1
|
Natsrita P, Charoenkwan P, Shoombuatong W, Mahalapbutr P, Faksri K, Chareonsudjai S, Rungrotmongkol T, Pipattanaboon C. Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes. Sci Rep 2024; 14:17165. [PMID: 39060292 PMCID: PMC11282219 DOI: 10.1038/s41598-024-67487-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
Several computational methods have been developed to identify neutralizing antibodies (NAbs) covering four dengue virus serotypes (DENV-1 to DENV-4); however, limitations of the dataset and the resulting performance remain. Here, we developed a new computational framework to predict potent and stable NAbs against DENV-1 to DENV-4 using only antibody (CDR-H3) and epitope sequences as input. Specifically, our proposed computational framework employed sequence-based ML and molecular dynamic simulation (MD) methods to achieve more accurate identification. First, we built a novel dataset (n = 1108) by compiling the interactions of CDR-H3 and epitope sequences with the half maximum inhibitory concentration (IC50) values, which represent neutralizing activities. Second, we achieved an accurately predictive ML model that showed high AUC values of 0.879 and 0.885 by tenfold cross-validation and independent tests, respectively. Finally, our computational framework could be applied to filter approximately 2.5 million unseen antibodies into two final candidates that showed strong and stable binding to all four serotypes. In addition, the most potent and stable candidate (1B3B9_V21) was evaluated for its development potential as a therapeutic agent by molecular docking and MD simulations. This study provides an antibody computational approach to facilitate the high-throughput identification of NAbs and accelerate the development of therapeutic antibodies.
Collapse
Affiliation(s)
- Piyatida Natsrita
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Panupong Mahalapbutr
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Kiatichai Faksri
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
- Research and Diagnostic Center for Emerging Infectious Diseases, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Sorujsiri Chareonsudjai
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Thanyada Rungrotmongkol
- Center of Excellent in Biocatalyst and Sustainable Biotechnology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Chonlatip Pipattanaboon
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand.
| |
Collapse
|
2
|
Karimi Alavijeh M, Lee YY, Gras SL. A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments. Eng Life Sci 2024; 24:e2400023. [PMID: 38975020 PMCID: PMC11223373 DOI: 10.1002/elsc.202400023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 07/09/2024] Open
Abstract
Bioreactor scale-up and scale-down have always been a topical issue for the biopharmaceutical industry and despite considerable effort, the identification of a fail-safe strategy for bioprocess development across scales remains a challenge. With the ubiquitous growth of digital transformation technologies, new scaling methods based on computer models may enable more effective scaling. This study aimed to evaluate the potential application of machine learning (ML) algorithms for bioreactor scale-up, with a specific focus on the prediction of scaling parameters. Factors critical to the development of such models were identified and data for bioreactor scale-up studies involving CHO cell-generated mAb products collated from the literature and public sources for the development of unsupervised and supervised ML models. Comparison of bioreactor performance across scales identified similarities between the different processes and primary differences between small- and large-scale bioreactors. A series of three case studies were developed to assess the relationship between cell growth and scale-sensitive bioreactor features. An embedding layer improved the capability of artificial neural network models to predict cell growth at a large-scale, as this approach captured similarities between the processes. Further models constructed to predict scaling parameters demonstrated how ML models may be applied to assist the scaling process. The development of data sets that include more characterization data with greater variability under different gassing and agitation regimes will also assist the future development of ML tools for bioreactor scaling.
Collapse
Affiliation(s)
- Masih Karimi Alavijeh
- Department of Chemical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
- The Bio21 Molecular Science and Biotechnology InstituteThe University of MelbourneParkvilleVictoriaAustralia
| | | | - Sally L. Gras
- Department of Chemical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
- The Bio21 Molecular Science and Biotechnology InstituteThe University of MelbourneParkvilleVictoriaAustralia
| |
Collapse
|
3
|
Smith MD, Case MA, Makowski EK, Tessier PM. Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. Bioinformatics 2023; 39:btad446. [PMID: 37478351 PMCID: PMC10477941 DOI: 10.1093/bioinformatics/btad446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/21/2023] [Accepted: 07/20/2023] [Indexed: 07/23/2023] Open
Abstract
MOTIVATION Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. RESULTS Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. AVAILABILITY AND IMPLEMENTATION All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.
Collapse
Affiliation(s)
- Matthew D Smith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Marshall A Case
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Emily K Makowski
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Peter M Tessier
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Protein Folding Disease Initiative, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Michigan Alzheimer’s Disease Center, University of Michigan, Ann Arbor, MI 48109-2200, United States
| |
Collapse
|
4
|
Williams AH, Zhan CG. Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management. BioDrugs 2023; 37:649-674. [PMID: 37464099 DOI: 10.1007/s40259-023-00611-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/28/2023] [Indexed: 07/20/2023]
Abstract
In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.
Collapse
Affiliation(s)
- Alexander H Williams
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- GSK Upper Providence, 1250 S. Collegeville Road, Collegeville, PA, 19426, USA
| | - Chang-Guo Zhan
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
| |
Collapse
|
5
|
Smith MD, Case MA, Makowski EK, Tessier PM. Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.548448. [PMID: 37503142 PMCID: PMC10369870 DOI: 10.1101/2023.07.10.548448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Motivation Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. Results Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. Availability All deep sequencing datasets and code to do the analyses presented within are available via GitHub. Contact Peter Tessier, ptessier@umich.edu. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
|
6
|
Barnawi A, Boulares M, Somai R. Simple and Powerful PCG Classification Method Based on Selection and Transfer Learning for Precision Medicine Application. Bioengineering (Basel) 2023; 10:bioengineering10030294. [PMID: 36978685 PMCID: PMC10045405 DOI: 10.3390/bioengineering10030294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/03/2023] [Accepted: 02/15/2023] [Indexed: 03/03/2023] Open
Abstract
The World Health Organization (WHO) highlights that cardiovascular diseases (CVDs) are one of the leading causes of death globally, with an estimated rise to over 23.6 million deaths by 2030. This alarming trend can be attributed to our unhealthy lifestyles and lack of attention towards early CVD diagnosis. Traditional cardiac auscultation, where a highly qualified cardiologist listens to the heart sounds, is a crucial diagnostic method, but not always feasible or affordable. Therefore, developing accessible and user-friendly CVD recognition solutions can encourage individuals to integrate regular heart screenings into their routine. Although many automatic CVD screening methods have been proposed, most of them rely on complex prepocessing steps and heart cycle segmentation processes. In this work, we introduce a simple and efficient approach for recognizing normal and abnormal PCG signals using Physionet data. We employ data selection techniques such as kernel density estimation (KDE) for signal duration extraction, signal-to-noise Ratio (SNR), and GMM clustering to improve the performance of 17 pretrained Keras CNN models. Our results indicate that using KDE to select the appropriate signal duration and fine-tuning the VGG19 model results in excellent classification performance with an overall accuracy of 0.97, sensitivity of 0.946, precision of 0.944, and specificity of 0.946.
