Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation

Zhao, Shenghao1,2; Yang, Xulei1; Zeng, Zeng1; Qian, Peisheng1; Zhao, Ziyuan1; Dai, Lingyun3,4; Prabhu, Nayana3; Nordlund, Paer3,5; Tam, Wai Leong6

2024-01-22

10.1038/s41598-024-51193-6

SCIENTIFIC REPORTS   影响因子和分区

2045-2322

Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of drug mechanisms of action and the dissection of protein interaction dynamics in different cellular states. One of the barriers to the wide applications of MS-CETSA is that MS-CETSA experiments must be performed on the specific cell lines of interest, which is typically time-consuming and costly in terms of labeling reagents and mass spectrometry time. In this study, we aim to predict CETSA features in various cell lines by introducing a computational framework called CycleDNN based on deep neural network technology. For a given set of n cell lines, CycleDNN comprises n auto-encoders. Each auto-encoder includes an encoder to convert CETSA features from one cell line into latent features in a latent space Z. It also features a decoder that transforms the latent features back into CETSA features for another cell line. In such a way, the proposed CycleDNN creates a cyclic prediction of CETSA features across different cell lines. The prediction loss, cycle-consistency loss, and latent space regularization loss are used to guide the model training. Experimental results on a public CETSA dataset demonstrate the effectiveness of our proposed approach. Furthermore, we confirm the validity of the predicted MS-CETSA data from our proposed CycleDNN through validation in protein-protein interaction prediction.

SCI

英语

其他

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:001148428500076

NATURE PORTFOLIO

Web of Science

1.ASTAR, Inst Infocomm Res I2R, Singapore City 138632, Singapore
2.Natl Univ Singapore NUS, Singapore City 119077, Singapore
3.ASTAR, Inst Mol & Cell Biol IMCB, Singapore City 138632, Singapore
4.Jinan Univ, Southern Univ Sci & Technol, Shenzhen Peoples Hosp, Affiliated Hosp 1, Shenzhen 518020, Peoples R China
5.Karolinska Inst, Dept Oncol & Pathol, S-17177 Stockholm, Sweden
6.ASTAR, Genome Inst Singapore GIS, Singapore City 138632, Singapore

Zhao, Shenghao,Yang, Xulei,Zeng, Zeng,et al. Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation[J]. SCIENTIFIC REPORTS,2024,14(1).

Zhao, Shenghao.,Yang, Xulei.,Zeng, Zeng.,Qian, Peisheng.,Zhao, Ziyuan.,...&Tam, Wai Leong.(2024).Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation.SCIENTIFIC REPORTS,14(1).

Zhao, Shenghao,et al."Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation".SCIENTIFIC REPORTS 14.1(2024).