Physics-based method to validate and repair flaws in protein structures

Abstract

A method that makes use of information provided by the combination of 13 C α and 13 C β chemical shifts, computed at the density functional level of theory, enables one to (i) validate, at the residue level, conformations of proteins and detect backbone or side-chain flaws by taking into account an ensemble average of chemical shifts over all of the conformations used to represent a protein, with a sensitivity of ∼90%; and (ii) provide a set of (χ1/χ2) torsional angles that leads to optimal agreement between the observed and computed 13 C α and 13 C β chemical shifts. The method has been incorporated into the CheShift-2 protein validation Web server. To test the reliability of the provided set of (χ1/ χ2) torsional angles, the side chains of all reported conformations of five NMR-determined protein models were refined by a simple routine, without using NOE-based distance restraints. The refinement of each of these five proteins leads to optimal agreement between the observed and computed 13 C α and 13 C β chemical shifts for ∼94% of the flaws, on average, without introducing a significantly large number of violations of the NOE-based distance restraints for a distance range ≤ 0.5 Å, in which the largest number of distance violations occurs. The results of this work suggest that use of the provided set of (χ1/χ2) torsional angles together with other observables, such as NOEs, should lead to a fast and accurate refinement of the side-chain conformations of protein models.S ince the seminal observation by Kendrew (1) that "it is the spatial relations between the side-chains which determine the chemical behavior and biological specificity of the protein molecule as a whole and these relations cannot be determined, except in a fragmentary manner, by purely chemical techniques" interest has been focused on the development of accurate methods to validate and determine side-chain conformations in proteins (ref. 2 and references therein). Side-chain chemical shifts have also been used for protein structure validation because these observables are highly sensitive to protein structural packing (3). The latter interest arises because it is largely accepted that a proper protein structure determination requires validation methods in which the observable values used to validate the structures are not used in their determination (4, 5), and it will assure spectroscopists and other users that a given protein model is a good representation of the native structure in solution. However, the validation process involves two crucial steps: (i) detecting flaws in the structure at the residue level and (ii) providing details as to how these flaws can be repaired. Existing validation methods are mainly concerned with determining the quality of the whole structure but only sometimes with highlighting where the flaws are located at the residue level (3,(6)(7)(8)(9)(10)(11)(12). To the best of our knowledge, these validation methods do not provide detailed information as to how such flaws can be eliminated. Therefore, it is left to the sp...

ProCS15: A DFT-based chemical shift predictor for backbone and C β atoms in proteins

“…These accuracies are very similar to DFT-based predictions made by other researchers (e.g. (Zhu et al, 2012), (Zhu et al, 2013), (Exner et al, 2012)) as well as CheShift-2 (Martin et al, 2013), which is another DFT-based chemical shift predictor for Cα and Cβ atoms. The RMSD values computed using ProCS15 for Ubiquitin can be reduced by as much as 0.7, 0.1, and 0.5 ppm for carbon, hydrogen, and nitrogen by using NMR-derived structural ensembles.…”

“…The agreement with experiment is quite remarkable with RMSD values around 1, 0.3, and 2 ppm for carbon, hydrogen, and nitrogen atoms. Chemical shift predictions based on quantum mechanical (QM) calculations (mostly density functional theory, DFT) are becoming increasingly feasible for small proteins (Zhu et al, 2012(Zhu et al, , 2013Exner et al, 2012;Sumowski et al, 2014;Swails et al, 2015) and Vila, Scheraga and co-workers have gone on to develop a DFT-based chemical shift predictor for Cα and Cβ atoms called CheShift-2 (Martin et al, 2013). Generally, these QM-based methods yield chemical shifts that deviate significantly more from experiment than the empirical methods, with RMSD values that generally are at least twice as large.…”

My 65 years in protein chemistry

“…This procedure offers a criterion for an accurate assessment of the quality of NMR-derived conformations, examines whether x-ray or NMR-solved structures are better representations of the observed 13 C α and 13 C β chemical shifts in solution, provides evidence indicating that the proposed methodology is more accurate than automated predictors for validation of protein structures, sheds light as to whether the agreement between computed and observed 13 C α and 13 C β chemical shifts is influenced by the identity of amino acid residues or by their location in the sequence, and provides evidence confirming the presence of dynamics of proteins in solution, hence showing that an ensemble of conformations is a better representation of the structure in solution than any single conformation, as pointed out in Section 15.6. Vila & Scheraga (2009), Vila et al (2009a, b), and Martin et al (2012, 2013) provided additional details for these validation procedures, including an internet server to enable experimentalists to validate their own NMR or x-ray structures.…”

Classification of RNA backbone conformation into rotamers using 13C′ chemical shifts: How far we can go?

“…The suite is defined from sugar-to-sugar (or from the δ torsional angle of residue i-1 to the δ torsional angle of residue i ), and it is contained within the dinucleotide subunit (see Figure 1). 13 C chemical shifts have been successfully used by our and other groups for protein and glycan structural determination, validation and refinement (4,5,6,7,8).…”