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Intrinsically disordered proteins as important players during desiccation stress of the soybean radicles Yun Liu, Jiahui Wu, Nan Sun, Chengjian Tu, Xiaoying Shi, Hua Cheng, Simu Liu, Shuiming Li, Yong Wang, Yizhi Zheng, and Vladimir N. Uversky J. Proteome Res., Just Accepted Manuscript • Publication Date (Web): 19 May 2017 Downloaded from http://pubs.acs.org on May 25, 2017

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Journal of Proteome Research

Intrinsically disordered proteins as important players during desiccation stress of the soybean radicles

Yun Liu†*, Jiahui Wu†, Nan Sun†, Chengjian Tu ‡, Xiaoying Shi†, Hua Cheng†, Simu Liu†, Shuiming Li †, Yong Wang†, Yizhi Zheng †,*, and Vladimir N. Uversky§,ǁ,*

†

Guangdong Provincial Key Laboratory for Plant Epigenetics, Shenzhen Key Laboratory of

Microbial Genetic Engineering, College of Life Sciences and Oceanography, Shenzhen University, Nanhai Ave 3688, Shenzhen, Guangdong, 518060, China; ‡

Department of Pharmaceutical Sciences, State University of New York at Buffalo, 285

Kapoor Hall, Buffalo, New York 14260 United States; §

Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute,

Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Blvd. MDC07, Tampa, Florida, USA; ǁ

Laboratory of new Methods in Biology, Institute for Biological Instrumentation of the

Russian Academy of Sciences, Institutskaya str., 7, Pushchino, Moscow region, 142290 Russia;

*To whom correspondence should be addressed: Y. L. Tel: +86-755-26535286. Fax:+86-755-26534274. E-mail: [email protected] Y. Z.: Tel: +86-755-26535286. Fax:+86-755-26534274. E-mail: [email protected] V. N. U.: Tel:+ 01-813-974-5816. Fax:+01-813-974-7357. E-mail: [email protected]

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ACS Paragon Plus Environment


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ABSTRACT Intrinsically disordered proteins (IDPs) play a variety of important physiological roles in all living organisms. However, there is no comprehensive analysis of the abundance of IDPs associated with environmental stress in plants. Here, we show that a set of heat-stable proteins (i.e., proteins that do not denature after boiling at 100°C for 10 min) was present in R0mm and R15mm radicles (i.e., before the radicle emergence and the 15 mm long radicles) of soybean (Glycine max) seeds. This set of 795 iTRAQ-quantified heat-stable proteins contained a high proportion of wholly or highly disordered proteins (15%), which was significantly higher than that estimated for the whole soybean proteome containing 55,787 proteins (9%). The heat-stable proteome of soybean radicles that contain many IDPs could protect lactate dehydrogenase (LDH) during freeze-thaw cycles. Comparison of the 795 heat-stable proteins in the R0mm and R15mm soybean radicles revealed that many of these proteins changed abundance during seedling growth, with 170 and 89 proteins being more abundant in R0mm and R15mm, respectively. KEGG analysis identified 18 proteins from the cysteine and methionine metabolism pathways and 9 proteins from the phenylpropanoid biosynthesis pathway. As an important type of IDP related to stress, 30 late embryogenesis abundant (LEA) proteins were also found. Ten selected proteins with high levels of predicted intrinsic disorder were able to efficiently protect LDH from the freeze-thaw-induced inactivation, but the protective ability was not correlated with the disorder content of these proteins. These observations suggest that protection of the enzymes and other proteins in a stressed cell can be one of the biological functions of plant IDPs. Key words: intrinsically disordered protein; late embryogenesis abundant protein; iTRAQ; stress resistance 2


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INTRODUCTION Many biologically important proteins are known to lack stable secondary and/or tertiary structure entirely (i.e., are fully disordered) or contain disordered regions under physiological conditions.1-4 These intrinsically disordered proteins (IDPs) and hybrid proteins composed of ordered domains and IDP regions (IDPRs)5 are typically characterized by noticeable biases of their amino acid sequences, possessing a high proportion of charged and polar residues, as well as many proline residues, which are known to promote disorder, while they typically contain a low proportion of hydrophobic residues.6-9 Furthermore, IDPs/IDPRs are often typified by a low sequence complexity.6-9 Structurally, IDPs are distinguished by high spatio-temporal heterogeneity, where instead of being folded into unique 3-D structures with relatively fixed atomic coordinates and low conformational dynamics, these proteins/regions exist as highly flexible structural ensembles of rapidly interconverting conformations.10-14 Importantly, despite the inability of such proteins/regions/domains to form stable tertiary structures, they do play important physiological roles,6, 13, 15-30 where they can be involved in various signaling processes,31, protein protection,42,

