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dc.contributor.authorLiou, Yi-Fanen_US
dc.contributor.authorVasylenko, Tamaraen_US
dc.contributor.authorYeh, Chia-Lunen_US
dc.contributor.authorLin, Wei-Chunen_US
dc.contributor.authorChiu, Shih-Hsiangen_US
dc.contributor.authorCharoenkwan, Phasiten_US
dc.contributor.authorShu, Li-Sunen_US
dc.contributor.authorHo, Shinn-Yingen_US
dc.contributor.authorHuang, Hui-Lingen_US
dc.date.accessioned2019-04-03T06:41:58Z-
dc.date.available2019-04-03T06:41:58Z-
dc.date.issued2015-12-09en_US
dc.identifier.issn1471-2164en_US
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2164-16-S12-S6en_US
dc.identifier.urihttp://hdl.handle.net/11536/135692-
dc.description.abstractBackground: Identifying putative membrane transport proteins (MTPs) and understanding the transport mechanisms involved remain important challenges for the advancement of structural and functional genomics. However, the transporter characters are mainly acquired from MTP crystal structures which are hard to crystalize. Therefore, it is desirable to develop bioinformatics tools for the effective large-scale analysis of available sequences to identify novel transporters and characterize such transporters. Results: This work proposes a novel method (SCMMTP) based on the scoring card method (SCM) using dipeptide composition to identify and characterize MTPs from an existing dataset containing 900 MTPs and 660 non-MTPs which are separated into a training dataset consisting 1,380 proteins and an independent dataset consisting 180 proteins. The SCMMTP produced estimating propensity scores for amino acids and dipeptides as MTPs. The SCMMTP training and test accuracy levels respectively reached 83.81% and 76.11%. The test accuracy of support vector machine (SVM) using a complicated classification method with a low possibility for biological interpretation and position-specific substitution matrix (PSSM) as a protein feature is 80.56%, thus SCMMTP is comparable to SVM-PSSM. To identify MTPs, SCMMTP is applied to three datasets including: 1) human transmembrane proteins, 2) a photosynthetic protein dataset, and 3) a human protein database. MTPs showing a-helix rich structure is agreed with previous studies. The MTPs used residues with low hydration energy. It is hypothesized that, after filtering substrates, the hydrated water molecules need to be released from the pore regions. Conclusions: SCMMTP yields estimating propensity scores for amino acids and dipeptides as MTPs, which can be used to identify novel MTPs and characterize transport mechanisms for use in further experiments. Availability: http://iclab.life.nctu.edu.tw/iclab_webtools/SCMMTP/en_US
dc.language.isoen_USen_US
dc.titleSCMMTP: identifying and characterizing membrane transport proteins using propensity scores of dipeptidesen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/1471-2164-16-S12-S6en_US
dc.identifier.journalBMC GENOMICSen_US
dc.citation.volume16en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.department生物資訊研究中心zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.contributor.departmentCenter for Bioinformatics Researchen_US
dc.identifier.wosnumberWOS:000376930700007en_US
dc.citation.woscount5en_US
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