Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR parameters back to protein sequence and structure requires experimental data. In this perspective we examine what the biological NMR community needs to do, in order to store and share its data better so that we can make effective use of AI methods to further our understanding of biological molecules.

Protein NMR assignment by isotope pattern recognition.

The current standard method for amino acid signal identification in protein NMR spectra is sequential assignment using triple-resonance experiments. Good software and elaborate heuristics exist, but the process remains laboriously manual. Machine learning does help, but its training databases need millions of samples that cover all relevant physics and every kind of instrumental artifact.

Restraint validation of biomolecular structures determined by NMR in the Protein Data Bank.

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NEF and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints.

Restraint Validation of Biomolecular Structures Determined by NMR in the Protein Data Bank.

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints.

Biochemistry and pathophysiology of the Transient Potential Receptor Vanilloid 6 (TRPV6) calcium channel.

TRPV6 is a Transient Receptor Potential Vanilloid (TRPV) cation channel with high selectivity for Ca ions. First identified in 1999 in a search for the gene which mediates intestinal Ca absorption, its far more extensive repertoire as a guardian of intracellular Ca has since become apparent. Studies on TRPV6-deficient mice demonstrated additional important roles in placental Ca transport, fetal bone development and male fertility.