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regulation of numerous pathways,33-40 cell protection,41

controlled cell death,44-48 and cellular homeostasis.49,

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Several

computational studies unequivocally revealed that IDPs are common in nature, with their abundance increasing with an increase in the complexity of the organism.1-4 For example, almost 50% of proteins in eukaryotic proteomes are IDPs, and 75% of transcriptional factors contain long functional IDPRs.1, 4, 21, 51 Currently, IDP is used as a generic term to denote a protein that contains extensive disorder that is important for function. The amount of IDPs/IDPRs in various proteomes typically serves as a reflection of both 3



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evolution and adaptation to the environment.4,

51, 52

On the evolutionary side, the

aforementioned fact that the percentage of IDPs in proteomes is increasing from bacteria and archaebacteria, to fungi, and to eukaryotic organisms serves as a reflection of the evolutionary importance of intrinsic disorder.4, 51-53 On the other hand, the role of disorder in adaptation to the environment can be illustrated by the fact that the salt, pH, and/or temperature-tolerant bacteria and Achaea typically contain more IDPs than their mesophilic and salt/pH-sensitive counterparts.52,

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All of these observations are consistent with the

conclusion that IDP might have a variety of important physiological functions in different organisms. Based on the analysis of the available literature data it has been concluded that IDPs can be divided into six functional groups, such as (i) entropic chains, (ii) scavengers, (iii) effectors, (iv) assemblers, (v) display sites, and (vi) chaperones.21, 54, 55 Because of their lack of fixed structure, IDPs/IDPRs are known to be promiscuous binders engaged in interactions with many often unrelated partners. By virtue of their ability to participate in low affinity-high specificity signaling interactions, IDPs/IDPRs are involved in various processes related to signal transduction and cellular cycle control. Intrinsic disorder is usually associated with sites of phosphorylation and many other enzymatically catalyzed posttranslational modifications (PTMs).12, 56-59 Obviously, this represents another reason for the broad involvement of IDPs/IDPRs in various regulatory processes, since phosphorylation and other PTMs are known to modulate the activity of numerous proteins involved in signal transduction, and regulate the binding affinity of transcription factors to their coactivators and DNA, thereby altering gene expression, cell growth and differentiation.57, 60-62 In comparison with animal IDPs, much less is known about IDPs in plants.63, 64 The 4


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disorder-based functions in plant proteins can be classified into five groups: (i) stress tolerance, (ii) transcription regulation, (iii) cell cycle regulation, (iv) molecular chaperons, and (v) development regulation.54, 63 Although quantitatively there are generally fewer IDPs in plant than in many animals, plants contain more IDPs related to the environmental adaptation.54, 63-69 Among these adaptation- and/or stress tolerance-related disordered proteins in plants are late embryogenesis abundant (LEA) and GRAS [this name is derived from the three initially identified family members, GAI (gibberellic acid-insensitive mutant protein), RGA (GAI-related sequence), and SCR (S locus cysteine-rich protein A)] proteins, which are rather abundant in various plants.67, 70, 71 For example, more than 50 LEA proteins have been found in Arabidopsis thaliana.70 Although several proteomic techniques have been widely used to reveal proteins related to various physiological processes, only a few large-scale experimental analyses of the abundance of IDPs actually expressed in living cells have been performed so far.72-76 The disordered structure and the peculiar amino acid compositions might help some IDPs to remain stable under conditions of low pH and/or high temperature. A heat treatment was shown to effectively enrich cell extracts in IDPs,73 such as ribosomal proteins, GroES, and acyl carrier protein in E.coli, S. cerevisiae, plants, and mammals.74, 75 However, to the best of our knowledge, there is no comprehensive report on the abundance of IDPs associated with environmental stress in plant. Therefore, the goal of this study was to fill this gap by analyzing the unfoldome (or intrinsically disordered side of the proteome) of the soybean radicles. It is known that many stress-related proteins accumulate in the embryo during dehydration of the orthodox seeds.77-81 Furthermore, the stress tolerance of seeds is known to 5



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increase with the enhancement of physical desiccation. When germination happens, the seeds are known to lose their stress tolerance likely due to a decrease in the abundance of these stress-related proteins. In order to find out how the IDP might function in stress tolerance, soybean radicles of 0 and 15 mm were used for comparative desiccation resistance and proteomic analyses in this paper.

EXPERIMENTAL SECTION Plant Material and Treatments Glycine max L. Merr. cv (Bainong 6#) seeds were kindly provided by the Institute of Agriculture Science in Baicheng City (Jilin Province, P.R. China). The seeds were allowed to imbibe on gauze in distilled water at 25°C in the dark. When the radicle emerged from the seed coat, seeds with 0 mm, 5 mm, and 15 mm long radicles (R0mm, R5mm, and R15mm, respectively) were used for desiccation resistance experiments by embedding seeds into a silica gel for 24 h. The survival ratios after dehydration were calculated after the seeds had been rehydrated in water for 48 h.

