CheShift-2: graphic validation of protein structures

MIM-ML: A Novel Quantum Chemical Fragment-Based Random Forest Model for Accurate Prediction of NMR Chemical Shifts of Nucleic Acids

We developed a random forest machine learning (ML) model for the prediction of 1H and 13C NMR chemical shifts of nucleic acids. Our ML model is trained entirely on reproducing computed chemical shifts obtained previously on 10 nucleic acids using a Molecules-in-Molecules (MIM) fragment-based density functional theory (DFT) protocol including microsolvation effects. Our ML model includes structural descriptors as well as electronic descriptors from an inexpensive low-level semiempirical calculation (GFN2-xTB) and trained on a relatively small number of DFT chemical shifts (2080 1H chemical shifts and 1780 13C chemical shifts on the 10 nucleic acids). The ML model is then used to make chemical shift predictions on 8 new nucleic acids ranging in size from 600 to 900 atoms and compared directly to experimental data. Though no experimental data was used in the training, the performance of our model is excellent (mean absolute deviation of 0.34 ppm for 1H chemical shifts and 2.52 ppm for 13C chemical shifts for the test set), despite having some nonstandard structures. A simple analysis suggests that both structural and electronic descriptors are critical for achieving reliable predictions. This is the first attempt to combine ML from fragment-based DFT calculations to predict experimental chemical shifts accurately, making the MIM-ML model a valuable tool for NMR predictions of nucleic acids.

Exploring the quality of protein structural models from a Bayesian perspective

We explore how ideas and practices common in Bayesian modeling can be applied to help assess the quality of 3D protein structural models. The basic premise of our approach is that the evaluation of a Bayesian statistical model's fit may reveal aspects of the quality of a structure when the fitted data is related to protein structural properties. Therefore, we fit a Bayesian hierarchical linear regression model to experimental and theoretical ¹³ C^α chemical shifts. Then, we propose two complementary approaches for the evaluation of such fitting: (a) in terms of the expected differences between experimental and posterior predicted values; (b) in terms of the leave-one-out cross-validation point-wise predictive accuracy. Finally, we present visualizations that can help interpret these evaluations. The analyses presented in this article are aimed to aid in detecting problematic residues in protein structures. The code developed for this work is available on: https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation.

Assessing SIRAH's Capability to Simulate Intrinsically Disordered Proteins and Peptides

The challenges posed by intrinsically disordered proteins (IDPs) to atomistic and coarse-grained (CG) simulations are boosting efforts to develop and reparametrize current force fields. An assessment of the dynamical behavior of IDPs' and unstructured peptides with the CG SIRAH force field suggests that the current version achieves a fair description of IDPs' conformational flexibility. Moreover, we found a remarkable capability to capture the effect of point mutations in loosely structured peptides.

PyVibMS: a PyMOL plugin for visualizing vibrations in molecules and solids

Visualizing vibrational motions calculated with different ab initio packages requires dedicated post-processing tools. Here, we present a PyMOL plugin called PyVibMS for visualizing the vibrational motions for both molecular and solid systems calculated by mainstream quantum chemical computer programs including Gaussian, Q-Chem, VASP, and CRYSTAL. Benefiting from the continuing development of the PyMOL platform, PyVibMS provides powerful functionalities and user-friendly interface. PyVibMS was written in Python and its open-source nature makes it flexible and sustainable. As an example, the motions of the Konkoli-Cremer local vibrational modes are shown in this work for the first time. PyVibMS is freely available at https://github.com/smutao/PyVibMS . Graphical abstract In this work, a PyMOL plugin named PyVibMS is developed to visualize molecular and lattice vibrations.

PyShifts: A PyMOL Plugin for Chemical Shift-Based Analysis of Biomolecular Ensembles

Here, we present PyShifts-a PyMOL plugin for chemical shift-based analysis of biomolecular ensembles. With PyShifts, users can compare and visualize differences between experimentally measured and computationally predicted chemical shifts. When analyzing multiple conformations of a biomolecule with PyShifts, users can also sort a set of conformations based on chemical shift differences and identify the conformers that exhibit the best agreement between measured and predicted chemical shifts. Although we have integrated PyShifts with the chemical shift predictors LARMOR^D and LARMOR^Cα, PyShifts can read in chemical shifts from any source, and so, users can employ PyShifts to analyze biomolecular structures using chemical shifts computed by any chemical shift predictor. We envision, therefore, that PyShifts (https://github.com/atfrank/PyShifts) will find utility as a general-purpose tool for exploring chemical shift-structure relationships in biomolecular ensembles.

