Background: Membrane proteins are embedded into biological membranes and interact
with them, playing a large range of roles from transporting materials to catalyzing interactions in the
cellular processes. The functions of membrane proteins are closely associated with types they belong
to. Membrane proteins have simultaneously more than one type, but most of the computational predictions
can deal with only one type.
Objective and Method: To bridge the gap, we proposed a multi-label method based on the sequence
homology and pseudo amino acid composition for predicting human membrane protein types. The
method is a two-step decision. The uncharacterized membrane protein firstly was aligned against the
database consisting of membrane proteins with known types and types of the most homological membrane
protein were transferred to it. If it had no homological membrane protein, the pseudo amino acid
composition-based method was used to predict its types.
Results: The predictive accuracies of the leave-one-out cross-validation test on these three benchmark
datasets are 0.8817, 0.8206 and 0.7276, respectively, better than our previous algorithm. We collected
5752 manually reviewed human membrane proteins with annotated types as the training set, and developed
a program MemPred for predicting multi-label types of membrane proteins.
Conclusion: We have proposed a multi-label computational method for predicting membrane protein
types and achieved a better performance. The advantage of the proposed method is that it can predict
simultaneously more than one type.