JSAI2024

Presentation information

Poster Session

Poster session » Poster session

[4Xin2] Poster session 2

Fri. May 31, 2024 12:00 PM - 1:40 PM Room X (Event hall 1)

[4Xin2-76] Generation of mutant protein amino acid sequence with Variational Autoencoder

〇Hiroya Ijima1, Yoshihiro Osakabe1, Ryoichi Takase1, Hikaru Koyama1, Akinori Asahara1 (1.Hitachi, Ltd.)

Keywords:bioinformatics, generative model

In order to improve the efficiency of functional protein development, there are widespread attempts to have amino acid sequence generation models propose promising protein candidates. However, sequences similar to known sequences output by generative models are not always promising in terms of performance. In this study, we propose a method to learn not only sequence similarity but also performance similarity at the same time. This method uses a Variational Autoencoder trained to correlate one component of a latent vector with protein performance, and generates mutant protein amino acid sequences extrapolated from known ones to enhance performance. A simulated evaluation using a performance prediction model confirms the effectiveness of the proposed method in improving development efficiency in the design of candidate proteins.

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