<p>Sequence modelling is used for analysing newly sequenced proteins, giving indication of the 3-D structure and functionality. Current approaches to the modelling of protein families are either based on discrete or probabilistic methods. Here we present an approach for combining these two approaches in a hybrid model, where discrete patterns are used to model conserved regions and probabilistic models are used for variable regions. When hidden Markov models are used to model the variable regions, the hybrid method gives increased classification accuracy, compared to pure discrete or probabilistic models.</p>

Sequence modelling is used for analysing newly sequenced proteins, giving indication of the 3-D structure and functionality. Current approaches to the modelling of protein families are either based on discrete or probabilistic methods. Here we present an approach for combining these two approaches in a hybrid model, where discrete patterns are used to model conserved regions and probabilistic models are used for variable regions. When hidden Markov models are used to model the variable regions, the hybrid method gives increased classification accuracy, compared to pure discrete or probabilistic models.