Figures and data in Predicting the sequence-dependent backbone dynamics of intrinsically disordered proteins

Figures
Tables
Additional files

9 figures, 2 tables and 1 additional file

Figures

Figure 1

Download asset Open asset

Clock-like tree plot showing lack of homology among the 45 IDPs.

The level of homology between two sequences is measured by the distance from their convergence point to the center of the clock. The highest level of apparent identity is between A1-LCD and TDP-43, at 25%, but these two proteins differ in both secondary structure formation and $R_{2}$ characteristics. There is, however, a 20-residue overlap between the N-terminus of MBP-xα2 and the C-terminus of rmBG21.

Figure 2

Download asset Open asset

Representative conformations of five IDPs.

(**A–E**) MKK4, α-synuclein, Mev-P_NTD, Sev-NT, and CBP-ID4. Conformations were initially generated using TraDES (http://trades.blueprint.org; Feldman and Hogue, 2002), selected to have radius of gyration close to predicted by a scaling function $R_{g} = 2.54 N^{0.522}$ (Å) (Bernadó and Blackledge, 2009). Conformations for residues predicted as helical by PsiPred plus filtering were replaced by an ideal helix. Finally residues are colored according to a scheme ranging from green for low predicted $R_{2}$ to red for high predicted $R_{2}$ .

Figure 3 with 1 supplement

Download asset Open asset

Properties of the 45 IDPs in the training set.

(A) Histograms of means and standard deviations, calculated for individual proteins. Curves are drawn to guide the eye. Inset: correlation between ${\bar{R}}_{2}$ and $σ_{R_{2}}$ . (B) Experimental mean scaled $R_{2}$ ( $m s R_{2}$ ) and SeqDYN $q$ parameters, for the 20 types of amino acids. Note that Pro residues have low $m s R_{2}$ for the lack of backbone amide proton. Amino acids are in descending order of $q$ .

Figure 3—source data 1 Source data for Figure 3.: https://cdn.elifesciences.org/articles/88958/elife-88958-fig3-data1-v1.xlsx
Download elife-88958-fig3-data1-v1.xlsx

Figure 3—figure supplement 1

Download asset Open asset

Possible effects of sequence length, temperature, and magnetic field on $R_{2}$ .

(A) Lack of dependence of ${\bar{R}}_{2}$ on sequence length. (B) Counts of IDPs with $R_{2}$ measured at various temperatures. (C) Matching of $R_{2}$ profiles of Sev-NT measured at two temperatures after uniform scaling. (D) Matching of $R_{2}$ profiles of A1-LCD measured at two temperatures after uniform scaling. (E) Counts of IDPs with $R_{2}$ measured at various magnetic fields.

Figure 3—figure supplement 1—source data 1 Source data for Figure 3—figure supplement 1.: https://cdn.elifesciences.org/articles/88958/elife-88958-fig3-figsupp1-data1-v1.xlsx
Download elife-88958-fig3-figsupp1-data1-v1.xlsx

Figure 4 with 1 supplement

Download asset Open asset

SeqDYN model parameters.

(A) Correlation between $m s R_{2}$ and $q$ . The values are also displayed as bars in Figure 3B. (B) Correlation of $m s R_{2}$ and $q$ with amino-acid molecular mass. (C) Correlation of $m s R_{2}$ and $q$ with bulkiness. (D) The optimal correlation length and deterioration of SeqDYN prediction as the correlation length is moved away from the optimal value.

Figure 4—source data 1 Source data for Figure 4.: https://cdn.elifesciences.org/articles/88958/elife-88958-fig4-data1-v1.xlsx
Download elife-88958-fig4-data1-v1.xlsx

Figure 4—figure supplement 1

Download asset Open asset

T-test of on the $q$ parameters for pairs of amino acids.

$q$ parameters were obtained from five-fold cross-validation training, resulting in five independent values for each $q$ parameter. Mean presented as red bars; standard deviation presented as error bar. *, p<0.05; **, p<0.01; ***, p<0.001; ns, not significant. $q$ parameters for all neighboring pairs not explicitly indicated are not significantly different.

Figure 4—figure supplement 1—source data 1 Source data for Figure 4—figure supplement 1.: https://cdn.elifesciences.org/articles/88958/elife-88958-fig4-figsupp1-data1-v1.xlsx
Download elife-88958-fig4-figsupp1-data1-v1.xlsx

Figure 5 with 1 supplement

Download asset Open asset

Quality of SeqDYN predictions.

(A) Histogram of RMSE(–1). Letters indicate RMSE(–1) values of the IDPs to be presented in panels (**B–F**). (**B–F**) Measured (bars) and predicted (curves) $R_{2}$ profiles for MKK4, α-synuclein, Mev-P_NTD, Sev-NT, and CBP-ID4. In (E) and (F), green curves are SeqDYN predictions and red curves are obtained after a helix boost.

