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Updated: Jul 9, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Protein sub-cellular localisation prediction by analysis of short-range residue correlations
Jian Guo1, Yuanlie Lin, Zhirong Sun
1Laboratory of Statistical Computing and Bioinformatics, Department of Mathematical Sciences, Tsinghua University, Beijing, PR China. guojian99@tsinghua.org.cn
Abstract:
Sub-cellular localisation performs an important role in genome analysis. This paper describes a new residue-couple model using a support vector machine to predict the sub-cellular localisation of proteins. This new approach provides better predictions than the existing methods. The total prediction accuracies on Reinhardt and Hubbard's dataset reach 92.0% for prokaryotic protein sequences and 86.9% for eukaryotic protein sequences with fivefold cross validation. For a new dataset with 8304 proteins located in eight sub-cellular locations, the total accuracy achieves 88.9%. Meanwhile, the model shows robust against N-terminal errors in the sequences.
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