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E-pRSA: Embeddings Improve the Prediction of Residue Relative Solvent Accessibility in Protein Sequence E-pRSA:嵌入改进了蛋白质序列中残基相对溶剂可及性的预测
IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-09-01 DOI: 10.1016/j.jmb.2024.168494

Knowledge of the solvent accessibility of residues in a protein is essential for different applications, including the identification of interacting surfaces in protein–protein interactions and the characterization of variations. We describe E-pRSA, a novel web server to estimate Relative Solvent Accessibility values (RSAs) of residues directly from a protein sequence. The method exploits two complementary Protein Language Models to provide fast and accurate predictions. When benchmarked on different blind test sets, E-pRSA scores at the state-of-the-art, and outperforms a previous method we developed, DeepREx, which was based on sequence profiles after Multiple Sequence Alignments. The E-pRSA web server is freely available at https://e-prsa.biocomp.unibo.it/main/ where users can submit single-sequence and batch jobs.

了解蛋白质中残基的溶剂可及性对不同的应用都至关重要,包括识别蛋白质-蛋白质相互作用中的相互作用表面和表征变异。我们介绍了 E-pRSA,这是一种新型网络服务器,可直接从蛋白质序列估算残基的相对溶剂可及性值(RSA)。该方法利用两个互补的蛋白质语言模型来提供快速准确的预测。在不同的盲测试集上进行基准测试时,E-pRSA 的得分达到了最先进水平,并优于我们之前开发的基于多重序列对齐后序列剖面的 DeepREx 方法。E-pRSA 网络服务器可在 https://e-prsa.biocomp.unibo.it/main/ 免费使用,用户可以提交单序列和批处理作业。
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引用次数: 0
CHARMM-GUI PDB Reader and Manipulator: Covalent Ligand Modeling and Simulation CHARMM-GUI PDB 阅读器和操纵器:共价配体建模与仿真
IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-09-01 DOI: 10.1016/j.jmb.2024.168554

Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands.

分子建模和模拟在分子水平探索蛋白质的生物学功能方面发挥着重要作用,是对实验的补充。CHARMM-GUI (https://www.charmm-gui.org) 是一个基于网络的图形用户界面,可生成复杂的分子模拟系统和输入文件,我们一直在不断开发和扩展其功能,以方便各种复杂的分子建模,使科学界更容易获得分子动力学模拟。目前,共价药物发现已成为一个热门的重要领域。共价药物能与目标蛋白质上的特定残基形成化学键,具有长效抑制作用的优势。尽管对带有各种共价配体类型的 PDB 蛋白结构建模提出了更高的要求,但共价配体的正确建模仍具有挑战性。本研究在 CHARMM-GUI PDB Reader & Manipulator 中提出了一种新功能,它可以处理多种配体-氨基酸连接类型,并通过使用 RCSB PDB 中超过 1000 个共价配体结构进行仔细的基准研究验证了这一功能。我们希望这一新功能能促进共价配体的建模和模拟研究。
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引用次数: 0
mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations mACPpred 2.0:利用集成空间和概率特征表征的堆叠深度学习进行抗癌肽预测
IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-09-01 DOI: 10.1016/j.jmb.2024.168687

Anticancer peptides (ACPs), naturally occurring molecules with remarkable potential to target and kill cancer cells. However, identifying ACPs based solely from their primary amino acid sequences remains a major hurdle in immunoinformatics. In the past, several web-based machine learning (ML) tools have been proposed to assist researchers in identifying potential ACPs for further testing. Notably, our meta-approach method, mACPpred, introduced in 2019, has significantly advanced the field of ACP research. Given the exponential growth in the number of characterized ACPs, there is now a pressing need to create an updated version of mACPpred. To develop mACPpred 2.0, we constructed an up-to-date benchmarking dataset by integrating all publicly available ACP datasets. We employed a large-scale of feature descriptors, encompassing both conventional feature descriptors and advanced pre-trained natural language processing (NLP)-based embeddings. We evaluated their ability to discriminate between ACPs and non-ACPs using eleven different classifiers. Subsequently, we employed a stacked deep learning (SDL) approach, incorporating 1D convolutional neural network (1D CNN) blocks and hybrid features. These features included the top seven performing NLP-based features and 90 probabilistic features, allowing us to identify hidden patterns within these diverse features and improve the accuracy of our ACP prediction model. This is the first study to integrate spatial and probabilistic feature representations for predicting ACPs. Rigorous cross-validation and independent tests conclusively demonstrated that mACPpred 2.0 not only surpassed its predecessor (mACPpred) but also outperformed the existing state-of-the-art predictors, highlighting the importance of advanced feature representation capabilities attained through SDL. To facilitate widespread use and accessibility, we have developed a user-friendly for mACPpred 2.0, available at https://balalab-skku.org/mACPpred2/.

