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Protein Secondary Structure Prediction Using Soft Computing Techniques
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AI
DOI：10.1002/bab.70127.png)
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Abstract
En 中文
Accurate prediction of protein secondary structure is a critical step toward understanding protein function and facilitating structure-based drug discovery. We present a template-independent, single sequence method utilizing a shallow feed-forward artificial neural network (ANN) with one hot (binary) amino acid encoding and a sliding window input. The network is trained and evaluated on two datasets: (i) a curated, nonhomologous Protein Data Bank (PDB) set with a strict maximum pairwise sequence identity, annotated with STRIDE; and (ii) a homologous human papillomavirus (HPV) set (L1, L2, E1-E7) whose labels are obtained from the Proteus predictor and used solely for a system specific, post hoc analysis. To improve transparency regarding generalization, we report the all-vs-all sequence identity distribution for the nonhomologous set (matrix and histogram). The model achieves competitive Q3 accuracy on the nonhomologous PDB benchmark and yields Q3-agreement (Proteus) on the HPV case study. We explicitly frame the HPV evaluation as agreement with a labeling tool rather than accuracy versus experiment. Despite its simplicity and lack of evolutionary profiles, the ANN demonstrates robust sequence-only performance, offering a lightweight baseline that is easy to reproduce and deploy on the CPU. We discuss limitations (dataset size, lack of cross-tool bake-offs, absence of long-range features) and delineate concrete avenues for future work.
Keywords:
artificial neural network
protein secondary structure prediction
STRIDE
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