Collapse
Affiliation(s)
- Ahmed Barnawi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Correspondence:
| | - Mehrez Boulares
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Research Laboratory of Technologies of Information and Communication and Electrical Engineering (LaTICE), Higher National School of Engineers of Tunis (ENSIT), University of Tunis, Tunis 1008, Tunisia
| | - Rim Somai
- ESPRIT School of Engineering, Tunis 2035, Tunisia
| |
Collapse
|
7
|
Boorla VS, Chowdhury R, Ramasubramanian R, Ameglio B, Frick R, Gray JJ, Maranas CD. De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD). Proteins 2023; 91:196-208. [PMID: 36111441 PMCID: PMC9538105 DOI: 10.1002/prot.26422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/17/2022] [Accepted: 09/06/2022] [Indexed: 01/11/2023]
Abstract
The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.
Collapse
Affiliation(s)
- Veda Sheersh Boorla
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802
| | - Ratul Chowdhury
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802
| | | | - Brandon Ameglio
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Rahel Frick
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802,Corresponding author:
| |
Collapse
|
8
|
Rawat P, Sharma D, Prabakaran R, Ridha F, Mohkhedkar M, Janakiraman V, Gromiha MM. Ab-CoV: a curated database for binding affinity and neutralization profiles of coronavirus-related antibodies. Bioinformatics 2022; 38:4051-4052. [PMID: 35771624 DOI: 10.1093/bioinformatics/btac439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/05/2022] [Accepted: 06/28/2022] [Indexed: 12/24/2022] Open
Abstract
SUMMARY We have developed a database, Ab-CoV, which contains manually curated experimental interaction profiles of 1780 coronavirus-related neutralizing antibodies. It contains more than 3200 datapoints on half maximal inhibitory concentration (IC50), half maximal effective concentration (EC50) and binding affinity (KD). Each data with experimentally known three-dimensional structures are complemented with predicted change in stability and affinity of all possible point mutations of interface residues. Ab-CoV also includes information on epitopes and paratopes, structural features of viral proteins, sequentially similar therapeutic antibodies and Collier de Perles plots. It has the feasibility for structure visualization and options to search, display and download the data. AVAILABILITY AND IMPLEMENTATION Ab-CoV database is freely available at https://web.iitm.ac.in/bioinfo2/ab-cov/home. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Puneet Rawat
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Divya Sharma
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - R Prabakaran
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Fathima Ridha
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Mugdha Mohkhedkar
- Infection Biology Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Vani Janakiraman
- Infection Biology Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - M Michael Gromiha
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| |
Collapse
|
9
|
Dasgupta A, Bakshi A, Mukherjee S, Das K, Talukdar S, Chatterjee P, Mondal S, Das P, Ghosh S, Som A, Roy P, Kundu R, Sarkar A, Biswas A, Paul K, Basak S, Manna K, Saha C, Mukhopadhyay S, Bhattacharyya NP, De RK. Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2022; 12:e1462. [PMID: 35942397 PMCID: PMC9350133 DOI: 10.1002/widm.1462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 03/28/2022] [Accepted: 04/28/2022] [Indexed: 05/02/2023]
Abstract
World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.
Collapse
Affiliation(s)
- Abhijit Dasgupta
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Abhisek Bakshi
- Department of Information TechnologyBengal Institute of TechnologyKolkataWest BengalIndia
| | - Srijani Mukherjee
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Kuntal Das
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Soumyajeet Talukdar
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Pratyayee Chatterjee
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Sagnik Mondal
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Puspita Das
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Subhrojit Ghosh
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Archisman Som
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Pritha Roy
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Rima Kundu
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Akash Sarkar
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Arnab Biswas
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Karnelia Paul
- Department of BiotechnologyUniversity of CalcuttaKolkataWest BengalIndia
| | - Sujit Basak
- Department of Physiology and BiophysicsStony Brook UniversityStony BrookNew YorkUSA
| | - Krishnendu Manna
- Department of Food and NutritionUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Chinmay Saha
- Department of Genome Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Satinath Mukhopadhyay
- Department of Endocrinology and MetabolismInstitute of Post Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial HospitalKolkataWest BengalIndia
| | - Nitai P. Bhattacharyya
- Department of Endocrinology and MetabolismInstitute of Post Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial HospitalKolkataWest BengalIndia
| | - Rajat K. De
- Machine Intelligence UnitIndian Statistical InstituteKolkataWest BengalIndia
| |
Collapse
|
10
|
Sengottiyan S, Malakar K, Kathiravan A, Velusamy M, Mikolajczyk A, Puzyn T. Integrated Approach to Interaction Studies of Pyrene Derivatives with Bovine Serum Albumin: Insights from Theory and Experiment. J Phys Chem B 2022; 126:3831-3843. [PMID: 35583491 PMCID: PMC9169062 DOI: 10.1021/acs.jpcb.2c00778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
This work aimed to
investigate the interaction of bovine serum
albumin with newly synthesized potent new pyrene derivatives (PS1
and PS2), which might prove useful to have a better antibacterial
character as found for similar compounds in the previous report [Low et al. 2014, 12, 2269−2284]. However, to date, binding studies with
plasma protein are still unknown. Steady-state fluorescence spectroscopy
and lifetime fluorescence studies show that the static interaction
binding mode and binding constants of PS1 and PS2 are 7.39 and 7.81
[Kb × 105 (M–1)], respectively. The experimental results suggest that hydrophobic
forces play a crucial role in interacting pyrene derivatives with
BSA protein. To verify this, molecular docking and molecular dynamics
simulations were performed to predict the nature of the interaction
and the dynamic behavior of the two compounds in the BSA complex,
PS1 and PS2, under physiological conditions of pH = 7.1. In addition,
the free energies of binding for the BSA-PS1 and BSA-PS2 complexes
were estimated at 300 K based on the molecular mechanics of the Poisson–Boltzmann
surface (MMPBSA) with the Gromacs package. PS2 was found to have a
higher binding affinity than PS1. To determine the behavior of the
orbital transitions in the ground state geometry, we found that both
compounds have similar orbital transitions from HOMO–LUMO via
π → π* and HOMO–1–LUMO+1 via n →
π*, which was included in the FMO analysis. A cytotoxicity study
was performed to determine the toxicity of the compounds. Based on
the MD study, the stability of the compounds with BSA and the dynamic
binding modes were further revealed, as well as the nature of the
binding force components involved and the important residues involved
in the binding process. From the binding energy analysis, it can be
assumed that PS2 may be more active than PS1.
