2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts

Chemical shifts of amino acids in proteins are the most sensitive and easily obtainable NMR parameters that reflect the primary, secondary, and tertiary structures of the protein. In recent years, chemical shifts have been used to identify secondary structure in peptides and proteins, and it has been confirmed that ¹H^α, ¹³C^α, ¹³C^β, and ¹³C′ NMR chemical shifts for all 20 amino acids are sensitive to their secondary structure. Currently, most of the methods are purely based on one-dimensional statistical analyses of various chemical shifts for each residue to identify protein secondary structure. However, it is possible to achieve an increased accuracy from the two-dimensional analyses of these chemical shifts. The 2DCSi approach performs two-dimension cluster analyses of ¹H^α, ¹H^N, ¹³C^α, ¹³C^β, ¹³C′, and ¹⁵N^H chemical shifts to identify protein secondary structure and the redox state of cysteine residue. For the analysis of paired chemical shifts of 6 data sets, each of the 20 amino acids has its own 15 two-dimension cluster scattering diagrams. Accordingly, the probabilities for identifying helix and extended structure were calculated by using our scoring matrix. Compared with existing the chemical shift-based methods, it appears to improve the prediction accuracy of secondary structure identification, particularly in the extended structure. In addition, the probability of the given residue to be helix or extended structure is displayed, allows the users to make decisions by themselves. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Grant sponsor: National Science Council of ROC; Grant numbers: NSC-94-2323-B006- 001, NSC-93-2212-E-006.     

ORB, a homology-based program for the prediction of protein NMR chemical shifts

A computer program (ORB) has been developed to predict ¹H,¹³C and ¹⁵N NMR chemical shifts of previouslyunassigned proteins. The program makes use of the information contained in achemical shift database of previously assigned proteins supplemented by astatistically derived averaged chemical shift database in which the shifts arecategorized according to their residue, atom and secondary structure type[Wishart et al. (1991) J. Mol. Biol., 222, 311–333]. The predictionprocess starts with a multiple alignment of all previously assigned proteinswith the unassigned query protein. ORB uses the sequence and secondarystructure alignment program XALIGN for this task [Wishart et al. (1994)CABIOS, 10, 121–132; 687–688]. The prediction algorithm in ORB isbased on a scoring of the known shifts for each sequence. The scores dependon global sequence similarity, local sequence similarity, structuralsimilarity and residue similarity and determine how much weight one particularshift is given in the prediction process. In situations where no applicablepreviously assigned chemical shifts are available, the shifts derived from theaveraged database are used. In addition to supplying the user with predictedchemical shifts, ORB calculates a confidence value for every prediction. Theseconfidence values enable the user to judge which predictions are the mostaccurate and they are particularly useful when ORB is incorporated into acomplete autoassignment package. The usefulness of ORB was tested on threemedium-sized proteins: an interleukin-8 analog, a troponin C synthetic peptideheterodimer and cardiac troponin C. Excellent results are obtained if ORB isable to use the chemical shifts of at least one highly homologous sequence.ORB performs well as long as the sequence identity between proteins with knownchemical shifts and the new sequence is not less than 30%.     

Hash: a program to accurately predict protein Hα shifts from neighboring backbone shifts

Chemical shifts provide not only peak identities for analyzing nuclear magnetic resonance (NMR) data, but also an important source of conformational information for studying protein structures. Current structural studies requiring H^α chemical shifts suffer from the following limitations. (1) For large proteins, the H^α chemical shifts can be difficult to assign using conventional NMR triple-resonance experiments, mainly due to the fast transverse relaxation rate of C^α that restricts the signal sensitivity. (2) Previous chemical shift prediction approaches either require homologous models with high sequence similarity or rely heavily on accurate backbone and side-chain structural coordinates. When neither sequence homologues nor structural coordinates are available, we must resort to other information to predict H^α chemical shifts. Predicting accurate H^α chemical shifts using other obtainable information, such as the chemical shifts of nearby backbone atoms (i.e., adjacent atoms in the sequence), can remedy the above dilemmas, and hence advance NMR-based structural studies of proteins. By specifically exploiting the dependencies on chemical shifts of nearby backbone atoms, we propose a novel machine learning algorithm, called Hash, to predict H^α chemical shifts. Hash combines a new fragment-based chemical shift search approach with a non-parametric regression model, called the generalized additive model, to effectively solve the prediction problem. We demonstrate that the chemical shifts of nearby backbone atoms provide a reliable source of information for predicting accurate H^α chemical shifts. Our testing results on different possible combinations of input data indicate that Hash has a wide rage of potential NMR applications in structural and biological studies of proteins.     