抗癌肽(ACPs)是天然存在的分子,具有靶向和杀死癌细胞的巨大潜力。然而,仅根据主要氨基酸序列来识别抗癌肽仍然是免疫信息学的一大障碍。过去,人们提出了一些基于网络的机器学习(ML)工具,以帮助研究人员识别潜在的 ACPs,并进行进一步测试。值得注意的是,我们在 2019 年推出的元方法 mACPpred 极大地推动了 ACP 研究领域的发展。鉴于表征 ACP 的数量呈指数级增长,现在迫切需要创建 mACPpred 的更新版本。为了开发 mACPpred 2.0,我们整合了所有公开的 ACP 数据集,构建了一个最新的基准数据集。我们采用了大规模的特征描述器,包括传统的特征描述器和基于自然语言处理(NLP)的高级预训练嵌入。我们使用 11 种不同的分类器评估了它们区分 ACP 和非 ACP 的能力。随后,我们采用了叠加深度学习(SDL)方法,将一维卷积神经网络(1D CNN)块和混合特征结合在一起。这些特征包括基于 NLP 的前七种表现特征和 90 种概率特征,使我们能够识别这些不同特征中隐藏的模式,提高 ACP 预测模型的准确性。这是第一项整合空间和概率特征表征来预测 ACP 的研究。严格的交叉验证和独立测试最终证明,mACPpred 2.0 不仅超越了其前身(mACPpred),而且还优于现有的最先进预测器,这凸显了通过 SDL 获得的高级特征表示能力的重要性。为了便于广泛使用和访问,我们为 mACPpred 2.0 开发了用户友好型网站 https://balalab-skku.org/mACPpred2/。
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引用次数: 0
Enricherator: A Bayesian Method for Inferring Regularized Genome-wide Enrichments from Sequencing Count Data Enricherator:从测序计数数据推断正规化全基因组富集度的贝叶斯方法
IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-09-01 DOI: 10.1016/j.jmb.2024.168567

A pervasive question in biological research studying gene regulation, chromatin structure, or genomics is where, and to what extent, does a signal of interest arise genome-wide? This question is addressed using a variety of methods relying on high-throughput sequencing data as their final output, including ChIP-seq for protein-DNA interactions,1 GapR-seq for measuring supercoiling,2 and HBD-seq or DRIP-seq for R-loop positioning.3, 4 Current computational methods to calculate genome-wide enrichment of the signal of interest usually do not properly handle the count-based nature of sequencing data, they often do not make use of the local correlation structure of sequencing data, and they do not apply any regularization of enrichment estimates. This can result in unrealistic estimates of the true underlying biological enrichment of interest, unrealistically low estimates of confidence in point estimates of enrichment (or no estimates of confidence at all), unrealistic gyrations in enrichment estimates at very close (<10 bp) genomic loci due to noise inherent in sequencing data, and in a multiple-hypothesis testing problem during interpretation of genome-wide enrichment estimates. We developed a tool called Enricherator to infer genome-wide enrichments from sequencing count data. Enricherator uses the variational Bayes algorithm to fit a generalized linear model to sequencing count data and to sample from the approximate posterior distribution of enrichment estimates (https://github.com/jwschroeder3/enricherator). Enrichments inferred by Enricherator more precisely identify known binding sites in cases where low coverage between binding sites leads to false-positive peak calls in these noisy regions of the genome; these benefits extend to published datasets.

