Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction
University of Cambridge（剑桥大学）；The National Institute of Agricultural Botany（英国国家农业植物学研究所）；University College London（伦敦大学学院）；Alan Turing Institute（艾伦·图灵研究所）；Chalmers University（查尔姆斯理工大学）
Abbi Abdel-Rehim, Oghenejokpeme Orhobor, Hang Lou, Hao Ni, Ross D. King
The goal of Protein Structure Prediction (PSP) problem is to predict a protein's 3D structure (confirmation) from its amino acid sequence. The problem has been a 'holy grail' of science since the Noble prize-winning work of Anfinsen demonstrated that protein conformation was determined by sequence. A recent and important step towards this goal was the development of AlphaFold2, currently the best PSP method. AlphaFold2 is probably the highest profile application of AI to science. Both AlphaFold2 and RoseTTAFold (another impressive PSP method) have been published and placed in the public domain (code & models). Stacking is a form of ensemble machine learning ML in which multiple baseline models are first learnt, then a meta-model is learnt using the outputs of the baseline level model to form a model that outperforms the base models. Stacking has been successful in many applications. We developed the ARStack PSP method by stacking AlphaFold2 and RoseTTAFold. ARStack significantly outperforms AlphaFold2. We rigorously demonstrate this using two sets of non-homologous proteins, and a test set of protein structures published after that of AlphaFold2 and RoseTTAFold. As more high quality prediction methods are published it is likely that ensemble methods will increasingly outperform any single method.
Improving Protein Sequence Design through Designability Preference Optimization
University of Technology Sydney（技术科技大学）；University of Washington（华盛顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Zhejiang University（浙江大学）
Improving Protein Optimization with Smoothed Fitness Landscapes
Massachusetts Institute of Technology（麻省理工学院）；Czech Academy of Sciences（捷克科学院）；Czech Technical University in Prague（捷克布拉格技术大学）；University of Chemistry and Technology, Prague（布拉格化学与技术大学）
Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction
From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；School of Medicine, Tsinghua University（清华大学医学院）；KTH Stockholm（瑞典皇家理工学院）；Microsoft Research Asia（微软亚洲研究院）；School of Life Science and Technology, Xidian University（西安电子科技大学生命科学技术学院）；School of Engineering, Westlake University（西湖大学工程学院）
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
Shanghai AI Laboratory（上海人工智能实验室）；Mila, Université de Montréal（蒙特利尔大学米拉研究所）；Zelixir Biotech（泽列西尔生物科技公司）；Fudan University（复旦大学）；The Chinese University of Hong Kong（香港中文大学）
Improving homology-directed repair by small molecule agents for genetic engineering in unconventional yeast? -- Learning from the engineering of mammalian systems
Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
NVIDIA（英伟达）；University of Oxford（牛津大学）；Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；HEC Montréal（蒙特利尔高等商学院）；CIFAR AI Chair（加拿大国际人工智能研究院（CIFAR）AI讲席教授职位）
Evolutionary Profiles for Protein Fitness Prediction
Zhejiang University（浙江大学）；Peking University（北京大学）；East China University of Science and Technology（华东理工大学）；Chengdu University of Information Technology（成都信息工程大学）；Massachusetts Institute of Technology（麻省理工学院）；Zhejiang University of Technology（浙江工业大学）
Department of Artificial Intelligence, Nostrum Biodiscovery（人工智能系，诺斯特姆生物发现）；Department of Drug Discovery, Nostrum Biodiscovery（药物发现系，诺斯特姆生物发现）；Department of R&D, Nostrum Biodiscovery（研发部，诺斯特姆生物发现）