Structural origins of pH‐dependent chemical shifts in the B1 domain of protein G

We report chemical shifts for H^N, N, and C′ nuclei in the His‐tagged B1 domain of protein G (GB1) over a range of pH values from pH 2.0 to 9.0, which fit well to standard pH‐dependent equations. We also report a 1.2 Å resolution crystal structure of GB1 at pH 3.0. Comparison of this crystal structure with published crystal structures at higher pHs provides details of the structural changes in GB1 associated with protonation of the carboxylate groups, in particular a conformational change in the C‐terminus of the protein at low pH. An additional change described recently is not seen in the crystal structure because of crystal contacts. We show that the pH‐dependent changes in chemical shifts can be almost entirely understood based on structural changes, thereby providing insight into the relationship between structure and chemical shift. In particular, we describe through‐bond effects extending up to five bonds, affecting N and C′ but not H^N; through‐space effects of carboxylates, which fit well to a simple electric field model; and effects due to conformational change, which have a similar magnitude to many of the direct effects. Finally, we discuss cooperative effects, demonstrating a lack of cooperative unfolding in the helix, and the existence of a β‐sheet “iceberg” extending over three of the four strands. This study therefore extends the application of chemical shifts to understanding protein structure. Proteins 2010; © 2010 Wiley‐Liss, Inc.     

PPM: a side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles

The combination of the wide availability of protein backbone and side-chain NMR chemical shifts with advances in understanding of their relationship to protein structure makes these parameters useful for the assessment of structural-dynamic protein models. A new chemical shift predictor (PPM) is introduced, which is solely based on physical?Cchemical contributions to the chemical shifts for both the protein backbone and methyl-bearing amino-acid side chains. To explicitly account for the effects of protein dynamics on chemical shifts, PPM was directly refined against 100?ns long molecular dynamics (MD) simulations of 35 proteins with known experimental NMR chemical shifts. It is found that the prediction of methyl-proton chemical shifts by PPM from MD ensembles is improved over other methods, while backbone C??, C??, C??, N, and H^N chemical shifts are predicted at an accuracy comparable to the latest generation of chemical shift prediction programs. PPM is particularly suitable for the rapid evaluation of large protein conformational ensembles on their consistency with experimental NMR data and the possible improvement of protein force fields from chemical shifts.     

SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and ¹³C^β chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and ¹³C^β atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for δ¹⁵N, δ¹³C’, δ¹³C^α, δ¹³C^β, δ¹H^α and δ¹H^N, respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2–10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.     

Structural model of Dex protein from Penicillium minioluteumand its implications in the mechanism of catalysis

The DEX gene encodes an extracellular dextranase (EC 3.2.1.11); this enzyme hydrolyzes the α(1,6) glucosidic bond contained in dextran to release small isomaltosaccharides. Sequence analysis has revealed only one homologous sequence, CB-8 protein, from Arthrobacter sp., with 30% sequence identity. The secondary structure prediction for Dex was corroborated by circular dichroism measurements. To explore the possibility that Dex protein might adopt a fold similar to any known structure, we conducted a threading search of a three-dimensional structure database. This search revealed that the Dex sequence is compatible with the galactose oxidase/methanol dehydrogenase/sialidase fold. A structural model of Dex based on these results is physically and biologically plausible and leads to testable predictions, including the prediction that Asp²⁴⁶ and Glu²⁹⁹ might be catalytic residues. Also, according to this model the Dex enzyme has a mechanism of hydrolysis with net inversion of anomeric configuration. Proteins 31:345–354, 1998. © 1998 Wiley-Liss, Inc.     

