PSSRcomp: a detailed analysis of secondary protein structure prediction

Abstract

The field of protein structure research has seen a significant transformation with the introduction of deep learning. Based on protein primary structure sequences, predicting secondary structures plays a crucial role in determining and understanding the roles of an amino acid sequence. Protein structural analysis was traditionally performed using time-consuming and sometimes imprecise technologies. We composed four types of prebuilt models - LSTM, RNN, GNN, ANN, etc, to predict secondary structures from different latest datasets of PDBbind and PISCES to understand the learning process HMM, Sequence embedding, and PSSM in a broader sense of machine readability. The q3 and q8 states of the secondary sequence are measured and analyzed for their distribution throughout the data set to comprehend their structural properties. In the post-AlphaFold era, these amino acid sequence data were theoretically capped at 94% accuracy. Our research hyper-optimized those approaches to find effective sequence prediction methods for better accuracy. Our optimized model acquired 0.9031 accuracy with impressive test results and custom evaluation metrics. This integration facilitates the relationship between protein structural properties to identify differences between protein learning models. Personalized treatment, drug development, and a better understanding of illnesses have all been made possible by the capacity to predict protein structures using these cutting-edge deep learning approaches reliably. However, it is important to use these computational models as a backup. Human expertise and domain knowledge are essential to interpret and validate data.