Embeddings from deep learning transfer GO annotations beyond homology.
Embeddings from deep learning transfer GO annotations beyond homology.
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来自深度学习迁移的嵌入使标注超越了同源。
DOI：
10.1038/s41598-020-80786-0
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发表时间：
2021-01-13
影响因子：
4.6
通讯作者：
Rost B
中科院分区：
文献类型：
--
作者：
Littmann M;Heinzinger M;Dallago C;Olenyi T;Rost B
了解蛋白质功能对于推进分子和医学生物学至关重要，但通过基因本体 (GO) 进行的实验功能注释仅占所有已知蛋白质的 0.5%。计算方法通常通过基于同源性的注释转移（通过识别具有已知功能的序列相似蛋白质）或通过使用进化信息的预测方法来弥补这种序列注释差距。在这里，我们建议通过基于 SeqVec 嵌入中而不是序列空间中蛋白质的邻近性的注释转移来预测 GO 术语。这些嵌入源自蛋白质序列 (SeqVec) 的深度学习语言模型 (LM)，传输从预测 3300 万个蛋白质序列中的下一个氨基酸中获得的知识。复制 CAFA3 的条件，我们的方法对于 BPO、MFO 和 CCO 的 Fmax 分别达到 37±±2%、50±±3% 和 57±±2%。从数字上看，这似乎接近前十名 CAFA3 方法。当将注释转移限制为与查询具有 < 20%配对序列同一性的蛋白质时，性能下降（Fmax BPO 33 ± 2%，MFO 43 ± 3%，CCO 53 ± 2%）；这仍然优于基于序列的简单传输。 CAFA4 的初步结果似乎证实了这些发现。总的来说，这个新概念可能会改变蛋白质的注释，特别是对于来自较小家族的蛋白质或具有本质上无序区域的蛋白质。
Knowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-annotation gap typically through homology-based annotation transfer by identifying sequence-similar proteins with known function or through prediction methods using evolutionary information. Here, we propose predicting GO terms through annotation transfer based on proximity of proteins in the SeqVec embedding rather than in sequence space. These embeddings originate from deep learned language models (LMs) for protein sequences (SeqVec) transferring the knowledge gained from predicting the next amino acid in 33 million protein sequences. Replicating the conditions of CAFA3, our method reaches an Fmax of 37 ± 2%, 50 ± 3%, and 57 ± 2% for BPO, MFO, and CCO, respectively. Numerically, this appears close to the top ten CAFA3 methods. When restricting the annotation transfer to proteins with < 20% pairwise sequence identity to the query, performance drops (Fmax BPO 33 ± 2%, MFO 43 ± 3%, CCO 53 ± 2%); this still outperforms naïve sequence-based transfer. Preliminary results from CAFA4 appear to confirm these findings. Overall, this new concept is likely to change the annotation of proteins, in particular for proteins from smaller families or proteins with intrinsically disordered regions.
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影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
影响因子：
4.6
作者：
Goldberg T;Rost B;Bromberg Y
通讯作者：
Bromberg Y
DOI：
10.1093/bioinformatics/bts565
发表时间：
2012-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Fu L;Niu B;Zhu Z;Wu S;Li W
通讯作者：
Li W
影响因子：
4.1
作者：
Krebs, HA;Johnson, WA
通讯作者：
Johnson, WA
影响因子：
12.3
作者：
Jiang Y;Oron TR;Clark WT;Bankapur AR;D'Andrea D;Lepore R;Funk CS;Kahanda I;Verspoor KM;Ben-Hur A;Koo da CE;Penfold-Brown D;Shasha D;Youngs N;Bonneau R;Lin A;Sahraeian SM;Martelli PL;Profiti G;Casadio R;Cao R;Zhong Z;Cheng J;Altenhoff A;Skunca N;Dessimoz C;Dogan T;Hakala K;Kaewphan S;Mehryary F;Salakoski T;Ginter F;Fang H;Smithers B;Oates M;Gough J;Törönen P;Koskinen P;Holm L;Chen CT;Hsu WL;Bryson K;Cozzetto D;Minneci F;Jones DT;Chapman S;Bkc D;Khan IK;Kihara D;Ofer D;Rappoport N;Stern A;Cibrian-Uhalte E;Denny P;Foulger RE;Hieta R;Legge D;Lovering RC;Magrane M;Melidoni AN;Mutowo-Meullenet P;Pichler K;Shypitsyna A;Li B;Zakeri P;ElShal S;Tranchevent LC;Das S;Dawson NL;Lee D;Lees JG;Sillitoe I;Bhat P;Nepusz T;Romero AE;Sasidharan R;Yang H;Paccanaro A;Gillis J;Sedeño-Cortés AE;Pavlidis P;Feng S;Cejuela JM;Goldberg T;Hamp T;Richter L;Salamov A;Gabaldon T;Marcet-Houben M;Supek F;Gong Q;Ning W;Zhou Y;Tian W;Falda M;Fontana P;Lavezzo E;Toppo S;Ferrari C;Giollo M;Piovesan D;Tosatto SC;Del Pozo A;Fernández JM;Maietta P;Valencia A;Tress ML;Benso A;Di Carlo S;Politano G;Savino A;Rehman HU;Re M;Mesiti M;Valentini G;Bargsten JW;van Dijk AD;Gemovic B;Glisic S;Perovic V;Veljkovic V;Veljkovic N;Almeida-E-Silva DC;Vencio RZ;Sharan M;Vogel J;Kansakar L;Zhang S;Vucetic S;Wang Z;Sternberg MJ;Wass MN;Huntley RP;Martin MJ;O'Donovan C;Robinson PN;Moreau Y;Tramontano A;Babbitt PC;Brenner SE;Linial M;Orengo CA;Rost B;Greene CS;Mooney SD;Friedberg I;Radivojac P
通讯作者：
Radivojac P