Global fold of human cannabinoid type 2 receptor probed by solid‐state 13C‐, 15N‐MAS NMR and molecular dynamics simulations

The global fold of human cannabinoid type 2 (CB₂) receptor in the agonist‐bound active state in lipid bilayers was investigated by solid‐state ¹³C‐ and ¹⁵N magic‐angle spinning (MAS) NMR, in combination with chemical‐shift prediction from a structural model of the receptor obtained by microsecond‐long molecular dynamics (MD) simulations. Uniformly ¹³C‐ and ¹⁵N‐labeled CB₂ receptor was expressed in milligram quantities by bacterial fermentation, purified, and functionally reconstituted into liposomes. ¹³C MAS NMR spectra were recorded without sensitivity enhancement for direct comparison of C_α, C_β, and C?O bands of superimposed resonances with predictions from protein structures generated by MD. The experimental NMR spectra matched the calculated spectra reasonably well indicating agreement of the global fold of the protein between experiment and simulations. In particular, the ¹³C chemical shift distribution of C_α resonances was shown to be very sensitive to both the primary amino acid sequence and the secondary structure of CB₂. Thus the shape of the C_α band can be used as an indicator of CB₂ global fold. The prediction from MD simulations indicated that upon receptor activation a rather limited number of amino acid residues, mainly located in the extracellular Loop 2 and the second half of intracellular Loop 3, change their chemical shifts significantly (≥1.5 ppm for carbons and ≥5.0 ppm for nitrogens). Simulated two‐dimensional ¹³C_α(i)? ¹³C?O(i) and ¹³C?O(i)? ¹⁵NH(i + 1) dipolar‐interaction correlation spectra provide guidance for selective amino acid labeling and signal assignment schemes to study the molecular mechanism of activation of CB₂ by solid‐state MAS NMR. Proteins 2014; 82:452–465. © 2013 Wiley Periodicals, Inc.     

Cover Image,Volume 87, Issue 12

CASP (critical assessment of structure prediction) assesses the state of the art in modeling protein structure from amino acid sequence. The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates (historically “ab initio” modeling). Progress was driven by the successful application of deep learning techniques to predict inter-residue distances. In turn, these results drove dramatic improvements in three-dimensional structure accuracy: With the proviso that there are an adequate number of sequences known for the protein family, the new methods essentially solve the long-standing problem of predicting the fold topology of monomeric proteins. Further, the number of sequences required in the alignment has fallen substantially. There is also substantial improvement in the accuracy of template-based models. Other areas—model refinement, accuracy estimation, and the structure of protein assemblies—have again yielded interesting results. CASP13 placed increased emphasis on the use of sparse data together with modeling and chemical crosslinking, SAXS, and NMR all yielded more mature results. This paper summarizes the key outcomes of CASP13. The special issue of PROTEINS contains papers describing the CASP13 assessments in each modeling category and contributions from the participants.     

Type I and II β-turns prediction using NMR chemical shifts

A method for predicting type I and II β-turns using nuclear magnetic resonance (NMR) chemical shifts is proposed. Isolated β-turn chemical-shift data were collected from 1,798 protein chains. One-dimensional statistical analyses on chemical-shift data of three classes β-turn (type I, II, and VIII) showed different distributions at four positions, (i) to (i + 3). Considering the central two residues of type I β-turns, the mean values of C_ο, C_α, H_N, and N_H chemical shifts were generally (i + 1) > (i + 2). The mean values of C_β and H_α chemical shifts were (i + 1) < (i + 2). The distributions of the central two residues in type II and VIII β-turns were also distinguishable by trends of chemical shift values. Two-dimensional cluster analyses on chemical-shift data show positional distributions more clearly. Based on these propensities of chemical shift classified as a function of position, rules were derived using scoring matrices for four consecutive residues to predict type I and II β-turns. The proposed method achieves an overall prediction accuracy of 83.2 and 84.2 % with the Matthews correlation coefficient values of 0.317 and 0.632 for type I and II β-turns, indicating that its higher accuracy for type II turn prediction. The results show that it is feasible to use NMR chemical shifts to predict the β-turn types in proteins. The proposed method can be incorporated into other chemical-shift based protein secondary structure prediction methods.     

Structure determination of proteins in <Superscript>2</Superscript>H<Subscript>2</Subscript>O solution aided by a deuterium-decoupled 3D HCA(N)CO experiment

We developed an NMR pulse sequence, 3D HCA(N)CO, to correlate the chemical shifts of protein backbone ¹Hα and ¹³Cα to those of ¹³C′ in the preceding residue. By applying ²H decoupling, the experiment was accomplished with high sensitivity comparable to that of HCA(CO)N. When combined with HCACO, HCAN and HCA(CO)N, the HCA(N)CO sequence allows the sequential assignment using backbone ¹³C′ and amide ¹⁵N chemical shifts without resort to backbone amide protons. This assignment strategy was demonstrated for ¹³C/¹⁵N-labeled GB1 dissolved in ²H₂O. The quality of the GB1 structure determined in ²H₂O was similar to that determined in H₂O in spite of significantly smaller number of NOE correlations. Thus this strategy enables the determination of protein structures in ²H₂O or H₂O at high pH values.     