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

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

13C Chemical Shifts in Proteins: A Rich Source of Encoded Structural Information

Despite the formidable progress in Nuclear Magnetic Resonance (NMR) spectroscopy, quality assessment of NMR-derived structures remains as an important problem. Thus, validation of protein structures is essential for the spectroscopists, since it could enable them to detect structural flaws and potentially guide their efforts in further refinement. Moreover, availability of accurate and efficient validation tools would help molecular biologists and computational chemists to evaluate quality of available experimental structures and to select a protein model which is the most suitable for a given scientific problem. The ¹³C^α nuclei are ubiquitous in proteins, moreover, their shieldings are easily obtainable from NMR experiments and represent a rich source of encoded structural information that makes ¹³C^α chemical shifts an attractive candidate for use in computational methods aimed at determination and validation of protein structures. In this chapter, the basis of a novel methodology of computing, at the quantum chemical level of theory, the ¹³C^α shielding for the amino acid residues in proteins is described. We also identify and examine the main factors affecting the ¹³C^α-shielding computation. Finally, we illustrate how the information encoded in the ¹³C chemical shifts can be used for a number of applications, viz., from protein structure prediction of both α-helical and β-sheet conformations, to determination of the fraction of the tautomeric forms of the imidazole ring of histidine in proteins as a function of pH or to accurate detection of structural flaws, at a residue-level, in NMR-determined protein models.

Sensitivity of ab Initio vs Empirical Methods in Computing Structural Effects on NMR Chemical Shifts for the Example of Peptides

The structural sensitivity of NMR chemical shifts as computed by quantum chemical methods is compared to a variety of empirical approaches for the example of a prototypical peptide, the 38-residue kaliotoxin KTX comprising 573 atoms. Despite the simplicity of empirical chemical shift prediction programs, the agreement with experimental results is rather good, underlining their usefulness. However, we show in our present work that they are highly insensitive to structural changes, which renders their use for validating predicted structures questionable. In contrast, quantum chemical methods show the expected high sensitivity to structural and electronic changes. This appears to be independent of the quantum chemical approach or the inclusion of solvent effects. For the latter, explicit solvent simulations with increasing number of snapshots were performed for two conformers of an eight amino acid sequence. In conclusion, the empirical approaches neither provide the expected magnitude nor the patterns of NMR chemical shifts determined by the clearly more costly ab initio methods upon structural changes. This restricts the use of empirical prediction programs in studies where peptide and protein structures are utilized for the NMR chemical shift evaluation such as in NMR refinement processes, structural model verifications, or calculations of NMR nuclear spin relaxation rates.

Pan-genome dynamics of Pseudomonas gene complements enriched across hexachlorocyclohexane dumpsite

Background

Phylogenetic heterogeneity across Pseudomonas genus is complemented by its diverse genome architecture enriched by accessory genetic elements (plasmids, transposons, and integrons) conferring resistance across this genus. Here, we sequenced a stress tolerant genotype i.e. Pseudomonas sp. strain RL isolated from a hexachlorocyclohexane (HCH) contaminated pond (45 mg of total HCH g⁻¹ sediment) and further compared its gene repertoire with 17 reference ecotypes belonging to P. stutzeri, P. mendocina, P. aeruginosa, P. psychrotolerans and P. denitrificans, representing metabolically diverse ecosystems (i.e. marine, clinical, and soil/sludge). Metagenomic data from HCH contaminated pond sediment and similar HCH contaminated sites were further used to analyze the pan-genome dynamics of Pseudomonas genotypes enriched across increasing HCH gradient.

