Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.

The training set, validation set and test set for training and validating the deep learning model before the blind test are randomly selected according the commonly used 8:1:1 ratio.
All the training data and three of four test datasets were collected by third party and are blind to us. The proteins in the fourth test dataset were downloaded from the Protein Data Bank (PDB) -a public database. The proteins in the fourth dataset were deposited in the PDB by third-party long after the proteins in the training dataset. The sequences in the test dataset that are similar to those in the training dataset were removed according to a stringent 30% identity threshold. Therefore, the fourth test dataset can also be considered a blind test dataset that has no overlap with the training dataset.
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