Figure 5—source data 1 Source data for Figure 5.: https://cdn.elifesciences.org/articles/88958/elife-88958-fig5-data1-v1.xlsx
Download elife-88958-fig5-data1-v1.xlsx

Figure 5—figure supplement 1

Download asset Open asset

Close reproduction (curve) of the measured $R_{2}$ profile (bars) of CBP-ID4 when that set of data alone was used to parameterize SeqDYN.

The resulting model has no value for predicting $R_{2}$ for other proteins.

Figure 5—figure supplement 1—source data 1 Source data for Figure 5—figure supplement 1.: https://cdn.elifesciences.org/articles/88958/elife-88958-fig5-figsupp1-data1-v1.xlsx
Download elife-88958-fig5-figsupp1-data1-v1.xlsx

Figure 6

Download asset Open asset

Measured (bars) and predicted (curves) $R_{2}$ profiles for ChiZ N-terminal region, TIA1 prion-like domain, Pdx1 C-terminal region, synaptobrevin-2, α-endosulfine, YAP, AMOTL1, FtsQ, and CAHS-8.

In (C), $R_{2}$ does not fall off at the N-terminus because the sequence is preceded by an expression tag MGSSHHHHHHHHHHHHS. In (H) and (I), green curves are SeqDYN predictions and red curves are obtained after a helix boost.

Figure 6—source data 1 Source data for Figure 6.: https://cdn.elifesciences.org/articles/88958/elife-88958-fig6-data1-v1.xlsx
Download elife-88958-fig6-data1-v1.xlsx

Figure 7

Download asset Open asset

$R_{2}$ profiles predicted (curves) by SeqDYN show close agreement with those measured (bars) on structured proteins in the unfolded state.

(A) Wild-type lysozyme (8 M urea; pH 2; cysteine-methylated). (B) Lysozyme with Trp62 to Gly mutation (pH 2). Methylated cysteines were treated as Ala in the SeqDYN predictions. (C) Apomyoglogin (8 M urea; pH 2.3). (D) Ubiquitin (8 M urea; pH 2).

Figure 7—source data 1 Source data for Figure 7.: https://cdn.elifesciences.org/articles/88958/elife-88958-fig7-data1-v1.xlsx
Download elife-88958-fig7-data1-v1.xlsx

Figure 8

Download asset Open asset

Comparison between SeqDYN prediction (curves) and effective transverse relaxation rate (bars) from ¹H dispersion relaxation experiment.

(A) $R_{2, e f f}$ in the high- $ω_{e f f}$ limit. (B) $R_{2, e f f}$ at low $ω_{e f f}$ .

Figure 8—source data 1 Source data for Figure 8.: https://cdn.elifesciences.org/articles/88958/elife-88958-fig8-data1-v1.xlsx
Download elife-88958-fig8-data1-v1.xlsx

Figure 9

Download asset Open asset

Correlation between the stickiness parameters (λ) and the NMR relaxation parameters (q).

The regression line is shown as dashes.

Figure 9—source data 1 Source data for Figure 9.: https://cdn.elifesciences.org/articles/88958/elife-88958-fig9-data1-v1.xlsx
Download elife-88958-fig9-data1-v1.xlsx

Tables

Table 1

Experimental conditions, mean and standard deviation of measured $R_{2}$ , and SeqDYN prediction RMSE.