在研究基因调控、染色质结构或基因组学的生物学研究中,一个普遍存在的问题是,感兴趣的信号在全基因组范围内的什么地方以及在多大程度上出现?解决这一问题的方法多种多样,其最终输出都依赖于高通量测序数据,包括用于蛋白质-DNA 相互作用的 ChIP-seq、用于测量超卷曲的 GapR-seq 以及用于 R 环定位的 HBD-seq 或 DRIP-seq。目前计算感兴趣信号的全基因组富集度的计算方法通常不能正确处理测序数据基于计数的性质,它们往往没有利用测序数据的局部相关结构,也没有对富集度估计值进行任何正则化处理。这可能会导致对感兴趣的真实潜在生物富集度的不切实际的估计、对富集度点估计值的不切实际的低置信度估计(或根本没有置信度估计)、由于测序数据固有的噪声而导致非常接近(<10 bp)基因组位点的富集度估计值出现不切实际的回旋,以及在解释全基因组富集度估计值时出现多重假设检验问题。我们开发了一种名为 Enricherator 的工具,用于从测序计数数据中推断全基因组富集度。Enricherator 使用变异贝叶斯算法对测序计数数据拟合广义线性模型,并从富集估计值的近似后验分布中采样()。当结合位点之间的低覆盖率导致基因组中这些嘈杂区域出现假阳性峰值调用时,Enricherator推断出的富集度能更精确地识别已知的结合位点;这些优势已扩展到已发表的数据集。
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引用次数: 0
NucMap 2.0: An Updated Database of Genome-wide Nucleosome Positioning Maps Across Species NucMap 2.0:跨物种全基因组核糖体定位图最新数据库。
IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-09-01 DOI: 10.1016/j.jmb.2024.168655

Nucleosome dynamics plays important roles in many biological processes, such as DNA replication and gene expression. NucMap (https://ngdc.cncb.ac.cn/nucmap) is the first database of genome-wide nucleosome positioning maps across species. Here, we present an updated version, NucMap 2.0, by incorporating more species and MNase-seq samples. In addition, we integrate other related omics data for each MNase-seq sample to provide a comprehensive view of nucleosome positioning, such as gene expression, transcription factor binding sites, histone modifications and DNA methylation. In particular, NucMap 2.0 integrates and pre-analyzes RNA-seq data and ChIP-seq data of human-related samples, which facilitates the interpretation of nucleosome positioning in humans. All processed data are integrated into an in-built genome browser, and users can make comprehensive side-by-side analyses. In addition, more online analytical functions are developed, which allows researchers to identify differential nucleosome regions and explore potential gene regulatory regions. All resources are open access with a user-friendly web interface.

核小体动力学在 DNA 复制和基因表达等许多生物过程中发挥着重要作用。NucMap (https://ngdc.cncb.ac.cn/nucmap) 是第一个跨物种的全基因组核糖体定位图数据库。在此,我们将纳入更多物种和 MNase-seq 样本,推出更新版 NucMap 2.0。此外,我们还为每个MNase-seq样本整合了其他相关的omics数据,以提供一个全面的核小体定位视图,如基因表达、转录因子结合位点、组蛋白修饰和DNA甲基化。特别是,NucMap 2.0 整合并预分析了人类相关样本的 RNA-seq 数据和 ChIP-seq 数据,这有助于解读人类的核小体定位。所有处理过的数据都集成到内置的基因组浏览器中,用户可以进行全面的并排分析。此外,还开发了更多在线分析功能,使研究人员能够识别差异核糖体区域并探索潜在的基因调控区域。所有资源都是开放式的,具有用户友好的网络界面。
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引用次数: 0
PPInterface: A Comprehensive Dataset of 3D Protein-Protein Interface Structures PPInterface:三维蛋白质-蛋白质界面结构综合数据集。
IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-09-01 DOI: 10.1016/j.jmb.2024.168686

The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein–protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein–protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset’s utility and accessibility.

The dataset and web server can be found at https://3dpath.ku.edu.tr/PPInt/.

PPInterface 数据集包含 815,082 个界面结构,提供了最全面的蛋白质-蛋白质界面结构信息。该资源提取自蛋白质数据库(PDB)中存储的 215,000 多个三维蛋白质结构。该数据集包含各种蛋白质复合物,为研究蛋白质-蛋白质相互作用结构特性的研究人员提供了丰富的信息。配套的网络服务器具有用户友好的界面,可实现高效的搜索和下载功能。研究人员可以获取蛋白质界面结构的详细信息,将其可视化,并探索各种功能,从而提高数据集的实用性和可访问性。数据集和网络服务器可在 https://3dpath.ku.edu.tr/PPInt/ 上找到。
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引用次数: 0
CATH 2024: CATH-AlphaFlow Doubles the Number of Structures in CATH and Reveals Nearly 200 New Folds CATH 2024:CATH-AlphaFlow 使 CATH 中的结构数量翻了一番,并揭示了近 200 个新折叠。
IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-09-01 DOI: 10.1016/j.jmb.2024.168551

CATH (https://www.cathdb.info) classifies domain structures from experimental protein structures in the PDB and predicted structures in the AlphaFold Database (AFDB). To cope with the scale of the predicted data a new NextFlow workflow (CATH-AlphaFlow), has been developed to classify high-quality domains into CATH superfamilies and identify novel fold groups and superfamilies. CATH-AlphaFlow uses a novel state-of-the-art structure-based domain boundary prediction method (ChainSaw) for identifying domains in multi-domain proteins. We applied CATH-AlphaFlow to process PDB structures not classified in CATH and AFDB structures from 21 model organisms, expanding CATH by over 100%.