Collapse
Affiliation(s)
- Selvaraj Sengottiyan
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk, 80-308 Poland
| | - Kakoli Malakar
- Department of Chemistry, North Eastern Hill University, Shillong 793 022, Meghalaya, India
| | - Arunkumar Kathiravan
- Department of Chemistry, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Avadi, Chennai 600 062, Tamil Nadu, India
| | - Marappan Velusamy
- Department of Chemistry, North Eastern Hill University, Shillong 793 022, Meghalaya, India
| | - Alicja Mikolajczyk
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk, 80-308 Poland.,QSAR Lab Ltd., ul. Trzy Lipy 3, Gdansk, 80-266 Poland
| | - Tomasz Puzyn
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk, 80-308 Poland.,QSAR Lab Ltd., ul. Trzy Lipy 3, Gdansk, 80-266 Poland
| |
Collapse
|
11
|
Aruleba RT, Adekiya TA, Ayawei N, Obaido G, Aruleba K, Mienye ID, Aruleba I, Ogbuokiri B. COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods. Bioengineering (Basel) 2022; 9:153. [PMID: 35447713 PMCID: PMC9024895 DOI: 10.3390/bioengineering9040153] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 12/15/2022] Open
Abstract
As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large.
Collapse
Affiliation(s)
- Raphael Taiwo Aruleba
- Department of Molecular and Cell Biology, Faculty of Science, University of Cape Town, Cape Town 7701, South Africa;
| | - Tayo Alex Adekiya
- Department of Pharmacy and Pharmacology, School of Therapeutic Science, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 7 York Road, Parktown 2193, South Africa;
| | - Nimibofa Ayawei
- Department of Chemistry, Bayelsa Medical University, Yenagoa PMB 178, Bayelsa State, Nigeria;
| | - George Obaido
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0404, USA
| | - Kehinde Aruleba
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Ibomoiye Domor Mienye
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (I.D.M.); (I.A.)
| | - Idowu Aruleba
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (I.D.M.); (I.A.)
| | - Blessing Ogbuokiri
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
| |
Collapse
|
12
|
|
13
|
|
14
|
Abstract
The SARS-CoV-2 virus, the COVID-19 disease, and the resulting pandemic have reshaped the entire world in an unprecedented manner. Massive efforts have been made by AI communities to combat the pandemic. What roles has AI played in tackling COVID-19? How has AI performed in the battle against COVID-19? Where are the gaps and opportunities? What lessons can we learn to enhance the ability of AI to battle future pandemics? These questions, despite being fundamental, are yet to be answered in full or systematically. They need to be addressed by AI communities as a priority despite the easing of the omicron infectiousness and threat. This article reviews these issues with reflections on global AI research and the literature on tackling COVID-19. It is envisaged that the demand and priority of developing "pandemic AI" will increase over time, with smart global epidemic early warning systems to be developed by a global collaborative AI effort.
Collapse
Affiliation(s)
- Longbing Cao
- University of Technology SydneySydneyNSW2007Australia
| |
Collapse
|
15
|
Gianti E, Percec S. Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go? Biomacromolecules 2022; 23:576-591. [PMID: 35133143 DOI: 10.1021/acs.biomac.1c01436] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of biomacromolecules. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, learn, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules.
Collapse
Affiliation(s)
- Eleonora Gianti
- Institute for Computational Molecular Science (ICMS), Temple University, Philadelphia, Pennsylvania 19122, United States.,Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Simona Percec
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| |
Collapse
|
16
|
Xu L, Magar R, Farimani AB. Forecasting COVID-19 new cases using deep learning methods. Comput Biol Med 2022; 144:105342. [PMID: 35247764 PMCID: PMC8864960 DOI: 10.1016/j.compbiomed.2022.105342] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/20/2022] [Accepted: 02/20/2022] [Indexed: 12/23/2022]
Abstract
After nearly two years since the first identification of SARS-CoV-2 virus, the surge in cases because of virus mutations is a cause of grave public health concern across the globe. As a result of this health crisis, predicting the transmission pattern of the virus is one of the most vital tasks for preparing and controlling the pandemic. In addition to mathematical models, machine learning tools, especially deep learning models have been developed for forecasting the trend of the number of patients affected by SARS-CoV-2 with great success. In this paper, three deep learning models, including CNN, LSTM, and the CNN-LSTM have been developed to predict the number of COVID-19 cases for Brazil, India and Russia. We also compare the performance of our models with the previously developed deep learning models and notice significant improvements in prediction performance. Although our models have been used only for forecasting cases in these three countries, the models can be easily applied to datasets of other countries. Among the models developed in this work, the LSTM model has the highest performance when forecasting and shows an improvement in the forecasting accuracy compared with some existing models. The research will enable accurate forecasting of the COVID-19 cases and support the global fight against the pandemic.
