About this Research Topic
The latest advancements in numerical representation of protein sequence feature representation, combined with statistical and machine learning methods, are expected to discover or summarize the sequence characteristics that determine the secondary structure of a protein. These developments are also expected to improve universal high-precision prediction methods applicable to protein secondary structure, to help better understand the sequence model characteristics of some special protein secondary structures (such as transmembrane protein), and to use more accurate protein secondary structure predictors for protein function, protein folding, or drug discovery, etc.
This Research Topic welcomes Original Research Articles and Reviews on the topics of:
• Databases or new benchmark datasets relative to protein secondary structures
• New machine learning methods or tools for protein secondary structures prediction
• New sequence features representation methods for protein secondary structures prediction
• Prediction of the specific secondary structures, such as transmembrane proteins, π helices and 310 helices, etc.
• Application of protein secondary structures prediction in protein function, protein-protein interaction, protein-RNA interaction, drug discovery and other fields.
• Using protein modelling to uncover and explore structure-function relationships and identify new, non-natural protein function.
Keywords: Protein Secondary Structure, Feature Representation, Machine Learning, Deep Learning, Bioinformactics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.