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Scoring Card Method Membrane Transport Proteins : Identifying and characterizing membrane transport proteins using propensity scores of dipeptides

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Background: Identifying putative membrane transport proteins (MTPs) and understanding transport mechanisms remain important challenges for the advancement of structural and functional genomics. However, the transport mechanisms are mainly acquired from MTP crystal structures which are hard to crystalize. Therefore, it is desirable to develop bioinformatics tools for effective large-scale analysis of available sequences in order to identify novel transporters and characterize transport mechanism.

Results: This work proposes a novel method (SCMMTP) based on scoring card method (SCM) which utilizes dipeptide composition as a feature to identify and characterize MTPs from an existing dataset containing 900 MTPs and 660 non-MTPs which are separated a training dataset consisting 1,380 proteins and an independent dataset consisting 180 proteins. The SCMMTP produced the estimating propensity scores of amino acids and dipeptides to be MTPs. The training and test accuracies of SCMMTP are 83.81% and 76.11%, respectively. 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%, SCMMTP comparable SVM-PSSM. SCMMTP is applied to three datasets including: 1) human transmembrane proteins, 2) photosynthetic protein dataset, and 3) a human protein database, to identify MTPs. MTPs showing a-helix rich structure is agreed with previous studies. The MTPs used the residues having low hydration energy. It is hypothesized that after filtering substrates the hydrated water molecules need to be released from the pore regions.



The flowchart of system designs for predicting and characterizing membrane transport proteins (MTPs).

Scoring card of membrane transport protein propensity scores

Contact with:
Hui-Ling Huang, Shinn-Ying Ho

Related publications of SCM

Huang HL, Charoenkwan P, Kao TF, Lee HC, Chang FL, Huang WL, Ho SJ, Shu LS, Chen WL, Ho SY: Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition. Bmc Bioinformatics 2012, 13.

Charoenkwan P, Shoombuatong W, Lee HC, Chaijaruwanich J, Huang HL, Ho SY: SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs. Plos One 2013, 8(9).

Tamara Vasylenko, Yi-Fan Liou, Hong-An Chen, Phasit Charoenkwan, Hui-Ling Huang* and Shinn-Ying Ho*, SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method, BMC Bioinformatics, 16 (Suppl 1):S8, 2015

Yi-Fan Liou, Phasit Charoenkwan, Yerukala Sathipati Srinivasulu, Tamala Vasylenko, Shih-Chung Lai, Hua-Chin Lee, Yi-Hsiung Chen, Hui-Ling Huang* and Shinn-Ying Ho*, "SCMHBP: Prediction and analysis of heme binding proteins using propensity scores of dipeptides," BMC Bioinformatics, 15 Suppl 16:S4, Dec. 2014.