Collapse
|
17
|
Moradi M, Golmohammadi R, Najafi A, Moosazadeh Moghaddam M, Fasihi-Ramandi M, Mirnejad R. A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis. INFORMATICS IN MEDICINE UNLOCKED 2022; 28:100862. [PMID: 35079621 PMCID: PMC8776350 DOI: 10.1016/j.imu.2022.100862] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 01/05/2023] Open
Abstract
In the last century, the emergence of in silico tools has improved the quality of healthcare studies by providing high quality predictions. In the case of COVID-19, these tools have been advantageous for bioinformatics analysis of SARS-CoV-2 structures, studying potential drugs and introducing drug targets, investigating the efficacy of potential natural product components at suppressing COVID-19 infection, designing peptide-mimetic and optimizing their structure to provide a better clinical outcome, and repurposing of the previously known therapeutics. These methods have also helped medical biotechnologists to design various vaccines; such as multi-epitope vaccines using reverse vaccinology and immunoinformatics methods, among which some of them have showed promising results through in vitro, in vivo and clinical trial studies. Moreover, emergence of artificial intelligence and machine learning algorithms have helped to classify the previously known data and use them to provide precise predictions and make plan for future of the pandemic condition. At this contemporary review, by collecting related information from the collected literature on valuable data sources; such as PubMed, Scopus, and Web of Science, we tried to provide a brief outlook regarding the importance of in silico tools in managing different aspects of COVID-19 pandemic infection and how these methods have been helpful to biomedical researchers.
Collapse
Affiliation(s)
- Mohammad Moradi
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
- Department of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | - Reza Golmohammadi
- Baqiyatallah Research Center for Gastroenterology and Liver Diseases (BRCGL), Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Ali Najafi
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | | | - Mahdi Fasihi-Ramandi
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Reza Mirnejad
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| |
Collapse
|
18
|
Lim YW, Adler AS, Johnson DS. Predicting antibody binders and generating synthetic antibodies using deep learning. MAbs 2022; 14:2069075. [PMID: 35482911 PMCID: PMC9067455 DOI: 10.1080/19420862.2022.2069075] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/19/2022] [Indexed: 11/30/2022] Open
Abstract
The antibody drug field has continually sought improvements to methods for candidate discovery and engineering. Historically, most such methods have been laboratory-based, but informatics methods have recently started to make an impact. Deep learning, a subfield of machine learning, is rapidly gaining prominence in the biomedical research. Recent advances in microfluidics technologies and next-generation sequencing have not only revolutionized therapeutic antibody discovery, but also contributed to a vast amount of antibody repertoire sequencing data, providing opportunities for deep learning-based applications. Previously, we used microfluidics, yeast display, and deep sequencing to generate a panel of binder and non-binder antibody sequences to the cancer immunotherapy targets PD-1 and CTLA-4. Here we encoded the antibody light and heavy chain complementarity-determining regions (CDR3s) into antibody images, then built and trained convolutional neural network models to classify binders and non-binders. To improve model interpretability, we performed in silico mutagenesis to identify CDR3 residues that were important for binder classification. We further built generative deep learning models using generative adversarial network models to produce synthetic antibodies against PD-1 and CTLA-4. Our models generated variable length CDR3 sequences that resemble real sequences. Overall, our study demonstrates that deep learning methods can be leveraged to mine and learn patterns in antibody sequences, offering insights into antibody engineering, optimization, and discovery.
Collapse
Affiliation(s)
- Yoong Wearn Lim
- GigaGen Inc. (A Grifols Company), South San Francisco, CA, USA
| | - Adam S. Adler
- GigaGen Inc. (A Grifols Company), South San Francisco, CA, USA
| | | |
Collapse
|
19
|
Bali A, Bali N. Role of artificial intelligence in fast-track drug discovery and vaccine development for COVID-19. NOVEL AI AND DATA SCIENCE ADVANCEMENTS FOR SUSTAINABILITY IN THE ERA OF COVID-19 2022. [PMCID: PMC9069021 DOI: 10.1016/b978-0-323-90054-6.00006-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
COVID-19 is a global pandemic spread across more than 200 countries and several measures are being taken to control it. Researchers in pharmaceutical academia/industry are incessantly targeting this disease through vaccine and drug development protocols. Artificial intelligence is being extensively explored for surveillance, diagnostics, contact tracing, and for clinical management of COVID-19. The most common application has been for repurposing of existing drugs through various AI tools. Successful training of artificial neural networks based on identification of specific patterns in binding of known antiviral drugs with protein sequences from diverse virus species have generated models giving good predictions for molecules against SARS-CoV-2 virus, in sync with clinical studies. ML tools have also been used to investigate immunogenic components of the virus to be exploited as vaccine candidates. In this chapter, the utilization of artificial intelligence to accelerate drug-design and vaccine design research for COVID-19 has been reviewed.
Collapse
|
20
|
Čolaković A, Avdagić-Golub E, Begović M, Memić B, Hasković-Džubur A. Application of machine learning in the fight against the COVID-19 pandemic: A review. ACTA FACULTATIS MEDICAE NAISSENSIS 2022. [DOI: 10.5937/afmnai39-38354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Methods: This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim: This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion: ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.
Collapse
|
21
|
Shah H, Shah S, Tanwar S, Gupta R, Kumar N. Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends. MULTIMEDIA SYSTEMS 2022; 28:1189-1222. [PMID: 34276140 PMCID: PMC8275905 DOI: 10.1007/s00530-021-00818-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 05/29/2021] [Indexed: 05/05/2023]
Abstract
The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.
