Two artificial intelligence (AI)-based methods for protein structure prediction, AlphaFold 2 and RoseTTAFold, increase dramatically the quality of structural modeling from sequence, nearing experimental accuracy. Protein language models encode the written language of proteins, allowing for more accurate annotations and predictions than homology-based methods. Most model organisms, neglected disease pathogens, and proteins with curated annotations have models available with varying quality, aiding wet-laboratory experiments targeting single-question issues. Ultrafast alignment tools can traverse the protein space by both sequence and structure to identify remote evolutionary relations previously precluded to older and slower methods. Preliminary analyses of predicted AlphaFold 2 3D-models from 21 model organisms suggest that the majority (>90%) of globular domains in proteins can be assigned to currently characterized domain evolutionary superfamilies. Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community. [ABSTRACT FROM AUTHOR]