Prediction of protein tertiary structure using pre-Trained self-supervised learning based on transformer

Alif Kurniawan, Wisnu Jatmiko, Rukman Hertadi, Novian Habibie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Information of 3D protein structure plays an important role in various fields, including health, biotechnology, biomaterials, and so on. Knowledge of the 3D structure of proteins will help in understanding the interactions that can take place with other molecules such as drug molecules and other effector molecules. To get the 3D protein structure, it is generally carried out experimentally using x-ray diffraction and NMR (Nuclear Magnetic Resonance) methods. The experimental method requires a relatively long time and expertise to handle the completion of the structure. Until now, not all protein structures can be determined because the level of complexity varies from one protein to another. One approach is to use machine learning that leverages evolution information and deep learning method. The results given can improve the accuracy of the prediction compared to the conventional approach. However, the level of accuracy is still influenced by the number of homologous proteins in the database. Therefore, this study propose to replace the process of extracting the evolutionary information from Multiple Sequence Analysis (MSA) into Transformer-based self-supervised pre-Trained. To test these changes, an experiment was carried out on a 3D protein structure prediction model based on Long Short-Term Memory (LSTM) and Universal Transformer. The proposed method results show a decrease in the value of distance Root-Mean Square Deviation (dRMSD) of 0.561 Angstrom and Root Mean Square Error (RMSE) torsion angle of 0.11 degree on the Universal Transformer predictor. The results of the T-Test show that the decrease in the two indicators shows a significant result. Therefore, pre-Trained data can be used as an evolutionary information only for the Universal Transformer predictor.

Original languageEnglish
Title of host publication2020 International Workshop on Big Data and Information Security, IWBIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages73-78
Number of pages6
ISBN (Electronic)9781728190983
DOIs
Publication statusPublished - 17 Oct 2020
Event5th International Workshop on Big Data and Information Security, IWBIS 2020 - Depok, Indonesia
Duration: 17 Oct 202018 Oct 2020

Publication series

Name2020 International Workshop on Big Data and Information Security, IWBIS 2020

Conference

Conference5th International Workshop on Big Data and Information Security, IWBIS 2020
CountryIndonesia
CityDepok
Period17/10/2018/10/20

Keywords

  • Protein Structure Prediction
  • Self-Supervised Pre-Trained
  • Transformer

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