2 HaDeX functionalities
2.1 Data import
The HaDeX web server works only on data in the DynamX datafile format (Waters Corp.). The data from other sources may be also adjusted to the format accepted by HaDeX provided it has following columns:
Although data can be imported into R using other tools, we strongly advise to rely on the read_hdx() function:
dat <- read_hdx(system.file(package = "HaDeX",
"HaDeX/data/KD_190304_Nucb2_EDTA_CaCl2_test02_clusterdata.csv"))Currently, read_hdx() supports .csv, .tsv and .xls files fulfilling the data structure described above. Files with data from multiple proteins are supported. The user need to indicate which protein is of interest for calculations.
2.2 Computation of deuteration levels
The computation of the level of deuteration involves several pre-processing steps, all of which are described in this section. These steps are performed automatically in the GUI or by the prepare_dataset() function in the console.
2.2.1 Measured data into overall peptide mass
The results of HDX-MS measurements as given in the DynamX data files are represented as the measured mass of peptides plus proton mass to charge ratio (\(Center\)). For later use, this value has to be transformed into an overall mass of a peptide measured after specific time point from a protein in a specific state, as shown in equation 1:
\[pepMass = z \times (Center-protonMass)\tag{1}\] where:
\(pepMass\) - expected mass of the peptide after incubation (Da),
\(protonMass\) - the mass of the proton (Da),
\(z\) - charge of the peptide,
\(Center\) - experimentally measured peptide mass plus proton mass to charge ratio \(\left(\frac{m}{z}\right)\).
HDX-MS experiments are often repeated (by the rule of thumb at least three times). Thus, we aggregate the results of replicates as a weighted mean mass into a single result per peptide using equation 2:
\[aggMass = \sum_{k = 1}^{N}\frac{Inten_k}{N}{\times}pepMass_k\tag{2}\] where:
\(aggMass\) - weighted mean mass of the peptide (Da),
\(k\) - replicate index,
\(Inten\) - intensity,
\(N\) - number of replicates.
This data manipulation results from an original data structure. Each repetition of the measurement gives the data for given peptide in a given time per possible value of \(z\) as shown in the example below. We need to use the value of \(pepMass\) as shown in equation 1 but still keep the information about original measurement - that’s why we use the weighted mass.
## Sequence File z RT Inten
## 1 KQFEHLNHQNPDTFEPKDLDML KD_190119_gg_Nucb2_CaCl2_10s_01 3 5.396196 95419
## 2 KQFEHLNHQNPDTFEPKDLDML KD_190119_gg_Nucb2_CaCl2_10s_01 4 5.392023 194073
## Center
## 1 901.7158
## 2 676.4348The uncertainty of a measurement is variability associated with the precision of measuring instrumentation. We present here a novel derivation of uncertainty formulas for HDX-MS data according to the ISO guidelines (Joint Committee for Guides in Metrology 2008). Input files always encompass results of more than one measurement. We assume uncorrelatedness of replicates as they come from different samples. Therefore, we average measurements of replicates for each time point and for all protein states. Thus, we compute peptide mass uncertainty \(u\) as uncertainty for aggregate estimate using the formula for standard deviation of the mean:
\[u(x) = \sqrt{\frac{ \sum_{i=1}^n \left( x_{i} - \overline{x} \right)^2}{n(n-1)}}\tag{3}\] where:
\(x_{i}\) - measurement,
\(\overline{x}\) - mean value,
\(n\) - number of measurements.
After obtaining the mass of the peptide, we can compute the deuteration level depending on the chosen maximum deuteration level. The maximum deuteration can also be computed in two different ways: either as theoretical (where the maximum deuteration depends on the theoretical deuteration levels) and experimental (where the maximum deuteration is assumed to be equal to the deuteration measured at the last time point).