Results

Although strain RL demonstrated clear species demarcation (ANI ≤ 80.03%) from the rest of its phylogenetic relatives, it was found to be closest to P. stutzeri clade which was further complemented functionally. Comparative functional analysis elucidated strain specific enrichment of metabolic pathways like α-linoleic acid degradation and carbazole degradation in Pseudomonas sp. strain RL and P. stutzeri XLDN-R, respectively. Composition based methods (%codon bias and %G + C difference) further highlighted the significance of horizontal gene transfer (HGT) in evolution of nitrogen metabolism, two-component system (TCS) and methionine metabolism across the Pseudomonas genomes used in this study. An intact mobile class-I integron (3,552 bp) with a captured gene cassette encoding for dihydrofolate reductase (dhfra1) was detected in strain RL, distinctly demarcated from other integron harboring species (i.e. P. aeruginosa, P. stutzeri, and P. putida). Mobility of this integron was confirmed by its association with Tnp21-like transposon (95% identity) suggesting stress specific mobilization across HCH contaminated sites. Metagenomics data from pond sediment and recently surveyed HCH adulterated soils revealed the in situ enrichment of integron associated transposase gene (TnpA6100) across increasing HCH contamination (0.7 to 450 mg HCH g⁻¹ of soil).

Conclusions

Unlocking the potential of comparative genomics supplemented with metagenomics, we have attempted to resolve the environment and strain specific demarcations across 18 Pseudomonas gene complements. Pan-genome analyses of these strains indicate at astoundingly diverse metabolic strategies and provide genetic basis for the cosmopolitan existence of this taxon.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1488-2) contains supplementary material, which is available to authorized users.

My 65 years in protein chemistry

This is a tour of a physical chemist through 65 years of protein chemistry from the time when emphasis was placed on the determination of the size and shape of the protein molecule as a colloidal particle, with an early breakthrough by James Sumner, followed by Linus Pauling and Fred Sanger, that a protein was a real molecule, albeit a macromolecule. It deals with the recognition of the nature and importance of hydrogen bonds and hydrophobic interactions in determining the structure, properties, and biological function of proteins until the present acquisition of an understanding of the structure, thermodynamics, and folding pathways from a linear array of amino acids to a biological entity. Along the way, with a combination of experiment and theoretical interpretation, a mechanism was elucidated for the thrombin-induced conversion of fibrinogen to a fibrin blood clot and for the oxidative-folding pathways of ribonuclease A. Before the atomic structure of a protein molecule was determined by x-ray diffraction or nuclear magnetic resonance spectroscopy, experimental studies of the fundamental interactions underlying protein structure led to several distance constraints which motivated the theoretical approach to determine protein structure, and culminated in the Empirical Conformational Energy Program for Peptides (ECEPP), an all-atom force field, with which the structures of fibrous collagen-like proteins and the 46-residue globular staphylococcal protein A were determined. To undertake the study of larger globular proteins, a physics-based coarse-grained UNited-RESidue (UNRES) force field was developed, and applied to the protein-folding problem in terms of structure, thermodynamics, dynamics, and folding pathways. Initially, single-chain and, ultimately, multiple-chain proteins were examined, and the methodology was extended to protein-protein interactions and to nucleic acids and to protein-nucleic acid interactions. The ultimate results led to an understanding of a variety of biological processes underlying natural and disease phenomena.

Factors affecting the computation of the 13C shielding in disaccharides

Knowledge of the three-dimensional structures of glycans and glycoproteins is useful for a full understanding of molecular processes in which glycans are involved, such as antigen-recognition and virus infection, to name a few. Among the ubiquitous nuclei in glycan molecules, the (13)C nucleus is an attractive candidate for computation of theoretical chemical shifts at the quantum chemical level of theory to validate and determine glycan structures. For this purpose, it is important to determine, first, which carbons can be used as probes to sense conformational changes and, second, all factors that affect the computation of the shielding, at the density functional theory (DFT) level of theory, of those carbons. To answer such questions, we performed a series of analyses on low-energy conformations, obtained by sampling the glycosidic torsional angles (ϕ, ψ) every 10°, of 12 disaccharides. Our results provide evidence that: (i) the carbons that participate in the glycosidic linkage are the most sensitive probes with which to sense conformational changes of disaccharides; (ii) the rotation of the hydroxyl groups closest to the glycosidic linkage significantly affects the computation of the shieldings of the carbons that participate in the glycosidic linkage; (iii) it is not possible to obtain the shieldings of one disaccharide from the computed values of a different disaccharide or from those disaccharides that differ in the anomeric state; and (iv) a proper basis set distribution, a functional, and a step size, with which to sample the conformational space, are necessary to compute shieldings accurately and rapidly.

