Abstract
BackgroundDriven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction.ResultsTo explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist’s real-value distance prediction is 0.896 Å2 when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å2 of DeepDist’s multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist’s multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment.ConclusionsDeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone.
Highlights
Driven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction
After deep learning was introduced for contact prediction in 2012 [8], different deep learning architectures have been designed to integrate traditional sequence features with inter-residue coevolution scores to substantially improve contact/distance prediction [9,10,11,12], even for some targets with shallow multiple sequence alignment (MSA)
We further show that predicting both real-value distance map and multi-class distance map simultaneously is more accurate than only predicting realvalue distance map, demonstrating the advantage of DeepDist multi-task learning framework to improve protein distance prediction
Summary
Inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. The accuracy of protein inter-residue contact prediction has been substantially increased due to the development of residue-residue co-evolution analysis methods effectively detecting the directly correlated mutations of contacted residues in the sequences of a protein family, such as Direct Coupling Analysis (DCA) [1], plmDCA [2], GREMLIN [3], CCMpred [4], and PSICOV [5] The capability of these methods to extract the correlated mutation information for contact prediction largely depends on the number of effective sequences in multiple sequence alignment (MSA) of a target protein. After deep learning was introduced for contact prediction in 2012 [8], different deep learning architectures have been designed to integrate traditional sequence features with inter-residue coevolution scores to substantially improve contact/distance prediction [9,10,11,12], even for some targets with shallow MSAs
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