Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design. De facto methods utilize autoregressive generation, but this eschews higher order interactions that could be exploited to improve inference speed. We describe a non-autoregressive alternative that performs inference using a constant number of calls resulting in a 23 times speed up without a loss in performance on the CATH benchmark. Conditioned on the 3D structure, we fine-tune ProteinMPNN to perform discrete diffusion with a purity prior over the index sampling order. Our approach gives the flexibility in trading off inference speed and accuracy by modulating the diffusion speed. Code: https://github.com/johnyang101/pmpnndiff
Background: RNA exhibits a variety of structural configurations. Here we consider a structure to be tantamount to the noncrossing Watson-Crick and \pairGU -base pairings (secondary structure) and additional cross-serial base pairs. These interactions are called pseudoknots and are observed across the whole spectrum of RNA functionalities. In the context of studying natural RNA structures, searching for new ribozymes and designing artificial RNA, it is of interest to find RNA sequences folding into a specific structure and to analyze their induced neutral networks. Since the established inverse…
Mask prior-guided denoising diffusion improves inverse protein folding
School of Computer Science, University of Sheffield（谢菲尔德大学计算机科学学院）；Biologics Engineering, Oncology R&D, AstraZeneca（阿斯利康生物制药工程与肿瘤研发部）；Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca（阿斯利康医药化学与研发部）；School of Biological Sciences, University of Southampton（南安普顿大学生物科学学院）；Centre for Machine Intelligence, University of Sheffield（谢菲尔德大学智能中心）
ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；AI Lab, Research Center for Industries of the Future, Westlake University（未来产业研究院人工智能实验室）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高陵人工智能学院）
Bridge-IF: Learning Inverse Protein Folding with Markov Bridges
Liangzhu Laboratory, Zhejiang University（浙江大学良渚实验室）；College of Pharmaceutical Sciences, Zhejiang University（浙江大学药学院）；Alibaba Cloud Computing（阿里云计算）；School of Public Health, Zhejiang University（浙江大学公共卫生学院）；School of Artificial Intelligence and Data Science, University of Science and Technology of China（中国科学技术大学人工智能与数据科学学院）；The Second Affiliated Hospital Zhejiang University School of Medicine（浙江大学医学院附属第二医院）
Structure-Aware Antibody Design with Affinity-Optimized Inverse Folding
University of Texas Health Science Center at Houston（休斯顿德克萨斯大学健康科学中心）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Worcester Polytechnic Institute（伍斯特理工学院）
Lattice Protein Folding with Variational Annealing
Department of Computer Science, Indiana University Bloomington（印第安纳大学布卢明顿分校计算机科学系）；Perimeter Institute for Theoretical Physics（理论物理研究所）；Department of Physics and Astronomy, University of Waterloo（多伦多大学物理与天文学系）；Department of Applied Mathematics, University of Waterloo（多伦多大学应用数学系）；Vector Institute（向量研究所）
Dark energy: the cost of function in protein evolution
Centre for Genomic Regulation, The Barcelona Institute of Science and Technology（基因组调控中心，巴塞罗那科学技术研究所）；Protein Physiology Lab, Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires（布宜诺斯艾利斯大学理学院生物化学系蛋白质生理实验室）；Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales, Consejo Nacional de Investigaciones Científicas y Técnicas - Universidad de Buenos Aires（布宜诺斯艾利斯大学理学院生物化学研究所，阿根廷国家科学研究委员会-布宜诺斯艾利斯大学）
Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling
DP Technology（DP技术公司）；AI for Science Institute（人工智能科学研究院）；School of Mathematical Sciences（数学科学学院）；Center for Machine Learning Research（机器学习研究中心）；Institute for Artificial Intelligence（人工智能研究院）
Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
University of Chicago（芝加哥大学）；Data Science Institute（数据科学研究所）；Department of Biochemistry and Molecular Biology（生物化学与分子生物学系）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）；Argonne National Laboratory（阿贡国家实验室）；Princeton University（普林斯顿大学）
Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design
University of Florida（佛罗里达大学）；University of Chicago（芝加哥大学）；Data Science Institute, University of Chicago（芝加哥大学数据科学研究所）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）；Mayo Clinic（梅奥诊所）；Argonne National Laboratory（阿贡国家实验室）
University of Texas at San Antonio（得克萨斯大学圣安东尼奥分校）；Argonne National Laboratory（阿贡国家实验室）；University of Central Florida（中佛罗里达大学）；Air Force Research Laboratory（空军研究实验室）；SRI International（斯坦福国际研究院）