Collapse
Affiliation(s)
- Het Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Saiyam Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Sudeep Tanwar
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Rajesh Gupta
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Neeraj Kumar
- Department of Computer Science Engineering, Thapar Institute of Engineering and Technology, Deemed to be University, Patiala, India
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand India
- King Abdul Aziz University, Jeddah, Saudi Arabia
| |
Collapse
|
22
|
Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
Collapse
|
23
|
Chang Z, Zhan Z, Zhao Z, You Z, Liu Y, Yan Z, Fu Y, Liang W, Zhao L. Application of artificial intelligence in COVID-19 medical area: a systematic review. J Thorac Dis 2021; 13:7034-7053. [PMID: 35070385 PMCID: PMC8743418 DOI: 10.21037/jtd-21-747] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 09/02/2021] [Indexed: 01/08/2023]
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics. METHODS We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic. RESULTS We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice. DISCUSSION This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians' role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.
Collapse
Affiliation(s)
- Zhoulin Chang
- College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan, China
| | - Zhiqing Zhan
- The Third Clinical College, Guangzhou Medical University, Guangzhou, China
| | - Zifan Zhao
- Nanshan College, Guangzhou Medical University, Guangzhou, China
| | - Zhixuan You
- Nanshan College, Guangzhou Medical University, Guangzhou, China
| | - Yang Liu
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Zhihong Yan
- Kuangji Medical Technology (Guangdong Hengqin) Co., Ltd., Zhuhai, China
| | - Yong Fu
- Kuangji Medical Technology (Guangdong Hengqin) Co., Ltd., Zhuhai, China
| | - Wenhua Liang
- Department of Thoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lei Zhao
- Department of Physiology, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
| |
Collapse
|
24
|
Tayara H, Abdelbaky I, To Chong K. Recent omics-based computational methods for COVID-19 drug discovery and repurposing. Brief Bioinform 2021; 22:6355836. [PMID: 34423353 DOI: 10.1093/bib/bbab339] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 07/09/2021] [Indexed: 12/22/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.
Collapse
Affiliation(s)
- Hilal Tayara
- School of international Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Jeollabukdo 54896, Republic of Korea.,Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
| |
Collapse
|
25
|
Mullick B, Magar R, Jhunjhunwala A, Barati Farimani A. Understanding mutation hotspots for the SARS-CoV-2 spike protein using Shannon Entropy and K-means clustering. Comput Biol Med 2021; 138:104915. [PMID: 34655896 PMCID: PMC8492016 DOI: 10.1016/j.compbiomed.2021.104915] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/17/2021] [Accepted: 09/29/2021] [Indexed: 12/16/2022]
Abstract
The SARS-CoV-2 virus like many other viruses has transformed in a continual manner to give rise to new variants by means of mutations commonly through substitutions and indels. These mutations in some cases can give the virus a survival advantage making the mutants dangerous. In general, laboratory investigation must be carried to determine whether the new variants have any characteristics that can make them more lethal and contagious. Therefore, complex and time-consuming analyses are required in order to delve deeper into the exact impact of a particular mutation. The time required for these analyses makes it difficult to understand the variants of concern and thereby limiting the preventive action that can be taken against them spreading rapidly. In this analysis, we have deployed a statistical technique Shannon Entropy, to identify positions in the spike protein of SARS Cov-2 viral sequence which are most susceptible to mutations. Subsequently, we also use machine learning based clustering techniques to cluster known dangerous mutations based on similarities in properties. This work utilizes embeddings generated using language modeling, the ProtBERT model, to identify mutations of a similar nature and to pick out regions of interest based on proneness to change. Our entropy-based analysis successfully predicted the fifteen hotspot regions, among which we were able to validate ten known variants of interest, in six hotspot regions. As the situation of SARS-COV-2 virus rapidly evolves we believe that the remaining nine mutational hotspots may contain variants that can emerge in the future. We believe that this may be promising in helping the research community to devise therapeutics based on probable new mutation zones in the viral sequence and resemblance in properties of various mutations.
Collapse
Affiliation(s)
- Baishali Mullick
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Rishikesh Magar
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Aastha Jhunjhunwala
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Amir Barati Farimani
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA,Corresponding author
| |
Collapse
|
26
|
Hu F, Huang M, Sun J, Zhang X, Liu J. An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2021; 73:11-21. [PMID: 33679271 PMCID: PMC7919532 DOI: 10.1016/j.inffus.2021.02.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/18/2020] [Accepted: 02/21/2021] [Indexed: 05/04/2023]
Abstract
Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.
Collapse
Affiliation(s)
- Fang Hu
- College of Information Engineering, Hubei University of Chinese Medicine, Wuhan 430065, PR China
- Department of Mathematics and Statistics, University of West Florida, Pensacola 32514, USA
| | - Mingfang Huang
- College of Information Engineering, Hubei University of Chinese Medicine, Wuhan 430065, PR China
| | - Jing Sun
- Department of Data Center, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430060, PR China
| | - Xiong Zhang
- Department of Geriatrics, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430060, PR China
| | - Jifen Liu
- Department of Data Center, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430060, PR China
| |
Collapse
|
27
|
Santo AAE, Feliciano GT. Genetic Algorithms Applied to Thermodynamic Rational Design of Mimetic Antibodies Based on the GB1 Domain of Streptococcal Protein G: An Atomistic Simulation Study. J Phys Chem B 2021; 125:7985-7996. [PMID: 34264671 DOI: 10.1021/acs.jpcb.1c03324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The development of mimetic antibodies (MA) capable of combining the high affinity and selectivity of antibodies with the small size of the peptides has enormous potential for applications in current biotechnology. In this work, we demonstrate that in silico MA design is possible through genetic algorithms (GA) developed from shell scripts capable of combining software commonly used for atomistic simulation. Our results demonstrate that, using the GB1 domain of the streptococcal G protein as a model, it is possible to optimize the molecular recognition capacity of a large MA population in a few generations. In the first case, GA was able to generate 10 MA with binding free energy (BFE) less than the vascular endothelial cell growth factor conjugated with the fms-type tyrosine kinase receptor. In the second case, it generated 13 MA with BFE less than that of the hepatitis C-E2 viral envelope conjugate with the antibody. Through the GA developed in this work, we demonstrate the use of a new protocol, capable of guiding experimental methods for the design of bioactive peptides that can assist in the development of new therapeutic molecules.
