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Accurate and timely detection of disease biomarkers is crucial for effective diagnosis and treatment across various medical conditions. Traditional methods, such as SELEX, used for screening Aptamers that bind to specific biomarkers, are often slow, costly, and lack the precision needed for diverse applications. To overcome these limitations, we introduce MAGA: Make Aptamer Generally Applied—a universal machine learning-based platform designed to predict Aptamer sequences that can target a wide range of disease biomarkers with high specificity and affinity. MAGA_A thrombin aptamer is derived by inputting the thrombin sequence into the MAGA model.
 
Accurate and timely detection of disease biomarkers is crucial for effective diagnosis and treatment across various medical conditions. Traditional methods, such as SELEX, used for screening Aptamers that bind to specific biomarkers, are often slow, costly, and lack the precision needed for diverse applications. To overcome these limitations, we introduce MAGA: Make Aptamer Generally Applied—a universal machine learning-based platform designed to predict Aptamer sequences that can target a wide range of disease biomarkers with high specificity and affinity. MAGA_A thrombin aptamer is derived by inputting the thrombin sequence into the MAGA model.
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The MAGA methodology consists of three parts:
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1.'''Pretrained Feature Extractor''':
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We pretrained protein and nucleic acid feature extractors (encoders) using extensive datasets from publicly available sources such as the Protein Data Bank (PDB) and GenBank. These encoders are designed to capture the complex structural and sequence-based features of proteins and nucleic acids, forming the foundation of our predictive modeling.
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2.'''Affinity Prediction Model''':
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The second component of MAGA involves a predictive model trained to estimate the binding affinity between proteins and corresponding Aptamers. By leveraging the extracted features from the pretrained encoders, our model accurately predicts the strength and specificity of the Aptamer-protein interactions, providing crucial insights into their potential as biomarkers.
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3.'''Monte Carlo Search Optimization''':
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To identify the optimal Aptamer sequences for specific proteins, we employed a Monte Carlo search strategy. This approach systematically explores the sequence space, guided by the predicted affinities, to find the most suitable Aptamers. This method ensures that our predictions are not only accurate but also optimized for practical use in various diagnostic applications.
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'''Figure 1 | The interaction of the Thrombin_HD22_29mer with human thrombin.''' (a) the site of crosslinking between thrombin_HD22_29mer and thrombin. The crosslinked tryptic peptide of thrombin is shown in boldface letters, and the arrow denotes the crosslink between T12 within the G-quadruplex of the DNA and Phe245 of thrombin. (b) the ribbon diagram of human thrombin, shown in purple, interacted with Thrombin_HD22_29mer in blue. (Modified from [1])<br>
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'''Figure 1 | The methodology of MAGA.'''<br>
  
 
===Characterization===
 
===Characterization===
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After thrombin and aptamers were diluted with proper buffer, reaction systems were built with a gradient of aptamers. MAGA_A and MAGA_C aptamers were tested, and a gradient of concentration of thrombin were applied to reflect the binding affinity. After the aptamers were co-incubated with thrombin for 60 min, an 12% non-denaturing polyacrylamide gel electrophoresis was performed. The gel was then stained by fluorescent dye. GelRed was used as the DNA dye.  
 
After thrombin and aptamers were diluted with proper buffer, reaction systems were built with a gradient of aptamers. MAGA_A and MAGA_C aptamers were tested, and a gradient of concentration of thrombin were applied to reflect the binding affinity. After the aptamers were co-incubated with thrombin for 60 min, an 12% non-denaturing polyacrylamide gel electrophoresis was performed. The gel was then stained by fluorescent dye. GelRed was used as the DNA dye.  
  
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The electrophoresis showed that the aptamers showed strong binding affinity ('''Figure 2'''). The shift bands became more clear as the concentration of thrombin increased. 15-mer and 29-mer aptamers had a clear shift band and a clear non-shift band. 40-mer aptamer was suspected to form multimers, causing a strong band at the sampling hole and unclear bands at the target sites.
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The electrophoresis showed that the aptamers showed strong binding affinity ('''Figure 2'''). The shift bands became more clear as the concentration of thrombin increased, and the free bands became less.
  
