PROSHIFT: protein chemical shift prediction using artificial neural networks

The importance of protein chemical shift values for the determination of three-dimensional protein structure has increased in recent years because of the large databases of protein structures with assigned chemical shift data. These databases have allowed the investigation of the quantitative relationship between chemical shift values obtained by liquid state NMR spectroscopy and the three-dimensional structure of proteins. A neural network was trained to predict the ¹H, ¹³C, and ¹⁵N of proteins using their three-dimensional structure as well as experimental conditions as input parameters. It achieves root mean square deviations of 0.3 ppm for hydrogen, 1.3 ppm for carbon, and 2.6 ppm for nitrogen chemical shifts. The model reflects important influences of the covalent structure as well as of the conformation not only for backbone atoms (as, e.g., the chemical shift index) but also for side-chain nuclei. For protein models with a RMSD smaller than 5 Å a correlation of the RMSD and the r.m.s. deviation between the predicted and the experimental chemical shift is obtained. Thus the method has the potential to not only support the assignment process of proteins but also help with the validation and the refinement of three-dimensional structural proposals. It is freely available for academic users at the PROSHIFT server: www.jens-meiler.de/proshift.html     

Improved chemical shift prediction by Rosetta conformational sampling

Chemical shift frequencies represent a time-average of all the conformational states populated by a protein. Thus, chemical shift prediction programs based on sequence and database analysis yield higher accuracy for rigid rather than flexible protein segments. Here we show that the prediction accuracy can be significantly improved by averaging over an ensemble of structures, predicted solely from amino acid sequence with the Rosetta program. This approach to chemical shift and structure prediction has the potential to be useful for guiding resonance assignments, especially in solid-state NMR structural studies of membrane proteins in proteoliposomes.     

CheckShift improved: fast chemical shift reference correction with high accuracy

The construction of a consistent protein chemical shift database is an important step toward making more extensive use of this data in structural studies. Unfortunately, progress in this direction has been hampered by the quality of the available data, particularly with respect to chemical shift referencing, which is often either inaccurate or inconsistently annotated. Preprocessing of the data is therefore required to detect and correct referencing errors. In an earlier study we developed CheckShift, a program for performing this task automatically. Now we spent substantial effort in improving the running time of the CheckShift algorithm, which resulted in an running time decrease of 90%, thereby achieving equivalent quality to the former version of CheckShift. The reason for the running time decrease is twofold. Firstly we improved the search for the optimal re-referencing offset considerably. Secondly, as CheckShift is based on a secondary structure prediction from the amino acid sequence (formally PsiPred was used), we evaluated a wide range of available secondary structure prediction programs focusing on the special needs of the CheckShift algorithm. The results of this evaluation prove empirically that we can use faster secondary structure prediction programs than PsiPred without sacrificing CheckShift’s accuracy. Very recently Wang and Markley (2009) gave a small list of extreme outliers of the former version of the CheckShift web-server. Those were due to the empirical reduction of the search space implemented in the old version. The new version of CheckShift now gives very similar results to RefDB and LACS for all outliers mentioned in Table 1 of Wang and Markley (2009).     

Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information

We introduce a Python-based program that utilizes the large database of ¹³C and ¹⁵N chemical shifts in the Biological Magnetic Resonance Bank to rapidly predict the amino acid type and secondary structure from correlated chemical shifts. The program, called PACSYlite Unified Query (PLUQ), is designed to help assign peaks obtained from 2D ¹³C–¹³C, ¹⁵N–¹³C, or 3D ¹⁵N–¹³C–¹³C magic-angle-spinning correlation spectra. We show secondary-structure specific 2D ¹³C–¹³C correlation maps of all twenty amino acids, constructed from a chemical shift database of 262,209 residues. The maps reveal interesting conformation-dependent chemical shift distributions and facilitate searching of correlation peaks during amino-acid type assignment. Based on these correlations, PLUQ outputs the most likely amino acid types and the associated secondary structures from inputs of experimental chemical shifts. We test the assignment accuracy using four high-quality protein structures. Based on only the Cα and Cβ chemical shifts, the highest-ranked PLUQ assignments were 40–60 % correct in both the amino-acid type and the secondary structure. For three input chemical shifts (CO–Cα–Cβ or N–Cα–Cβ), the first-ranked assignments were correct for 60 % of the residues, while within the top three predictions, the correct assignments were found for 80 % of the residues. PLUQ and the chemical shift maps are expected to be useful at the first stage of sequential assignment, for combination with automated sequential assignment programs, and for highly disordered proteins for which secondary structure analysis is the main goal of structure determination.     

