End-to-end differentiable protein design using variational AutoEncoder = 변분 오토인코더를 이용한 단백질 디자인

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Protein is an essential polymer that performs various functions in living things. Protein design means creating proteins with novel structures or functions. Computational protein design has been tried for a long time, but there were limitations because it could only deal with limited protein space compared to massive protein space. Recently, due to the advent of a deep generative model, proteins can be designed faster than conventional methods. In this research, protein sequences and corresponding structures were mapped into latent space through the variational autoencoder (VAE) model. Then, protein sequences and dihedral angles were generated from the sampled latent variable through the decoder. Protein structures were approximated by fixed backbone bond lengths and bond angles and appended new atoms with predicted dihedral angles. End to end differentiable protein structure generative model was built. Through a case study of globin superfamily, the performance of the VAE model was examined. New globin proteins were designed through our VAE model, and its properties were analyzed. VAE model was also tried up to mainly alpha helix class level.
Advisors
Kim, Dongsupresearcher김동섭researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2020.8,[iv, 34 p. :]

Keywords

protein design▼adeep learning▼avariational autoencoder▼aconvolutional neural network▼adeep generative model; 단백질 디자인▼a딥러닝▼a변분 자동 인코더▼a합성곱 신경망▼a심층 생성 모델

URI
http://hdl.handle.net/10203/284933
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925092&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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