 
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'''Figure 3 | Native-PAGE results of EMSA.''' In the two figures, lane 1, marker; lane 2, control group with 15-mer, 29-mer and 40-mer aptamers and NO thrombin. All aptamers were at a concentration of 10 pM. A: lane 3-5, 0.45 pM thrombin incubated with respectively 29-mer+40-mer, 15-mer+40-mer, 15-mer+29-mer; lane 6-8, 15-mer aptamer incubated with gradient thrombin concentration of 0.45, 0.9 and 1.8 pM. B: lane 3-5, 29-mer aptamer incubated with gradient thrombin concentration of 0.45, 0.9 and 1.8 pM.; lane 6-8, 40-mer aptamer incubated with gradient thrombin concentration of 0.45, 0.9 and 1.8 pM.
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'''Figure 2 | Native-PAGE results of EMSA.''' lane 1, marker; lane 2, control group with MAGA_A thrombin aptamer and NO thrombin; lane 6, control group with MAGA_C thrombin aptamer and NO thrombin. All aptamers were at a concentration of 10 pM. lane 3-5, MAGA_A thrombin aptamer incubated with gradient thrombin concentration of 2.0, 4.0 and 8.0 μM. lane 7-9, MAGA_C thrombin aptamer incubated with gradient thrombin concentration of 2.0, 4.0 and 8.0 μM.
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===References===
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1. Tasset, D. M., Kubik, M. F., & Steiner, W. (1997). Oligonucleotide inhibitors of human thrombin that bind distinct epitopes. ''Journal of molecular biology'', ''272''(5), 688–698. https://doi.org/10.1006/jmbi.1997.1275<br>
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Latest revision as of 06:08, 21 September 2024

MAGA_A thrombin aptamer

Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]

Usage and Biology

Accurate and timely detection of disease biomarkers is crucial for effective diagnosis and treatment across various medical conditions. Traditional methods, such as SELEX, used for screening Aptamers that bind to specific biomarkers, are often slow, costly, and lack the precision needed for diverse applications. To overcome these limitations, we introduce MAGA: Make Aptamer Generally Applied—a universal machine learning-based platform designed to predict Aptamer sequences that can target a wide range of disease biomarkers with high specificity and affinity. MAGA_A thrombin aptamer is derived by inputting the thrombin sequence into the MAGA model.

The MAGA methodology consists of three parts:

1.Pretrained Feature Extractor:

We pretrained protein and nucleic acid feature extractors (encoders) using extensive datasets from publicly available sources such as the Protein Data Bank (PDB) and GenBank. These encoders are designed to capture the complex structural and sequence-based features of proteins and nucleic acids, forming the foundation of our predictive modeling.

2.Affinity Prediction Model:

The second component of MAGA involves a predictive model trained to estimate the binding affinity between proteins and corresponding Aptamers. By leveraging the extracted features from the pretrained encoders, our model accurately predicts the strength and specificity of the Aptamer-protein interactions, providing crucial insights into their potential as biomarkers.

3.Monte Carlo Search Optimization:

To identify the optimal Aptamer sequences for specific proteins, we employed a Monte Carlo search strategy. This approach systematically explores the sequence space, guided by the predicted affinities, to find the most suitable Aptamers. This method ensures that our predictions are not only accurate but also optimized for practical use in various diagnostic applications.



Figure 1 | The methodology of MAGA.

Characterization

Electrophoretic mobility shift assay (EMSA)

An electrophoretic mobility shift assay (EMSA) is a common affinity electrophoresis technique used to study protein-DNA or protein-RNA interactions. This procedure can determine if a protein or mixture of proteins is capable of binding to a given DNA or RNA sequence. In the present study, EMSA was employed for affinity test of the aptamers.

After thrombin and aptamers were diluted with proper buffer, reaction systems were built with a gradient of aptamers. MAGA_A and MAGA_C aptamers were tested, and a gradient of concentration of thrombin were applied to reflect the binding affinity. After the aptamers were co-incubated with thrombin for 60 min, an 12% non-denaturing polyacrylamide gel electrophoresis was performed. The gel was then stained by fluorescent dye. GelRed was used as the DNA dye.

The electrophoresis showed that the aptamers showed strong binding affinity (Figure 2). The shift bands became more clear as the concentration of thrombin increased, and the free bands became less.

Figure 2 | Native-PAGE results of EMSA. lane 1, marker; lane 2, control group with MAGA_A thrombin aptamer and NO thrombin; lane 6, control group with MAGA_C thrombin aptamer and NO thrombin. All aptamers were at a concentration of 10 pM. lane 3-5, MAGA_A thrombin aptamer incubated with gradient thrombin concentration of 2.0, 4.0 and 8.0 μM. lane 7-9, MAGA_C thrombin aptamer incubated with gradient thrombin concentration of 2.0, 4.0 and 8.0 μM.