Improving 3D structure prediction from chemical shift data

We report advances in the calculation of protein structures from chemical shift nuclear magnetic resonance data alone. Our previously developed method, CS-Rosetta, assembles structures from a library of short protein fragments picked from a large library of protein structures using chemical shifts and sequence information. Here we demonstrate that combination of a new and improved fragment picker and the iterative sampling algorithm RASREC yield significant improvements in convergence and accuracy. Moreover, we introduce improved criteria for assessing the accuracy of the models produced by the method. The method was tested on 39 proteins in the 50–100 residue size range and yields reliable structures in 70 % of the cases. All structures that passed the reliability filter were accurate (<2 Å RMSD from the reference).     

Protein secondary structure prediction using NMR chemical shift data

Accurate determination of protein secondary structure from the chemical shift information is a key step for NMR tertiary structure determination. Relatively few work has been done on this subject. There needs to be a systematic investigation of algorithms that are (a) robust for large datasets; (b) easily extendable to (the dynamic) new databases; and (c) approaching to the limit of accuracy. We introduce new approaches using k-nearest neighbor algorithm to do the basic prediction and use the BCJR algorithm to smooth the predictions and combine different predictions from chemical shifts and based on sequence information only. Our new system, SUCCES, improves the accuracy of all existing methods on a large dataset of 805 proteins (at 86% Q(3) accuracy and at 92.6% accuracy when the boundary residues are ignored), and it is easily extendable to any new dataset without requiring any new training. The software is publicly available at http://monod.uwaterloo.ca/nmr/succes.     

RASP: rapid and robust backbone chemical shift assignments from protein structure

Chemical shift prediction has an unappreciated power to guide backbone resonance assignment in cases where protein structure is known. Here we describe Resonance Assignment by chemical Shift Prediction (RASP), a method that exploits this power to derive protein backbone resonance assignments from chemical shift predictions. Robust assignments can be obtained from a minimal set of only the most sensitive triple-resonance experiments, even for spectroscopically challenging proteins. Over a test set of 154 proteins RASP assigns 88 % of residues with an accuracy of 99.7 %, using only information available from HNCO and HNCA spectra. Applied to experimental data from a challenging 34 kDa protein, RASP assigns 90 % of manually assigned residues using only 40 % of the experimental data required for the manual assignment. RASP has the potential to significantly accelerate the backbone assignment process for a wide range of proteins for which structural information is available, including those for which conventional assignment strategies are not feasible.     

A structural alphabet for local protein structures: improved prediction methods

Three-dimensional protein structures can be described with a library of 3D fragments that define a structural alphabet. We have previously proposed such an alphabet, composed of 16 patterns of five consecutive amino acids, called Protein Blocks (PBs). These PBs have been used to describe protein backbones and to predict local structures from protein sequences. The Q16 prediction rate reaches 40.7% with an optimization procedure. This article examines two aspects of PBs. First, we determine the effect of the enlargement of databanks on their definition. The results show that the geometrical features of the different PBs are preserved (local RMSD value equal to 0.41 A on average) and sequence-structure specificities reinforced when databanks are enlarged. Second, we improve the methods for optimizing PB predictions from sequences, revisiting the optimization procedure and exploring different local prediction strategies. Use of a statistical optimization procedure for the sequence-local structure relation improves prediction accuracy by 8% (Q16 = 48.7%). Better recognition of repetitive structures occurs without losing the prediction efficiency of the other local folds. Adding secondary structure prediction improved the accuracy of Q16 by only 1%. An entropy index (Neq), strongly related to the RMSD value of the difference between predicted PBs and true local structures, is proposed to estimate prediction quality. The Neq is linearly correlated with the Q16 prediction rate distributions, computed for a large set of proteins. An "expected" prediction rate QE16 is deduced with a mean error of 5%.     

