Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N

Summary

Chemical shifts are obtained at the first stage of any protein structural study by NMR spectroscopy. Chemical shifts are known to be impacted by a wide range of structural factors and the artificial neural network based TALOS-N program has been trained to extract backbone and sidechain torsion angles from ¹H, ¹⁵N and ¹³C shifts. The program is quite robust, and typically yields backbone torsion angles for more than 90% of the residues, and sidechain χ₁ rotamer information for about half of these, in addition to reliably predicting secondary structure. The use of TALOS-N is illustrated for the protein DinI, and torsion angles obtained by TALOS-N analysis from the measured chemical shifts of its backbone and ¹³C^β nuclei are compared to those seen in a prior, experimentally determined structure. The program is also particularly useful for generating torsion angle restraints, which then can be used during standard NMR protein structure calculations.

1 Introduction

1.1 Relations between chemical shifts and protein structure

The first step of any protein structural study by NMR spectroscopy typically involves assignment of the multitude of NMR resonances to individual nuclei. Originally, for proteins extracted from natural sources, this only involved assignment of the hydrogen NMR spectra (1, 2). However, due to extensive resonance overlap in ¹H NMR spectra, this technology was restricted to relatively small proteins. With advances in molecular biology, the vast majority of today’s structural studies focus on cloned proteins, typically overexpressed in Escherichia coli (3–5). By using suitable isotopically enriched growth media, it then is readily feasible to obtain essentially full incorporation of the NMR-observable stable isotopes ¹³C and ¹⁵N. These nuclei not only are key to dispersing the crowded NMR spectra in three or four orthogonal frequency dimensions, dramatically reducing the resonance overlap problem, the ¹³C and ¹⁵N chemical shifts themselves have proven to be important reporters on local backbone conformation (6–8). NMR chemical shifts in proteins are exquisitely sensitive to local conformation. However, they depend on many different factors, including backbone and side-chain torsion angles, neighboring residues, ring currents caused by nearby aromatic groups, hydrogen bonding, electric fields, local strain and geometric distortions, as well as solvent exposure (9–15). This not only has made it difficult to separately quantify the relation between each of these parameters and the chemical shift, it also makes it impossible to uniquely attribute such a structural parameter to any individual chemical shift.

For protein NMR spectroscopy, triple resonance correlation experiments, which link the resonances of directly bonded ¹H, ¹³C and ¹⁵N nuclei are commonly used to assign the chemical shifts of ¹H, ¹³C, and ¹⁵N nuclei in proteins (16–18). The chemical shift assignment procedure usually consists of two steps: (1) sequence-specific assignment of the backbone atoms, and (2) side-chain assignments. Nearly complete chemical shift assignments for backbone and side-chain atoms are commonly required to assign nuclear Overhauser enhancement (NOE) spectra, which classically are used to derive interproton distances that serve as the primary experimental restraints for calculating the protein structure. The backbone (¹H^α, ¹³C′, ¹³C^α, ¹⁵N and ¹H^N) and ¹³C^β chemical shifts, which are generally obtained in the earliest stage of any protein NMR study, are particularly useful reporters on local conformation. Their link to secondary structure, as well as to hydrogen bonding and χ₁ sidechain torsion angles, has been long recognized and has been the focus of both empirical studies as well as quantum-chemical calculations (11–15, 19, 20).

1.2 Protein backbone and side-chain conformation from NMR chemical shifts

The rapid increase in the number of proteins for which both high-resolution structural coordinates have been deposited in the Protein Data Bank (PDB) (21) and NMR chemical shift assignments are available in the BioMagResBank (BRMB) (22), have stimulated the development of quantitative empirical methods to study the relation between protein structure and chemical shifts (23). Among the wide array of empirical methods, TALOS (20) and its two successors TALOS+ (24) and TALOS-N (25) have become particularly widely used for making accurate ϕ/ψ backbone torsion angle predictions on the basis of the backbone (¹³C^α, ¹³C′, ¹⁵N, ¹H^α and ¹H^N) and ¹³C^β chemical shift assignments. These ϕ/ψ predictions can be used to validate NOE-derived NMR structures that didn’t use chemical shift derived input parameters or, conversely, to generate additional restraints as input to the protein structure calculation and refinement protocols.

