蛋白质是人类生命的重要组成部分,其结构对于功能和机制分析很重要。最近的工作表明了AI驱动方法对蛋白质结构预测的潜力。但是,新模型的开发受到数据集和基准测试培训程序的限制。据我们所知,现有的开源数据集远不足以满足现代蛋白质序列相关研究的需求。为了解决这个问题,我们介绍了具有高覆盖率和多样性的第一个百万级蛋白质结构预测数据集,称为PSP。该数据集由570K真实结构序列(10TB)和745K互补蒸馏序列(15TB)组成。此外,我们还提供了该数据集上SOTA蛋白结构预测模型的基准测试训练程序。我们通过参与客串比赛验证该数据集的实用程序进行培训,我们的模特赢得了第一名。我们希望我们的PSP数据集以及培训基准能够为AI驱动的蛋白质相关研究提供更广泛的AI/生物学研究人员社区。
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In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence alone has always been a highly valuable problem for de novo antibody design. AlphaFold2, a breakthrough in the field of structural biology, provides a solution to predict protein structure based on protein sequences and computationally expensive coevolutionary multiple sequence alignments (MSAs). However, the computational efficiency and undesirable prediction accuracy of antibodies, especially on the complementarity-determining regions (CDRs) of antibodies limit their applications in the industrially high-throughput drug design. To learn an informative representation of antibodies, we employed a deep antibody language model (ALM) on curated sequences from the observed antibody space database via a transformer model. We also developed a novel model named xTrimoABFold to predict antibody structure from antibody sequence based on the pretrained ALM as well as efficient evoformers and structural modules. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame-aligned point loss. xTrimoABFold outperforms AlphaFold2 and other protein language model based SOTAs, e.g., OmegaFold, HelixFold-Single, and IgFold with a large significant margin (30+\% improvement on RMSD) while performing 151 times faster than AlphaFold2. To the best of our knowledge, xTrimoABFold achieved state-of-the-art antibody structure prediction. Its improvement in both accuracy and efficiency makes it a valuable tool for de novo antibody design and could make further improvements in immuno-theory.
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RNA结构的确定和预测可以促进靶向RNA的药物开发和可用的共性元素设计。但是,由于RNA的固有结构灵活性,所有三种主流结构测定方法(X射线晶体学,NMR和Cryo-EM)在解决RNA结构时会遇到挑战,这导致已解决的RNA结构的稀缺性。计算预测方法作为实验技术的补充。但是,\ textit {de从头}的方法都不基于深度学习,因为可用的结构太少。取而代之的是,他们中的大多数采用了耗时的采样策略,而且它们的性能似乎达到了高原。在这项工作中,我们开发了第一种端到端的深度学习方法E2FOLD-3D,以准确执行\ textit {de de novo} RNA结构预测。提出了几个新的组件来克服数据稀缺性,例如完全不同的端到端管道,二级结构辅助自我鉴定和参数有效的骨干配方。此类设计在独立的,非重叠的RNA拼图测试数据集上进行了验证,并达到平均sub-4 \ aa {}根平方偏差,与最先进的方法相比,它表现出了优越的性能。有趣的是,它在预测RNA复杂结构时也可以取得令人鼓舞的结果,这是先前系统无法完成的壮举。当E2FOLD-3D与实验技术耦合时,RNA结构预测场可以大大提高。
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Protein structure prediction aims to determine the three-dimensional shape of a protein from its amino acid sequence 1 . This problem is of fundamental importance to biology as the structure of a protein largely determines its function 2 but can be hard to determine experimentally. In recent years, considerable progress has been made by leveraging genetic information: analysing the co-variation of homologous sequences can allow one to infer which amino acid residues are in contact, which in turn can aid structure prediction 3 . In this work, we show that we can train a neural network to accurately predict the distances between pairs of residues in a