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SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method

Home || Characterization | Download | Prediction | Release 1.10, Last update: Aug 5 2014 


SCMPSP

Photosynthetic proteins (PSPs) from plants, algae and photosynthetic bacteria greatly differ in their structure and function as they are involved in numerous subprocesses, including the harvest of solar energy, diffusive transport, energy conversion, electron and ion transport reactions from water to NADP+, ATP generation and series of enzymatic reactions in the stroma of the chloroplast.

This study proposes a novel SCMPSP method to predict and characterize PSPs based on a scoring card method (SCM) from sequences. We first established a new dataset from SwissPort consisting of 649 PS proteins (selected using a Gene Ontology term GO:0015979 and its child terms) and 649 non-PS proteins (selected from 44,536 putative non-PS proteins) with identity 25%. Consequently, the propensity scores of 400 dipeptides and 20 amino acids to be PSPs were estimated. Finally, we identified informative physico-chemical properties by utilizing the estimated propensity scores of PSPs. The training and mean test accuracies of SCMPS on three independent test datasets are 71.54%, 62.20%, and 64.38% respectively.

The used datasets and source codes of SCMPSP are available at HERE .


The flowchart of system designs for predicting and characterizing photosynthetic binding proteins (PSPs).

Heat map of the photosynthetic protein propensity scores of dipeptides

The Pearson correlation coefficient (R =0.7955) between BLAS910101 and AA Score of SCMPS
The Pearson correlation coefficient (R = 0.7597) between WOLR810101and AA Score of SCMPS
The Pearson correlation coefficient (R = -0.7948) between PUNT030101 and AA Score of SCMPS

Contact with:
Hui-Ling Huang,

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).