Predicting drug–protein interaction using quasi-visual question answering system

By Shuangjia Zheng, Yongjian Li, Sheng Chen, Jun Xu, Yuedong Yang. 2019

291 citations recorded by the source corpus.

Identifying novel drug–protein interactions is crucial for drug discovery. For this purpose, many machine learning-based methods have been developed based on drug descriptors and one-dimensional protein sequences. However, protein sequences cannot accurately reflect the interactions in three-dimensional space. However, direct input of three-dimensional structure is of low efficiency due to the sparse three-dimensional matrix, and is also prevented by the limited number of co-crystal structures available for training. Here we propose an end-to-end deep learning framework to predict the interactions by representing proteins with a two-dimensional distance map from monomer structures (Image) and drugs with molecular linear notation (String), following the visual question answering mode. For efficient training of the system, we introduce a dynamic attentive convolutional neural network to learn fixed-size representations from the variable-length distance maps and a self-attentional sequential model to automatically extract semantic features from the linear notations. Extensive experiments demonstrate that our model obtains competitive performance against state-of-the-art baselines on the directory of useful decoys, enhanced (DUD-E), human and BindingDB benchmark datasets. Further attention visualization provides biological interpretation to depict highlighted regions of both protein and drug molecules. When predicting the interaction of proteins with potential drugs, the protein can be encoded as its one-dimensional sequence or a three-dimensional structure, which can capture more relevant features of the protein, but also makes the task to predict the interactions harder. A new method predicts these interactions using a two-dimensional distance matrix representation of a protein, which can be processed like a two-dimensional image, striking a balance between the data being simple to process and rich in relevant structures.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, methodology): “…directly from protein-ligand pairs Wang and Dokholyan [2021], Li et al. [2020], Tsubaki et al. [2019], Gao et al. [2018], Karimi et al. [2019], Zheng et al. [2020] or make predictions through complex structure that has been previously obtained by experimental or docking approaches Jiménez et…”
Interpretable bilinear attention network with domain adaptation improves drug–target prediction (2022, unknown): “However, many previous studies learn global representations in their separate encoders, without explicitly learning local interactions 2,11,13,19,20 .”
AlphaFold2-aware protein-DNA binding site prediction using graph transformer (2022, background): “Effective learning of protein structure remains a challenging task, even though 1DCNN [31], 2DCNN [32], 3DCNN [33], GNN and its variants [34, 35] have been widely adopted.”
Geometric deep learning on molecular representations (2021, methodology): “Recurrent neural networks String notations (1D grid) Generative molecular design [19, 44, 45], synthesis planning [46], protein structure prediction [47, 48] and prediction of properties in drug discovery [49, 50]”