Structural genomics: past, present and future

Abstract

The exponential growth of genomics sequence information in the 1990s led to significant knowledge gaps in our understanding of biological systems. It was true then, and it is still true now that the sequence information bore little insights about the functions encoded in the genomes. The fi eld of Structural Genomics (SG) arose to address these gaps. The mission of SG programs was to facilitate rapid de novo structure determination for proteins representing new protein families to provide meaningful structural coverage of the genomes. There were significant challenges to advance technologies for the prepara tion of thousands of proteins and for their structural and functional characterization. The SG programs quickly addressed barriers, and deficiencies, improved effectiveness, and reproducibility, and created highly integrated and cost - effective pipelines for p rotein production and structure determination. The improvements in experimental methods developed by the SG consortia resulted in fast progress in molecular and structural biology, enhanced structure quality, and significantly benefitted biological and biomedical research, providing insights into novel structural and functional space. The experimental three - dimensional models were promptly made public through the Protein Data Bank structure repository, facilitating the structure determination of other m embers of the family, and helping to understand their molecular and biochemical functions. The light sources and dedicated macromolecular crystallography beamlines, advanced software, and computing resources have contributed to SG success and expanded biology community competence in determining protein structures. Structural biology research was set to undergo a major transformation. The advancements resulted in the determination of thousands of protein structures, mostly from unique protein families, and increased structural coverage of the rapidly expanding protein universe. These structures contributed to AlphaFold/RozeTTAFold AI algorithms allowing accurate structure prediction of millions of proteins. In principle, the original goal propo sed by the National Institutes of Health Protein Structure Initiative, that structures of all proteins should be available to the community experimentally or computationally, has been accomplished. At