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| <title>UniKP Kinetic Values Prediction</title> | |
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| <h1>UniKP Kinetic Values Prediction</h1> | |
| <p> <a href="/docs" target="_blank">API Usage</a></p> | |
| <div class="box"> | |
| <form id="predictionForm"> | |
| <label for="sequence">Sequence:</label> | |
| <textarea id="sequence" name="sequence"></textarea> | |
| <label for="smiles">SMILES:</label><br /> | |
| <textarea id="smiles" name="smiles"></textarea> | |
| <input type="submit" value="Submit" /> | |
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| <div id="predictionResults" class="box"></div> | |
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| <h2>UniKP</h2> | |
| <h3>What is UniKP?</h3> | |
| <p> | |
| UniKP is a unified framework for the prediction of enzyme | |
| kinetic parameters. It is a machine learning model that predicts | |
| the kinetic parameters of enzymes based on their amino acid | |
| sequences and SMILES representations of a substrate. | |
| </p> | |
| <h3>Why is this interesting?</h3> | |
| <p> | |
| UniKP can be used to predict the kinetic parameters of enzymes, | |
| which can be used for feature extraction in enzyme engineering. | |
| Knowing the kinetic parameters of an enzyme can help to | |
| understand its function and can be used to optimize its | |
| performance. | |
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| <h2>References</h2> | |
| <ul> | |
| <li> | |
| <a | |
| href="https://github.com/Luo-SynBioLab/UniKP" | |
| target="_blank" | |
| ><img | |
| class="devicon" | |
| src="https://cdn.jsdelivr.net/gh/devicons/devicon@latest/icons/github/github-original-wordmark.svg" | |
| alt="UniKP" | |
| /></a> | |
| </li> | |
| <li> | |
| <a | |
| href="https://www.nature.com/articles/s41467-023-44113-1" | |
| target="_blank" | |
| >Yu, H., Deng, H., He, J. et al. UniKP: a unified | |
| framework for the prediction of enzyme kinetic | |
| parameters. Nat Commun 14, 8211 (2023)</a | |
| > | |
| </li> | |
| <li><a href="https://www.ml6.eu/" target="_blank">ML6</a></li> | |
| <li> | |
| <a href="https://www.decypher.bio/" target="_blank" | |
| >deCYPher</a | |
| > | |
| </li> | |
| </ul> | |
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