Protein Preparation and iTRAQ labeling The R0mm and R15mm radicles from seeds (i.e., before the radicle emergence and the 15 mm long radicles) were collected to give 0.1 g fresh weight, frozen, and ground into powder in liquid nitrogen. Then, 1 ml of 100 mM HEPES buffer containing 0.01% (v/v) β-mercaptoethanol, 0.5 M PMSF (phenylmethane sulfonyl fluoride), and 0.1% (w/v) phosphatase inhibitor (Roche, Germany) was added to the powder, and the resulting mixture 6


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was centrifuged at 13,000 rpm for 30 min at 4˚C. The soluble proteins in the supernatant were transferred to a fresh tube, boiled at 100°C for 10 min, and then cooled on ice for 15 min. After centrifuging of the resulting sample at 13,000 rpm for 30 min at 4°C, 600 µl of the supernatant (the heat-stable proteins) were mixed with 800 µl of precooled acetone. The mixture was incubated at -30˚C for overnight. After centrifuging at 13,000 rpm for 30 min at 4°C, the pellet was washed three times with acetone. Then, the pellet was dried to powder in the vacuum freeze-drier. The powder was dissolved in 100 µl of 40 mM Tris-Cl buffer containing 4% CHAPS, 2 M thiourea, 6 M urea, 65 mM DTT, and 0.1% (w/v) phosphatase inhibitor, pH 8.0. A total of 100 µg protein of each sample was typically digested with sequence-grade modified Trypsin (Dingguo, China) for 16 h at 37°C. The resulting peptide mixture was dried using vacuum centrifugation and the powder was re-dissolved in 0.5 M TEAB, pH 8.5. The iTRAQ reagent (Applied Biosystems, USA) was used for labeling the sample as described in the manufacturer’s protocol. iTRAQ tags 113-115 and 116-118 were added to peptides extracted in biological triplicates from the R0mm and R15mm radicles, respectively (here, biological triplicates correspond to samples obtained from three different and independent protein extracts for radicles of each diameter). Water was added to the stop reaction when the labeled peptides with the isobaric tags had been incubated at room temperature for 2 h.

Fractionation by High-pH Reverse Chromatography The solution from the previous step was put into one tube and dried in Speed-vacuum concentrator (savant DNA 120, Thermo Scientific, USA), subjected to the first dimensional 7



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fractionation procedure on high-pH reverse chromatography column (Agilent, ZORBAX Extended-C18 2.1) using a separation gradient with buffer B (10 mM ammonium formate dissolved in 90% acetonitrile, pH 10.0) which linearly increased from 5% up to 30% in 40 min at a flow rate of 0.3 ml/min. A total of 40 equal fractions were collected every one minute and every four of the fractions were combined into a new fraction, ten fractions were finally obtained and prepared for the following LC-MS analysis.

Reverse Phase Nano-Flow HPLC and Tandem Mass Spectrometry The reverse phase nano-LCMS/MS analysis was performed on the Eksigent nanoLC-Ultra™ 2D System (AB SCIEX, Canada). The lyophilized fractions were suspended in 2% (v/v) acetonitrile, 0.1% (v/v) formic acid, and loaded on ChromXP C18 (3 µm, 120 Å) nanoLC trap column. The online trapping, desalting procedure were carried out at 2 µL/min for 10 min with 100% solvent A. Solvents were composed of water/acetonitrile/formic acid (A, 98/2/0.1%; B, 2/98/0.1%). Then, an elution gradient of 5-35% acetonitrile (0.1% formic acid) in 70 min was employed on an analytical column (75 µm x 15 cm, C18, 3 µm, 120 Å, ChromXP Eksigent). LC MS/MS analysis was performed on a Triple TOF 5600 System (AB SCIEX, Canada) fitted with a NanosprayIII source (AB SCIEX, Canada). Data was acquired using an ion spray voltage of 2.4 kV, curtain gas of 30 PSI, nebulizer gas of 5 PSI, and an interface heater temperature of 150oC. The MS was operated with TOF-MS scan ranges of 350 to 1250 m/z. For information-dependent acquisition (IDA), survey scans start from 100 to 1500 m/z and were acquired in 250 ms and as many as 30 product ion scans (80 ms) were collected if exceeding a threshold of 200 counts per second (counts/s) and with a +2 to +5 8


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charge state. A rolling collision energy setting was applied to all precursor ions for collision-induced dissociation. dynamic exclusion was set for ½ of peak width (~16 s).

Protein Identification and Quantification The MS/MS data were conducted using ProteinPilot Software v.4.5 (Sciex Inc., USA) utilizing the Paragon and Progroup algorithms.82 The global false discovery rate (FDR) was estimated with the PSPEP tool83 integrated in the ProteinPilot Software. Here the global FDR was set to be 1.0% after searching against a concatenated target-decoy database consisting of the Glycine max protein database (55,787 entries) and corresponding reverse decoy database. The database search parameters were set as follows: iTRAQ 8-plex peptides labelling quantification, cysteine modified with iodoacetamide, trypsin digestion, thorough searching mode and minimum protein threshold of 95% confidence (unused protein score > 1.3). At least 1 unique peptide was required for each confidently identified protein. All the proteins identified in this study are listed in Table S1, including proteins with the p-values exceeding 0.05 threshold, and 795 proteins were further selected according to the minimal p-value of 0.05. The means of the iTRAQ ratios from the R0mm and R15mm were compared as described in ref.84 The statistical analysis was performed using unpaired two-sample Student t-test. A 2-fold change and p-value less than 0.05 were used to determine differentially expressed proteins.

