DomNet: protein domain boundary prediction using enhanced general regression network and new profiles

Abstract

The accurate and stable prediction of protein domain boundaries is an important avenue for the prediction of protein structure, function, evolution, and design. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques. In this paper, we propose a new machine learning based domain predictor namely, DomNet that can show a more accurate and stable predictive performance than the existing state-of-the-art models. The DomNet is trained using a novel compact domain profile, secondary structure, solvent accessibility information, and interdomain linker index to detect possible domain boundaries for a target sequence. The performance of the proposed model was compared to nine different machine learning models on the Benchmark_2 dataset in terms of accuracy, sensitivity, specificity, and correlation coefficient. The DomNet achieved the best performance with 71% accuracy for domain boundary identification in multidomains proteins. With the CASP7 benchmark dataset, it again demonstrated superior performance to contemporary domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut, and DomainDiscovery.