Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence
The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing...
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| Published in | Molecules (Basel, Switzerland) Vol. 29; no. 19; p. 4626 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
01.10.2024
MDPI |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1420-3049 1420-3049 |
| DOI | 10.3390/molecules29194626 |
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| Abstract | The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology. |
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| AbstractList | The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology. The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology. |
| Audience | Academic |
| Author | Park, Yongho Kim, Hyunsoo Park, Jongham Son, Ahrum Lee, Sangwoon Yoon, Yoonki Kim, Woojin |
| AuthorAffiliation | 3 Department of Convergent Bioscience and Informatics, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea 4 Protein AI Design Institute, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea 1 Department of Molecular Medicine, Scripps Research, La Jolla, CA 92037, USA; ahson@scripps.edu 5 SCICS, Prove beyond AI, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea 2 Department of Bio-AI Convergence, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea; 975pjh@gmail.com (J.P.); woojin1544@gmail.com (W.K.); dbsrl0218@gmail.com (Y.Y.); sanguni088@gmail.com (S.L.); kmalrpkr13@gmail.com (Y.P.) |
| AuthorAffiliation_xml | – name: 5 SCICS, Prove beyond AI, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea – name: 1 Department of Molecular Medicine, Scripps Research, La Jolla, CA 92037, USA; ahson@scripps.edu – name: 3 Department of Convergent Bioscience and Informatics, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea – name: 4 Protein AI Design Institute, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea – name: 2 Department of Bio-AI Convergence, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea; 975pjh@gmail.com (J.P.); woojin1544@gmail.com (W.K.); dbsrl0218@gmail.com (Y.Y.); sanguni088@gmail.com (S.L.); kmalrpkr13@gmail.com (Y.P.) |
| Author_xml | – sequence: 1 givenname: Ahrum orcidid: 0000-0003-2706-1340 surname: Son fullname: Son, Ahrum – sequence: 2 givenname: Jongham orcidid: 0009-0001-5734-3800 surname: Park fullname: Park, Jongham – sequence: 3 givenname: Woojin orcidid: 0009-0001-2592-5486 surname: Kim fullname: Kim, Woojin – sequence: 4 givenname: Yoonki surname: Yoon fullname: Yoon, Yoonki – sequence: 5 givenname: Sangwoon surname: Lee fullname: Lee, Sangwoon – sequence: 6 givenname: Yongho surname: Park fullname: Park, Yongho – sequence: 7 givenname: Hyunsoo orcidid: 0000-0002-8441-3376 surname: Kim fullname: Kim, Hyunsoo |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39407556$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_arr_2025_102726 crossref_primary_10_1016_j_ijbiomac_2025_142136 crossref_primary_10_1016_j_compbiomed_2025_109833 |
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| Keywords | computational biology synthetic biology protein engineering de novo protein design molecular design artificial intelligence therapeutic proteins |
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