Ensemble model for accuracy prediction of protein secondary structure

Srushti C. Shivaprasad (University Visvesvaraya College of Engineering (UVCE))
Prathibhavani P. Maruthi (University Visvesvaraya College of Engineering (UVCE))
Teja Shree Venkatesh (University Visvesvaraya College of Engineering (UVCE))
Venugopal K. Rajuk (Former Vice Chancellor, Bangalore University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 32, No 3: December 2023

Predicting a protein’s secondary structure is crucial for understanding the working of proteins. Despite advancements over the years, the top predictors have achieved only 80% Q8 accuracy when sequence profile information is the sole input. An ensemble approach is proposed using convolutional neural network (CNN) and a classifier known as support vector machine (SVM) on both the partial and the whole CullPDB datasets. The protein secondary structure (PSS) has a complex hierarchical structure, as well as the ability to take into account the reliance between neighbouring labels. A detailed experiment yielding high levels of Q8 accuracy with scores of 97.91%, 85.13%, and 78.02% using 20%, 80%, and 100% respectively of the protein residues on the new predicted dataset CullPDB6133 which is better than the accuracies predicted by similar models. The proposed methodology highlights the use of CNN as a general framework, for efficiently predicting eight-state (Q8) accuracy of secondary protein structures with a low time and space complexity.