Domains not classified in existing CATH superfamilies or fold groups were used to seed novel folds, giving 253 new folds from PDB structures (September 2023 release) and 96 from AFDB structures of proteomes of 21 model organisms. Where possible, functional annotations were obtained using (i) predictions from publicly available methods (ii) annotations from structural relatives in AFDB/UniProt50. We also predicted functional sites and highly conserved residues. Some folds are associated with important functions such as photosynthetic acclimation (in flowering plants), iron permease activity (in fungi) and post-natal spermatogenesis (in mice).

CATH-AlphaFlow will allow us to identify many more CATH relatives in the AFDB, further characterising the protein structure landscape.

CATH (https://www.cathdb.info) 根据 PDB 中的实验蛋白质结构和 AlphaFold 数据库 (AFDB) 中的预测结构对结构域进行分类。为了应对预测数据的规模,我们开发了一个新的 NextFlow 工作流程(CATH-AlphaFlow),将高质量的结构域归入 CATH 超家族,并识别新的折叠组和超家族。CATH-AlphaFlow 采用了最先进的基于结构的新型结构域边界预测方法(ChainSaw)来识别多结构域蛋白质中的结构域。我们应用 CATH-AlphaFlow 处理了未在 CATH 中分类的 PDB 结构和来自 21 个模式生物的 AFDB 结构,将 CATH 扩大了 100%。未被归入现有 CATH 超家族或折叠组的结构域被用来培育新的折叠,从 PDB 结构(2023 年 9 月发布)中产生了 253 个新折叠,从 21 个模式生物的蛋白质组的 AFDB 结构中产生了 96 个新折叠。在可能的情况下,功能注释是通过 (i) 公开方法预测 (ii) AFDB/UniProt50 中的结构亲缘注释获得的。我们还预测了功能位点和高度保守的残基。一些褶皱与一些重要功能有关,如光合适应(开花植物)、铁渗透酶活性(真菌)和产后精子发生(小鼠)。CATH-AlphaFlow 将使我们能够在 AFDB 中识别出更多的 CATH 亲缘结构,从而进一步确定蛋白质结构的特征。
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引用次数: 0
GalaxySagittarius-AF: Predicting Targets for Drug-Like Compounds in the Extended Human 3D Proteome GalaxySagittarius-AF:在扩展的人类三维蛋白质组中预测类药物的靶点
IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-09-01 DOI: 10.1016/j.jmb.2024.168617

In recent years, advancements in deep learning techniques have significantly expanded the structural coverage of the human proteome. GalaxySagittarius-AF translates these achievements in structure prediction into target prediction for druglike compounds by incorporating predicted structures. This web server searches the database of human protein structures using both similarity- and structure-based approaches, suggesting potential targets for a given druglike compound. In comparison to its predecessor, GalaxySagittarius, GalaxySagittarius-AF utilizes an enlarged structure database, incorporating curated AlphaFold model structures alongside their binding sites and ligands, predicted using an updated version of GalaxySite. GalaxySagittarius-AF covers a large human protein space compared to many other available computational target screening methods. The structure-based prediction method enhances the use of expanded structural information, differentiating it from other target prediction servers that rely on ligand-based methods. Additionally, the web server has undergone enhancements, operating two to three times faster than its predecessor. The updated report page provides comprehensive information on the sequence and structure of the predicted protein targets. GalaxySagittarius-AF is accessible at https://galaxy.seoklab.org/sagittarius_af without the need for registration.

近年来,深度学习技术的进步极大地扩展了人类蛋白质组的结构覆盖范围。GalaxySagittarius-AF 将这些结构预测方面的成就转化为类药物的靶点预测,将预测的结构纳入其中。该网络服务器使用相似性和基于结构的方法搜索人类蛋白质结构数据库,为给定的类药物化合物推荐潜在靶点。与它的前身 GalaxySagittarius 相比,GalaxySagittarius-AF 利用了更大的结构数据库,将经过策划的 AlphaFold 模型结构与利用 GalaxySite 更新版预测的结合位点和配体结合在一起。与许多其他可用的计算目标筛选方法相比,GalaxySagittarius-AF 覆盖了一个巨大的人类蛋白质空间。基于结构的预测方法加强了对扩展结构信息的利用,使其有别于其他依赖配体方法的靶标预测服务器。此外,网络服务器也进行了改进,运行速度比其前身快了两到三倍。更新后的报告页面提供了有关预测蛋白质靶标序列和结构的全面信息。GalaxySagittarius-AF 可通过 https://galaxy.seoklab.org/sagittarius_af 访问,无需注册。
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引用次数: 0
Fungtion: A Server for Predicting and Visualizing Fungal Effector Proteins Fungtion:预测和可视化真菌效应蛋白的服务器
IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-09-01 DOI: 10.1016/j.jmb.2024.168613

