Improved prediction of protein-protein interactions using AlphaFold2.
Improved prediction of protein-protein interactions using AlphaFold2.
复制标题
DOI：
10.1038/s41467-022-28865-w
复制
发表时间：
2022-03-10
影响因子：
16.6
通讯作者：
Elofsson A
中科院分区：
文献类型：
--
作者：
Bryant P;Pozzati G;Elofsson A
不幸的是，预测蛋白质链的结构是迈向蛋白质功能的基本步骤。生成可接受的质量（dockq≥0.23）的模型，以与预测的界面产生一个简单的函数，以预测可接受的分数，从而使用预测的dockq corvors conders conterfient frots conteriate from conteriation from conteriation from conteriation from conteriation from conteriate from conteriation from frots from from conteriate from conteriate from。
在这里预测蛋白质复合物的结构非常困难。
Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR. Predicting the structure of protein complexes is extremely difficult. Here, authors apply AlphaFold2 with optimized multiple sequence alignments to model complexes of interacting proteins, enabling prediction of both if and how proteins interact with state-of-art accuracy.
登录
查看更多内容
影响因子：
16.6
作者：
Green AG;Elhabashy H;Brock KP;Maddamsetti R;Kohlbacher O;Marks DS
通讯作者：
Marks DS
影响因子：
2.9
作者：
Lensink MF;Brysbaert G;Mauri T;Nadzirin N;Velankar S;Chaleil RAG;Clarence T;Bates PA;Kong R;Liu B;Yang G;Liu M;Shi H;Lu X;Chang S;Roy RS;Quadir F;Liu J;Cheng J;Antoniak A;Czaplewski C;Giełdoń A;Kogut M;Lipska AG;Liwo A;Lubecka EA;Maszota-Zieleniak M;Sieradzan AK;Ślusarz R;Wesołowski PA;Zięba K;Del Carpio Muñoz CA;Ichiishi E;Harmalkar A;Gray JJ;Bonvin AMJJ;Ambrosetti F;Vargas Honorato R;Jandova Z;Jiménez-García B;Koukos PI;Van Keulen S;Van Noort CW;Réau M;Roel-Touris J;Kotelnikov S;Padhorny D;Porter KA;Alekseenko A;Ignatov M;Desta I;Ashizawa R;Sun Z;Ghani U;Hashemi N;Vajda S;Kozakov D;Rosell M;Rodríguez-Lumbreras LA;Fernandez-Recio J;Karczynska A;Grudinin S;Yan Y;Li H;Lin P;Huang SY;Christoffer C;Terashi G;Verburgt J;Sarkar D;Aderinwale T;Wang X;Kihara D;Nakamura T;Hanazono Y;Gowthaman R;Guest JD;Yin R;Taherzadeh G;Pierce BG;Barradas-Bautista D;Cao Z;Cavallo L;Oliva R;Sun Y;Zhu S;Shen Y;Park T;Woo H;Yang J;Kwon S;Won J;Seok C;Kiyota Y;Kobayashi S;Harada Y;Takeda-Shitaka M;Kundrotas PJ;Singh A;Vakser IA;Dapkūnas J;Olechnovič K;Venclovas Č;Duan R;Qiu L;Xu X;Zhang S;Zou X;Wodak SJ
通讯作者：
Wodak SJ
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：
2.9
作者：
Huang, Sheng-You;Zou, Xiaoqin
通讯作者：
Zou, Xiaoqin
影响因子：
8
作者：
Kundrotas, Petras J.;Anishchenko, Ivan;Vakser, Ilya A.
通讯作者：
Vakser, Ilya A.