Palistha Shrestha (전북대학교)

한빛사논문

조회104

Palistha ShresthaPalistha Shrestha

전북대학교

Nat. Commun., Aug 07 2024, 15 (1) | https://doi.org/10.1038/s41467-024-51071-9 Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model

Palistha Shrestha ^1,5, Jeevan Kandel ^2,5, Hilal Tayara ³& Kil To Chong^1,4

¹Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Jeollabuk-do, Republic of Korea.

²Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju, Jeollabuk-do, Republic of Korea.

³School of International Engineering and Science, Jeonbuk National University, Jeonju, Jeollabuk-do, Republic of Korea.

⁴Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju, Jeollabukdo, Republic of Korea.

⁵These authors contributed equally: Palistha Shrestha, Jeevan Kandel.

Corresponding authors

Correspondence to Hilal Tayara or Kil To Chong.

Abstract

Post-translational modifications (PTMs) are pivotal in modulating protein functions and influencing cellular processes like signaling, localization, and degradation. The complexity of these biological interactions necessitates efficient predictive methodologies. In this work, we introduce PTMGPT2, an interpretable protein language model that utilizes prompt-based fine-tuning to improve its accuracy in precisely predicting PTMs. Drawing inspiration from recent advancements in GPT-based architectures, PTMGPT2 adopts unsupervised learning to identify PTMs. It utilizes a custom prompt to guide the model through the subtle linguistic patterns encoded in amino acid sequences, generating tokens indicative of PTM sites. To provide interpretability, we visualize attention profiles from the model's final decoder layer to elucidate sequence motifs essential for molecular recognition and analyze the effects of mutations at or near PTM sites to offer deeper insights into protein functionality. Comparative assessments reveal that PTMGPT2 outperforms existing methods across 19 PTM types, underscoring its potential in identifying disease associations and drug targets.

논문정보

형식Research article
게재일2024년 08월 (BRIC 등록일 2024-08-07)
연구진국내 연구진
분야 바이오·의료융합 > 다학제 융합/복합

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