Collapse
Affiliation(s)
- Anderson A E Santo
- Institute of Chemistry, São Paulo State University, Araraquara, SP, Brazil
| | | |
Collapse
|
28
|
Applications of Machine Learning and High-Performance Computing in the Era of COVID-19. APPLIED SYSTEM INNOVATION 2021. [DOI: 10.3390/asi4030040] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a vital role. These technologies helped to contain the disease’s spread by predicting contaminated people/places, as well as forecasting future trends. In this article, we provide insights into the applications of machine learning (ML) and high-performance computing (HPC) in the era of COVID-19. We discuss the person-specific data that are being collected to lower the COVID-19 spread and highlight the remarkable opportunities it provides for knowledge extraction leveraging low-cost ML and HPC techniques. We demonstrate the role of ML and HPC in the context of the COVID-19 era with the successful implementation or proposition in three contexts: (i) ML and HPC use in the data life cycle, (ii) ML and HPC use in analytics on COVID-19 data, and (iii) the general-purpose applications of both techniques in COVID-19’s arena. In addition, we discuss the privacy and security issues and architecture of the prototype system to demonstrate the proposed research. Finally, we discuss the challenges of the available data and highlight the issues that hinder the applicability of ML and HPC solutions on it.
Collapse
|
29
|
Nguyen DC, Ding M, Pathirana PN, Seneviratne A. Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:95730-95753. [PMID: 34812398 PMCID: PMC8545197 DOI: 10.1109/access.2021.3093633] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/27/2021] [Indexed: 05/02/2023]
Abstract
The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.
Collapse
Affiliation(s)
- Dinh C. Nguyen
- School of EngineeringDeakin UniversityWaurn PondsVIC3216Australia
| | | | | | - Aruna Seneviratne
- School of Electrical Engineering and TelecommunicationsUniversity of New South Wales (UNSW)SydneyNSW2052Australia
| |
Collapse
|
30
|
El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu JW, El-Sappagh S. Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics (Basel) 2021; 11:1155. [PMID: 34202587 PMCID: PMC8303306 DOI: 10.3390/diagnostics11071155] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/11/2022] Open
Abstract
Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
Collapse
Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Samir Abdelrazik
- Information System Department, Faculty of Computer Science and Information Systems, Mansoura University, Mansoura 13518, Egypt;
| | - Tamer Abuhmed
- College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea
| | - Eslam Amer
- Faculty of Computer Science, Misr International University, Cairo 11828, Egypt;
| | - Farman Ali
- Department of Software, Sejong University, Seoul 05006, Korea;
| | - Jong-Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| |
Collapse
|
31
|
Riahi S, Lee JH, Wei S, Cost R, Masiero A, Prades C, Olfati-Saber R, Wendt M, Park A, Qiu Y, Zhou Y. Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers. Antib Ther 2021; 4:109-122. [PMID: 34396040 PMCID: PMC8344454 DOI: 10.1093/abt/tbab011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 01/07/2023] Open
Abstract
As the COVID-19 pandemic continues to spread, hundreds of new initiatives including
studies on existing medicines are running to fight the disease. To deliver a potentially
immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new
collaborations and ways of sharing are required to create as many paths forward as
possible. Here, we leverage our expertise in computational antibody engineering to
rationally design/engineer three previously reported SARS-CoV neutralizing antibodies and
share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing
antibodies, m396, 80R and CR-3022 were chosen as templates due to their diversified
epitopes and confirmed neutralization potency against SARS-CoV (but not SARS-CoV-2 except
for CR3022). Structures of variable fragment (Fv) in complex with receptor binding domain
(RBD) from SARS-CoV or SARS-CoV-2 were subjected to our established in silico antibody
engineering platform to improve their binding affinity to SARS-CoV-2 and developability
profiles. The selected top mutations were ensembled into a focused library for each
antibody for further screening. In addition, we convert the selected binders with
different epitopes into the trispecific format, aiming to increase potency and to prevent
mutational escape. Lastly, to avoid antibody-induced virus activation or enhancement, we
suggest application of NNAS and DQ mutations to the Fc region to eliminate effector
functions and extend half-life.