The original TALOS program (Torsion Angle Likeliness Obtained from Shifts) searches a protein database, consisting originally of only 20 proteins, but later expanded to ca 200 proteins, with both high-resolution X-ray coordinates and NMR chemical shift assignments. TALOS identifies the ten tripeptide fragments that represent the best match in terms of chemical shifts and residue types to those of a tripeptide segment whose assignments are known and whose structure is under study (the “target protein”). The assumption underlying TALOS is that fragments with similar chemical shifts and residue type typically have similar backbone conformations. Thus, if these ten best matched fragments have consistent, narrowly clustered values for the ϕ/ψ angles of their center residue, their averages and standard deviations are used as a prediction for the ϕ/ψ angles of the center residue of the target protein tripeptide. If the ϕ/ψ angles of the center residue of these ten best matched tripeptides fall in different regions of the Ramachandran map, the matches are declared ambiguous, and no prediction is made for the central residue. With this quality control criterion, TALOS predicted ϕ/ψ torsion angles for, on average, ca 72% of the residues in any given target protein. For TALOS validation proteins, where the true ϕ/ψ angles are known, only about 1.8% of the predictions were inconsistent with crystallographically determined ϕ/ψ torsion angles. Excluding these 1.8% erroneous predictions, a root mean square (RMS) difference of ca 13° is observed between predicted and crystallographically observed ϕ/ψ torsion angles.

Although rather robust, the original TALOS program was unable to make definitive predictions for about 28% of the residues in any given protein. Most of these 28% are located outside regular secondary structure, exactly those regions where backbone torsion angle information is most needed. TALOS+ was developed to address this shortcoming, and to extend the coverage of the program {Shen, 2009 #4712}. For a given residue in the target protein, TALOS+ first uses an artificial neural network (ANN) module to predict its three-state distribution in the Ramachandran map, i.e., α, β, and positive-ϕ. This three-state distribution is subsequently used to guide the database search procedure for the 10 best matches. With the incorporation of the ANN, TALOS+ is able to increase its coverage to ca 88%, without sacrificing accuracy. Thus, compared to the original TALOS program, the fraction of residues whose backbone angles cannot be predicted is reduced from ~28% to ~12%. Importantly, most of the additional ϕ/ψ torsion angle predictions are made for residues in loop or turn regions, where this information is needed most.

The recently introduced TALOS-N program relies far more extensively on neural network analysis of the input chemical shift data than TALOS+, thereby further increasing coverage, accuracy and reliability. In addition, TALOS-N is the first program to generate quite accurate predictions for the side-chain χ₁ torsion angles (Fig. 1).

Fig. 1.

Flowchart of the TALOS-N program (reproduced from (25) with permission from Springer).

For the ϕ/ψ torsion angle prediction of a given residue i in the target protein, a well-trained two-level feed-forward multilayer ANN, referred to as a (ϕ,ψ)-ANN, is first used by TALOS-N to predict the 324-state ϕ/ψ distribution of residue i on the basis of the NMR chemical shifts and residue type of itself and its adjacent residues (i−2 to i+2). Here, the 324-state ϕ/ψ distribution corresponds to the likelihood that residue i adopts torsion angles that fall in any of the 324 voxels, of 20°×20° each, that make up the Ramachandran map. The ANN-predicted ϕ/ψ distribution is then used solely to search a large crystallographic database (containing 9523 proteins, with chemical shifts added by a computational method (26)), for a pool of 1000 heptapeptide fragments with ϕ/ψ angles that best match the 324-state ϕ/ψ distribution. These top 1000 fragments then are further evaluated for the agreement between their computed chemical shifts and experimental values of the corresponding heptapeptide segment (i−3 to i+3) in the target protein. The 25 best-matched database heptapeptides are retained, and the ϕ/ψ angles of their center residues are inspected by using an advanced clustering analysis, and subsequently used to make a prediction for the ϕ/ψ angles of the query residue. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥ 90% fraction of the residues, with an error rate of ca 3.5% when using an acceptance criterion that is nearly two-fold tighter than that used previously by TALOS and TALOS+. The RMS difference between predicted and crystallographically observed ϕ/ψ torsion angles is ca 12°, also slightly better than what was obtained with the earlier versions of the program.

