Designing Protein Structures from Natural Language
University of Washington（华盛顿大学）；National University of Singapore（新加坡国立大学）；Allen Institute for AI (Ai2)（艾伦人工智能研究所）；Howard Hughes Medical Institute（霍华德·休斯医学研究所）
Joseph Lee, Yanjing Li, Bo Qiang, Gregory Lau, Allyson Ettinger, Noah A. Smith, Rory Donovan-Maiye, David Baker, Hanna Hajishirzi, Pang Wei Koh
A central challenge in de novo protein design is generating proteins with desired structural and functional properties. Existing design models typically require a dedicated input representation for each property, limiting the design objectives they support. We develop Prompteina, the first all-atom structure generation model that supports new and broader design objectives specified in natural language, analogous to text-to-image generation. To train Prompteina, we construct Prompteina-Data, a paired text--protein structure dataset of 1.8M structures and 50M+ captions describing global and residue-specific structural and functional properties. We adapt Proteina-Complexa, a pretrained structure generation model, through text-conditioned pretraining and finetuning that aligns residues with their corresponding text. Prompteina outperforms baselines on 15 of 18 structural properties and generates proteins that more closely match descriptions of their intended functions. In binder design, it leads baselines on at least 13 of 20 targets and improves interface confidence by 14\% over the strongest baseline. With iterative in silico feedback, Prompteina's gain over non-iterative generation is 57\% larger than the next-best baseline on the hardest targets. Finally, we experimentally validate Prompteina designs against GM2A, a target with no reported de novo binders. We identify nine binders, with the strongest at an apparent $K_{\mathrm{D}}$ of 107 nM. These results establish natural language as an effective and powerful conditioning interface for protein structure generation.
ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction
Rutgers University（罗格斯大学）；University of Liverpool（利物浦大学）；Peking University（北京大学）；Massachusetts Institute of Technology（麻省理工学院）；New Jersey Institute of Technology（新泽西理工学院）
ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training
Beihang University（北京航空航天大学）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；State Grid Smart Grid Research Institute Co., Ltd.（国网智能电网研究院有限公司）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
InstructPro: Natural Language Guided Ligand-Binding Protein Design
Language Technologies Institute, School of Computer Science, Carnegie Mellon University（语言技术研究所，计算机科学学院，卡内基梅隆大学）；Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University（Ray和Stephanie Lane计算生物学系，计算机科学学院，卡内基梅隆大学）；Lambda Inc（Lambda公司）
The SARS-CoV-2 pandemic has created a global race for a cure. One approach focuses on designing a novel variant of the human angiotensin-converting enzyme 2 (ACE2) that binds more tightly to the SARS-CoV-2 spike protein and diverts it from human cells. Here we formulate a novel protein design framework as a reinforcement learning problem. We generate new designs efficiently through the combination of a fast, biologically-grounded reward function and sequential action-space formulation. The use of Policy Gradients reduces the compute budget needed to reach consistent, high-quality designs by at…
Loop-Diffusion: an equivariant diffusion model for designing and scoring protein loops
University of Washington（华盛顿大学）；Paul G. Allen School of Computer Science and Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Department of Applied Mathematics, University of Washington（华盛顿大学应用数学系）；Fred Hutch Cancer Research Center（弗雷德·哈钦森癌症研究中心）
Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering. Challenges exist due to the complex sequence--fold relationship, as well as the difficulties to capture the diversity of the sequences (therefore structures and functions) within a fold. To overcome these challenges, we propose Fold2Seq, a novel transformer-based generative framework for designing protein sequences conditioned on a specific target fold. To model the complex sequence--structure relationship, Fold2Seq jointly learns a sequence embedding using a transf…
School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；College of Foreign Languages and Literatures, Fudan University（复旦大学外国语言文学学院）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究所）
Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in pro…
IBM Research Europe（IBM欧洲研究院）；ETH Zurich（苏黎世联邦理工学院）
With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents. Bridging systems biology and drug discovery, we propose a deep learning framework for conditional de novo design of antiviral candidate drugs tailored against given protein targets. First, we train a multimodal ligand--protein binding affinity model on predicting affinities of antiviral compounds to target proteins and couple this model with pharmacological toxicity predictors. Exploiting this multi-objective as a reward function of a c…
BioGeometry；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）
Proteins are macromolecules that perform essential functions in all living organisms. Designing novel proteins with specific structures and desired functions has been a long-standing challenge in the field of bioengineering. Existing approaches generate both protein sequence and structure using either autoregressive models or diffusion models, both of which suffer from high inference costs. In this paper, we propose a new approach capable of protein sequence and structure co-design, which iteratively translates both protein sequence and structure into the desired state from random initializati…
Zhejiang University（浙江大学）；Westlake University（西湖大学）；Westlake Institute for Advanced Study（西湖高等研究院）