Fungal pathogens pose significant threats to plant health by secreting effectors that manipulate plant-host defences. However, identifying effector proteins remains challenging, in part because they lack common sequence motifs. Here, we introduce Fungtion (Fungal effector prediction), a toolkit leveraging a hybrid framework to accurately predict and visualize fungal effectors. By combining global patterns learned from pretrained protein language models with refined information from known effectors, Fungtion achieves state-of-the-art prediction performance. Additionally, the interactive visualizations we have developed enable researchers to explore both sequence- and high-level relationships between the predicted and known effectors, facilitating effector function discovery, annotation, and hypothesis formulation regarding plant-pathogen interactions. We anticipate Fungtion to be a valuable resource for biologists seeking deeper insights into fungal effector functions and for computational biologists aiming to develop future methodologies for fungal effector prediction: https://step3.erc.monash.edu/Fungtion/.

真菌病原体通过分泌效应蛋白操纵植物宿主的防御系统,对植物健康构成重大威胁。然而,识别效应蛋白仍然具有挑战性,部分原因是它们缺乏共同的序列基序。在这里,我们介绍 Fungtion(真菌效应物预测),这是一个利用混合框架准确预测和可视化真菌效应物的工具包。通过将从预训练蛋白质语言模型中学到的全局模式与已知效应物的细化信息相结合,Fungtion 实现了最先进的预测性能。此外,我们开发的交互式可视化技术还能让研究人员探索预测的效应物与已知效应物之间的序列关系和高层关系,从而促进效应物功能的发现、注释以及有关植物病原体相互作用的假设的提出。我们预计 Fungtion 将成为生物学家深入了解真菌效应物功能的宝贵资源,也将成为计算生物学家开发未来真菌效应物预测方法的宝贵资源:https://step3.erc.monash.edu/Fungtion/。
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引用次数: 0
3dRNA/DNA: 3D Structure Prediction from RNA to DNA 3dRNA/DNA:从 RNA 到 DNA 的三维结构预测
IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-09-01 DOI: 10.1016/j.jmb.2024.168742
Yi Zhang, Yiduo Xiong, Chenxi Yang, Yi Xiao

There is an increasing need for determining 3D structures of DNAs, e.g., for increasing the efficiency of DNA aptamer selection. Recently, we have proposed a computational method of 3D structure prediction of DNAs, called 3dDNA, which has been integrated into our original web server 3dRNA, now renamed 3dRNA/DNA (http://biophy.hust.edu.cn/new/3dRNA). Currently, 3dDNA can only output the predicted DNA 3D structures for users but cannot rank them as an energy function for assessing DNA 3D structures is still lacking. Here, we first provide a brief introduction to 3dDNA and then introduce a new energy function, 3dDNAscore, for the assessment of DNA 3D structures. 3dDNAscore is an all-atom knowledge-based potential by integrating 86 atomic types from nucleic acids. Benchmarks demonstrate that 3dDNAscore can effectively identify near-native structures from the decoys generated by 3dDNA, thus enhancing the completeness of 3dDNA.

现在越来越需要确定 DNA 的三维结构,例如提高 DNA 合体选择的效率。最近,我们提出了一种名为 3dDNA 的 DNA 三维结构预测计算方法,并将其集成到了我们最初的网络服务器 3dRNA,现在更名为 3dRNA/DNA(http://biophy.hust.edu.cn/new/3dRNA)。目前,3dDNA 只能为用户输出预测的 DNA 3D 结构,但不能对其进行排序,因为还缺乏评估 DNA 3D 结构的能量函数。在此,我们首先简要介绍 3dDNA,然后介绍一种用于评估 DNA 3D 结构的新能量函数 3dDNAscore。3dDNAscore 是一种基于全原子知识的势能,它整合了核酸中的 86 种原子类型。基准测试表明,3dDNAscore 能从 3dDNA 生成的诱饵中有效识别近原生结构,从而提高 3dDNA 的完整性。
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引用次数: 0
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Journal of Molecular Biology
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