Design of metalloproteins and novel protein folds using variational autoencoders.
Design of metalloproteins and novel protein folds using variational autoencoders.
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DOI：
10.1038/s41598-018-34533-1
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发表时间：
2018-11-01
影响因子：
4.6
通讯作者：
Jones DT
中科院分区：
文献类型：
--
作者：
Greener JG;Moffat L;Jones DT
The design of novel proteins has many applications but remains an attritional process with success in isolated cases. Meanwhile, deep learning technologies have exploded in popularity in recent years and are increasingly applicable to biology due to the rise in available data. We attempt to link protein design and deep learning by using variational autoencoders to generate protein sequences conditioned on desired properties. Potential copper and calcium binding sites are added to non-metal binding proteins without human intervention and compared to a hidden Markov model. In another use case, a grammar of protein structures is developed and used to produce sequences for a novel protein topology. One candidate structure is found to be stable by molecular dynamics simulation. The ability of our model to confine the vast search space of protein sequences and to scale easily has the potential to assist in a variety of protein design tasks.
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影响因子：
14.9
作者：
Andreini C;Cavallaro G;Lorenzini S;Rosato A
通讯作者：
Rosato A
影响因子：
64.8
作者：
Chevalier A;Silva DA;Rocklin GJ;Hicks DR;Vergara R;Murapa P;Bernard SM;Zhang L;Lam KH;Yao G;Bahl CD;Miyashita SI;Goreshnik I;Fuller JT;Koday MT;Jenkins CM;Colvin T;Carter L;Bohn A;Bryan CM;Fernández-Velasco DA;Stewart L;Dong M;Huang X;Jin R;Wilson IA;Fuller DH;Baker D
通讯作者：
Baker D
影响因子：
3.7
作者：
Asgari E;Mofrad MR
通讯作者：
Mofrad MR
DOI：
10.1073/pnas.1218319110
发表时间：
2013-04-23
影响因子：
11.1
作者：
Dagliyan, Onur;Shirvanyants, David;Dokholyan, Nikolay V.
通讯作者：
Dokholyan, Nikolay V.
影响因子：
5.8
作者：
Capra, John A.;Singh, Mona
通讯作者：
Singh, Mona