TransConv: convolution-infused transformer for protein secondary structure prediction

Context: Protein secondary structure prediction is essential for understanding protein function and characteristics and can also facilitate drug discovery. Traditional methods for experimentally determining protein structures are both time-consuming and costly. Computational biology offers a viable alternative by predicting protein structures from their sequences. Protein secondary structure is defined by the local spatial arrangement of the protein backbone, resulting from hydrogen bonds between amino acids.

Methods: In this study, we introduce TransConv, a model that combines transformer models with convolutional blocks to predict protein secondary structures from amino acid sequences. Transformer models are effective at capturing long-range dependencies through self-attention mechanisms. We integrate convolutional blocks into the transformer architecture to improve the detection of important local features. This hybrid model captures both long-range interactions and local features, leading to more accurate predictions of protein secondary structures, thus offering an efficient solution for this critical task. The experimental outcomes on the benchmark datasets depict the superiority of the proposed approach over the state-of-the-art (SOTA) models in the literature.