Proton,carbon, and nitrogen chemical shifts accurately delineate differences and similarities in secondary structure between the homologous proteins IRAP and IL-1β

Summary ¹H,¹³C, and¹⁵N secondary chemical shifts, defined as the difference between the observed value and the random coil value, have been calculated for interleukin-1 receptor antagonist protein and interleukin-1. Averaging of the secondary chemical shifts with those of adjacent residues was used to smooth out local effects and to obtain a correlation dependent on secondary structure. Differences and similarities in the placement of secondary structure elements in the primary segdences of these structurally homologous proteins are manifested in the smoothed secondary chemical shifts of all three types of nuclei. The close correlation observed between the secondary chemical shifts and the previously defined locations of secondary structure, as defined by traditional methods, exemplifies the advantage of chemical shifts to delineate regions of secondary structure.     

Aromatic claw: A new fold with high aromatic content that evades structural prediction

We determined the NMR structure of a highly aromatic (13%) protein of unknown function, Aq1974 from Aquifex aeolicus (PDB ID: 5SYQ). The unusual sequence of this protein has a tryptophan content five times the normal (six tryptophan residues of 114 or 5.2% while the average tryptophan content is 1.0%) with the tryptophans occurring in a WXW motif. It has no detectable sequence homology with known protein structures. Although its NMR spectrum suggested that the protein was rich in β‐sheet, upon resonance assignment and solution structure determination, the protein was found to be primarily α‐helical with a small two‐stranded β‐sheet with a novel fold that we have termed an Aromatic Claw. As this fold was previously unknown and the sequence unique, we submitted the sequence to CASP10 as a target for blind structural prediction. At the end of the competition, the sequence was classified a hard template based model; the structural relationship between the template and the experimental structure was small and the predictions all failed to predict the structure. CSRosetta was found to predict the secondary structure and its packing; however, it was found that there was little correlation between CSRosetta score and the RMSD between the CSRosetta structure and the NMR determined one. This work demonstrates that even in relatively small proteins, we do not yet have the capacity to accurately predict the fold for all primary sequences. The experimental discovery of new folds helps guide the improvement of structural prediction methods.     

A probabilistic model for secondary structure prediction from protein chemical shifts

Protein chemical shifts encode detailed structural information that is difficult and computationally costly to describe at a fundamental level. Statistical and machine learning approaches have been used to infer correlations between chemical shifts and secondary structure from experimental chemical shifts. These methods range from simple statistics such as the chemical shift index to complex methods using neural networks. Notwithstanding their higher accuracy, more complex approaches tend to obscure the relationship between secondary structure and chemical shift and often involve many parameters that need to be trained. We present hidden Markov models (HMMs) with Gaussian emission probabilities to model the dependence between protein chemical shifts and secondary structure. The continuous emission probabilities are modeled as conditional probabilities for a given amino acid and secondary structure type. Using these distributions as outputs of first‐ and second‐order HMMs, we achieve a prediction accuracy of 82.3%, which is competitive with existing methods for predicting secondary structure from protein chemical shifts. Incorporation of sequence‐based secondary structure prediction into our HMM improves the prediction accuracy to 84.0%. Our findings suggest that an HMM with correlated Gaussian distributions conditioned on the secondary structure provides an adequate generative model of chemical shifts. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Conformational analysis of the full‐length M2 protein of the influenza A virus using solid‐state NMR

