Assessing the One-Bond C_α–H Spin–Spin Coupling Constants in Proteins: Pros and Cons of Different Approaches

Abstract

In the present work we explore three different approaches for the computation of the one-bond spin-spin coupling constants (SSCC) 1 J CαH in proteins: DFT calculation, Karplus-like equation and Gaussian Process regression. The main motivation of this work is to select the best method for a fast and accurate computation of the 1 J CαH SSCC, for its use on everyday applications in protein structure validation, refinement and/or determination. Our initial results showed a poor agreement between the DFT computed and observed 1 J CαH SSCC values. Further analysis lead us to the understanding that the model chosen for the DFT computations is inappropriate, and that more complex models will requiere a higher if not prohibitively computational cost. Finally, we show that Karplus-like equation and Gaussian Process regression provide faster and more accurate results than DFT-based calculations.

Theoretical Investigation of Glycine Micro-Solvated. Energy and NMR Spin Spin Coupling Constants Calculations

“…Hence, the proton transfer of the neutral glycine, stable in the gas phase, to zwitterionic conformer, stable in aqueous solution, has an estimated free energy barrier of about 7.3 kcal mol [1][2][3]5,6,8,[10][11][12][13][14]. Some other recent works aim to study small clusters of Glycine: (H 2 O) n complexes in order to elucidate structures and binding energies [15][16][17][18], or the stabilization of Z-Glycine in water and its NMR signature [19] and the election of a method for fast accurate of 1 J(C, H) for application in proteins [20].…”

Exploring the quality of protein structural models from a Bayesian perspective

“…Bayesian statistics offers very suitable theoretical advantages for developing models involving bio-molecular structural data and has been applied in numerous tools and methods in this context. [1][2][3][4][5][6] Furthermore, Bayesian methods are capable of accounting for errors and noise of variable source and nature, which is suitable for working with bio-molecular experimental data. 7 In statistics, partially pooling data means to separate observations into groups, while allowing the groups to remain somehow linked in order to influence each other.…”

Exploring the quality of protein structural models from a Bayesian perspective

“…Bayesian statistics offers very suitable theoretical advantages for developing models involving bio-molecular structural data and it has been applied in numerous tools and methods in this context [1][2][3][4][5][6]. Furthermore, Bayesian methods are capable of accounting for errors and noise of variable source and nature, which is suitable for working with bio-molecular experimental data [7].…”