2.2.2 Experimental deuteration level
The experimental deuteration level is computed as the deuteration level of the peptide from a protein in a specific state and after incubation time \(t\) compared to the deuteration level measured at the start of the incubation (\(t_0\)). It yields a value for the chosen state and chosen time \(t\).
\[D = D_{t} - D_{t_0}\tag{4}\]
where:
\(D\) - deuteration level (Da),
\(D_{t_0}\) - experimentally measured deuteration at the beginning of the incubation (0 or close to 0),
The equation 4 produces only absolute deuteration levels. The computations of relative deuteration levels follows a similar logic and is normalized by the difference of deuteration between the start (\(t_0\)) and the end of the experiment (\(t_out\)) as shown in the equation 4a:
\[D = \frac{D_{t} - D_{t_0}}{D_{t_{out}} - D_{t_0}}\tag{4a}\]
All functions in the HaDeX package contain the logical parameter \(relative\) to determine if they should return absolute or relative deuteration levels.
2.2.2.1 Uncertainty calculations
We describe the methodology of the uncertainty calculations for relative deuteration levels. The uncertainty for absolute deuteration levels is computed similarly, but without scaling.
To calculate uncertainty related to functions of more than one variables (e. g., equation 4) the Law of propagation of uncertainty is defined by equation 5:
\[u_{c}(y) = \sqrt{\sum_{k} \left[ \frac{\partial y}{\partial x_{k}} u(x_{k}) \right]^2}\tag{5}\]
As the variable of interest is \(D\), we apply the general formula to the deuteration level \(D\) (equation 6):
\[u_{c}(D) = \sqrt{\sum_{k} \left[ \frac{\partial D}{\partial D_{k}} u(D_{k}) \right]^2 }\tag{6}\]
where:
\(k \in \{0, t, out\}\),
\(D_{k}\) - deuteration in \(k\) time (Da),
\(u(D_{k})\) - an uncertainty associated with \(D_{k}\) as standard deviation of the mean value,
Then, expanding the equation 6:
\[u_{c}(D) = \sqrt{ \left[ \frac{1}{D_{t_{out}}-D_{t_0}} u(D_{t}) \right]^2 + \left[ \frac{D_{t} - D_{t_{out}}}{(D_{t_{out}}-D_{t_0})^2} u(D_{t_0}) \right]^2 + \left[ \frac{D_{t_0} - D_{t}}{(D_{t_{out}}-D_{t_0})^2} u (D_{t_{out}}) \right]^2}\tag{7}\] As expected, the uncertainty associated with \(D_{t}\) has the biggest impact on \(u_{c}(D)\).
2.2.3 Theoretical deuteration level
As opposed to the experimental deuteration levels, theoretical deuteration level only partially depends on the experimental data. Here, the maximum deuteration level is based on a hypothetical peptide where all hydrogens were replaced by deuterons, as it is shown in equation 8:
\[D = \frac{D_{t}-MHP}{MaxUptake \times protonMass}\tag{8}\]
where:
\(D_{t}\) - deuteration measured in a chosen time point (Da),
\(MHP\) - theoretical mass of the peptide (constant) (Da),
\(MaxUptake\) - the maximum proton uptake for the peptide (theoretical constant) (Da),
\(protonMass\) - mass of a proton (constant) (Da).
The absolute deuteration level is calculated as in equation 8 but without scaling (equation 8a):
\[D = D_{t} - MHP\tag{8a}\]
2.2.3.1 Uncertainty calculations
For functions of one variable uncertainty reduces to:
\[u(y) = \left| \frac{dy}{dx} u(x) \right|.\tag{9}\]
Substituting \(D\) from equation 8, we have
\[u(D) = \left|\frac{1}{MaxUptake \times protonMass} u(D_{t}) \right|\tag{10}\]
For the absolute values, \(u(D)\) is identical with \(u(D_{t})\), based on equations 8a and 9.
2.3 Difference of deuteration levels between two states
The differences of deuteration levels between two states are associated with a different level of protection of hydrogens. Therefore, we are especially interested in the differential analysis of the deuteration levels. Thus, the deuteration level in one state \((D_{2})\) is subtracted from deuteration level in the other state \((D_{1})\):
\[diff = D_{1} - D_{2}\tag{11}\]
and the uncertainty is a function of two variables (based on equation 11 and 5):
\[u_{c}(diff) = \sqrt{u(D_{1})^2 + u(D_{2})^2}\tag{12}\]
calc_dat <- prepare_dataset(dat,
in_state_first = "gg_Nucb2_EDTA_0.001",
chosen_state_first = "gg_Nucb2_EDTA_25",
out_state_first = "gg_Nucb2_EDTA_1440",
in_state_second = "gg_Nucb2_CaCl2_0.001",
chosen_state_second = "gg_Nucb2_CaCl2_25",
out_state_second = "gg_Nucb2_CaCl2_1440") 2.4 Visual data analysis
2.4.1 Comparison of states
Comparison plots show the deuteration level of all peptides in selected states in a given time. The x-axis represents positions of amino acids in the sequence. The y-axis shows the deuteration level, expressed either as a relative or absolute deuteration. The chart below shows peptide deuteration after 25 minutes of the incubation along with the confidence intervals.