To predict the χ₁ rotameric state (g⁻, g⁺ or t) for a given residue i (of residue type a) in the target protein, TALOS-N uses another set of ANNs, referred to as (χ₁)_a-ANNs. The (χ₁)_a-ANN has been trained to correlate the center residue likelihood of adopting each of the three χ₁ rotameric states to the differences between its observed chemical shifts and those expected on the basis of its backbone conformation. A separate database search procedure is subsequently used to estimate the three-state probability of residue i to adopt the three χ₁ rotameric states. With an optimized error control criterion, TALOS-N predicts χ₁ rotameric states for ca 50% of the residues, with an “error rate” of ca 10% when comparing the predicted χ₁ rotameric state to that of any given reference structure. However, we note that the true error is likely to be much lower, as for proteins that have multiple available independently solved X-ray structures, the χ₁ rotameric states of any “erroneous” χ₁ prediction is typically in agreement with that of another X-ray structure (25).

Similar to TALOS+, TALOS-N is also implemented with an ANN-based module for predicting secondary structure (SS) from the NMR chemical shifts. For this purpose, TALOS-N uses two separate ANNs, referred to as SS-ANN and SS_seq-ANN, which are trained to correlate the three-state secondary structure classification (helix, sheet, and coil) of a residue to both the chemical shifts and amino acid sequence, or to amino acid sequence alone, respectively. The output of these two ANNs is used in a hybrid manner to predict secondary structure for any residue in a protein, regardless of the completeness of chemical shift assignments. The overall correctness of the SS prediction is ca 88% when NMR chemical shifts are available, dropping to ca 81% when no chemical shifts are available. In the absence of chemical shifts, TALOS-N matches the accuracy of the best sequence-only secondary structure prediction programs (27, 28).

2 Materials

In this chapter, we use the protein DinI (29) to illustrate the use of TALOS-N for predicting its backbone ϕ/ψ and side-chain χ₁ torsion angles, as well as its secondary structure classification. To follow the examples, both the TALOS-N software package and an input file with correctly formatted chemical shift assignments are needed.

2.1 Software Requirements

The TALOS-N software package, including the required binaries for three of the most common operating systems, Linux, Mac OS X and Windows, as well as the requisite protein database and scripts can be downloaded from http://spin.niddk.nih.gov/bax/software/TALOS-N/ and installed straightforwardly (see ^{Note 1}). Alternatively, a server version of TALOS-N can be used directly, without installing the TALOS-N software (http://spin.niddk.nih.gov/bax/nmrserver/talosn/).

2.2 Data Requirements

An input table containing both the full amino acid sequence and the NMR chemical shift assignments is required, to be prepared with a specific data format (general purpose NMRPipe table format). As an example, an excerpt of such a file is shown below for the protein DinI:

DATA FIRST_RESID 2
DATA SEQUENCE RIEVTIAKT SPLPAGAIDA LAGELSRRIQ YAFPDNEGHV SVRYAAANNL
DATA SEQUENCE SVIGATKEDK QRISEILQET WESADDWFVS E
VARS    RESID RESNAME ATOMNAME SHIFT
FORMAT %4d %1s %4s %8.3f
    2  R    C  174.123
    2  R   CA   55.537
    2  R   CB   32.786
    2  R    H    8.772
    2  R   HA    4.994
    2  R    N  123.394
    3  I     C  173.941
    3  I    CA   60.986

