Improved global protein homolog detection with major gains in function identification.
Improved global protein homolog detection with major gains in function identification.
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DOI：
10.1073/pnas.2211823120
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发表时间：
2023-02-28
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
--
作者：
同源物检测，即寻找与未知蛋白质相似的蛋白质，通常是了解该蛋白质的作用和功能的第一步。然而，如果查询蛋白质和目标蛋白质之间的蛋白质序列同一性较低（< 30%），传统工具就很难区分正确匹配和随机匹配，无法识别重要的相似性。我们使用深度学习语言模型中的蛋白质表示来解决这个问题。减小这些表示的大小显着提高了同源物检测能力。截至 2022 年 3 月，我们的工具可以找到超过 93% 无法分配功能的人类蛋白质的推定同源物。蛋白质序列有数亿个，但现有同源物检测方法无法完全获得它们之间的关系。迫切需要一种改进的方法来将同源物检测推向较低水平的序列同一性。这里使用的方法依赖于语言模型在矩阵中以数字方式表示蛋白质（嵌入），并使用离散余弦变换来压缩数据以提取最重要的部分，从而显着减小数据大小。该蛋白质直系同源搜索工具 (PROST) 的线性运行时间明显更快，最重要的是，它可以计算蛋白质序列对之间的距离，以比以前显着降低序列同一性水平产生同源物。蛋白质变构效应的程度指出了结构和序列整体方面的重要性。 PROST 擅长全局同源性检测，但不擅长检测局部同源性。结果通过相应结构对之间的强烈相似性得到验证。检测到的远程同源物的数量显着增加，并将有效序列匹配推入更深处的暮色区。目前还没有指定功能的人类蛋白质序列现在在 93% 的病例中发现了大量的推定同源物，并且在 76.4% 的病例中发现了结构验证的指定功能。数据压缩使得能够在较短的搜索时间内大量搜索同源物，同时显着增加检测到的远程同源物的数量。该方法足够有效，可以进行全基因组/蛋白质组比较。 PROST Web 服务器可通过 https://mesihk.github.io/prost 访问。
Homolog detection, finding similar proteins to an unknown protein, is usually the first step in understanding the role and function of that protein. However, if the identity of protein sequences between query and target proteins is low (< 30%), traditional tools struggle to distinguish a correct match from a random one, failing to identify important similarities. We have used protein representations from deep learning language models to solve this problem. Reducing the size of these representations significantly improved homolog detection capabilities. Our tool can find putative homologs for more than 93% of human proteins that were not able to assign a function as of March 2022. There are several hundred million protein sequences, but the relationships among them are not fully available from existing homolog detection methods. There is an essential need for an improved method to push homolog detection to lower levels of sequence identity. The method used here relies on a language model to represent proteins numerically in a matrix (an embedding) and uses discrete cosine transforms to compress the data to extract the most essential part, significantly reducing the data size. This PRotein Ortholog Search Tool (PROST) is significantly faster with linear runtimes, and most importantly, computes the distances between pairs of protein sequences to yield homologs at significantly lower levels of sequence identity than previously. The extent of allosteric effects in proteins points out the importance of global aspects of structure and sequence. PROST excels at global homology detection but not at detecting local homologs. Results are validated by strong similarities between the corresponding pairs of structures. The number of remote homologs detected increased significantly and pushes the effective sequence matches more deeply into the twilight zone. Human protein sequences presently having no assigned function now find significant numbers of putative homologs for 93% of cases and structurally verified assigned functions for 76.4% of these cases. The data compression enables massive searches for homologs with short search times while yielding significant gains in the numbers of remote homologs detected. The method is sufficiently efficient to permit whole-genome/proteome comparisons. The PROST web server is accessible at https://mesihk.github.io/prost.
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影响因子：
14.9
作者：
Lees JG;Lee D;Studer RA;Dawson NL;Sillitoe I;Das S;Yeats C;Dessailly BH;Rentzsch R;Orengo CA
通讯作者：
Orengo CA
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：
14.9
作者：
UniProt Consortium
通讯作者：
UniProt Consortium
影响因子：
5.7
作者：
Ingles-Prieto, Alvaro;Ibarra-Molero, Beatriz;Delgado-Delgado, Asuncion;Perez-Jimenez, Raul;Fernandez, Julio M.;Gaucher, Eric A.;Sanchez-Ruiz, Jose M.;Gavira, Jose A.
通讯作者：
Gavira, Jose A.
影响因子：
5.6
作者：
Blake, JD;Cohen, FE
通讯作者：
Cohen, FE