Protein name	# of res	Temp(K)	B₀ (MHz)	${\bar{R}}_{2}$ (s^–1)	$σ_{R_{2}}$ (s^–1)	RMSE(s^–1)	PMID; ref
Training set (45 IDPs) ^*
A1-LCD	131	298	800	2.68	0.46	0.60	32029630; Martin et al., 2020
Aβ40	40	278	600	3.40	0.92	0.38	31181936; Rezaei-Ghaleh et al., 2019
Ash1	83	278	800	9.80	1.40	1.41	27807972; Martin et al., 2016
Beclin1	165	288	800	5.37	1.03	1.14	27288992; Yao et al., 2016
CAPRIN1	103	303	600	5.34	0.88	0.72	31898464; Wong et al., 2020
CBP-ID4	207	283	700	5.45	2.55	2.01;1.90^†	29790640; Murrali et al., 2018
GbnD4-DHD	91	280	700	6.81	1.55	1.28	29309054; Jenner et al., 2018
ERD14	185	288	600	3.96	0.87	0.54	21336827; Szalainé Ágoston et al., 2011
ExsE	88	298	600	3.18	0.88	0.76	22138394; Zheng et al., 2012
FCP1	85	298	500	2.94	0.54	0.43	26286791; Lawrence and Showalter, 2012
FUS	163	298	850	3.48	0.51	0.54	26455390; Burke et al., 2015
GAb1	82	298	500	3.99	0.88	0.89	34929201; Gruber et al., 2022
hACTR	69	304	600	3.26	0.47	0.49	18177052; Ebert et al., 2008
Hahellin	92	298	800	9.94	2.69	2.85	24671380; Patel et al., 2014
hCSD1	141	298	500	3.56	0.93	0.99	18537264; Kiss et al., 2008
HOX-DFD	90	298	600	6.98	3.15	1.99	30802457; Maiti et al., 2019
hZIP4-ICL2	100	283	800	9.54	2.37	1.58	30793391; Bafaro et al., 2019
Jaburetox	94	298	800	6.01	2.30	2.27	25605001; Lopes et al., 2015
KRS-NT	72	303	600	3.26	0.93	0.83	24983501; Cho et al., 2014
MBP-xα2	70	295	600	3.83	0.60	0.54	25343306; De Avila et al., 2014
MKK4	86	278	850	4.49	1.42	0.63	29276882; Delaforge et al., 2018
N-Cby	63	298		4.19	1.20	1.25	21182262; Mokhtarzada et al., 2011
Niv-P_NTD	406	288	700	5.41	1.82	1.66	33177626; Schiavina et al., 2020
NS5A-D2D3	268	278	800	8.62	3.85	2.14	26445449; Sólyom et al., 2015
NUPR1	93	298	600	2.98	0.82	0.76	31325636; Neira et al., 2019
OPN	220	310	800	2.59	0.82	0.54	31794728; Mateos et al., 2020
p53TAD	73	298	850	2.72	0.66	0.33	30240067; Xie et al., 2018
PDEγ	87	298		3.96	1.05	0.71	18230733; Song et al., 2008
PKIα	75	300	900	3.41	0.87	0.52	32338601; Olivieri et al., 2020
Mev-P_NTD	304	298	950	2.92	0.59	0.48	30140745; Milles et al., 2018
ProTα	113	283	800	3.40	0.56	0.43	29466338; Borgia et al., 2018
Pup	64	298	850	2.66	0.51	0.43	30240067; Xie et al., 2018
rmBG21	199	300	600	4.06	0.90	0.63	17676872; Ahmed et al., 2007
RPB1	201	277	850	6.48	1.74	1.33	28945358; Janke et al., 2018
securin	202	283	500	5.49	1.13	1.08	19053469; Csizmok et al., 2008
Sev-NT	124	298	600	3.20	1.42	0.76;0.38^†	27112095; Abyzov et al., 2016
Sic1	92	278	500	3.34	0.59	0.48	20399186; Mittag et al., 2010
SKIPN	71	298		5.64	1.05	1.46	20007319; Wang et al., 2010
SLBP-NT	113	298	600	3.96	1.40	1.61	15260482; Thapar et al., 2004
α-synuclein	140	298	600	2.96	0.53	0.44	30184304; Rezaei-Ghaleh et al., 2018
SOCS5-JIR	70	303	800	4.32	2.36	1.91	26173083; Chandrashekaran et al., 2015
tau K18	129	283	700	4.12	0.95	0.83	23740819; Barré and Eliezer, 2013
TC1	106	298	600	4.65	1.61	1.24	23189168; Cino et al., 2012
TDP-43	151	283	500	4.07	1.51	0.96	27545621; Conicella et al., 2016
γ-tubulin-CT	39	288	500	2.23	0.35	0.27	29127738; Harris et al., 2018
Test set (9 IDPs)
AMOTL1	207	283	800	8.45	2.55	2.04	35481651; Vogel et al., 2022
CAHS-8	233	303	850	4.43	3.25	2.36;1.92^†	34750927; Malki et al., 2022
ChiZ	64	298	800	4.33	0.89	0.74	32585849; Hicks et al., 2020
α-endosulfine	121	298	800	3.21	0.81	0.48	34346186; Thapa et al., 2022
FtsQ	99	305	800	6.44	3.78	2.32;1.71^†	36959324; Smrt et al., 2023
Pdx1	83	298	500	2.98	0.70	0.76	30525611; Cook et al., 2019
synaptobrevin-2	96	278	600	5.54	1.80	0.72	30975750; Lakomek et al., 2019
TIA-1	91	310	800	4.01	0.89	0.55	36112647; Sekiyama et al., 2022
YAP	122	298	800	3.19	1.44	1.23	35378854; Feichtinger et al., 2022

*

For training set, RMSE is calculated for prediction based on leave-one-out training (using 44 IDPs).
†

First number is for SeqDYN prediction; second number is after applying a helix boost.