The linear sequence of amino acids determines protein structure and function. Protein design, known as the inverse of protein structure prediction, aims to obtain a novel protein sequence that will fold into the defined structure. Recent works on computational protein design have studied designing sequences for the desired backbone structure with local positional information and achieved competitive performance. However, similar local environments in different backbone structures may result in different amino acids, indicating that protein structure's global context matters. Thus, we propose t…
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）
Multi-Scale Representation Learning for Protein Fitness Prediction
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）；Harvard Medical School（哈佛医学院）；IBM Research（IBM研究院）；Broad Institute（布罗德研究所）；HEC Montréal（蒙特利尔高等商学院）；CIFAR（加拿大高级研究所）
Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference
Siebel School of Data Science and Computing（数据科学与计算系）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；DOE Center for Advanced Bioenergy and Bioproducts Innovation（能源与生物产品创新部门）；Chemical and Biomolecular Engineering（化学与生物分子工程系）
Designing DNA and protein sequences with improved function has the potential to greatly accelerate synthetic biology. Machine learning models that accurately predict biological fitness from sequence are becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, this method suffers from vanishing gradients and may cause predictor pathologies …
GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow
Peking University（北京大学）；National Research Council Canada（加拿大国家研究委员会）；University of Ottawa（渥太华大学）；Mila - Québec AI Institute（Mila - 魁北克人工智能研究所）；HEC Montréal（蒙特利尔高等商学院）
TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering
Toursun Synbio（拓荪合成生物）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Cambridge（剑桥大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；City University of Hong Kong（香港城市大学）
Amazon Devices（亚马逊设备部门）；Amazon Machine Learning Solutions Lab（亚马逊机器学习解决方案实验室）；AWS Professional Services（亚马逊云科技专业服务部门）
Representation learning for proteins has primarily focused on the global understanding of protein sequences regardless of their length. However, shorter proteins (known as peptides) take on distinct structures and functions compared to their longer counterparts. Unfortunately, there are not as many naturally occurring peptides available to be sequenced and therefore less peptide-specific data to train with. In this paper, we propose a new peptide data augmentation scheme, where we train peptide language models on artificially constructed peptides that are small contiguous subsets of longer, wi…
ByteDance AI Lab（字节跳动人工智能实验室）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
This paper demonstrates that language models are strong structure-based protein designers. We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs), that have learned massive sequential evolutionary knowledge from the universe of natural protein sequences, to acquire an immediate capability to design preferable protein sequences for given folds. We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. During inference, iterative refinement is performed to effect…
ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods
Institute of AI for Health, Helmholtz Munich（亥姆霍兹慕尼黑人工智能健康研究所）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Helmholtz AI（亥姆霍兹人工智能研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Faculty of Mathematics, Informatics and Mechanics, University of Warsaw（华沙大学数学、信息学与力学学院）
Generative power of a protein language model trained on multiple sequence alignments
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Institute of Bioengineering（生物工程研究所）；School of Life Sciences（生命科学学院）；SIB Swiss Institute of Bioinformatics（瑞士生物信息学研究所）
ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design
Tsinghua University（清华大学）；International Digital Economy Academy（国际数字经济学院）；Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Nanyang Technological University（南洋理工大学）
Saudi Electronic University（沙特电子大学）；The Commonwealth Scientific and Industrial Research Organisation（澳大利亚联邦科学与工业研究组织）；The Australian National University（澳大利亚国立大学）；University of Technology Sydney（悉尼科技大学）；the University of Western Australia（西澳大利亚大学）
Accurate localization of proteins from fluorescence microscopy images is challenging due to the inter-class similarities and intra-class disparities introducing grave concerns in addressing multi-class classification problems. Conventional machine learning-based image prediction pipelines rely heavily on pre-processing such as normalization and segmentation followed by hand-crafted feature extraction to identify useful, informative, and application-specific features. Here, we demonstrate that deep learning-based pipelines can effectively classify protein images from different datasets. We prop…
Latent Retrieval Augmented Generation of Cross-Domain Protein Binders
Dept. of Comp. Sci. & Tech., Tsinghua University（计算机科学与技术系，清华大学）；Institute for AIR, Tsinghua University（空气智能研究所，清华大学）；Gaoling School of Artificial Intelligence, Renmin University of China（北京理工大学人工智能学院）
EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；School of Computer Science, Fudan University（复旦大学计算机学院）；Xiaohongshu Inc（小红书科技有限公司）；Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University（华东师范大学药学院人工智能与药物发现创新中心）
University of Toronto（多伦多大学）；Donnelly Centre for Cellular and Biomolecular Research（唐纳利细胞与生物分子研究中心）
Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. Those generative models can learn protein representations that are often more informative of protein structure and function than hand-engineered features. Furthermore, they can be used to quickly propose millions of nove…
Protein-Conditioned Multi-Objective Reinforcement Learning for Full-Length mRNA Design
Nanjing Foreign Language School（南京外国语学校）；School of Science, China Pharmaceutical University（中国药科大学理学院）；School of Pharmacy, China Pharmaceutical University（中国药科大学药学院）