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Gene Ontology (GO) and KEGG analysis The

GO

annotation

of

differentially

expressed

proteins

were

analyzed

at

http://bioinfo.cau.edu.cn/agriGO/index.php. The functional annotations were presented by corresponding GO terms. The hypergeometric test was used for GO enrichment analysis and p-value less than 0.05 was used as indication of statistical significance. In addition, the KEGG pathway analysis was performed at http://www.kegg.jp for altered proteins.

Semi-quantitative RT-PCR and Western-blotting In order to examine the expression levels of the query proteins, 17 genes were selected for semi-quantitative RT-PCR and 3 proteins were detected by Western blotting. For semi-quantitative RT-PCR, the actin-11 and 18S genes were used for internal reference. For Western blotting, the quantity of protein was detected by AuraECL chemiluminescence kit (Auragene, China).

Circular dichroism analysis Ten proteins were selected to be expressed in E.coli and then purified by HPLC. The His-tags were cut off by thrombase, and the proteins were purified by molecular sieve chromatography according to their mass. The purified proteins were dissolved in deionized water and analyzed by circular dichroism from 250 nm to 190 nm. The interval wavelength was 1 nm. The data were analyzed by the Dichroweb to obtain the secondary structure content of the query proteins.

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LDH activity assay Lactate dehydrogenase (LDH) from rabbit muscle (Roche, Germany) is sensitive to freeze-thaw cycles. The LDH was diluted in 100 mM sodium phosphate buffer (pH 7.0) to a final concentration of 0.357 M. Test proteins were added to equal volumes of LDH at molar ratios of 1:1, 5:1 or 10:1 (test protein: LDH). The enzyme with and without proteins were frozen in liquid nitrogen for 2 min and then thawed at 25°C for 5 min. The freeze-thaw cycles were repeated 3 times. The activity of LDH was detected according to the literature.85 In brief, 5 µL of the LDH-containing mixture was diluted to 2 mL with the assay buffer (100 mM PBS buffer containing 7.5 mM pyruvate and 0.1 mM NADH). The LDH activity was monitored by the increase in the absorbance at 340 nm for 2 min due to the conversion of NADH into NAD+ at 37°C. All of the values presented in this study are expressed as the percentage of the rate of the reaction relative to the rate of reaction in the untreated samples.

Evaluation of intrinsic disorder Global disorder analysis in the 795 proteins quantified by iTRAQ and the total set of soybean proteins was analyzed by ESpritz.86 Amino acid sequences of 10 selected proteins analyzed in this study (in FASTA format) were retrieved from UniProt.87 The corresponding UniProt IDs are I1K7E6, I1KV72, I1MAW4, P26585, C6T3P2, I1JGV5, C6TFS4, I1KID4, C6SYE5, and Q9S7N8. Intrinsic disorder propensities of target proteins were evaluated using four algorithms from the PONDR family, PONDR-FIT, PONDR® VLXT, PONDR® VSL2, and PONDR® VL3,7, 88-92

as well as the IUPred web server that evaluates the presence of both short and long

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IDPRs in a query protein.93 For each protein, after obtaining an average disorder score by each predictor, all predictor-specific average scores were averaged again to generate an average per-protein intrinsic disorder score. Use of consensus for evaluation of intrinsic disorder is motivated by empirical observations that this approach usually increases the predictive performance compared to the use of a single predictor.94-96 In addition to the aforementioned per-residue disorder predictors, we used two binary disorder classifiers, charge-hydropathy plot (CH-plot)2, 17and cumulative distribution function (CDF) analysis that evaluate the predisposition of a given protein to be ordered or disordered as a whole. A CH-plot represents an input protein as a point within the 2D graph, where the mean Kyte-Doolittle hydrophobicity and the mean absolute net charge are used as the X- and Y-coordinates, respectively. In the corresponding CH-plot, fully structured proteins and fully disordered proteins can be separated by a boundary line. All proteins located above this boundary line are highly likely to be extended, while proteins located below this line are likely to be compact.2, 17 CDF analysis summarizes the per-residue disorder predictions by plotting PONDR scores against their cumulative frequency, which allows ordered and disordered proteins to be distinguished on the basis of the distribution of prediction scores.2 At any given point on the CDF curve, the ordinate gives the proportion of residues with a PONDR score less than or equal to the abscissa. The optimal boundary that provided the most accurate order-disorder classification was shown to represent seven points located in the 12th through 18th bins.2 Thus, in the CDF analysis, order-disorder classification is based on whether a CDF curve of a given protein is above (ordered) or below (disordered) a majority of boundary points.2 We also used a combined CH-plot – CDF analysis (CH-CDF analysis) 12


