title: Sixty-five years of the long march in protein secondary structure prediction: the final stretch?
abstract: Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82-84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88-90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cα-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction.
authors.affiliation: Insitute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD 4222, Australia.
authors.forename: Yuedong
authors.initials: Y
authors.lastname: Yang
authors.affiliation: School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China.
authors.forename: Jianzhao
authors.initials: J
authors.lastname: Gao
authors.affiliation: Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China.
authors.forename: Jihua
authors.initials: J
authors.lastname: Wang
authors.affiliation: Signal Processing Laboratory, Griffith University, Brisbane, 4122, Australia.
authors.forename: Rhys
authors.initials: R
authors.lastname: Heffernan
authors.affiliation: Signal Processing Laboratory, Griffith University, Brisbane, 4122, Australia.
authors.forename: Jack
authors.initials: J
authors.lastname: Hanson
authors.affiliation: Signal Processing Laboratory, Griffith University, Brisbane, 4122, Australia.
authors.forename: Kuldip
authors.initials: K
authors.lastname: Paliwal
authors.affiliation: Insitute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD 4222, Australia.|Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China.
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins