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Prediction of protein tertiary structure using pre-Trained self-supervised learning based on transformer
Kurniawan A.a, Jatmiko W.a, Hertadi R.b, Habibie N.c
a Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia
b Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Bandung, Indonesia
c Faculty of Engineering, University of Freiburg, Freiburg, Germany
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IEEE.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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Conventional approach,Evolutionary information,Experimental methods,Homologous proteins,Protein structure prediction,Protein tertiary structures,Root mean square deviations,Root mean square errors[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Protein Structure Prediction,Self-Supervised Pre-Trained,Transformer[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/IWBIS50925.2020.9255624[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]