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Prediction of protein subcellular localization
- Chin-Sheng Yu, Yu‐Ching Chen, Chih-Hao Lu, Jenn-Kang Hwang
- Biology, Computer ScienceProteins
- 15 August 2006
An approach based on a two‐level support vector machine (SVM) system, which performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity and when compared with other approaches, this approach performed significantly better.
Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n‐peptide compositions
- Chin-Sheng Yu, Chih‐Jen Lin, Jenn-Kang Hwang
- BiologyProtein science : a publication of the Protein…
- 1 May 2004
This method uses the support vector machines trained by multiple feature vectors based on n‐peptide compositions to predict subcellular localization for Gram‐negative bacteria, and achieves the highest prediction rate ever reported.
KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns
A new web server, KinasePhos 2.0, incorporates support vector machines (SVM) with the protein sequence profile and protein coupling pattern, which is a novel feature used for identifying phosphorylation sites and performs better than other tools previously developed.
Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution
CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation
This platform is used to obtain a brief or detailed gene ontology (GO)-type categories, including subcellular localization(s), for the queried proteins by combining the CELLO localization-predicting and BLAST homology-searching approaches.
Crystal Structure of Helicobacter pylori Formamidase AmiF Reveals a Cysteine-Glutamate-Lysine Catalytic Triad*
- Chiu-Lien Hung, Jia-Hsin Liu, W. Chiu, Shao-Wei Huang, Jenn-Kang Hwang, Wen-Ching Wang
- ChemistryJournal of Biological Chemistry
- 20 April 2007
Molecular dynamic simulations show that the conserved triad has minimal fluctuations, catalyzing the hydrolysis of a specific nitrile or amide in the nitrilase superfamily efficiently.
Predicting RNA-binding sites of proteins using support vector machines and evolutionary information
- Cheng-Wei Cheng, E. C. Su, Jenn-Kang Hwang, Ting-Yi Sung, W. Hsu
- BiologyBMC Bioinformatics
- 12 December 2008
This work proposes a method, RNAProB, which incorporates a new smoothed position-specific scoring matrix (PSSM) encoding scheme with a support vector machine model to predict RNA-binding sites in proteins, and shows that smoothed PSSM encoding significantly enhances the prediction performance, especially for sensitivity.
Proportion of solvent-exposed amino acids in a protein and rate of protein evolution.
- Yeong-shin Lin, W. Hsu, Jenn-Kang Hwang, Wen-Hsiung Li
- BiologyMolecular biology and evolution
- 1 April 2007
This work shows that protein structure is also an important determinant of protein evolutionary rate in yeast, and suggests that proteins with many residues under selective constraint tend to evolve slowly, supporting the "fitness (functional) density" hypothesis.
(PS)2: protein structure prediction server
- Chih-Chieh Chen, Jenn-Kang Hwang, Jinn-Moon Yang
- Computer Science, BiologyNucleic Acids Res.
- 1 July 2006
An automatic protein structure prediction server, (PS)2, which uses an effective consensus strategy both in template selection and target–template alignment integrating PSI-BLAST, IMPALA and T-Coffee, and which is considerably faster than other methods that rely on the additional structural consensus of templates.
MIB: Metal Ion-Binding Site Prediction and Docking Server
- Yu-Feng Lin, Chih-Wen Cheng, C. Shih, Jenn-Kang Hwang, Chin-Sheng Yu, Chih-Hao Lu
- Chemistry, BiologyJ. Chem. Inf. Model.
- 15 December 2016
The web server reported here was built to predict metal ion-binding residues and to generate the predictedMetal ion-bound 3D structure, which provides the metal ions docking after prediction.