Protein Fold Family Recognition From Unassigned Residual Dipolar Coupling Data
This work aims to improve protein fold family recognition for structural biologists, but it appears incremental as it builds on existing NMR data and computational methods without claiming major breakthroughs.
The paper tackles the problem of limited adoption of computational protein structure modeling by experimental biologists due to insufficient structural libraries and lack of experimental validation, proposing Probability Density Profile Analysis (PDPA) that uses unassigned residual dipolar coupling data from NMR experiments to address these issues.
Despite many advances in computational modeling of protein structures, these methods have not been widely utilized by experimental structural biologists. Two major obstacles are preventing the transition from a purely-experimental to a purely-computational mode of protein structure determination. The first problem is that most computational methods need a large library of computed structures that span a large variety of protein fold families, while structural genomics initiatives have slowed in their ability to provide novel protein folds in recent years. The second problem is an unwillingness to trust computational models that have no experimental backing. In this paper we test a potential solution to these problems that we have called Probability Density Profile Analysis (PDPA) that utilizes unassigned residual dipolar coupling data that are relatively cheap to acquire from NMR experiments.