The protein’s amino acid sequence should be provided in one or more lines starting with the tag “DATA SEQUENCE”. Only the one-character residue name is allowed (see ^{Note 2}) and space characters in the sequence are ignored. An optional line beginning with a tag of “DATA FIRST_RESID” is needed to specify the first residue number of the amino acid sequence listed in the “DATA SEQUENCE” line if the first residue listed is not residue number 1. For the chemical shift table, columns for residue number, one-character residue type (see ^{Note 2}), atom name (see ^{Note 3}) and chemical shift value must be included, and their definitions (“RESID”, “RESNAME”, “ATOMNAME”, and “SHIFT” respectively) must be pre-declared in a line beginning with a “VARS” tag; a line beginning with a “FORMAT” tag is also required (immediately after the “VARS” line) to define the data type of each corresponding column of the table.

Note that all chemical shifts used as input for TALOS-N are required to be properly referenced (see ^{Note 4}) to ensure the accuracy and reliability of the predictions. If the protein sample used to collect the NMR chemical shift data is perdeuterated, ²H isotope corrections (30) need to be applied for ¹³C^α and ¹³C^β chemical shifts (see ^{Note 5}).

Other standard chemical shift formats, such as the NMR-Star format used by the BMRB database, can also be used as input after a format conversion. A conversion script is provided in the TALOS-N package for this purpose (see ^{Note 6}). The server version of TALOS-N includes automated chemical shift format identification and can use the NMR-Star format chemical shift file directly as input, without requiring prior format conversion.

3. Methods

3.1 TALOS-N prediction

The TALOS-N prediction can be performed for DinI with an input chemical shift file of name “inCS.tab” by typing the command:

   talosn -in inCS.tab

The program first converts the chemical shifts (δ) of each query residue to its corresponding secondary chemical shifts (Δδ) by subtracting a residue-type dependent random coil value, as well as corrections to account for the residue types of its two immediate neighbors. The converted secondary chemical shifts are stored in a file named “predAdjCS.tab” (in the “SHIFT” column), together with the original chemical shifts (“CS_OBS”) and the corresponding corrections (“CS_ADJ”, which is the random coil value including nearest neighbor (i±1) residue type correction) used to calculate the secondary chemical shifts. To make a ϕ/ψ angle prediction, the converted secondary chemical shifts together with the amino acid type information are used as inputs for the (ϕ/ψ)-ANN to calculate the 324-state ϕ/ψ distribution for each predictable residue (see ^{Note 7}), with the output stored in a file named “predANN.tab”. A database search step is then performed to search a 9523-protein database for the 25 best matched heptapeptides in terms of the 324-state ϕ/ψ angle distribution, the secondary chemical shifts, and the amino acid type. A single file, “predAll.tab”, is generated in this step to store the information of those best database matches for each of the residues in the target protein. A final summarization and quality control step is performed to identify outliers in the 25 best-matching heptapeptides by evaluating the clustering of the ϕ/ψ angles of their center resides in the Ramachandran map, or by using the observed ϕ/ψ of a reference structure if such a structure is available (this requires an additional option “-ref ref.pdb” in the command line, where “ref.pdb” is the name for the reference structure). A summary file “pred.tab” is then created, displaying the average ϕ and ψ values (in the PHI and PSI columns) and their respective standard deviations (DPHI and DPSI), as well as an aggregate, weighted χ² score (DIST, see eq 12 of reference (25)), reflecting how well the target protein chemical shifts match those of the database fragments. An excerpt of this file for DinI is shown below:

VARS    RESID RESNAME PHI PSI DPHI DPSI DIST S2 COUNT CS_COUNT CLASS
FORMAT %4d %s %8.3f %8.3f %8.3f %8.3f %8.3f %5.3f %2d %2d %s
    2  R -107.206   129.115    9.502    7.843    0.293 0.873  25 16 Strong
    3  I -117.237   126.352    6.691    6.523    0.180 0.883  25 18 Strong