The influenza A M2 protein forms a proton channel for virus infection and mediates virus assembly and budding. While extensive structural information is known about the transmembrane helix and an adjacent amphipathic helix, the conformation of the N‐terminal ectodomain and the C‐terminal cytoplasmic tail remains largely unknown. Using two‐dimensional (2D) magic‐angle‐spinning solid‐state NMR, we have investigated the secondary structure and dynamics of full‐length M2 (M2FL) and found them to depend on the membrane composition. In 2D ¹³C DARR correlation spectra, 1,2‐dimyristoyl‐sn‐glycero‐3‐phosphocholine (DMPC)‐bound M2FL exhibits several peaks at β‐sheet chemical shifts, which result from water‐exposed extramembrane residues. In contrast, M2FL bound to cholesterol‐containing membranes gives predominantly α‐helical chemical shifts. Two‐dimensional J‐INADEQUATE spectra and variable‐temperature ¹³C spectra indicate that DMPC‐bound M2FL is highly dynamic while the cholesterol‐containing membranes significantly immobilize the protein at physiological temperature. Chemical‐shift prediction for various secondary‐structure models suggests that the β‐strand is located at the N‐terminus of the DMPC‐bound protein, while the cytoplasmic domain is unstructured. This prediction is confirmed by the 2D DARR spectrum of the ectodomain‐truncated M2(21–97), which no longer exhibits β‐sheet chemical shifts in the DMPC‐bound state. We propose that the M2 conformational change results from the influence of cholesterol, and the increased helicity of M2FL in cholesterol‐rich membranes may be relevant for M2 interaction with the matrix protein M1 during virus assembly and budding. The successful determination of the β‐strand location suggests that chemical‐shift prediction is a promising approach for obtaining structural information of disordered proteins before resonance assignment.     

An Empirical Correlation between Secondary Structure Content and Averaged Chemical Shifts in Proteins

It is shown that the averaged chemical shift (ACS) of a particular nucleus in the protein backbone empirically correlates well to its secondary structure content (SSC). Chemical shift values of more than 200 proteins obtained from the Biological Magnetic Resonance Bank are used to calculate ACS values, and the SSC is estimated from the corresponding three-dimensional coordinates obtained from the Protein Data Bank. ACS values of ¹H_α show the highest correlation to helical and sheet structure content (correlation coefficient of 0.80 and 0.75, respectively); ¹H_N exhibits less reliability (0.65 for both sheet and helix), whereas such correlations are poor for the heteronuclei. SSC estimated using this correlation shows a good agreement with the conventional chemical shift index-based approach for a set of proteins that only have chemical shift information but no NMR or x-ray determined three-dimensional structure. These results suggest that even chemical shifts averaged over the entire protein retain significant information about the secondary structure. Thus, the correlation between ACS and SSC can be used to estimate secondary structure content and to monitor large-scale secondary structural changes in protein, as in folding studies.     

Beyond the Twilight Zone: Automated prediction of structural properties of proteins by recursive neural networks and remote homology information

The prediction of 1D structural properties of proteins is an important step toward the prediction of protein structure and function, not only in the ab initio case but also when homology information to known structures is available. Despite this the vast majority of 1D predictors do not incorporate homology information into the prediction process. We develop a novel structural alignment method, SAMD, which we use to build alignments of putative remote homologues that we compress into templates of structural frequency profiles. We use these templates as additional input to ensembles of recursive neural networks, which we specialise for the prediction of query sequences that show only remote homology to any Protein Data Bank structure. We predict four 1D structural properties – secondary structure, relative solvent accessibility, backbone structural motifs, and contact density. Secondary structure prediction accuracy, tested by five‐fold cross‐validation on a large set of proteins allowing less than 25% sequence identity between training and test set and query sequences and templates, exceeds 82%, outperforming its ab initio counterpart, other state‐of‐the‐art secondary structure predictors (Jpred 3 and PSIPRED) and two other systems based on PSI‐BLAST and COMPASS templates. We show that structural information from homologues improves prediction accuracy well beyond the Twilight Zone of sequence similarity, even below 5% sequence identity, for all four structural properties. Significant improvement over the extraction of structural information directly from PDB templates suggests that the combination of sequence and template information is more informative than templates alone. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

Background

Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3–4 Å from the native structure). Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure.

Results

We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that C _α trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10%) yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 Å threshold (as per CASP assessment) from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8 Å predictions on the CASP7 targets using a pre-CASP7 PDB, and find that both predictors are state-of-the-art, with the template-based one far outperforming the best CASP7 systems if templates with sequence identity to the query of 10% or better are available. Although this is not the main focus of this paper we also report on reconstructions of C _α traces based on both ab initio and template-based 4-class map predictions, showing that the latter are generally more accurate even when homology is dubious.

Conclusion

Accurate predictions of multi-class maps may provide valuable constraints for improved ab initio and template-based prediction of protein structures, naturally incorporate multiple templates, and yield state-of-the-art binary maps. Predictions of protein structures and 8 Å contact maps based on the multi-class distance map predictors described in this paper are freely available to academic users at the url http://distill.ucd.ie/.