GENERALIST: A latent space based generative model for protein sequence families.
GENERALIST: A latent space based generative model for protein sequence families.
复制标题
DOI：
10.1371/journal.pcbi.1011655
复制
发表时间：
2023-11
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
蛋白质序列家族的生成模型是蛋白质科学家和工程师的重要工具。然而，最先进的生成方法在对中等大小的蛋白质和/或具有低序列覆盖度的蛋白质家族建模时面临推断、准确性和过拟合相关的障碍。在这里，我们提出了一个简单的学习，可调的，和准确的生成模型，GENERAIST：GENERAtive非线性tenSor-factorizaTion蛋白质序列。GENERALIST准确地捕获了氨基酸协变的几个高阶汇总统计量。GENERALIST还预测保守的局部最优序列，这些序列可能在稳定的3D结构中折叠。重要的是，与目前的方法不同，GENERALIST模型的序列集合中的序列密度与相应的自然集合非常相似。最后，GENERALIST将蛋白质序列嵌入到信息潜在空间中。GENERALIST将成为研究蛋白质序列变异性的重要工具。蛋白质序列家族显示出巨大的序列变异。然而，据认为，功能序列空间的很大一部分仍然未被探索。生成模型是一种机器学习方法，它允许我们使用天然存在的蛋白质序列来学习是什么使蛋白质发挥功能。在这里，我们提出了一种新型的生成模型GENERALIST：GENERAtive nonLInear tenSor-factorizaTion蛋白质序列，是准确的，易于实现，并与非常小的数据集。我们相信GENERALIST将成为蛋白质科学家和工程师的重要工具。
Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting- related obstacles when modeling moderately sized to large proteins and/or protein families with low sequence coverage. Here, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. GENERALIST accurately captures several high order summary statistics of amino acid covariation. GENERALIST also predicts conservative local optimal sequences which are likely to fold in stable 3D structure. Importantly, unlike current methods, the density of sequences in GENERALIST-modeled sequence ensembles closely resembles the corresponding natural ensembles. Finally, GENERALIST embeds protein sequences in an informative latent space. GENERALIST will be an important tool to study protein sequence variability. Protein sequence families show tremendous sequence variation. Yet, it is thought that a large portion of the functional sequence space remains unexplored. Generative models are machine learning methods that allow us to learn what makes proteins functional using sequences of naturally occurring proteins. Here, we present a new type of generative model GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences that is accurate, easy to implement, and works with very small datasets. We believe that GENERALIST will be an important tool in the repertoire of protein scientists and engineers alike.
登录
查看更多内容
影响因子：
3
作者：
Muntoni AP;Pagnani A;Weigt M;Zamponi F
通讯作者：
Zamponi F
影响因子：
3.7
作者：
Ferguson AL;Falkowska E;Walker LM;Seaman MS;Burton DR;Chakraborty AK
通讯作者：
Chakraborty AK
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
5.6
作者：
David A;Islam S;Tankhilevich E;Sternberg MJE
通讯作者：
Sternberg MJE
影响因子：
4.3
作者：
Zhao X;Plata G;Dixit PD
通讯作者：
Dixit PD