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that is known to provide additional important information on the classification of protein disorder.97-99 Potential disorder-based protein binding sites of query proteins (molecular recognition features, MoRFs) were identified by the ANCHOR algorithm.100, 101 This algorithm utilizes the pair-wise energy estimation approach originally used by IUPred.93, 102 This approach acts on the hypothesis that long regions of disorder include localized potential binding sites, which are not capable of folding on their own because they are unable to form sufficiently favorable intrachain interactions, but can obtain the energy to stabilize via interaction with a globular protein partner.100, 101

RESULTS AND DISCUSSION The desiccation-tolerance and heat-stable protein content of soybean radicles during germination Nearly 85% of R0mm survived, while none of the R15mm soybean seeds survived desiccation (Table 1). Accordingly, the analysis of the relative contents of heat-stable proteins revealed that 22% of the proteins in R0mm soybeans are heat-stable, which is twice that of R15mm soybeans. This indicates that higher levels of heat-stable proteins might confer desiccation tolerance to the R0mm soybeans.

The protective effect of the heat-stable proteins on LDH To validate whether heat-stable proteome of soybean could protect cellular proteins under stress conditions, the protective effects of soluble or heat-stable proteins on LDH during 13



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freeze-thaw cycles were analyzed. Figure 1 shows that when the mass ratio of soluble or heat-stable proteome to LDH was 4:1, the heat-stable proteins gave a significantly better protection of LDH against freeze-thaw-induced inactivation (pI1K7E6 (70%) >Q9S7N8 (62%) >I1JGV5 (58%) >I1MAW4 (55%) >P26585 (45%) >I1KV72 (41%)>I1KID4 (34%). The exceptionally high disorder levels in the selected proteins, the presence of more than one AIBS in a protein, and the overall high content of residues that can be involved in disorder-based binding suggests that the analyzed proteins commonly utilize disorder for their interactions with binding partners, and that these proteins are involved either in the polyvalent interactions by using multiple binding sites to interact with one binding partner or in scaffolding-like interactions by using multiple binding sites to interact with multiple binding partners. The wide spread of lengths of identified AIBSs also suggests the presence of multiple disorder-based binding mechanisms (ranging from local folding-on-binding of short regions via wrapping around binding mode to global binding-induced folding of large regions).

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The protective effect of proteins on LDH during freeze-thaw cycles Because the heat-stable proteome showed a better LDH protection from the freeze-thaw-induced inactivation (Figure 1), the protective function of these ten selected proteins were analyzed. As shown in Figure 8A, all these proteins possessed a significant protective effect against the LDH deactivation compared with the negative control, lysozyme. However, the protective effects were not significantly correlated with the percentages of disorder in query proteins (Figure 8B).

Conclusions Intrinsically disordered proteins (IDPs) are widespread in organismal proteomes, especially in eukaryotes. IDPs have many important cellular functions, such as recognition and regulation of transcription and various cellular signaling processes. The previous analysis of the heat-stable protein fraction of imbibed radicles of Medicago truncatula seeds revealed a higher content of IDPs in the desiccation-tolerant material.117 LEA proteins are among the most abundant IDPs in plants. Although it is known that LEA proteins contribute to the stress resistance of plants, animals, and microorganisms,42, 118 it is not known how other IDPs function under stress. The goal of our study was to fill this gap and to analyze what various IDPs can do in a plant. Our analysis revealed that there were more wholly or highly disordered proteins among the upregulated proteins in the desiccation tolerant R0mm. The 10 selected IDPs as well as the entire heat-stable proteomes of the soybean radicles were shown to more efficiently protect the LDH from the inactivation induced by the freeze-thaw cycles than the total set of soluble proteins isolated from soybean radicles. These observations 21



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suggest that under the stress conditions, IDPs serve as important cellular protectors. Intrinsically disordered proteins are known to be multifunctional. To see if this could be also the case for the heat-stable proteome of soybean radicles, ten IDPs were purified for the structural and functional analysis. All of these proteins were shown to be mostly disordered. In addition to their reported biological functions that ranges from cytochrome c oxidase, to transcription activation and to mRNA splicing, all these proteins were found to protect an enzyme against freeze-thaw cycles. The ANCHOR analysis revealed the presence of multiple disorder-based binding sites in these proteins. Their intrinsically disordered nature may help the proteins to interact with DNA, RNA, and/or other proteins. Furthermore, these proteins can potentially interact with multiple binding partners, serving as scaffolds, or can act as hub proteins in signaling networks. For all of these ten IDPs, the enzyme-protective function is reported for the first time. Curiously, the protective effects were not obviously correlated with the percentages of disorder in query proteins. These results provide experimental support for the idea that IDPs are multifunctional moonlighting proteins that have protective functions in the organism.