Estimation of protein secondary structure content directly from NMR spectra using an improved empirical correlation with averaged chemical shift

We have recently shown that the averaged chemical shift (ACS) of a nucleus in the protein backbone correlates well empirically to its secondary structure content (SSC). This allows the estimation of SSC directly from the NMR spectrum without the time intensive process of chemical shift assignment. Here, we present an empirical correlation that accounts both for contributions to the relevant protein and chemical shift databases made subsequent to the original analysis, and for missing or inconsistently referenced resonances. Our results affirm that this method provides a significant tool for initial structural prediction from NMR data prior to complete chemical shift assignment.     

Automated 1H and 13C chemical shift prediction using the BioMagResBank

A computer program has been developed to accurately and automatically predict the ¹H and ¹³C chemical shifts of unassigned proteins on the basis of sequence homology. The program (called SHIFTY) uses standard sequence alignment techniques to compare the sequence of an unassigned protein against the BioMagResBank – a public database containing sequences and NMR chemical shifts of nearly 200 assigned proteins [Seavey et al. (1991) J. Biomol. NMR, 1, 217–236]. From this initial sequence alignment, the program uses a simple set of rules to directly assign or transfer a complete set of ¹H or ¹³C chemical shifts (from the previously assigned homologues) to the unassigned protein. This homologous assignment protocol takes advantage of the simple fact that homologous proteins tend to share both structural similarity and chemical shift similarity. SHIFTY has been extensively tested on more than 25 medium-sized proteins. Under favorable circumstances, this program can predict the ¹H or ¹³C chemical shifts of proteins with an accuracy far exceeding any other method published to date. With the expo- nential growth in the number of assigned proteins appearing in the literature (now at a rate of more than 150 per year), we believe that SHIFTY may have widespread utility in assigning individual members in families of related proteins, an endeavor that accounts for a growing portion of the protein NMR work being done today.     

Rapid and reliable protein structure determination via chemical shift threading

Protein structure determination using nuclear magnetic resonance (NMR) spectroscopy can be both time-consuming and labor intensive. Here we demonstrate how chemical shift threading can permit rapid, robust, and accurate protein structure determination using only chemical shift data. Threading is a relatively old bioinformatics technique that uses a combination of sequence information and predicted (or experimentally acquired) low-resolution structural data to generate high-resolution 3D protein structures. The key motivations behind using NMR chemical shifts for protein threading lie in the fact that they are easy to measure, they are available prior to 3D structure determination, and they contain vital structural information. The method we have developed uses not only sequence and chemical shift similarity but also chemical shift-derived secondary structure, shift-derived super-secondary structure, and shift-derived accessible surface area to generate a high quality protein structure regardless of the sequence similarity (or lack thereof) to a known structure already in the PDB. The method (called E-Thrifty) was found to be very fast (often?<?10 min/structure) and to significantly outperform other shift-based or threading-based structure determination methods (in terms of top template model accuracy)—with an average TM-score performance of 0.68 (vs. 0.50–0.62 for other methods). Coupled with recent developments in chemical shift refinement, these results suggest that protein structure determination, using only NMR chemical shifts, is becoming increasingly practical and reliable. E-Thrifty is available as a web server at http://ethrifty.ca.     

A probabilistic approach for validating protein NMR chemical shift assignments

It has been estimated that more than 20% of the proteins in the BMRB are improperly referenced and that about 1% of all chemical shift assignments are mis-assigned. These statistics also reflect the likelihood that any newly assigned protein will have shift assignment or shift referencing errors. The relatively high frequency of these errors continues to be a concern for the biomolecular NMR community. While several programs do exist to detect and/or correct chemical shift mis-referencing or chemical shift mis-assignments, most can only do one, or the other. The one program (SHIFTCOR) that is capable of handling both chemical shift mis-referencing and mis-assignments, requires the 3D structure coordinates of the target protein. Given that chemical shift mis-assignments and chemical shift re-referencing issues should ideally be addressed prior to 3D structure determination, there is a clear need to develop a structure-independent approach. Here, we present a new structure-independent protocol, which is based on using residue-specific and secondary structure-specific chemical shift distributions calculated over small (3–6 residue) fragments to identify mis-assigned resonances. The method is also able to identify and re-reference mis-referenced chemical shift assignments. Comparisons against existing re-referencing or mis-assignment detection programs show that the method is as good or superior to existing approaches. The protocol described here has been implemented into a freely available Java program called “Probabilistic Approach for protein Nmr Assignment Validation (PANAV)” and as a web server () which can be used to validate and/or correct as well as re-reference assigned protein chemical shifts.     