For each predictable residue, or residue with sufficient input chemical shifts (see ^{Note 7}), a final classification is made (listed as the last “CLASS” column in the “pred.tab” file) for its ϕ/ψ angle prediction by a summarization step, detailed below. Prior to making this final classification, the program calculates the predicted backbone rigidity as reflected in the “random coil index” order parameter, RCI-S² (31), which scales between 0 (total disorder) and 1 (fully rigid). Its values are included under the “S2” column in the “pred.tab” file. Residues below the threshold RCI-S² ≤0.6 are assigned as dynamic (receiving a “Dyn” classification) in “pred.tab”. For other residues, a classification of strongly unambiguous is assigned (with a “Strong” tag) if the center residues of all 25 best matching heptapeptides locate in a consistent ϕ/ψ region in the Ramachandran map. A generously unambiguous classification is assigned (with a “Generous” tag) if the center residues of only the top 10 best matches cluster in a consistent ϕ/ψ region. All other cases are considered ambiguous (classified with a “Warn” tag), even though inspection of their Ramachandran map population may contain very useful information. For example, often the ambiguous residues will cluster in two distinct regions of the Ramachandran map, and the investigator can explore both options during structure calculations.

For the predictable residues, the ϕ/ψ angles are calculated by averaging the ϕ/ψ angles of the center residues of all 25 best matches (for residues classified as “Strong”) or from the top 10 best matches (for a “Generous” prediction), and shown in the “PHI”/“PSI” columns. The estimated uncertainties in the predicted ϕ/ψ angles are calculated from their standard deviations from these averages and listed in the “DPHI” and “DPSI” columns. Only when a known reference structure is provided as input to the program, the predicted ϕ/ψ values will be compared to the observed ϕ/ψ angles in this reference structure for all unambiguously predicted (“Strong” and “Generous”) residues. A prediction is labeled as “Bad” if the predicted and the observed ϕ/ψ angles are not consistent (see ^{Note 8}).

For DinI, 71 residues (out of a total of 81) are obtained with unambiguous ϕ/ψ angles prediction, 2 have an ambiguous ϕ/ψ angle prediction, and 6 are predicted as dynamic (Fig. 2). Among those 71 unambiguous predictions, 70 are classified as “Strong” and two (Ala15 and Gly16) are designated “Bad” after inspecting their consistency relative to the ϕ/ψ angles observed in the reference NMR structure. It is worth noting that in the reference structure, these latter two residues (with ϕ/ψ angles of −57°/49° and −176°/−18°, respectively) are located in very lowly populated regions of the Ramachandran map, i.e., they are statistically unlikely to occur. Without further experimental data, it is not possible to decide whether the “Bad” classification refers to the reference structure, or to the quality of the prediction.

Fig. 2.

Graphic TALOS-N inspection interface for protein DinI. (For details, see 3.2, or the TALOS-N webpage http://spin.niddk.nih.gov/bax/software/TALOS-N/)

After TALOS-N prediction of ϕ/ψ angles has been completed, another database search and ANN-based procedure is performed to predict the χ₁ rotameric states. A χ₁ rotamer prediction summary file “predChi1.tab” is created with an excerpt of this file shown below for DinI:

VARS    RESID RESNAME CS_COUNT CHI1_OBS Q_Gm Q_Gp Q_T CLASS
FORMAT %4d %s %2d %8.3f %5.3f %5.3f %5.3f %s
    2  R 16    -69.938 0.341 0.121 0.538 na
    3  I 18    -62.494 0.873 0.063 0.063 g-
    4  E 18    -61.087 0.312 0.093 0.595 na
    5  V 18   178.554 0.073 0.055 0.872 t
    6  T 18     66.182 0.302 0.464 0.235 na
    7  I 18    -75.725 0.713 0.143 0.143 g-