ASSOCIATED CONTENT Supporting information The Supporting information is available free of charge on the ACS Publications website at DOI: . Supplementary materials include Table S1 that represents the protein identifications and relative quantifications; Figure S1 that represents the annotated tandem mass spectra; and Figure S2 showing the far-UV CD spectra of ten proteins in the presence of SDS or TFE. 22


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Table S1. Quantitative information on 4,032 proteins quantified by the iTRAQ technique. Protein identification, major properties, and relative quantifications are listed. The table shows accession number, protein name, molecular weight, pI, used score, total number of spectra, percent sequence coverage, number of unique peptides, group ratios and p-value. The software used for protein qualification and quantitation is ProteinPilot (version 4.5) provided by the MS instrument vendor. Figure S1. Annotated tandem mass spectra. 284 peptides were randomly selected to show the information related to the protein and peptide qualification and quantitation. The Figure includes the reporter ion region and MS/MS spectrum of the unique peptide, whereas the peptide and protein qualification and quantitation also included in corresponding tables. The information about other 283 peptides was simplified according to the example shown below. 113-115 represents labeled R0mm, 116-118 represents labeled R15mm. Figure S2. Far-UV CD spectra of 10 selected proteins in aqueous solutions, SDS and TFE. Transcription activator-related, LOC100812629, PM35, Arpp13, and cwf18 proteins are natively unfolded (behave as random coils) in water and more α-helical structure can be induced by SDS and TFE. The other five proteins showed some α-helical structure in water, and could form more helices in the presence of SDS and TFE.

Acknowledgments: This work was supported by National Natural Science Foundation of China (31300215, 31370289) and China Scholarship Council (201508440389). We thank Xingfeng Yin from Jinan University for his help with the mass spectrometer analyses.

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A.; Ghavami, S.; Ghigo, E.; Ghosh, D.; Giammarioli, A. M.; Giampieri, F.; Giampietri, C.; Giatromanolaki, A.; Gibbings, D. J.; Gibellini, L.; Gibson, S. B.; Ginet, V.; Giordano, A.; Giorgini, F.; Giovannetti, E.; Girardin, S. E.; Gispert, S.; Giuliano, S.; Gladson, C. L.; Glavic, A.; Gleave, M.; Godefroy, N.; Gogal, R. M., Jr.; Gokulan, K.; Goldman, G. H.; Goletti, D.; Goligorsky, M. S.; Gomes, A. V.; Gomes, L. C.; Gomez, H.; Gomez-Manzano, C.; Gomez-Sanchez, R.; Goncalves, D. A.; Goncu, E.; Gong, Q.; Gongora, C.; Gonzalez, C. B.; Gonzalez-Alegre, P.; Gonzalez-Cabo, P.; Gonzalez-Polo, R. A.; Goping, I. S.; Gorbea, C.; Gorbunov, N. V.; Goring, D. R.; Gorman, A. M.; Gorski, S. M.; Goruppi, S.; Goto-Yamada, S.; Gotor, C.; Gottlieb, R. A.; Gozes, I.; Gozuacik, D.; Graba, Y.; Graef, M.; Granato, G. E.; Grant, G. D.; Grant, S.; Gravina, G. L.; Green, D. R.; Greenhough, A.; Greenwood, M. T.; Grimaldi, B.; Gros, F.; Grose, C.; Groulx, J. F.; Gruber, F.; Grumati, P.; Grune, T.; Guan, J. L.; Guan, K. L.; Guerra, B.; Guillen, C.; Gulshan, K.; Gunst, J.; Guo, C.; Guo, L.; Guo, M.; Guo, W.; Guo, X. G.; Gust, A. A.; Gustafsson, A. B.; Gutierrez, E.; Gutierrez, M. G.; Gwak, H. S.; Haas, A.; Haber, J. E.; Hadano, S.; Hagedorn, M.; Hahn, D. R.; Halayko, A. J.; Hamacher-Brady, A.; Hamada, K.; Hamai, A.; Hamann, A.; Hamasaki, M.; Hamer, I.; Hamid, Q.; Hammond, E. M.; Han, F.; Han, W.; Handa, J. T.; Hanover, J. A.; Hansen, M.; Harada, M.; Harhaji-Trajkovic, L.; Harper, J. W.; Harrath, A. H.; Harris, A. L.; Harris, J.; Hasler, U.; Hasselblatt, P.; Hasui, K.; Hawley, R. G.; Hawley, T. S.; He, C.; He, C. Y.; He, F.; He, G.; He, R. R.; He, X. H.; He, Y. W.; He, Y. Y.; Heath, J. K.; Hebert, M. J.; Heinzen, R. A.; Helgason, G. V.; Hensel, M.; Henske, E. P.; Her, C.; Herman, P. K.; Hernandez, A.; Hernandez, C.; Hernandez-Tiedra, S.; Hetz, C.; Hiesinger, P. R.; Higaki, K.; Hilfiker, S.; Hill, B. G.; Hill, J. A.; Hill, W. D.; Hino, K.; Hofius, D.; Hofman, P.; Hoglinger, G. U.; Hohfeld, J.; Holz, M. K.; Hong, Y.; Hood, D. A.; Hoozemans, J. J.; Hoppe, T.; Hsu, C.; Hsu, C. Y.; Hsu, L. C.; Hu, D.; Hu, G.; Hu, H. M.; Hu, H.; Hu, M. C.; Hu, Y. C.; Hu, Z. W.; Hua, F.; Hua, Y.; Huang, C.; Huang, H. L.; Huang, K. H.; Huang, K. Y.; Huang, S.; Huang, S.; Huang, W. P.; Huang, Y. R.; Huang, Y.; Huang, Y.; Huber, T. B.; Huebbe, P.; Huh, W. K.; Hulmi, J. J.; Hur, G. M.; Hurley, J. H.; Husak, Z.; Hussain, S. N.; Hussain, S.; Hwang, J. J.; Hwang, S.; Hwang, T. I.; Ichihara, A.; Imai, Y.; Imbriano, C.; Inomata, M.; Into, T.; Iovane, V.; Iovanna, J. L.; Iozzo, R. V.; Ip, N. Y.; Irazoqui, J. E.; Iribarren, P.; Isaka, Y.; Isakovic, A. J.; Ischiropoulos, H.; Isenberg, J. S.; Ishaq, M.; Ishida, H.; Ishii, I.; Ishmael, J. E.; Isidoro, C.; Isobe, K.; Isono, E.; Issazadeh-Navikas, S.; Itahana, K.; Itakura, E.; Ivanov, A. I.; Iyer, A. K.; Izquierdo, J. M.; Izumi, Y.; Izzo, V.; Jaattela, M.; Jaber, N.; Jackson, D. J.; Jackson, W. T.; Jacob, T. G.; Jacques, T. S.; Jagannath, C.; Jain, A.; Jana, N. R.; Jang, B. K.; Jani, A.; Janji, B.; Jannig, P. R.; Jansson, P. J.; Jean, S.; Jendrach, M.; Jeon, J. H.; Jessen, N.; Jeung, E. B.; Jia, K.; Jia, L.; Jiang, H.; Jiang, H.; Jiang, L.; Jiang, T.; Jiang, X.; Jiang, X.; Jiang, X.; Jiang, Y.; 28


