogenesis of ASD through various known biological pathways, which may be differential in distinct brain regions. Detailed investigation into ASD-related lncRNAs can provide clues for developing potential ASD diagnosis biomarkers and therapy.
Published in 2020
Identification of a repurposed drug as an inhibitor of Spike protein of human coronavirus SARS-CoV-2 by computational methods.
Abstract: Severe acute respiratory syndrome coronavirus (SARS-CoV-2) is an emerging new viral pathogen that causes severe respiratory disease. SARS-CoV-2 is responsible for the outbreak of COVID-19 pandemic worldwide. As there are no confirmed antiviral drugs or vaccines currently available for the treatment of COVID-19, discovering potent inhibitors or vaccines are urgently required for the benefit of humanity. The glycosylated Spike protein (S-protein) directly interacts with human angiotensin-converting enzyme 2 (ACE2) receptor through the receptor-binding domain (RBD) of S-protein. As the S-protein is exposed to the surface and is essential for entry into the host, the S-protein can be considered as a first-line therapeutic target for antiviral therapy and vaccine development. In silico screening, docking, and molecular dynamics simulation studies were performed to identify repurposing drugs using DrugBank and PubChem library against the RBD of S-protein. The study identified a laxative drug, Bisoxatin (DB09219), which is used for the treatment of constipation and preparation of the colon for surgical procedures. It binds nicely at the S-protein-ACE2 interface by making substantial pi-pi interactions with Tyr505 in the 'Site 1' hook region of RBD and hydrophilic interactions with Glu406, Ser494, and Thr500. Bisoxatin consistently binds to the protein throughout the 100 ns simulation. Taken together, we propose that the discovered molecule, Bisoxatin may be a promising repurposable drug molecule to develop new chemical libraries for inhibiting SARS-CoV-2 entry into the host.
Published in 2020
Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking.
Abstract: BACKGROUND: The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2. METHODS AND FINDINGS: We perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID-DB00701) would probably be the most effective drug based on the selected criterions. CONCLUSIONS: Our study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.
Published in 2020
Identification of L-asparaginases from Streptomyces strains with competitive activity and immunogenic profiles: a bioinformatic approach.
Abstract: The enzyme L-asparaginase from Escherichia coli is a therapeutic enzyme that has been a cornerstone in the clinical treatment of acute lymphoblastic leukemia for the last decades. However, treatment effectiveness is limited by the highly immunogenic nature of the protein and its cross-reactivity towards L-glutamine. In this work, a bioinformatic approach was used to identify, select and computationally characterize L-asparaginases from Streptomyces through sequence-based screening analyses, immunoinformatics, homology modeling, and molecular docking studies. Based on its predicted low immunogenicity and excellent enzymatic activity, we selected a previously uncharacterized L-asparaginase from Streptomyces scabrisporus. Furthermore, two putative asparaginase binding sites were identified and a 3D model is proposed. These promising features allow us to propose L-asparaginase from S. scabrisporus as an alternative for the treatment of acute lymphocytic leukemia.
Published in 2020
Identifying Effective Antiviral Drugs Against SARS-CoV-2 by Drug Repositioning Through Virus-Drug Association Prediction.
Abstract: A new coronavirus called SARS-CoV-2 is rapidly spreading around the world. Over 16,558,289 infected cases with 656,093 deaths have been reported by July 29th, 2020, and it is urgent to identify effective antiviral treatment. In this study, potential antiviral drugs against SARS-CoV-2 were identified by drug repositioning through Virus-Drug Association (VDA) prediction. 96 VDAs between 11 types of viruses similar to SARS-CoV-2 and 78 small molecular drugs were extracted and a novel VDA identification model (VDA-RLSBN) was developed to find potential VDAs related to SARS-CoV-2. The model integrated the complete genome sequences of the viruses, the chemical structures of drugs, a regularized least squared classifier (RLS), a bipartite local model, and the neighbor association information. Compared with five state-of-the-art association prediction methods, VDA-RLSBN obtained the best AUC of 0.9085 and AUPR of 0.6630. Ribavirin was predicted to be the best small molecular drug, with a higher molecular binding energy of -6.39 kcal/mol with human angiotensin-converting enzyme 2 (ACE2), followed by remdesivir (-7.4 kcal/mol), mycophenolic acid (-5.35 kcal/mol), and chloroquine (-6.29 kcal/mol). Ribavirin, remdesivir, and chloroquine have been under clinical trials or supported by recent works. In addition, for the first time, our results suggested several antiviral drugs, such as FK506, with molecular binding energies of -11.06 and -10.1 kcal/mol with ACE2 and the spike protein, respectively, could be potentially used to prevent SARS-CoV-2 and remains to further validation. Drug repositioning through virus-drug association prediction can effectively find potential antiviral drugs against SARS-CoV-2.
Published in 2020
A ligand-based computational drug repurposing pipeline using KNIME and Programmatic Data Access: case studies for rare diseases and COVID-19.
Abstract: Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces (APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, we present a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basis of two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeted download of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data for GLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.
Published in 2020
NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment.
Abstract: Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.
Published in 2020
Combination of multicomponent KA(2) and Pauson-Khand reactions: short synthesis of spirocyclic pyrrolocyclopentenones.
Abstract: The Cu-catalyzed multicomponent ketone-amine-alkyne (KA(2)) reaction was combined with a Pauson-Khand cycloaddition to give access of unprecedented constrained spirocyclic pyrrolocyclopentenone derivatives following a DOS couple-pair approach. The polyfunctional molecular scaffolds were tested on the cyclopentenone reactivity to further expand the skeletal diversity, demonstrating the utility of this combined approach in generating novel spiro compounds as starting material for the generation of chemical libraries. The chemoinformatics characterization of the newly-synthesized molecules gave evidence about structural and physicochemical properties with respect to a set of blockbuster drugs, and showed that such scaffolds are drug-like but more spherical and three-dimensional in character than the drugs.
Showing 2071 to 2074 of 5186 entries