– theoretical:
- relative values:
comparison_plot(calc_dat = calc_dat,
theoretical = TRUE,
relative = TRUE,
state_first = "Nucb2 Factor 1",
state_second = "Nucb2 Factor 2") +
labs(title = "Theoretical fraction exchanged in state comparison in 25 min time")
- absolute values:
comparison_plot(calc_dat = calc_dat,
theoretical = TRUE,
relative = FALSE,
state_first = "Nucb2 Factor 1",
state_second = "Nucb2 Factor 2") +
labs(title = "Theoretical fraction exchanged in state comparison in 25 min time")
– experimental:
- relative values:
comparison_plot(calc_dat = calc_dat,
theoretical = FALSE,
relative = TRUE,
state_first = "Nucb2 Factor 1",
state_second = "Nucb2 Factor 2") +
labs(title = "Fraction exchanged in state comparison in 25 min time")
- absolute values:
comparison_plot(calc_dat = calc_dat,
theoretical = FALSE,
relative = FALSE,
state_first = "Nucb2 Factor 1",
state_second = "Nucb2 Factor 2") +
labs(title = "Fraction exchanged in state comparison in 25 min time")
2.5 Woods plot
Woods plots show the difference between the deuteration of all peptides in two different states in a specific time point as described by equation 11. Similarly to the comparison plot, HaDeX provides both experimental and theoretical deuteration levels using either relative or absolute values:
– theoretical:
- relative values:
woods_plot(calc_dat = calc_dat,
theoretical = TRUE,
relative = TRUE) +
labs(title = "Theoretical fraction exchanged between states in 25 min time")
- absolute values:
woods_plot(calc_dat = calc_dat,
theoretical = TRUE,
relative = FALSE) +
labs(title = "Theoretical fraction exchanged between states in 25 min time")
– experimental:
- relative values:
woods_plot(calc_dat = calc_dat,
theoretical = FALSE,
relative = TRUE) +
labs(title = "Theoretical fraction exchanged between states in 25 min time")
- absolute values:
woods_plot(calc_dat = calc_dat,
theoretical = FALSE,
relative = FALSE) +
labs(title = "Theoretical fraction exchanged between states in 25 min time")
2.5.1 Confidence limit in Woods plot
The function calculate_confidence_limit_values() calculates confidence limit values as it is described elsewhere (Houde, Berkowitz, and Engen 2011).
calculate_confidence_limit_values(calc_dat = calc_dat,
confidence_limit = 0.99,
theoretical = FALSE,
relative = TRUE) ## [1] -0.01619004 0.01619004The function add_stat_dependency() returns data extended by column describing relation of a given peptide with confidence limit.