protein which convey more about structure than contact predictions. With this information we construct a potential of mean force 4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimised by a simple gradient descent algorithm, to realise structures without the need for complex sampling procedures.The resulting system, named AlphaFold, has been shown to achieve high accuracy, even for sequences with relatively few homologous sequences. In the most recent Critical Assessment of Protein Structure Prediction 5 (CASP13), a blind assessment of the state of the field of protein structure prediction, AlphaFold created high-accuracy structures (with TM-scores † of 0.7 or higher) for 24 out of 43 free modelling domains whereas the next best method, using sampling and contact information, achieved such accuracy for only 14 out of 43 domains.AlphaFold represents a significant advance in protein structure prediction. We expect the increased accuracy of structure predictions for proteins to enable insights in understanding the function and malfunction of these proteins, especially in cases where no homologous proteins have been experimentally determined 7 .Proteins are at the core of most biological processes. Since the function of a protein is dependent on its structure, understanding protein structure has been a grand challenge in biology for decades. While several experimental structure determination techniques have been developed
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The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding. Recent advances have proved the power of language models (LMs) in processing the protein sequence databases, which inherit the advantages of attention networks and capture useful information in learning representations for proteins. The past two years have witnessed remarkable success in tertiary protein structure prediction (PSP), including evolution-based and single-sequence-based PSP. It seems that instead of using energy-based models and sampling procedures, protein language model (pLM)-based pipelines have emerged as mainstream paradigms in PSP. Despite the fruitful progress, the PSP community needs a systematic and up-to-date survey to help bridge the gap between LMs in the natural language processing (NLP) and PSP domains and introduce their methodologies, advancements and practical applications. To this end, in this paper, we first introduce the similarities between protein and human languages that allow LMs extended to pLMs, and applied to protein databases. Then, we systematically review recent advances in LMs and pLMs from the perspectives of network architectures, pre-training strategies, applications, and commonly-used protein databases. Next, different types of methods for PSP are discussed, particularly how the pLM-based architectures function in the process of protein folding. Finally, we identify challenges faced by the PSP community and foresee promising research directions along with the advances of pLMs. This survey aims to be a hands-on guide for researchers to understand PSP methods, develop pLMs and tackle challenging problems in this field for practical purposes.
