Reflecting on mutational and biophysical analysis of Gaussia princeps Luciferase from a structural perspective: a unique bioluminescent enzyme

Hidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMR

Recent advances in molecular modeling using deep learning can revolutionize our understanding of dynamic protein structures. NMR is particularly well-suited for determining dynamic features of biomolecular structures. The conventional process for determining biomolecular structures from experimental NMR data involves its representation as conformation-dependent restraints, followed by generation of structural models guided by these spatial restraints. Here we describe an alternative approach: generating a distribution of realistic protein conformational models using artificial intelligence-(AI-) based methods and then selecting the sets of conformers that best explain the experimental data. We applied this conformational selection approach to redetermine the solution NMR structure of the enzyme Gaussia luciferase. First, we generated a diverse set of conformer models using AlphaFold2 (AF2) with an enhanced sampling protocol. The models that best-fit NOESY and chemical shift data were then selected with a Bayesian scoring metric. The resulting models include features of both the published NMR structure and the standard AF2 model generated without enhanced sampling. This "AlphaFold-NMR" protocol also generated an alternative "open" conformational state that fits nearly as well to the overall NMR data but accounts for some NOESY data that is not consistent with first "closed" conformational state; while other NOESY data consistent with this second state are not consistent with the first conformational state. The structure of this "open" structural state differs from that of the "closed" state primarily by the position of a thumb-shaped loop between α-helices H5 and H6, revealing a cryptic surface pocket. These alternative conformational states of Gluc are supported by "double recall" analysis of NOESY data and AF2 models. Additional structural states are also indicated by backbone chemical shift data indicating partially-disordered conformations for the C-terminal segment. Considered as a multistate ensemble, these multiple states of Gluc together fit the NOESY and chemical shift data better than the "restraint-based" NMR structure and provide novel insights into its structure-dynamic-function relationships. This study demonstrates the potential of AI-based modeling with enhanced sampling to generate conformational ensembles followed by conformer selection with experimental data as an alternative to conventional restraint satisfaction protocols for protein NMR structure determination.

Computational biophysics and structural biology of proteins-a Special Issue in honor of Prof. Haruki Nakamura's 70th birthday

Receiving his initial training jointly in theoretical and applied physics at the University of Tokyo, Professor Haruki Nakamura has had a long and eventful scientific career, along the way helping to shape the way that biophysics is carried out in Japan. Concentrating his research efforts on the simulation of protein structure and function, he has, over his career arc, acted as director of the Institute for Protein Research (Osaka, Japan), director of the Protein Data Bank of Japan (PDBj), president of the Biophysical Society of Japan (BSJ), president of the Protein Science Society of Japan (PSSJ), and group leader and professor of Bioinformatics and Computational Structural Biology at Osaka University. In 2022, Prof. Haruki Nakamura turned 70 years old, and to mark this occasion, his scientific colleagues from around the world have combined their efforts to produce this Festschrift Issue of the IUPAB Biophysical Reviews journal around the theme of the computational biophysics and structural biology of proteins.