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 the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules. While local modules focus on relationships between neighbor amino acids, global modules explicitly capture non-local contexts. Experimental results demonstrate that the proposed GCA method outperforms state-of-the-arts on de novo protein design. Our code and pretrained model will be released.
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
Protein-ligand complex structures have been utilised to design benchmark machine learning methods that perform important tasks related to drug design such as receptor binding site detection, small molecule docking and binding affinity prediction. However, these methods are usually trained on only ligand bound (or holo) conformations of the protein and therefore are not guaranteed to perform well when the protein structure is in its native unbound conformation (or apo), which is usually the conformation available for a newly identified receptor. A primary reason for this is that the local struc…
West China Hospital, Sichuan University（四川大学华西医院）；University of Gothenburg（哥德堡大学）；The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)（中国科学院大学附属肿瘤医院（浙江省肿瘤医院））；Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences（中国科学院基础医学与肿瘤研究所（IBMC））
De novo molecular design has facilitated the exploration of large chemical space to accelerate drug discovery. Structure-based de novo method can overcome the data scarcity of active ligands by incorporating drug-target interaction into deep generative architectures. However, these strategies are bottlenecked by the small fraction of experimentally determined protein or complex structures. In addition, the cost of molecular generation is computationally expensive due to 3D representations of both molecule and protein. Here, we demonstrate a widely used and fast protein sequence-based reinforce…
Proteomics is the large-scale analysis of the proteins. The common method for identifying proteins and characterising their amino acid sequences is to digest the proteins into peptides, analyse the peptides using mass spectrometry and assign the resulting tandem mass spectra (MS/MS) to peptides using database search tools. However, database search algorithms are highly dependent on a reference protein database and they cannot identify peptides and proteins not included in the database. Therefore, de novo sequencing algorithms are developed to overcome the problem by directly reconstructing the…
De novo prediction of protein structures, the prediction of structures from amino-acid sequences which are not similar to those of hitherto resolved structures, has been one of the major challenges in molecular biophysics. In this paper, we develop a new method of de novo prediction, which combines the fragment assembly method and the simulation of physical folding process: Structures which have consistently assembled fragments are dynamically searched by Langevin molecular dynamics of conformational change. The benchmarking test shows that the prediction is improved when the candidate structu…
De novo design of molecules has recently enjoyed the power of generative deep neural networks. Current approaches aim to generate molecules either resembling the properties of the molecules of the training set or molecules that are optimized with respect to specific physicochemical properties. None of the methods generates molecules specific to a target protein. In the approach presented here, we introduce a method which is conditioned on the protein target sequence to generate de novo molecules that are relevant to the target. We use an implementation adapted from Google's "Show and Tell" ima…
De novo protein structure prediction from amino acid sequence is one of the most challenging problems in computational biology. As one of the extensively explored mathematical models for protein folding, Hydrophobic-Polar (HP) model enables thorough investigation of protein structure formation and evolution. Although HP model discretizes the conformational space and simplifies the folding energy function, it has been proven to be an NP-complete problem. In this paper, we propose a novel protein folding framework FoldingZero, self-folding a de novo protein 2D HP structure from scratch based on …
Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method gr…
The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just …
Modern algorithms for de novo prediction of protein structures typically output multiple full-length models (decoys) rather than a single solution. Subsequent clustering of such decoys is used both to gauge the success of the modelling and to decide on the most native-like conformation. At the same time, partial protein models are sufficient for some applications such as crystallographic phasing by molecular replacement (MR) in particular, provided these models represent a certain part of the target structure with reasonable accuracy. Here we propose a novel clustering algorithm that natively …
Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
LASIGE, Faculdade de Ciências da Universidade de Lisboa（里斯本大学科学学院LASIGE实验室）；BioISI, Faculdade de Ciências da Universidade de Lisboa（里斯本大学科学学院BioISI实验室）；iBET – Instituto de Biologia Experimental e Tecnológica（生物实验与技术研究所）