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数据驱动的预测方法可以有效,准确地将蛋白质序列转化为生物活性结构,对于科学研究和治疗发展非常有价值。使用共同进化信息确定准确的折叠格局是现代蛋白质结构预测方法的成功基础。作为最新的状态,AlphaFold2显着提高了准确性,而无需进行明确的共同进化分析。然而,其性能仍然显示出对可用序列同源物的强烈依赖。我们研究了这种依赖性的原因,并提出了一种元生成模型Evogen,以弥补较差的MSA靶标的Alphafold2的表现不佳。 Evogen使我们能够通过降低搜索的MSA或生成虚拟MSA来操纵折叠景观,并帮助Alphafold2在低数据表方面准确地折叠,甚至通过单序预测来实现令人鼓舞的性能。能够用很少的MSA做出准确的预测,不仅可以更好地概括为孤儿序列的Alphafold2,而且使其在高通量应用程序中的使用民主化。此外,Evogen与AlphaFold2结合产生了一种概率结构生成方法,该方法可以探索蛋白质序列的替代构象,并且序列生成的任务意识可区分算法将使包括蛋白质设计在内的其他相关任务受益。
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在三维分子结构上运行的计算方法有可能解决生物学和化学的重要问题。特别地,深度神经网络的重视,但它们在生物分子结构域中的广泛采用受到缺乏系统性能基准或统一工具包的限制,用于与分子数据相互作用。为了解决这个问题,我们呈现Atom3D,这是一个新颖的和现有的基准数据集的集合,跨越几个密钥的生物分子。我们为这些任务中的每一个实施多种三维分子学习方法,并表明它们始终如一地提高了基于单维和二维表示的方法的性能。结构的具体选择对于性能至关重要,具有涉及复杂几何形状的任务的三维卷积网络,在需要详细位置信息的系统中表现出良好的图形网络,以及最近开发的设备越多的网络显示出显着承诺。我们的结果表明,许多分子问题符合三维分子学习的增益,并且有可能改善许多仍然过分曝光的任务。为了降低进入并促进现场进一步发展的障碍,我们还提供了一套全面的DataSet处理,模型培训和在我们的开源ATOM3D Python包中的评估工具套件。所有数据集都可以从https://www.atom3d.ai下载。
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基于AI的蛋白质结构预测管道(例如AlphaFold2)已达到了几乎实验的准确性。这些高级管道主要依赖于多个序列比对(MSA)和模板作为输入来从同源序列中学习共进化信息。但是,从蛋白质数据库中搜索MSA和模板很耗时,通常需要数十分钟。因此,我们尝试通过仅使用蛋白质的主要序列来探索快速蛋白质结构预测的极限。提出了Helixfold单一的形式将大规模蛋白质语言模型与AlphaFold2的优质几何学习能力相结合。我们提出的方法,Helixfold单个,首先预先培训是一种大规模蛋白质语言模型(PLM),使用了数以千计的主要序列利用自我监督的学习范式,将用作MSA和模板的替代方法共同进化信息。然后,通过将预训练的PLM和AlphaFold2的必需组件组合在一起,我们获得了一个端到端可区分模型,以仅从主要序列预测原子的3D坐标。 Helixfold-Single在数据集CASP14和Cameo中得到了验证,通过基于MSA的方法,具有大型同源家庭的基于MSA的方法,从而实现了竞争精度。此外,与主流管道进行蛋白质结构预测相比,Helixfold单个的时间比主流管道的时间少得多,这表明其在需要许多预测的任务中的潜力。 HelixFold-Single的守则可在https://github.com/paddlepaddle/paddlehelix/tree/dev/dev/pprotein_folding/helixfold-single上获得,我们还在https://paddlehelix.baidu.com上提供稳定的Web服务。 /app/drug/protein-single/prevast。
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Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantity of structural data. Meanwhile, protein language models trained on substantial 1D sequences have shown burgeoning capabilities with scale in a broad range of applications. Nevertheless, no preceding studies consider combining these different protein modalities to promote the representation power of geometric neural networks. To address this gap, we make the foremost step to integrate the knowledge learned by well-trained protein language models into several state-of-the-art geometric networks. Experiments are evaluated on a variety of protein representation learning benchmarks, including protein-protein interface prediction, model quality assessment, protein-protein rigid-body docking, and binding affinity prediction, leading to an overall improvement of 20% over baselines and the new state-of-the-art performance. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin and can be generalized to complex tasks.
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尽管相似性搜索研究的不断发展,但它仍然面临着由于数据的复杂性而面临的相同挑战,例如维度和计算距离距离功能的诅咒。已证明,各种机器学习技术能够用简单的线性功能组合来代替精心设计的数学模型,通常以正式保证的准确性和正确的查询性能来获得速度和简单性。作者通过为3D蛋白质结构搜索的复杂问题提供了轻巧的解决方案来探索这一研究趋势的潜力。该解决方案由三个步骤组成:(i)将3D蛋白结构信息转换为非常紧凑的向量,(ii)使用概率模型将这些向量分组并通过返回给定数量的类似对象和(iii)来响应查询,并且)最终的过滤步骤,该步骤应用基本的向量距离函数来完善结果。
