Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Prediction of Aptamer Affinity Using an Artificial Intelligence Approach Publisher Pubmed



Fallah A1 ; Havaei SA2 ; Sedighian H3 ; Kachuei R4 ; Fooladi AAI3
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Bacteriology and Virology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Microbiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Applied Microbiology Research Center, Biomedicine Technologies Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
  4. 4. Molecular Biology Research Center, Biomedicine Technologies Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran

Source: Journal of Materials Chemistry B Published:2024


Abstract

Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesized. Even if the SELEX approach has been improved a lot, it is frequently challenging and time-consuming to identify aptamers experimentally. In particular, structure-based methods are the most used in computer-aided design and development of aptamers. For this purpose, numerous web-based platforms have been suggested for the purpose of forecasting the secondary structure and 3D configurations of RNAs and DNAs. Also, molecular docking and molecular dynamics (MD), which are commonly utilized in protein compound selection by structural information, are suitable for aptamer selection. On the other hand, from a large number of sequences, artificial intelligence (AI) may be able to quickly discover the possible aptamer candidates. Conversely, sophisticated machine and deep-learning (DL) models have demonstrated efficacy in forecasting the binding properties between ligands and targets during drug discovery; as such, they may provide a reliable and precise method for forecasting the binding of aptamers to targets. This research looks at advancements in AI pipelines and strategies for aptamer binding ability prediction, such as machine and deep learning, as well as structure-based approaches, molecular dynamics and molecular docking simulation methods. © 2024 The Royal Society of Chemistry.
Related Docs
View other Related Docs
1. Recent Advances in Aptamer Discovery, Modification and Improving Performance, Biochemistry and Biophysics Reports (2024)
2. Identification of Potential Vascular Endothelial Growth Factor Receptor Inhibitors Via Tree-Based Learning Modeling and Molecular Docking Simulation, Journal of Chemometrics (2024)
3. Identification of Novel 3-Hydroxy-Pyran-4-One Derivatives As Potent Hiv-1 Integrase Inhibitors Using in Silico Structure-Based Combinatorial Library Design Approach, Frontiers in Chemistry (2019)
4. Accelerating Big Data Analysis Through Lasso-Random Forest Algorithm in Qsar Studies, Bioinformatics (2022)
5. Linear and Nonlinear Qsar Modeling of 1,3,8-Substituted-9-Deazaxanthines As Potential Selective A2bar Antagonists, Medicinal Chemistry Research (2013)
6. Aptamers, the Bivalent Agents As Probes and Therapies for Coronavirus Infections: A Systematic Review, Molecular and Cellular Probes (2020)
7. Real-Time Pcr: An Appropriate Approach to Confirm Ssdna Generation From Pcr Product in Selex Process, Iranian Journal of Biotechnology (2017)
8. A Quantitative Structure-Activity Relationship (Qsar) Study of Some Diaryl Urea Derivatives of B-Raf Inhibitors, Research in Pharmaceutical Sciences (2016)
Experts (# of related papers)
View all Related Experts
Afshin Fassihi (12)
Lotfollah Saghaie Dehkordi (7)
Other Related Docs
9. 3D U-Net: A Voxel-Based Method in Binding Site Prediction of Protein Structure, Journal of Bioinformatics and Computational Biology (2021)
10. Insights Into the Human A1 Adenosine Receptor From Molecular Dynamics Simulation: Structural Study in the Presence of Lipid Membrane, Medicinal Chemistry Research (2015)
11. Exploring a Model of a Chemokine Receptor/Ligand Complex in an Explicit Membrane Environment by Molecular Dynamics Simulation: The Human Ccr1 Receptor, Journal of Chemical Information and Modeling (2011)
12. Classification and Similarity Analysis of Binding-Database: A Survey on Application of Multi-Class Classifiers for Deriving General Rules From Large Compound Databases, Journal of Isfahan Medical School (2017)
13. Qsar and Docking Studies of Some 1,2,3,4-Tetrahydropyrimidines: Evaluation of Gp41 As Possible Target for Anti-Hiv-1 Activity, Medicinal Chemistry Research (2015)
14. Selection and Characterization of Single-Stranded Dna Aptamers Against Interleukin-5, Research in Pharmaceutical Sciences (2019)
15. In Silico Activity of As1411 Aptamer Against Nucleolin of Cancer Cells, Iranian Journal of Blood and Cancer (2020)
16. Homology Modeling of Human Ccr5 and Analysis of Its Binding Properties Through Molecular Docking and Molecular Dynamics Simulation, Biochimica et Biophysica Acta - Biomembranes (2011)
17. Quantitative Structure Activities Relationships of Some 2-Mercaptoimidazoles As Ccr2 Inhibitors Using Genetic Algorithm-Artificial Neural Networks, Research in Pharmaceutical Sciences (2013)
18. Pyprotmodel: An Easy to Use Gui for Comparative Protein Modeling, Journal of Molecular Graphics and Modelling (2022)
19. Effect of Biomolecular Conformation on Docking Simulation: A Case Study on a Potent Hiv-1 Protease Inhibitor, Iranian Journal of Pharmaceutical Research (2015)
20. A Gu‑Net‑Based Architecture Predicting Ligand–Protein‑Binding Atoms, Journal of Medical Signals and Sensors (2023)
21. Discovery of Novel Rarα Agonists Using Pharmacophore-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Studies, PLoS ONE (2023)
22. Development of Α4 Integrin Dna Aptamer As a Potential Therapeutic Tool for Multiple Sclerosis, Journal of Cellular Biochemistry (2019)