Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, United States; Center for Computational Biology, Johns Hopkins University, Baltimore, United States
School of Biological Sciences, Seoul National University, Seoul, Republic of Korea; Artificial Intelligence Institute, Seoul National University, Seoul, Republic of Korea
Ales Varabyou
Center for Computational Biology, Johns Hopkins University, Baltimore, United States; Department of Computer Science, Johns Hopkins University, Baltimore, United States
Natalia Rincon
Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, United States; Center for Computational Biology, Johns Hopkins University, Baltimore, United States
Sukhwan Park
School of Biological Sciences, Seoul National University, Seoul, Republic of Korea; Artificial Intelligence Institute, Seoul National University, Seoul, Republic of Korea
Ilia Minkin
Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, United States; Center for Computational Biology, Johns Hopkins University, Baltimore, United States
Mihaela Pertea
Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, United States; Center for Computational Biology, Johns Hopkins University, Baltimore, United States
Martin Steinegger
School of Biological Sciences, Seoul National University, Seoul, Republic of Korea; Artificial Intelligence Institute, Seoul National University, Seoul, Republic of Korea; Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Republic of Korea
Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, United States; Center for Computational Biology, Johns Hopkins University, Baltimore, United States; Department of Computer Science, Johns Hopkins University, Baltimore, United States; Department of Biostatistics, Johns Hopkins University, Baltimore, United States
Recently developed methods to predict three-dimensional protein structure with high accuracy have opened new avenues for genome and proteome research. We explore a new hypothesis in genome annotation, namely whether computationally predicted structures can help to identify which of multiple possible gene isoforms represents a functional protein product. Guided by protein structure predictions, we evaluated over 230,000 isoforms of human protein-coding genes assembled from over 10,000 RNA sequencing experiments across many human tissues. From this set of assembled transcripts, we identified hundreds of isoforms with more confidently predicted structure and potentially superior function in comparison to canonical isoforms in the latest human gene database. We illustrate our new method with examples where structure provides a guide to function in combination with expression and evolutionary evidence. Additionally, we provide the complete set of structures as a resource to better understand the function of human genes and their isoforms. These results demonstrate the promise of protein structure prediction as a genome annotation tool, allowing us to refine even the most highly curated catalog of human proteins. More generally we demonstrate a practical, structure-guided approach that can be used to enhance the annotation of any genome.