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蛋白质RNA相互作用对各种细胞活性至关重要。已经开发出实验和计算技术来研究相互作用。由于先前数据库的限制,尤其是缺乏蛋白质结构数据,大多数现有的计算方法严重依赖于序列数据,只有一小部分使用结构信息。最近,alphafold彻底改变了整个蛋白质和生物领域。可预应学,在即将到来的年份,也将显着促进蛋白质-RNA相互作用预测。在这项工作中,我们对该字段进行了彻底的审查,调查绑定站点和绑定偏好预测问题,并覆盖常用的数据集,功能和模型。我们还指出了这一领域的潜在挑战和机遇。本调查总结了过去的RBP-RNA互动领域的发展,并预见到了alphafold时代未来的发展。
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虽然最近在许多科学领域都变得无处不在,但对其评估的关注较少。对于分子生成模型,最先进的是孤立或与其输入有关的输出。但是,它们的生物学和功能特性(例如配体 - 靶标相互作用)尚未得到解决。在这项研究中,提出了一种新型的生物学启发的基准,用于评估分子生成模型。具体而言,设计了三个不同的参考数据集,并引入了与药物发现过程直接相关的一组指标。特别是我们提出了一个娱乐指标,将药物目标亲和力预测和分子对接应用作为评估生成产量的互补技术。虽然所有三个指标均在测试的生成模型中均表现出一致的结果,但对药物目标亲和力结合和分子对接分数进行了更详细的比较,表明单峰预测器可能会导致关于目标结合在分子水平和多模式方法的错误结论,而多模式的方法是错误的结论。因此优选。该框架的关键优点是,它通过明确关注配体 - 靶标相互作用,将先前的物理化学域知识纳入基准测试过程,从而创建了一种高效的工具,不仅用于评估分子生成型输出,而且还用于丰富富含分子生成的输出。一般而言,药物发现过程。
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大规模蛋白质语言模型(PLM)在蛋白质预测任务中的性能提高,范围从3D结构预测到各种功能预测。特别是,Alphafold(一种开创性的AI系统)可能会重塑结构生物学。但是,尚未探索超出结构预测的AlphaFold,Evoformer的PLM模块的效用。在本文中,我们研究了三个流行PLM的表示能力:ESM-1B(单序),MSA转换器(多个序列比对)和Evoformer(结构),并特别关注Evoformer。具体而言,我们旨在回答以下关键问题:(i)作为Alphafold的一部分,Evoformer是否会产生可预测蛋白质功能的表示形式? (ii)如果是的,可以替换ESM-1B和MSA转换器? (iii)这些PLM多少依赖于进化相关的蛋白质数据?在这方面,他们彼此补充吗?我们通过实证研究以及新的见解和结论来比较这些模型。最后,我们发布代码和数据集以获得可重复性。
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Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advances by designing a novel deep learning architecture consisting of a 3-dimensional convolutional neural network utilizing channel-wise attention and two graph convolutional networks utilizing attention-based aggregation of node features. HAC-Net (Hybrid Attention-Based Convolutional Neural Network) obtains state-of-the-art results on the PDBbind v.2016 core set, the most widely recognized benchmark in the field. We extensively assess the generalizability of our model using multiple train-test splits, each of which maximizes differences between either protein structures, protein sequences, or ligand extended-connectivity fingerprints. Furthermore, we perform 10-fold cross-validation with a similarity cutoff between SMILES strings of ligands in the training and test sets, and also evaluate the performance of HAC-Net on lower-quality data. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction. All of our software is available as open source at https://github.com/gregory-kyro/HAC-Net/.
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最近,自我监督的神经语言模型最近已应用于生物序列数据,进步的结构,功能和突变效应预测。一些蛋白质语言模型,包括MSA变压器和Alphafold的Evoformer,将进化相关蛋白的多个序列比对作为输入。 MSA Transformer的行专注的简单组合导致了最新的无监督结构接触预测。我们证明,MSA变压器柱浓度的简单和通用组合与MSA中序列之间的锤距距离密切相关。因此,基于MSA的语言模型编码详细的系统发育关系。我们进一步表明,这些模型可以将编码功能和结构约束的共同进化信号与反映历史意义的系统发育相关性分开。为了评估这一点,我们从POTTS模型中生成了在天然MSA训练的POTTS模型的合成MSA。我们发现,当使用MSA变压器与推断的POTTS模型时,无监督的接触预测对系统发育噪声的弹性更大。
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基于注意的蛋白质序列训练的基于注意力的模型在分类和与人工智能驱动的蛋白质设计相关的分类和生成任务方面取得了令人难以置信的成功。但是,我们对非常大规模的模型和数据在有效的蛋白质模型开发中发挥作用。我们介绍了一套名为progen2的蛋白质语言模型的套件,该模型最高为6.4b参数,并在从基因组,宏基因组和免疫曲目数据库中绘制的不同序列数据集上进行了培训。 GEECEN2模型在捕获观察到的进化序列的分布,生成新型的可行序列并预测蛋白质适应性的情况下显示出最先进的性能,而无需额外的芬特。随着蛋白质序列的大型大小和原始数量继续变得更加广泛,我们的结果表明,越来越多的重点需要放在提供给蛋白质序列模型的数据分布上。我们在https://github.com/salesforce/progen上发布了PECEN2模型和代码。
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A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.
