DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity.
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity.
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DOI：
10.1093/bib/bbab160
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发表时间：
2021-11-05
影响因子：
9.5
通讯作者：
Salomonis N
中科院分区：
文献类型：
--
作者：
Li G;Iyer B;Prasath VBS;Ni Y;Salomonis N
溶细胞性T细胞通过寻找、结合和杀死在其表面上呈递外源抗原的细胞在适应性免疫系统中发挥重要作用。对T细胞免疫的进一步了解将极大地有助于开发新的癌症免疫疗法和针对威胁生命的病原体的疫苗。设计这种靶向疗法的核心是预测非天然肽引起T细胞应答的计算方法，然而，我们目前缺乏准确的免疫原性推断方法。另一个挑战是准确模拟特定人白细胞抗原等位基因的免疫原性肽的能力，用于合成生物学应用，以及增加真实的训练数据集。在这里，我们提出了一个β-二项分布的方法来获得肽免疫原性的潜力，从单独的序列。我们使用三个独立的事先验证的免疫原性肽集合（登革热病毒，癌症新抗原和SARS-CoV-2）对五种传统机器学习（ElasticNet，K-最近邻，支持向量机，随机森林和AdaBoost）和三种深度学习模型（卷积神经网络（CNN），残差网络和图神经网络）进行了系统的基准测试。我们选择CNN作为最佳预测模型，基于其对小型和大型数据集的适应性以及相对于现有方法的性能。除了优于两种常用的免疫原性预测算法外，DeepImmuno-CNN还正确预测了哪些残基对T细胞抗原识别最重要，并预测了SARS-CoV-2变体的新影响。我们的独立生成对抗网络（GAN）方法DeepImmuno-GAN进一步能够准确模拟具有与真实的抗原相似的物理化学性质和免疫原性预测的免疫原性肽。我们提供DeepImmuno-CNN作为源代码和易于使用的Web界面。
Cytolytic T-cells play an essential role in the adaptive immune system by seeking out, binding and killing cells that present foreign antigens on their surface. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life-threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native peptides to elicit a T-cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen alleles, for both synthetic biological applications, and to augment real training datasets. Here, we propose a beta-binomial distribution approach to derive peptide immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, K-nearest neighbors, support vector machine, Random Forest and AdaBoost) and three deep learning models (convolutional neural network (CNN), Residual Net and graph neural network) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-CoV-2). We chose the CNN as the best prediction model, based on its adaptivity for small and large datasets and performance relative to existing methods. In addition to outperforming two highly used immunogenicity prediction algorithms, DeepImmuno-CNN correctly predicts which residues are most important for T-cell antigen recognition and predicts novel impacts of SARS-CoV-2 variants. Our independent generative adversarial network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate immunogenic peptides with physicochemical properties and immunogenicity predictions similar to that of real antigens. We provide DeepImmuno-CNN as source code and an easy-to-use web interface.
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DOI：
10.1126/science.abe8499
发表时间：
2020-12-18
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Hou YJ;Chiba S;Halfmann P;Ehre C;Kuroda M;Dinnon KH 3rd;Leist SR;Schäfer A;Nakajima N;Takahashi K;Lee RE;Mascenik TM;Graham R;Edwards CE;Tse LV;Okuda K;Markmann AJ;Bartelt L;de Silva A;Margolis DM;Boucher RC;Randell SH;Suzuki T;Gralinski LE;Kawaoka Y;Baric RS
通讯作者：
Baric RS
MHC I类的性质提出了增强免疫原性的肽。
影响因子：
4.3
作者：
Calis JJ;Maybeno M;Greenbaum JA;Weiskopf D;De Silva AD;Sette A;Keşmir C;Peters B
通讯作者：
Peters B
影响因子：
3.7
作者：
Nielsen M;Lundegaard C;Blicher T;Lamberth K;Harndahl M;Justesen S;Røder G;Peters B;Sette A;Lund O;Buus S
通讯作者：
Buus S
影响因子：
64.5
作者：
Azizi E;Carr AJ;Plitas G;Cornish AE;Konopacki C;Prabhakaran S;Nainys J;Wu K;Kiseliovas V;Setty M;Choi K;Fromme RM;Dao P;McKenney PT;Wasti RC;Kadaveru K;Mazutis L;Rudensky AY;Pe'er D
通讯作者：
Pe'er D
通过结合局部和全局深度卷积神经网络来预测 RNA-蛋白质结合位点和基序。
影响因子：
5.8
作者：
Pan, Xiaoyong;Shen, Hong-Bin
通讯作者：
Shen, Hong-Bin