Collapse
Affiliation(s)
- Saleh Riahi
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Jae Hyeon Lee
- Data & Data Science, Sanofi, Cambridge, MA, United States
| | - Shuai Wei
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Robert Cost
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | | | | | | | - Maria Wendt
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Anna Park
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Yu Qiu
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Yanfeng Zhou
- Large Molecule Research, Sanofi, Framingham, MA, United States
| |
Collapse
|
32
|
Mustafa DAM, Saida L, Latifi D, Wismans LV, de Koning W, Zeneyedpour L, Luider TM, van den Hoogen B, van Eijck CHJ. Rintatolimod Induces Antiviral Activities in Human Pancreatic Cancer Cells: Opening for an Anti-COVID-19 Opportunity in Cancer Patients? Cancers (Basel) 2021; 13:cancers13122896. [PMID: 34207861 PMCID: PMC8227153 DOI: 10.3390/cancers13122896] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/31/2021] [Accepted: 06/02/2021] [Indexed: 01/08/2023] Open
Abstract
Simple Summary Specific treatment for COVID-19 infections in cancer patients is lacking while the demand for treatment is increasing. Therefore, we explored the effect of Rintatolimod, a Toll-like receptor 3 (TLR3) agonist, on human epithelial cancerous cells. Our results demonstrated that Rintatolimod stimulated an anti-viral effect by producing RNase L that blocks virus replication. Moreover, Rintatolimod activated the innate and the adaptive immune systems by activating a cascade of actions in human cancerous cells. We believe that Rintatolimod should be considered in the treatment regimens of cancer patients who suffer from SARS-CoV-2 infection. Abstract Severe acute respiratory virus-2 (SARS-CoV-2) has spread globally leading to a devastating loss of life. Large registry studies have begun to shed light on the epidemiological and clinical vulnerabilities of cancer patients who succumb to or endure poor outcomes of SARS-CoV-2. Specific treatment for COVID-19 infections in cancer patients is lacking while the demand for treatment is increasing. Therefore, we explored the effect of Rintatolimod (Ampligen®) (AIM ImmunoTech, Ocala, FL, USA), a Toll-like receptor 3 (TLR3) agonist, to treat uninfected human pancreatic cancer cells (HPACs). The direct effect of Rintatolimod was measured by targeted gene expression profiling and by proteomics measurements. Our results show that Rintatolimod induces an antiviral effect in HPACs by inducing RNase-L-dependent and independent pathways of the innate immune system. Treatment with Rintatolimod activated the interferon signaling pathway, leading to the overexpression of several cytokines and chemokines in epithelial cells. Furthermore, Rintatolimod treatment increased the expression of angiogenesis-related genes without promoting fibrosis, which is the main cause of death in patients with COVID-19. We conclude that Rintatolimod could be considered an early additional treatment option for cancer patients who are infected with SARS-CoV-2 to prevent the complicated severity of the disease.
Collapse
Affiliation(s)
- Dana A. M. Mustafa
- Department of Pathology, The Tumor Immuno-Pathology (TIP) Laboratory, Erasmus University Medical Center, 3015 Rotterdam, The Netherlands;
| | - Lawlaw Saida
- Department of Surgery, The Tumor Immuno-Pathology (TIP) Laboratory, Erasmus University Medical Center, 3015 Rotterdam, The Netherlands; (L.S.); (D.L.); (L.V.W.)
| | - Diba Latifi
- Department of Surgery, The Tumor Immuno-Pathology (TIP) Laboratory, Erasmus University Medical Center, 3015 Rotterdam, The Netherlands; (L.S.); (D.L.); (L.V.W.)
| | - Leonoor V. Wismans
- Department of Surgery, The Tumor Immuno-Pathology (TIP) Laboratory, Erasmus University Medical Center, 3015 Rotterdam, The Netherlands; (L.S.); (D.L.); (L.V.W.)
| | - Willem de Koning
- Clinical Bioinformatics Unit, Department of Pathology, The Tumor Immuno-Pathology (TIP) Laboratory, Erasmus University Medical Center, 3015 Rotterdam, The Netherlands;
| | - Lona Zeneyedpour
- Department of Neurology, Clinical and Cancer Proteomics, Erasmus University Medical Center, 3015 Rotterdam, The Netherlands; (L.Z.); (T.M.L.)
| | - Theo M. Luider
- Department of Neurology, Clinical and Cancer Proteomics, Erasmus University Medical Center, 3015 Rotterdam, The Netherlands; (L.Z.); (T.M.L.)
| | | | - Casper H. J. van Eijck
- Department of Surgery, The Tumor Immuno-Pathology (TIP) Laboratory, Erasmus University Medical Center, 3015 Rotterdam, The Netherlands; (L.S.); (D.L.); (L.V.W.)
- Correspondence: ; Tel.: +31-1-7044329
| |
Collapse
|
33
|
Kombe Kombe AJ, Zahid A, Mohammed A, Shi R, Jin T. Potent Molecular Feature-based Neutralizing Monoclonal Antibodies as Promising Therapeutics Against SARS-CoV-2 Infection. Front Mol Biosci 2021; 8:670815. [PMID: 34136533 PMCID: PMC8201996 DOI: 10.3389/fmolb.2021.670815] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 05/06/2021] [Indexed: 12/23/2022] Open
Abstract
The 2019-2020 winter was marked by the emergence of a new coronavirus (SARS-CoV-2) related disease (COVID-19), which started in Wuhan, China. Its high human-to-human transmission ability led to a worldwide spread within few weeks and has caused substantial human loss. Mechanical antiviral control approach, drug repositioning, and use of COVID-19 convalescent plasmas (CPs) were the first line strategies utilized to mitigate the viral spread, yet insufficient. The urgent need to contain this deadly pandemic has led searchers and pharmaceutical companies to develop vaccines. However, not all vaccines manufactured are safe. Besides, an alternative and effective treatment option for such an infectious disease would include pure anti-viral neutralizing monoclonal antibodies (NmAbs), which can block the virus at specific molecular targets from entering cells by inhibiting virus-cell structural complex formation, with more safety and efficiency than the CP. Indeed, there is a lot of molecular evidence about the protector effect and the use of molecular feature-based NmAbs as promising therapeutics to contain COVID-19. Thus, from the scientific publication database screening, we here retrieved antibody-related papers and summarized the repertory of characterized NmAbs against SARS-CoV-2, their molecular neutralization mechanisms, and their immunotherapeutic pros and cons. About 500 anti-SARS-CoV-2 NmAbs, characterized through competitive binding assays and neutralization efficacy, were reported at the writing time (January 2021). All NmAbs bind respectively to SARS-CoV-2 S and exhibit high molecular neutralizing effects against wild-type and/or pseudotyped virus. Overall, we defined six NmAb groups blocking SARS-CoV-2 through different molecular neutralization mechanisms, from which five potential neutralization sites on SARS-CoV-2 S protein are described. Therefore, more efforts are needed to develop NmAbs-based cocktails to mitigate COVID-19.
