Abstract
The use of statistical potentials in NMR structure calculation improves the accuracy of the final structure but also raises issues of double counting and possible bias. Because statistical potentials are averaged over a large set of structures, they may not reflect the preferences of a particular structure or data set. We propose a Bayesian method to incorporate a knowledge-based backbone dihedral angle potential into an NMR structure calculation. To avoid bias exerted through the backbone potential, we adjust its weight by inferring it from the experimental data. We demonstrate that an optimally weighted potential leads to an improvement in the accuracy and quality of the final structure, especially with sparse and noisy data. Our findings suggest that no universally optimal weight exists, and that the weight should be determined based on the experimental data. Other knowledge-based potentials can be incorporated using the same approach.
Highlights
Structural data measured by NMR spectroscopy are never complete
We need to interpret the data in the light of prior knowledge that is typically encoded in a potential function or force field [1]
Physical and statistical potentials are complementary in the sense that some interactions cannot be broken down into fundamental, physical contributions but are captured more effectively by potentials derived from known structures
Summary
Even the most carefully collected data will by themselves not allow us to determine the three-dimensional structure of a biomolecule with atomic resolution. We need to interpret the data in the light of prior knowledge that is typically encoded in a potential function or force field [1]. Potential functions quantify the forces and interactions within a biomolecule and with its environment. Physics-based force fields [3] aim to approximate the underlying physical laws. Statistical or knowledge-based potentials [4] are learned from a structure database and describe the effective forces resulting from all interactions including those with the solvent. Physical and statistical potentials are complementary in the sense that some interactions cannot be broken down into fundamental, physical contributions but are captured more effectively by potentials derived from known structures
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