The objective of our group is to predict aspects of protein function and structure from sequence. The wealth of evolutionary information available through comparing the whole bio-diversity of species makes such an ambitious goal achievable. Our particular niche is the combination of evolutionary information (EI) with machine learning (ML) and artificial intelligence (AI). 30 years ago, the marriage of machine learning and evolutionary information (in the form of Multiple Sequence Alignments) allowed a breakthrough in secondary structure prediction. The same principle has been underlying all state-of-the-art predictions of protein structure and function and is also the root for the program that broke through in protein structure prediction, namely AlphaFold2. Over the last two years, it has become possible to deep learn the language of life written in proteins through protein Language Models (pLMs) transfer extracted information to supervise learn protein prediction with annotations.