Protein folding and design are major biophysical problems, the solution of which would lead to important applications especially in medicine. Here a novel protein model capable of simultaneously provide quantitative protein design and folding is introduced. With computer simulations it is shown that, for a large set of real protein structures, the model produces designed sequences with similar physical properties to the corresponding natural occurring sequences. The designed sequences are not yet fully realistic and require further experimental testing. For an independent set of proteins, noto…
Protein structures and functions are determined by a contiguous arrangement of amino acid sequences. Designing novel protein sequences and structures with desired geometry and functions is a complex task with large state spaces. Here we develop a novel protein design pipeline consisting of two deep learning algorithms, ProteinSolver and AlphaFold2. ProteinSolver is a deep graph neural network that generates amino acid sequences such that the forces between interacting amino acids are favorable and compatible with the fold while AlphaFold2 is a deep learning algorithm that predicts the protein …
Recent development of high-resolution mass spectrometry (MS) instruments enables chemical cross-linking (XL) to become a high-throughput method for obtaining structural information about proteins. Restraints derived from XL-MS experiments have been used successfully for structure refinement and protein-protein docking. However, one formidable question is under which circumstances XL-MS data might be sufficient to determine a protein's tertiary structure de novo? Answering this question will not only include understanding the impact of XL-MS data on sampling and scoring within a de novo protein…
In recent work we reported the vibrational spectrum of more than 100,000 known protein structures, and a self-consistent sonification method to render the spectrum in the audible range of frequencies (Extreme Mechanics Letters, 2019). Here we present a method to transform these molecular vibrations into materialized vibrations of thin water films using acoustic actuators, leading to complex patterns of surface waves, and using the resulting macroscopic images in further processing using deep convolutional neural networks. Specifically, the patterns of water surface waves for each protein struc…
In the scope of drug discovery, the molecular design aims to identify novel compounds from the chemical space where the potential drug-like molecules are estimated to be in the order of 10^60 - 10^100. Since this search task is computationally intractable due to the unbounded search space, deep learning draws a lot of attention as a new way of generating unseen molecules. As we seek compounds with specific target proteins, we propose a Transformer-based deep model for de novo target-specific molecular design. The proposed method is capable of generating both drug-like compounds (without specif…
The University of Hong Kong（香港大学）；Peking University（北京大学）；Microsoft Research Asia（微软亚洲研究院）；University of Waterloo（滑铁卢大学）
Protein language models have excelled in a variety of tasks, ranging from structure prediction to protein engineering. However, proteins are highly diverse in functions and structures, and current state-of-the-art models including the latest version of AlphaFold rely on Multiple Sequence Alignments (MSA) to feed in the evolutionary knowledge. Despite their success, heavy computational overheads, as well as the de novo and orphan proteins remain great challenges in protein representation learning. In this work, we show that MSAaugmented models inherently belong to retrievalaugmented methods. Mo…
UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
Tsinghua University（清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Renmin University of China（中国人民大学）；Bytedance（字节跳动）
Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系生物数据科学实验室）；Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系）；Dept. of Chemistry, Middle East Technical University（中东技术大学化学系）；Dept. of Physics, Middle East Technical University（中东技术大学物理系）；Dept. Of Artificial Intelligence Engineering, Bahcesehir University（巴切塞希尔大学人工智能工程系）；Cancer Systems Biology Lab, Graduate School of Informatics, Middle East Technical University（中东技术大学信息研究生院癌症系统生物学实验室）；Dept. of Pharmaceutical Chemistry, Faculty of Pharmacy, Gazi University（加齐大学药学系药剂化学系）；Laboratory of Molecular Modeling, Evias Pharmaceutical R&D Ltd.（Evias制药研发有限公司分子建模实验室）；Dept. of Electrical and Electronics Engineering, İskenderun Technical University（伊斯肯德伦技术大学电子与电气工程系）；Institute for Computational Biomedicine, Heidelberg University（海德堡大学计算生物医学研究所）；Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University（哈切塔佩大学健康科学研究生院生物信息学系）
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
Massachusetts Institute of Technology（麻省理工学院）；Laboratory for Atomistic and Molecular Mechanics (LAMM)（原子与分子力学实验室）；Center for Computational Science and Engineering（计算科学与工程中心）；Schwarzman College of Computing（施瓦茨曼计算学院）
Deep Lead Optimization: Leveraging Generative AI for Structural Modification
College of Pharmaceutical Sciences, Zhejiang University（浙江大学药学院）；AI Lab, Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心AI实验室）
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）
University of Washington Bothell（华盛顿大学博塞尔分校）；University of Washington Seattle（华盛顿大学西雅图分校）；Saint Louis University（圣路易斯大学）；Pacific Lutheran University（太平洋路德大学）；University of Chicago（芝加哥大学）
Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-con…