For a query residue of residue type a (excluding Gly, Pro, and Ala) with sufficient input chemical shifts (see ^{Note 7}), TALOS-N first searches the database for the 1000 best-matched heptapeptides in terms of the backbone torsion angles and residue types. It then uses a trained (χ₁)_a-ANN to calculate a χ₁ matching score for each database match, which measures the likelihood of the center residue of the database heptapeptide to adopt the same χ₁ rotameric state as the query residue. The program then derives a three-state probability score, P_c, for the query residue to adopt each of the three χ₁ rotameric states (c = g⁻, g⁺ and t, stored in the columns “Q_Gm”, “Q_Gp” and “Q_T”, respectively, in “predChi1.tab”). TALOS-N then classifies the prediction for the query residue to adopt χ₁-rotamer state c (g⁻, g⁺ or t, as listed in the last column of “CLASS” in the “predChi1.tab” file) only when the predicted probability for state c is significantly higher than that for the other two states, by default P_c > 0.6. Otherwise, an ambiguous classification is assigned (with a “na” tag). Details of other contents in “predChi1.tab” are as follows: the column of “CS_COUNT” is for the count of the experimental chemical shifts of the target residue itself and its two neighbors; when a reference structure is provided, a “CHI1_OBS” column is provided to display the χ₁ angle observed in the reference structure. For DinI, TALOS-N makes χ₁ rotameric state predictions for 30 out of a total of 61 (non-Gly/-Pro/-Ala) residues, among which three (Asp35, Asn48 and Asp75) differ in their predicted χ₁ rotameric state from the reference NMR structure (PDB entry 1GHH).

Next to predicting ϕ, ψ and χ₁ torsion angles, TALOS-N also predicts the protein’s secondary structure. For residues with chemical shift assignments, a two-level neural network, SS-ANN, is trained to make a three-state secondary structure prediction (H, E or L, representing for α-helix, β-sheet and loop, respectively) on the basis of both the chemical shifts and the amino acid sequence information. In addition, another two-level ANN, referred to as SS_seq-ANN, is trained by using solely the amino acid sequence information. It can be used to make predictions for residues that lack chemical shift information. However, this SS_seq-ANN is used more generally by TALOS-N in a hybrid manner with the SS-ANN to make secondary structure prediction for proteins when chemical shift assignments are incomplete. TALOS-N generates an output file “predSS.tab” to store the predicted secondary structure. An excerpt of this file is shown below for DinI:

VARS RESID RESNAME CS_CNT CS_CNT_R2 Q_H Q_E Q_L CONFIDENCE SS_CLASS
FORMAT %4d %1s %2d %2d %8.3f %8.3f %8.3f %4.2f %s
    1  M 10  4     0.333     0.333   0.333 0.00 L
    2  R 16  6     0.097     0.740   0.162 0.58 E
    3  I 18  6     0.027     0.970   0.003 0.94 E
    4  E 18  6     0.006     0.968   0.026 0.94 E
    5  V 18  6     0.004     0.963   0.033 0.93 E
    6  T 18  6     0.009     0.970   0.021 0.95 E

Details of its contents are as follows: the column of “CS_CNT_R2” lists the number of experimental chemical shifts of the target residue; “CS_CNT” contains the count of experimental chemical shifts of the target residue plus its two immediate neighbors; the columns “Q_H”, “Q_E” and “Q_L” list the SS-ANN (or SS_seq-ANN) predicted probability for the target residue to be of secondary structure type “H”, “E” and “L”, respectively; the values shown in the “CONFIDENCE” column represent the confidence of the 3-state secondary structure prediction for a given target residue, calculated from the difference of maximal and median values of “Q_H”, “Q_E” and “Q_L”; the text listed in the “SS_CLASS” column shows the final secondary structure classification assigned by the program, i.e., one of the three states with the maximal predicted probability.

For DinI, when comparing to the output of the DSSP program (32) for the reference structure (PDB entry 1GHH), the overall correctness ratio of the TALOS-N predicted secondary structure is 70/81. In this respect, it is important to note that even for proteins of known structure, secondary structure assignment can be ambiguous, as reflected in only ca 90% agreement among the output of some of the most popular programs (23).

As mentioned above, TALOS-N predictions can either be made locally by downloading the requisite programs, or can be performed via the TALOS-N server (http://spin.niddk.nih.gov/bax/nmrserver/talosn/), which requires a chemical shift file as input, and an e-mail address to send back the prediction results, including all abovementioned output files, such as “pred.tab”, “predChi1.tab”, “predSS.tab”, “predS2.tab”, “predAll.tab”, “predAdjCS.tab” and “predANN.tab”.

