The design and generation of biological sequences with desired functional properties is a central challenge in biology. This problem is an active area of research and has been approached using generative models. These models that rely on representation learning often learn an entangled representation of the data, making the generation of new sequences hard to control. To address this issue, we apply and extend an algorithm for Restricted Boltzmann Machines (RBMs) that enables control over learned features, allowing for the targeted generation of sequences with specific functional characteristics. The approach is tested on a case study, the Lattice protein model, and is generalized for use on a real biological example, the WW domain family.