Accounting for a mirror-image conformation as a subtle effect in protein folding

By using local (free-energy profiles along the amino acid sequence and (13)C(α) chemical shifts) and global (principal component) analyses to examine the molecular dynamics of protein-folding trajectories, generated with the coarse-grained united-residue force field, for the B domain of staphylococcal protein A, we are able to (i) provide the main reason for formation of the mirror-image conformation of this protein, namely, a slow formation of the second loop and part of the third helix (Asp29-Asn35), caused by the presence of multiple local conformational states in this portion of the protein; (ii) show that formation of the mirror-image topology is a subtle effect resulting from local interactions; (iii) provide a mechanism for how protein A overcomes the barrier between the metastable mirror-image state and the native state; and (iv) offer a plausible reason to explain why protein A does not remain in the metastable mirror-image state even though the mirror-image and native conformations are at least energetically compatible.

13C Chemical Shifts in Proteins: A Rich Source of Encoded Structural Information

Despite the formidable progress in Nuclear Magnetic Resonance (NMR) spectroscopy, quality assessment of NMR-derived structures remains as an important problem. Thus, validation of protein structures is essential for the spectroscopists, since it could enable them to detect structural flaws and potentially guide their efforts in further refinement. Moreover, availability of accurate and efficient validation tools would help molecular biologists and computational chemists to evaluate quality of available experimental structures and to select a protein model which is the most suitable for a given scientific problem. The ¹³C^α nuclei are ubiquitous in proteins, moreover, their shieldings are easily obtainable from NMR experiments and represent a rich source of encoded structural information that makes ¹³C^α chemical shifts an attractive candidate for use in computational methods aimed at determination and validation of protein structures. In this chapter, the basis of a novel methodology of computing, at the quantum chemical level of theory, the ¹³C^α shielding for the amino acid residues in proteins is described. We also identify and examine the main factors affecting the ¹³C^α-shielding computation. Finally, we illustrate how the information encoded in the ¹³C chemical shifts can be used for a number of applications, viz., from protein structure prediction of both α-helical and β-sheet conformations, to determination of the fraction of the tautomeric forms of the imidazole ring of histidine in proteins as a function of pH or to accurate detection of structural flaws, at a residue-level, in NMR-determined protein models.

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

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 , 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.

Quality assessment of protein NMR structures

Biomolecular NMR structures are now routinely used in biology, chemistry, and bioinformatics. Methods and metrics for assessing the accuracy and precision of protein NMR structures are beginning to be standardized across the biological NMR community. These include both knowledge-based assessment metrics, parameterized from the database of protein structures, and model versus data assessment metrics. On line servers are available that provide comprehensive protein structure quality assessment reports, and efforts are in progress by the world-wide Protein Data Bank (wwPDB) to develop a biomolecular NMR structure quality assessment pipeline as part of the structure deposition process. These quality assessment metrics and standards will aid NMR spectroscopists in determining more accurate structures, and increase the value and utility of these structures for the broad scientific community.

CheShift-2 resolves a local inconsistency between two X-ray crystal structures

Since chemical shifts provide important and relatively accessible information about protein structure in solution, a Web server, CheShift-2, was developed for structure interrogation, based on a quantum mechanics database of (13)C( α ) chemical shifts. We report the application of CheShift-2 to a local inconsistency between two X-ray crystal structures (PDB IDs 1IKN and 1NFI) of the complex between the p65/p50 heterodimer of NFκB and its inhibitor IκBα. The availability of NMR resonance assignments that included the region of the inconsistency provided an opportunity for independent validation of the CheShift-2 server. Application of the server showed that the (13)C( α ) chemical shifts measured for the Gly270-Pro281 sequence close to the C-terminus of IκBα were unequivocally consistent with the backbone structure modeled in the 1IKN structure, and were inconsistent with the 1NFI structure. Previous NOE measurements had demonstrated that the position of a tryptophan ring in the region immediately N-terminal in this region was not consistent with either structure. Subsequent recalculation of the local structure in this region, based on the electron density of the deposited structure factors for 1IKN, confirmed that the local backbone structure was best modeled by 1IKN, but that the rotamer of Trp258 is consistent with the 1NFI structure, including the presence of a hydrogen bond between the ring NεH of Trp258 and the backbone carbonyl group of Gln278. The consensus between all of these measures suggests that the CheShift-2 server operates well under circumstances in which backbone chemical shifts are available but where local plasticity may render X-ray structural data ambiguous.