In Bioinformatics (Oxford, England)
MOTIVATION : Deep learning has become the dominant technology for protein contact prediction. However, the factors that affect the performance of deep learning in contact prediction have not been systematically investigated.
RESULTS : We analyzed the results of our three deep learning-based contact prediction methods (MULTICOM-CLUSTER, MULTICOM-CONSTRUCT, and MULTICOM-NOVEL) in the CASP13 experiment and identified several key factors (i.e. deep learning technique, multiple sequence alignment, distance distribution prediction, and domain-based contact integration) that influenced the contact prediction accuracy. We compared our convolutional neural network (CNN)-based contact prediction methods with three co-evolution-based methods on 75 CASP13 targets consisting of 108 domains. We demonstrated that the CNN-based multi-distance approach was able to leverage global co-evolutionary coupling patterns comprised of multiple correlated contacts for more accurate contact prediction than the local coevolution-based methods, leading to a substantial increase of precision by 19.2 percentage points. We also tested different alignment methods and domain-based contact prediction with the deep learning contact predictors. The comparison of the three methods showed deeper sequence alignments and the integration of domain-based contact prediction with the full-length contact prediction improved the performance of contact prediction. Moreover, we demonstrated that the domain-based contact prediction based on a novel ab initio approach of parsing domains from multiple sequence alignments alone without using known protein structures was a simple, fast approach to improve contact prediction. Finally, we showed that predicting the distribution of inter-residue distances in multiple distance intervals could capture more structural information and improve binary contact prediction.
AVAILABILITY : https://github.com/multicom-toolbox/DNCON2/.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.
Wu Tianqi, Hou Jie, Adhikari Badri, Cheng Jianlin