Predicting the three dimensional structure of proteins is a difficult task. In the last years several approaches have been proposed for performing this task taking into account different protein chemical and physical properties. As a result, a growing number of protein structure prediction tools is becoming available, some of them being specialized to work on either some aspects of the predictions or on some categories of proteins. In this context, it becomes useful to jointly apply different prediction techniques and combine their results in order to improve the quality of the predictions. However, several problems have to be solved in order to make this a viable possibility. In this paper we propose a framework allowing to (i) define a common reference applicative domain for different prediction techniques, (ii) characterize predictors through evaluating some quality parameters, (iii) characterize the performances of a team of predictors jointly applied over a prediction problem and (iv) obtain a unique prediction from the team. Finally, we highlight the application of this framework to the definition of a multi-agent system performing the team selection task, the integration of multiple, possibly heterogeneous, predictions and the translation of predictors inputs and outputs into a uniform data format.