Advanced Deep Learning Methods for Protein Structure Prediction and Design

• URL: http://arxiv.org/abs/2503.13522v3
• Date: Sat, 29 Mar 2025 13:08:27 GMT
• Title: Advanced Deep Learning Methods for Protein Structure Prediction and Design
• Authors: Yichao Zhang, Ningyuan Deng, Xinyuan Song, Ziqian Bi, Tianyang Wang, Zheyu Yao, Keyu Chen, Ming Li, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Liu, Li Zhang, Xuanhe Pan, Jinlang Wang, Pohsun Feng, Yizhu Wen, Lawrence KQ Yan, Hongming Tseng, Yan Zhong, Yunze Wang, Ziyuan Qin, Bowen Jing, Junjie Yang, Jun Zhou, Chia Xin Liang, Junhao Song,
• Abstract summary: We comprehensively explore advanced deep learning methods applied to protein structure prediction and design.<n>The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture.<n> Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored.
• Score: 28.575821996185024
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students.

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