Protein Secondary Structure Prediction: A Review of Progress and Directions

Abstract

Background: Over the last few decades, a search for the theory of protein folding has grown into a full-fledged research field at the intersection of biology, chemistry and informatics. Despite enormous effort, there are still open questions and challenges, like understanding the rules by which amino acid sequence determines protein secondary structure. Objective: In this review, we depict the progress of the prediction methods over the years and identify sources of improvement. Methods: The protein secondary structure prediction problem is described followed by the discussion on theoretical limitations, description of the commonly used data sets, features and a review of three generations of methods with the focus on the most recent advances. Additionally, methods with available online servers are assessed on the independent data set. Results: The state-of-the-art methods are currently reaching almost 88% for 3-class prediction and 76.5% for an 8-class prediction. Conclusion: This review summarizes recent advances and outlines further research directions.

ProteinUnet—An efficient alternative to SPIDER3‐single for sequence‐based prediction of protein secondary structures

“…[31,32] The secondary structure prediction for 1D sequences is analogous to the multi-label segmentation of 2D images, but to the best of our knowledge, U-Net architecture has not been used previously for protein structure prediction. [4,33,34] In our proposed architecture, each block in the contractive path contains three convolutional layers with zero padding and kernels of size 7 with stride 1, followed by a rectified linear unit (ReLU) activation. To decrease the number of the parameters and increase the correlation between Q8 and Q3 predictions, the output layer for states Q3 is calculated based on the output for Q8 (unlike in SPIDER3-Single where the outputs for Q8 and Q3 are parallel).…”

“…Experimental determination of the structure is costly and time-consuming compared to sequence determination [3] and the number of known sequences is even 1,000 times bigger than those of examined structures. [4] This creates a need for techniques and models that will computationally predict a protein structure from its primary sequence.…”

“…Accurate protein structure and function prediction rely, in part, on the accuracy of secondary structure prediction that has been extensively studied and resulting in many computational methods (e.g., see an overview [4] ). A number of researchers also concentrate on predicting structural properties of proteins like backbone dihedral angles leveraging this information for secondary structure prediction or calculation ( [7] based on the early/late-stage protein folding approach [8] ).…”

“…Experimental determination of the structure is costly and time‐consuming compared to sequence determination [ 3 ] and the number of known sequences is even 1,000 times bigger than those of examined structures. [ 4 ] This creates a need for techniques and models that will computationally predict a protein structure from its primary sequence. The challenge started in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone [ 3,5,6 ] and has not been solved yet.…”

“…Recently, developed state‐of‐the‐art methods of secondary structure prediction leverage deep neural network architectures and multiple sequence alignments (MSAs) of homologous sequences allowing them to achieve up to 88% Q3 accuracy, [ 4 ] especially for proteins with a large number of known homologous sequences. However, the majority of proteins do not have any known homologous sequences or very few of them.…”