De novo protein structure generation from incomplete chemical shift assignments

NMR chemical shifts provide important local structural information for proteins. Consistent structure generation from NMR chemical shift data has recently become feasible for proteins with sizes of up to 130 residues, and such structures are of a quality comparable to those obtained with the standard NMR protocol. This study investigates the influence of the completeness of chemical shift assignments on structures generated from chemical shifts. The Chemical-Shift-Rosetta (CS-Rosetta) protocol was used for de novo protein structure generation with various degrees of completeness of the chemical shift assignment, simulated by omission of entries in the experimental chemical shift data previously used for the initial demonstration of the CS-Rosetta approach. In addition, a new CS-Rosetta protocol is described that improves robustness of the method for proteins with missing or erroneous NMR chemical shift input data. This strategy, which uses traditional Rosetta for pre-filtering of the fragment selection process, is demonstrated for two paramagnetic proteins and also for two proteins with solid-state NMR chemical shift assignments. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

An approach to sequential NMR assignments of proteins: application to chemical shift restraint-based structure prediction

A procedure for the simultaneous acquisition of {HNCOCANH & HCCCONH} chemical shift correlation spectra employing sequential \(^{1}\hbox {H}\) data acquisition for moderately sized proteins is presented. The suitability of the approach for obtaining sequential resonance assignments, including complete \(^{15}\hbox {N}{},\, ^{1}\hbox {H}{}^{N},\, ^{13}\hbox {CO}{},\, ^{13}\hbox {C}^{\alpha }{},\, ^{13}\hbox {C}^{\beta }\) and \(^{1}\hbox {H}^{\alpha }\) chemical shift information, is demonstrated experimentally for a \(^{13}\hbox {C}\) and \(^{15}\hbox {N}\) labelled sample of the C-terminal winged helix (WH) domain of the minichromosome maintenance (MCM) complex of Sulfolobus solfataricus. The chemical shift information obtained was used to calculate the global fold of this winged helix domain via CS-Rosetta. This demonstrates that our procedure provides a reliable and straight-forward protocol for a quick global fold determination of moderately-sized proteins.     

Paircoil2: improved prediction of coiled coils from sequence

We introduce Paircoil2, a new version of the Paircoil program, which uses pairwise residue probabilities to detect coiled-coil motifs in protein sequence data. Paircoil2 achieves 98% sensitivity and 97% specificity on known coiled coils in leave-family-out cross-validation. It also shows superior performance compared with published methods in tests on proteins of known structure.     

Relationship between nuclear magnetic resonance chemical shift and protein secondary structure

An analysis of the 1H nuclear magnetic resonance chemical shift assignments and secondary structure designations for over 70 proteins has revealed some very strong and unexpected relationships. Similar studies, performed on smaller databases, for 13C and 15N chemical shifts reveal equally strong correlations to protein secondary structure. Among the more interesting results to emerge from this work is the finding that all 20 naturally occurring amino acids experience a mean alpha-1H upfield shift of 0.39 parts per million (from the random coil value) when placed in a helical configuration. In a like manner, the alpha-1H chemical shift is found to move downfield by an average of 0.37 parts per million when the residue is placed in a beta-strand or extended configuration. Similar changes are also found for amide 1H, carbonyl 13C, alpha-13C and amide 15N chemical shifts. Other relationships between chemical shift and protein conformation are also uncovered; in particular, a correlation between helix dipole effects and amide proton chemical shifts as well as a relationship between alpha-proton chemical shifts and main-chain flexibility. Additionally, useful relationships between alpha-proton chemical shifts and backbone dihedral angles as well as correlations between amide proton chemical shifts and hydrogen bond effects are demonstrated.