ArticleInternational journal of molecular sciences2016
A New Secondary Structure Assignment Algorithm Using Cα Backbone Fragments.
Article in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A Machine Learning Computational Framework Develops a Multiple Programmed Cell Death Index for Improving Clinical Outcomes in Bladder Cancer.Biochemical genetics · 2024Article
- Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops.Genes · 2024Article
- Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements fromBiomolecules · 2023Article
- A Perspective on the (Rise and Fall of) Protein β-Turns.International journal of molecular sciences · 2022Review
- Automated Protein Secondary Structure Assignment from CBiomolecules · 2022Article
- Assigning secondary structure in proteins using AI.Journal of molecular modeling · 2021Article
- Mathematical Basis of Predicting Dominant Function in Protein Sequences by a Generic HMM-ANN Algorithm.Acta biotheoretica · 2018Article
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6 authors.
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Abstract
The assignment of secondary structure elements in proteins is a key step in the analysis of their structures and functions. We have developed an algorithm, SACF (secondary structure assignment based on Cα fragments), for secondary structure element (SSE) assignment based on the alignment of Cα backbone fragments with central poses derived by clustering known SSE fragments. The assignment algorithm consists of three steps: First, the outlier fragments on known SSEs are detected. Next, the remaining fragments are clustered to obtain the central fragments for each cluster. Finally, the central fragments are used as a template to make assignments. Following a large-scale comparison of 11 secondary structure assignment methods, SACF, KAKSI and PROSS are found to have similar agreement with DSSP, while PCASSO agrees with DSSP best. SACF and PCASSO show preference to reducing residues in N and C cap regions, whereas KAKSI, P-SEA and SEGNO tend to add residues to the terminals when DSSP assignment is taken as standard. Moreover, our algorithm is able to assign subtle helices (310-helix, π-helix and left-handed helix) and make uniform assignments, as well as to detect rare SSEs in β-sheets or long helices as outlier fragments from other programs. The structural uniformity should be useful for protein structure classification and prediction, while outlier fragments underlie the structure-function relationship.
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