add_stat_dependency(calc_dat,
confidence_limit = 0.98,
theoretical = FALSE,
relative = TRUE)## # A tibble: 108 x 29
## Sequence Start End Med_Sequence frac_exch_state~ err_frac_exch_s~
## <chr> <int> <int> <dbl> <dbl> <dbl>
## 1 VPIDID 17 22 19.5 NaN NaN
## 2 KTKVKGEGHVEGEKIENPDTGL 23 44 33.5 NaN NaN
## 3 YYDEY 45 49 47 0.975 0.00984
## 4 YYDEYL 45 50 47.5 0.679 0.00549
## 5 YLRQVID 49 55 52 0.453 0.00309
## 6 YLRQVIDV 49 56 52.5 0.428 0.00341
## 7 YLRQVIDVL 49 57 53 0.417 0.00271
## 8 LRQVID 50 55 52.5 0.520 0.00660
## 9 LRQVIDV 50 56 53 0.450 0.00248
## 10 LRQVIDVL 50 57 53.5 0.414 0.00346
## # ... with 98 more rows, and 23 more variables: frac_exch_state_2 <dbl>,
## # err_frac_exch_state_2 <dbl>, diff_frac_exch <dbl>, err_frac_exch <dbl>,
## # abs_frac_exch_state_1 <dbl>, err_abs_frac_exch_state_1 <dbl>,
## # abs_frac_exch_state_2 <dbl>, err_abs_frac_exch_state_2 <dbl>,
## # abs_diff_frac_exch <dbl>, err_abs_diff_frac_exch <dbl>,
## # avg_theo_in_time_1 <dbl>, err_avg_theo_in_time_1 <dbl>,
## # avg_theo_in_time_2 <dbl>, err_avg_theo_in_time_2 <dbl>, ...2.6 Kinetic plots
By the term kinetics we understand deuteration change in time. To calculate deuteration values per peptide for different time points is used function calculate_kinetics(). This function uses the calculate_state_deuteration() function.
(kin_YYDEYL_gg_Nucb2_CaCl2 <- calculate_kinetics(dat = dat,
protein = "db_Nucb2",
sequence = "YYDEYL",
state = "gg_Nucb2_CaCl2",
start = 45,
end = 50,
time_in = 0.001,
time_out = 1440))## # A tibble: 5 x 15
## Protein Sequence Start End State time_chosen frac_exch_state
## <chr> <chr> <int> <int> <chr> <dbl> <dbl>
## 1 db_Nucb2 YYDEYL 45 50 gg_Nucb2_CaCl2 0.167 20.9
## 2 db_Nucb2 YYDEYL 45 50 gg_Nucb2_CaCl2 1 37.7
## 3 db_Nucb2 YYDEYL 45 50 gg_Nucb2_CaCl2 10 74.1
## 4 db_Nucb2 YYDEYL 45 50 gg_Nucb2_CaCl2 25 86.2
## 5 db_Nucb2 YYDEYL 45 50 gg_Nucb2_CaCl2 60 93.9
## # ... with 8 more variables: err_frac_exch_state <dbl>,
## # abs_frac_exch_state <dbl>, err_abs_frac_exch_state <dbl>,
## # avg_theo_in_time <dbl>, err_avg_theo_in_time <dbl>,
## # abs_avg_theo_in_time <dbl>, err_abs_avg_theo_in_time <dbl>,
## # Med_Sequence <dbl>(kin_YYDEYL_gg_Nucb2_EDTA <- calculate_kinetics(dat = dat,
protein = "db_Nucb2",
sequence = "YYDEYL",
state = "gg_Nucb2_EDTA",
start = 45,
end = 50,
time_in = 0.001,
time_out = 1440))## # A tibble: 5 x 15
## Protein Sequence Start End State time_chosen frac_exch_state
## <chr> <chr> <int> <int> <chr> <dbl> <dbl>
## 1 db_Nucb2 YYDEYL 45 50 gg_Nucb2_EDTA 0.167 16.4
## 2 db_Nucb2 YYDEYL 45 50 gg_Nucb2_EDTA 1 34.0
## 3 db_Nucb2 YYDEYL 45 50 gg_Nucb2_EDTA 10 60.7
## 4 db_Nucb2 YYDEYL 45 50 gg_Nucb2_EDTA 25 67.9
## 5 db_Nucb2 YYDEYL 45 50 gg_Nucb2_EDTA 60 79.0
## # ... with 8 more variables: err_frac_exch_state <dbl>,
## # abs_frac_exch_state <dbl>, err_abs_frac_exch_state <dbl>,
## # avg_theo_in_time <dbl>, err_avg_theo_in_time <dbl>,
## # abs_avg_theo_in_time <dbl>, err_abs_avg_theo_in_time <dbl>,
## # Med_Sequence <dbl>Calculated data can be shown next to each other on the plot for comparison. To visualize kinetic data we recommend plot_kinetics() function:
– theoretical:
- relative values:
bind_rows(kin_YYDEYL_gg_Nucb2_CaCl2, kin_YYDEYL_gg_Nucb2_EDTA) %>%
plot_kinetics(theoretical = TRUE,
relative = TRUE)
- absolute values:
bind_rows(kin_YYDEYL_gg_Nucb2_CaCl2, kin_YYDEYL_gg_Nucb2_EDTA) %>%
plot_kinetics(theoretical = TRUE,
relative = FALSE)
– experimental:
- relative values:
bind_rows(kin_YYDEYL_gg_Nucb2_CaCl2, kin_YYDEYL_gg_Nucb2_EDTA) %>%
plot_kinetics(theoretical = FALSE,
relative = TRUE)
- absolute values:
bind_rows(kin_YYDEYL_gg_Nucb2_CaCl2, kin_YYDEYL_gg_Nucb2_EDTA) %>%
plot_kinetics(theoretical = FALSE,
relative = FALSE)
2.7 Additional tools
HaDeX provides additional tools for assessment of experiments.
