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. 2022 Dec 13;17(5):175903. doi: 10.1007/s11704-022-2163-9

FragDPI: a novel drug-protein interaction prediction model based on fragment understanding and unified coding

Zhihui Yang 1, Juan Liu 1,✉, Xuekai Zhu 1, Feng Yang 1, Qiang Zhang 1, Hayat Ali Shah 1
PMCID: PMC9745276  PMID: 36532946

Abstract

Prediction of drug-protein binding is critical for virtual drug screening. Many deep learning methods have been proposed to predict the drug-protein binding based on protein sequences and drug representation sequences. However, most existing methods extract features from protein and drug sequences separately. As a result, they can not learn the features characterizing the drug-protein interactions. In addition, the existing methods encode the protein (drug) sequence usually based on the assumption that each amino acid (atom) has the same contribution to the binding, ignoring different impacts of different amino acids (atoms) on the binding. However, the event of drug-protein binding usually occurs between conserved residue fragments in the protein sequence and atom fragments of the drug molecule. Therefore, a more comprehensive encoding strategy is required to extract information from the conserved fragments.

In this paper, we propose a novel model, named FragDPI, to predict the drug-protein binding affinity. Unlike other methods, we encode the sequences based on the conserved fragments and encode the protein and drug into a unified vector. Moreover, we adopt a novel two-step training strategy to train FragDPI. The pre-training step is to learn the interactions between different fragments using unsupervised learning. The fine-tuning step is for predicting the binding affinities using supervised learning. The experiment results have illustrated the superiority of FragDPI.

Electronic Supplementary Material

Supplementary material is available for this article at 10.1007/s11704-022-2163-9 and is accessible for authorized users.

Keywords: affinity score, drug-protein interaction, BERT, Bi-Transformer, virtual drug screening

Electronic supplementary material

11704_2022_2163_MOESM1_ESM.pdf (366.2KB, pdf)

FragDPI: A Novel Drug-Protein Interaction Prediction Model Based on Fragment Understanding and Unified Coding

Acknowledgements

This work was supported by the National Key R&D Program of China (2019YFA0904303).

Footnotes

Zhihui Yang is a PhD candidate in the School of Computer Science, Wuhan University, China. His current research interests include synthetic biology, deep learning, metabolic pathway reconstruction, and metabolic flux analysis.

Juan Liu is a professor in the School of Computer Science, Wuhan University, China. Her research interests include machine learning, data mining, bioinformatics, pattern recognition, and artificial intelligence methods for medicine.

Xuekai Zhu is a master’s student in the School of Computer Science, Wuhan University, China. His current research interests are in artificial intelligence methods for bioinformatics.

Feng Yang is a PhD candidate in the School of Computer Science, Wuhan University, China. His current research interests include machine learning, retrosynthesis prediction and metabolic pathway design.

Qiang Zhang is a PhD candidate in the School of Computer Science, Wuhan University, China. Her current research interests include retrosynthesis prediction, metabolic pathway design, bioinformatics, and machine learning.

Hayat Ali Shah received his MS degree in Computer Science from Virtual University of Pakistan, Pakistan in 2018. He is currently a PhD candidate in the School of Computer Science, Wuhan University, China. His research interests are simulated alignments, multiple sequence alignments, machine learning, prediction and reconstruction of metabolic pathways.

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Supplementary Materials

11704_2022_2163_MOESM1_ESM.pdf (366.2KB, pdf)

FragDPI: A Novel Drug-Protein Interaction Prediction Model Based on Fragment Understanding and Unified Coding

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