Table 2

RMSEs (s^–1) of $R_{2}$ predictions by SeqDYN and MD for 10 IDPs.

IDP name	SeqDYN	MD
A1-LCD	0.60*	0.59 ^§^{, ¶}
Aβ40	0.38*	0.38 ^§
HOX-DFD	1.99*	1.40 ^§
α-synuclein	0.44*	0.50 ^§
p53TAD	0.33*	1.04 ^**
Pup	0.43*	1.00**
Sev-NT	0.38*^,†	1.10 ^§^,††
tau K18	0.83*	0.80 ^§
ChiZ	0.74 ^‡	1.40 ^{‡ ‡}
FtsQ	1.71 ^§^,†	1.70 ^{§ §}

*

Based on leave-one-out training (using 44 IDPs).
†

Helix boost applied.
‡

Based on training by the full training set (45 IDPs).
§

From Dey et al., 2022.
¶

RMSE is scaled down by a factor of 2.39, to correct for the effect of temperature (MD at 288 K; see Figure 3—figure supplement 1C).
**

From Yu and Brüschweiler, 2022.
††

RMSE is scaled down by a factor of 2.99, to correct for the effects of temperature and magnetic field (MD at 274 K and 850 MHz; see Figure 3—figure supplement 1B).
‡ ‡

Originally calculated in Hicks et al., 2020 with correction in Hicks et al., 2021.
§ §

From Smrt et al., 2023.

Additional files

MDAR checklist: https://cdn.elifesciences.org/articles/88958/elife-88958-mdarchecklist1-v1.pdf
Download elife-88958-mdarchecklist1-v1.pdf

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Mendeley

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Sanbo Qin
Huan-Xiang Zhou

(2024)

Predicting the sequence-dependent backbone dynamics of intrinsically disordered proteins

eLife 12:RP88958.

https://doi.org/10.7554/eLife.88958.3

Share this article

Cite this article

Clock-like tree plot showing lack of homology among the 45 IDPs.

Representative conformations of five IDPs.

Properties of the 45 IDPs in the training set.

Figure 3—source data 1

Possible effects of sequence length, temperature, and magnetic field on R2.

Figure 3—figure supplement 1—source data 1

SeqDYN model parameters.

Figure 4—source data 1

T-test of on the q parameters for pairs of amino acids.

Figure 4—figure supplement 1—source data 1

Quality of SeqDYN predictions.

Figure 5—source data 1

Close reproduction (curve) of the measured R2 profile (bars) of CBP-ID4 when that set of data alone was used to parameterize SeqDYN.

Figure 5—figure supplement 1—source data 1

Measured (bars) and predicted (curves) R2 profiles for ChiZ N-terminal region, TIA1 prion-like domain, Pdx1 C-terminal region, synaptobrevin-2, α-endosulfine, YAP, AMOTL1, FtsQ, and CAHS-8.

Figure 6—source data 1

R2 profiles predicted (curves) by SeqDYN show close agreement with those measured (bars) on structured proteins in the unfolded state.

Figure 7—source data 1

Comparison between SeqDYN prediction (curves) and effective transverse relaxation rate (bars) from 1H dispersion relaxation experiment.

Figure 8—source data 1

Correlation between the stickiness parameters (λ) and the NMR relaxation parameters (q).

Figure 9—source data 1

Experimental conditions, mean and standard deviation of measured R2, and SeqDYN prediction RMSE.

RMSEs (s–1) of R2 predictions by SeqDYN and MD for 10 IDPs.

MDAR checklist

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Possible effects of sequence length, temperature, and magnetic field on $R_{2}$ .

T-test of on the $q$ parameters for pairs of amino acids.

Close reproduction (curve) of the measured $R_{2}$ profile (bars) of CBP-ID4 when that set of data alone was used to parameterize SeqDYN.

Measured (bars) and predicted (curves) $R_{2}$ profiles for ChiZ N-terminal region, TIA1 prion-like domain, Pdx1 C-terminal region, synaptobrevin-2, α-endosulfine, YAP, AMOTL1, FtsQ, and CAHS-8.

$R_{2}$ profiles predicted (curves) by SeqDYN show close agreement with those measured (bars) on structured proteins in the unfolded state.

Comparison between SeqDYN prediction (curves) and effective transverse relaxation rate (bars) from ¹H dispersion relaxation experiment.

Experimental conditions, mean and standard deviation of measured $R_{2}$ , and SeqDYN prediction RMSE.

RMSEs (s^–1) of $R_{2}$ predictions by SeqDYN and MD for 10 IDPs.