The identification of interfaces in protein complexes is effective for the 
elucidation of protein function and helps us to understand their roles in 
biological processes. With the exponentially growing amount of protein sequence 
data, an exploration of new methods that predict protein interaction sites based 
solely on sequence information is becoming increasingly urgent. Because a 
combination of different methods could produce better results than a single 
method, interaction site prediction can be improved through the utilization of 
different methods. This paper describes a new method that predicts interaction 
sites based on protein sequences by integrating five different algorithms 
employing meta-method, Majority Vote and SVMhmm Regression techniques. The 
'metaPIS' web-server was implemented for meta-prediction. An evaluation of the 
meta-methods using independent datasets revealed that Majority Vote achieved the 
highest average Matthews correlation coefficient (0.181) among all the methods 
assessed. SVMhmm Regression achieved a lower score but provided a more stable 
result. The metaPIS server allows experimental biologists to speculate regarding 
protein function by identifying potential interaction sites based on protein 
sequence. As a web server, metaPIS is freely accessible to the public at 
http://202.116.74.5:84/metapis.