3.2 Manual inspection and adjustment

The TALOS-N predictions can be inspected and further adjusted by using a Java graphic program, jrama. Two examples of command line calls of this program are:

  jrama -in pred.tab
  jrama -in pred.tab -ref DinI.pdb

Fig. 2 shows the jrama graphic interface, loaded with the TALOS-N predicted results for DinI. The left panel of the graphic interface shows a map of the ϕ/ψ angles of the center residues of the 25 best matched heptapeptides in the database (green squares) and the query residue Thr-6 (blue, depicting the angles observed in the NMR-derived PDB entry 1GHH), superimposed on a Ramachandran map depicting in gray the “most favorable” ϕ/ψ angles for Thr, i.e., those most commonly observed in high-resolution crystal structures of a very large array of proteins. The 324 (ϕ/ψ)-ANN predicted scores for Thr-6 are shown as colored voxels, but only for those that are populated at least one standard deviation above the average predicted voxel density. The top right panel displays the amino acid sequence of DinI, with residues colored according to their ϕ/ψ prediction classification. Missing predictions (e.g., residue M1) are shown in light grey, consistent predictions in light or dark green (for “Strong” and “Generous” predictions, respectively), ambiguous predictions in yellow, and dynamic residues in blue. Three other panels correspond to the RCI-S² value, the predicted secondary structure (red, αhelix; aqua, β-sheet), with the height of the bars reflecting the probability assigned by the SS-ANN secondary structure prediction. The bottom right panel depicts the χ₁ rotamer predictions (red oval: g⁻; green: g⁺; yellow: t), with the height of the ovals corresponding to the probability assigned by the χ₁ rotameric state prediction.

The TALOS-N prediction (including the summary of TALOS-N predicted ϕ/ψ angles) is normally performed with the default parameters and settings. However, the left panel also can be used to manually adjust the prediction classification of a given query residue according to a user’s preference. The prediction files then will be overwritten to reflect any changes made interactively.

3.3 Generation of angular restraints

The TALOS-N output can be converted into ϕ and ψ torsion angle restraints that then can be used directly as input for a conventional protein NMR structure calculation procedure (33, 34). Two convenient scripts, “talos2dyana.com” and “talos2xplor.com”, are included in the TALOS-N software package for this purpose. These scripts read predicted ϕ and ψ angles from the TALOS-N prediction summary file “pred.tab”, and generate for each residue with an unambiguous TALOS-N prediction (classified as “Strong” or “Generous”) a ϕ and a ψ torsion angle restraint (see ^{Note 9}). These torsion angle restraints can be stored in either CYANA format, as shown below for residues 2 and 3 of DinI:

  2  ARG PHI  -127.2  -87.2
  2  ARG PSI   109.1  149.1
  3  ILE  PHI  -137.2  -97.2
  3  ILE  PSI   106.4  146.4

or in XPLOR format:

assign (resid   1 and name C   ) (resid   2 and name N   )
      (resid   2 and name CA ) (resid   2 and name C   )  1.0 -107.2 20.0 2
assign (resid   2 and name N   ) ( resid  2 and name CA )
      (resid   2 and name C   ) (resid   3 and name N   )  1.0  129.1 20.0 2
assign (resid   2 and name C   ) (resid   3 and name N   )
      (resid   3 and name CA ) (resid   3 and name C   )  1.0 -117.2 20.0 2
assign (resid   3 and name N   ) (resid   3 and name CA )
      (resid   3 and name C   ) (resid   4 and name N   )  1.0  126.4 20.0 2

These input restraints then can be used for protein structure calculations as a complement to the conventional NOE distance restraints. Note that such chemical shift derived torsion angle restraints alone are typically insufficient to reach a converged protein structure as each torsion angle contains a substantial uncertainty (±20°, in the above example), and these uncertainties rapidly accumulate when building the protein chain. Moreover, as mentioned above, predictions are generally only about 90% complete and may contain errors.