2.7.1 Peptide coverage
The sequence of the protein(s) is reconstructed from the peptides from the input file. Thus, amino acids not covered by peptides are marked as X according to the IUPAC convention. The sequence is reconstructed using the reconstruct_sequence() function.
reconstruct_sequence(dat)## [1] "xxxxxxxxxxxxxxxxVPIDIDKTKVKGEGHVEGEKIENPDTGLYYDEYLRQVIDVLETDKHFREKLQTADIEEIKSGKLSRELDLVSHHVRTRLDELKRQEVARLRMLIKAKMDSVQDTGIDHQALLKQFEHLNHQNPDTFEPKDLDMLIKAATSDLENYDKTRHEEFKKYEMxxxxxxxxxxxxLDEEKRQREESKFGEMxxxxxxxxxxxxxxxxxxxKEVWEEADGLDPNEFDPKTFFKLHDVNNDRFLDEQELEAxFTKELEKVYDPKNEEDDMVEMEEERLxxxxHVMNEVDINKDRLVTLEEFLRATEKKEFLEPDSWETLDQQQLFTEDELKEFESHISQQEDELRKKAEELQKQKEELQRQHDQLQAQEQELQQVVKQMEQKKLQQANPPAGPAGELK"Additionally, the coverage of peptides can be presented on a chart using the plot_coverage() and plot_position_frequency() functions.
plot_coverage(dat, chosen_state = "gg_Nucb2_CaCl2")
plot_position_frequency(dat, chosen_state = "gg_Nucb2_CaCl2")
The user can choose which state (or states) should be included in these plots. If this parameter is not provided, the first possible state is chosen. If a given peptide is available in more than one state, it is shown only once.
2.7.2 Quality control
The function quality_control() plots the change in the uncertainty of deuteration levels as a function of incubation time. The uncertainty is averaged over all peptides available at a given time point in a selected state. This chart has a double function: firstly, it allows checking if the measurement uncertainty is decreasing over time (which is the expected behavior) and the second one helps to plan the appropriate incubation length for the tested protein (whether we obtain the desired data reliability values).
In HDX-MS experiments, the average uncertainty of measurements decreases over time as the sample is more and more deuterated (even accounting for the back-propagation). Therefore, if the level of uncertainty is stable, the exchange is still ongoing and it would be advisable to prolong the reaction.
Moreover, the user can detect a time point after which the decrease of the deuteration uncertainty becomes too marginal to prolong the measurements. This function is most useful in case of multiple measurements of the same or very similar proteins because it helps to optimize the duration of the incubation. The result of this function can be easily visualized.
Example:
result <- quality_control(dat = dat,
state_first = "gg_Nucb2_EDTA",
state_second = "gg_Nucb2_CaCl2",
chosen_time = 1,
in_time = 0.001)ggplot(result) +
geom_line(aes(x = out_time, y = avg_err_state_first, color = "Average error (first state)")) +
geom_line(aes(x = out_time, y = avg_err_state_second, color = "Average error (second state)")) +
scale_x_log10() +
labs(x = "log(time) [min]", y = "Average uncertainty", title = "Uncertainty change") +
theme_bw(base_size = 11) +
theme(legend.position = "bottom",
legend.title = element_blank())
The chart below shows how we interpret the three most common results of quality control.