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Jiang, Y.; Jimenez, A.; Jin, C.; Jin, H.; Jin, L.; Jin, M.; Jin, S.; Jinwal, U. K.; Jo, E. K.; Johansen, T.; Johnson, D. E.; Johnson, G. V.; Johnson, J. D.; Jonasch, E.; Jones, C.; Joosten, L. A.; Jordan, J.; Joseph, A. M.; Joseph, B.; Joubert, A. M.; Ju, D.; Ju, J.; Juan, H. F.; Juenemann, K.; Juhasz, G.; Jung, H. S.; Jung, J. U.; Jung, Y. K.; Jungbluth, H.; Justice, M. J.; Jutten, B.; Kaakoush, N. O.; Kaarniranta, K.; Kaasik, A.; Kabuta, T.; Kaeffer, B.; Kagedal, K.; Kahana, A.; Kajimura, S.; Kakhlon, O.; Kalia, M.; Kalvakolanu, D. V.; Kamada, Y.; Kambas, K.; Kaminskyy, V. O.; Kampinga, H. H.; Kandouz, M.; Kang, C.; Kang, R.; Kang, T. C.; Kanki, T.; Kanneganti, T. D.; Kanno, H.; Kanthasamy, A. G.; Kantorow, M.; Kaparakis-Liaskos, M.; Kapuy, O.; Karantza, V.; Karim, M. R.; Karmakar, P.; Kaser, A.; Kaushik, S.; Kawula, T.; Kaynar, A. M.; Ke, P. Y.; Ke, Z. J.; Kehrl, J. H.; Keller, K. E.; Kemper, J. K.; Kenworthy, A. K.; Kepp, O.; Kern, A.; Kesari, S.; Kessel, D.; Ketteler, R.; Kettelhut Ido, C.; Khambu, B.; Khan, M. M.; Khandelwal, V. K.; Khare, S.; Kiang, J. G.; Kiger, A. A.; Kihara, A.; Kim, A. L.; Kim, C. H.; Kim, D. R.; Kim, D. H.; Kim, E. K.; Kim, H. Y.; Kim, H. R.; Kim, J. S.; Kim, J. H.; Kim, J. C.; Kim, J. H.; Kim, K. W.; Kim, M. D.; Kim, M. M.; Kim, P. K.; Kim, S. W.; Kim, S. Y.; Kim, Y. S.; Kim, Y.; Kimchi, A.; Kimmelman, A. C.; Kimura, T.; King, J. S.; Kirkegaard, K.; Kirkin, V.; Kirshenbaum, L. A.; Kishi, S.; Kitajima, Y.; Kitamoto, K.; Kitaoka, Y.; Kitazato, K.; Kley, R. A.; Klimecki, W. T.; Klinkenberg, M.; Klucken, J.; Knaevelsrud, H.; Knecht, E.; Knuppertz, L.; Ko, J. L.; Kobayashi, S.; Koch, J. C.; Koechlin-Ramonatxo, C.; Koenig, U.; Koh, Y. H.; Kohler, K.; Kohlwein, S. D.; Koike, M.; Komatsu, M.; Kominami, E.; Kong, D.; Kong, H. J.; Konstantakou, E. G.; Kopp, B. T.; Korcsmaros, T.; Korhonen, L.; Korolchuk, V. I.; Koshkina, N. V.; Kou, Y.; Koukourakis, M. I.; Koumenis, C.; Kovacs, A. L.; Kovacs, T.; Kovacs, W. J.; Koya, D.; Kraft, C.; Krainc, D.; Kramer, H.; Kravic-Stevovic, T.; Krek, W.; Kretz-Remy, C.; Krick, R.; Krishnamurthy, M.; Kriston-Vizi, J.; Kroemer, G.; Kruer, M. C.; Kruger, R.; Ktistakis, N. T.; Kuchitsu, K.; Kuhn, C.; Kumar, A. P.; Kumar, A.; Kumar, A.; Kumar, D.; Kumar, D.; Kumar, R.; Kumar, S.; Kundu, M.; Kung, H. J.; Kuno, A.; Kuo, S. H.; Kuret, J.; Kurz, T.; Kwok, T.; Kwon, T. K.; Kwon, Y. T.; Kyrmizi, I.; La