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现在,我们目睹了深度学习方法在各种蛋白质(或数据集)中的重大进展。但是,缺乏评估不同方法的性能的标准基准,这阻碍了该领域的深度学习进步。在本文中,我们提出了一种称为PEER的基准,这是一种用于蛋白质序列理解的全面和多任务基准。 PEER提供了一组不同的蛋白质理解任务,包括蛋白质功能预测,蛋白质定位预测,蛋白质结构预测,蛋白质 - 蛋白质相互作用预测和蛋白质 - 配体相互作用预测。我们评估每个任务的不同类型的基于序列的方法,包括传统的特征工程方法,不同的序列编码方法以及大规模的预训练蛋白质语言模型。此外,我们还研究了这些方法在多任务学习设置下的性能。实验结果表明,大规模的预训练蛋白质语言模型可实现大多数单个任务的最佳性能,共同训练多个任务进一步提高了性能。该基准的数据集和源代码均可在https://github.com/deepgraphlearning/peer_benchmark上获得
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病毒感染导致全世界的显着发病率和死亡率。理解特定病毒和人类蛋白质之间的相互作用模式在揭示病毒感染和发病机制的潜在机制方面发挥着至关重要的作用。这可以进一步帮助预防和治疗病毒相关疾病。然而,由于病毒 - 人类相互作用的稀缺数据和大多数病毒的快速突变率,预测新病毒和人体细胞之间的蛋白质 - 蛋白质相互作用的任务是非常挑战性的。我们开发了一种多任务转移学习方法,利用人类互乱组约2400万蛋白序列和相互作用模式的信息来解决小型训练数据集的问题。除了使用手工制作的蛋白质特征,而不是通过深语模型方法从巨大的蛋白质序列来源学习的统计学上丰富的蛋白质表示。此外,我们采用了额外的目的,旨在最大限度地提高观察人蛋白质蛋白质相互作用的可能性。这一附加任务目标充当规律器,还允许纳入域知识来告知病毒 - 人蛋白质 - 蛋白质相互作用预测模型。我们的方法在13个基准数据集中实现了竞争力,以及SAR-COV-2病毒受体的案例研究。实验结果表明,我们所提出的模型有效地用于病毒 - 人和细菌 - 人蛋白质 - 蛋白质 - 蛋白质相互作用预测任务。我们分享我们的重复性和未来研究代码,以便在https://git.l3s.uni-hannover.de/dong/multitastastastastastastastastastask-transfer。
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核磁共振(NMR)光谱是结构生物学的主要技术之一,蛋白质数据库中沉积了11,800多个蛋白质结构。 NMR可以阐明溶液,活细胞和固体中中小型蛋白质的结构和动力学,但受到乏味的数据分析过程的限制。通常,它需要训练有素的专家进行数周或数月的手动工作,以将NMR测量变成蛋白质结构。该过程的自动化是一个空旷的问题,在30年前在该领域中提出。在这里,我们提出了解决这一挑战的解决方案,该解决方案可以在完成测量后几小时内对蛋白质NMR数据进行完全自动化的分析。仅使用NMR光谱和蛋白质序列作为输入,我们的基于机器学习的方法,Artina,可严格地提供信号位置,共振分配和结构,而无需任何人类干预。 Artina在包含1329个多维NMR光谱的100个蛋白基准测试中进行了测试,展示了其以1.44 {\ aa}中位数RMSD求解结构的能力,并识别91.36%的正确NMR共振分配。非专家可以使用Artina,从而减少了NMR的蛋白质分配或结构确定的努力,从而基本上是在样品的制备和光谱测量中进行的。
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