Collapse
Affiliation(s)
- Arnaud John Kombe Kombe
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ayesha Zahid
- Hefei National Laboratory for Physical Sciences at Microscale, The CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ahmed Mohammed
- Hefei National Laboratory for Physical Sciences at Microscale, The CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ronghua Shi
- Hefei National Laboratory for Physical Sciences at Microscale, The CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Tengchuan Jin
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Laboratory for Physical Sciences at Microscale, The CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Science, Shanghai, China
| |
Collapse
|
34
|
Dong Y, Yao YD. IoT Platform for COVID-19 Prevention and Control: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:49929-49941. [PMID: 34812390 PMCID: PMC8545211 DOI: 10.1109/access.2021.3068276] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/09/2021] [Indexed: 05/18/2023]
Abstract
As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and low vaccination rates, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.
Collapse
Affiliation(s)
- Yudi Dong
- Department of Electrical and Computer EngineeringStevens Institute of TechnologyHobokenNJ07030USA
| | - Yu-Dong Yao
- Department of Electrical and Computer EngineeringStevens Institute of TechnologyHobokenNJ07030USA
| |
Collapse
|
35
|
Nguyen DC, Ding M, Pathirana PN, Seneviratne A. Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:95730-95753. [PMID: 34812398 DOI: 10.20944/preprints202004.0325.v1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/27/2021] [Indexed: 05/21/2023]
Abstract
The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.
Collapse
Affiliation(s)
- Dinh C Nguyen
- School of EngineeringDeakin University Waurn Ponds VIC 3216 Australia
| | - Ming Ding
- Data61CSIRO Eveleigh NSW 2015 Australia
| | | | - Aruna Seneviratne
- School of Electrical Engineering and TelecommunicationsUniversity of New South Wales (UNSW) Sydney NSW 2052 Australia
| |
Collapse
|
36
|
Poongodi M, Malviya M, Hamdi M, Rauf HT, Kadry S, Thinnukool O. The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:97906-97928. [PMID: 34812400 PMCID: PMC8545196 DOI: 10.1109/access.2021.3094400] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 06/29/2021] [Indexed: 05/06/2023]
Abstract
Different epidemics, specially Coronavirus, have caused critical misfortunes in various fields like monetary deprivation, survival conditions, thus diminishing the overall individual fulfillment. Various worldwide associations and different hierarchies of government fraternity are endeavoring to offer the necessary assistance in eliminating the infection impacts but unfortunately standing up to the non-appearance of resources and expertise. In contrast to all other pandemics, Coronavirus has proven to exhibit numerous requirements such that curated appropriation and determination of innovations are required to deal with the vigorous undertakings, which include precaution, detection, and medication. Innovative advancements are essential for the subsequent pandemics where-in the forthcoming difficulties can indeed be approached to such a degree that it facilitates constructive solutions more comprehensively. In this study, futuristic and emerging innovations are analyzed, improving COVID-19 effects for the general public. Large data sets need to be advanced so that extensive models related to deep analysis can be used to combat Coronavirus infection, which can be done by applying Artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to varying processing files. This article aims to furnish variation sets of innovations that can be utilized to eliminate COVID-19 and serve as a resource for the coming generations. At last, elaboration associated with future state-of-the-art technologies and the attainable sectors of AI methodologies has been mentioned concerning the post-COVID-19 world to enable the different ideas for dealing with the pandemic-based difficulties.
Collapse
Affiliation(s)
- M Poongodi
- College of Science and EngineeringHamad Bin Khalifa University, Qatar Foundation Doha Qatar
| | - Mohit Malviya
- Department of CTO 5GWipro Ltd. Bengaluru 560035 India
| | - Mounir Hamdi
- College of Science and EngineeringHamad Bin Khalifa University, Qatar Foundation Doha Qatar
| | - Hafiz Tayyab Rauf
- Centre for Smart SystemsAI and Cybersecurity, Staffordshire University Stoke-on-Trent ST4 2DE U.K
| | - Seifedine Kadry
- Faculty of Applied Computing and TechnologyNoroff University College 4608 Kristiansand Norway
| | - Orawit Thinnukool
- Research Group of Embedded Systems and Mobile Application in Health Science, College of Arts, Media and TechnologyChiang Mai University Chiang Mai 50200 Thailand
| |
Collapse
|
37
|
Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:130820-130839. [PMID: 34812339 DOI: 10.13140/rg.2.2.23518.38727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 07/11/2020] [Indexed: 05/24/2023]
Abstract
The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.
Collapse
Affiliation(s)
- Quoc-Viet Pham
- Research Institute of Computer, Information and CommunicationPusan National University Busan 46241 South Korea
| | - Dinh C Nguyen
- School of EngineeringDeakin University Waurn Ponds VIC 3216 Australia
| | - Thien Huynh-The
- ICT Convergence Research CenterKumoh National Institute of Technology Gumi 39177 South Korea
| | - Won-Joo Hwang
- Department of Biomedical Convergence EngineeringPusan National University Busan 46241 South Korea
- Department of Information Convergence Engineering (Artificial Intelligence)Pusan National University Busan 46241 South Korea
| | | |
Collapse
|
38
|
Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:130820-130839. [PMID: 34812339 PMCID: PMC8545324 DOI: 10.1109/access.2020.3009328] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 07/11/2020] [Indexed: 05/18/2023]
Abstract
The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.
Collapse
Affiliation(s)
- Quoc-Viet Pham
- Research Institute of Computer, Information and CommunicationPusan National UniversityBusan46241South Korea
| | - Dinh C. Nguyen
- School of EngineeringDeakin UniversityWaurn PondsVIC3216Australia
| | - Thien Huynh-The
- ICT Convergence Research CenterKumoh National Institute of TechnologyGumi39177South Korea
| | - Won-Joo Hwang
- Department of Biomedical Convergence EngineeringPusan National UniversityBusan46241South Korea
- Department of Information Convergence Engineering (Artificial Intelligence)Pusan National UniversityBusan46241South Korea
| | | |
Collapse
|