Spada, A. R.; Lafont, F.; Lahm, T.; Lakkaraju, A.; Lam, T.; Lamark, T.; Lancel, S.; Landowski, T. H.; Lane, D. J.; Lane, J. D.; Lanzi, C.; Lapaquette, P.; Lapierre, L. R.; Laporte, J.; Laukkarinen, J.; Laurie, G. W.; Lavandero, S.; Lavie, L.; LaVoie, M. J.; Law, B. Y.; Law, H. K.; Law, K. B.; Layfield, R.; Lazo, P. A.; Le Cam, L.; Le Roch, K. G.; Le Stunff, H.; Leardkamolkarn, V.; Lecuit, M.; Lee, B. H.; Lee, C. H.; Lee, E. F.; Lee, G. M.; Lee, H. J.; Lee, H.; Lee, J. K.; Lee, J.; Lee, J. H.; Lee, J. 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P.; Liton, P. B.; Liu, B.; Liu, C.; Liu, C. F.; Liu, F.; Liu, H. J.; Liu, J.; Liu, J. J.; Liu, J. L.; Liu, K.; Liu, L.; Liu, L.; Liu, Q.; Liu, R. Y.; Liu, S.; Liu, S.; Liu, W.; Liu, X. D.; Liu, X.; Liu, X. H.; Liu, X.; Liu, X.; Liu, X.; Liu, Y.; Liu, Y.; Liu, Z.; Liu, Z.; Liuzzi, J. P.; Lizard, G.; Ljujic, M.; Lodhi, I. J.; Logue, S. E.; Lokeshwar, B. L.; Long, Y. C.; Lonial, S.; Loos, B.; Lopez-Otin, C.; Lopez-Vicario, C.; Lorente, M.; Lorenzi, P. L.; Lorincz, P.; Los, M.; Lotze, M. T.; Lovat, P. E.; Lu, B.; Lu, B.; Lu, J.; Lu, Q.; Lu, S. M.; Lu, S.; Lu, Y.; Luciano, F.; Luckhart, S.; Lucocq, J. M.; Ludovico, P.; Lugea, A.; Lukacs, N. W.; Lum, J. J.; Lund, A. H.; Luo, H.; Luo, J.; Luo, S.; Luparello, C.; Lyons, T.; Ma, J.; Ma, Y.; Ma, Y.; Ma, Z.; Machado, J.; Machado-Santelli, G. M.; Macian, F.; MacIntosh, G. C.; MacKeigan, J. P.; Macleod, K. F.; MacMicking, J. D.; MacMillan-Crow, L. A.; Madeo, F.; Madesh, M.; Madrigal-Matute, J.; Maeda, A.; Maeda, T.; Maegawa, G.; Maellaro, E.; Maes, H.; Magarinos, M.; Maiese, K.; Maiti, T. K.; Maiuri, L.; Maiuri, M. C.; Maki, C. G.; Malli, R.; Malorni, W.; Maloyan, A.; Mami-Chouaib, F.; Man, N.; Mancias, J. D.; Mandelkow, E. M.; Mandell, M. A.; Manfredi, A. A.; Manie, S. N.; Manzoni, C.; Mao, K.; Mao, Z.; Mao, