Deep-syntax approaches to machine translation have emerged as an alternative to phrase-based statistical systems. TectoMT is an open source framework for transfer-based MT which works at the deep tectogrammatical level and combines linguistic knowledge and statistical techniques. When adapting to a domain, terminological resources improve results with simple techniques, e.g. force-translating domain-specific expressions. In such approaches, multiword entries are translated as if they were a single token-with-spaces, failing to represent the internal structure which makes TectoMT a powerful translation engine. In this work we enrich source and target multiword terms with syntactic structure, and seamlessly integrate them in the tree-based transfer phase of TectoMT. Our experiments on the IT domain using the Microsoft terminological resource show improvement in Spanish, Basque and Portuguese.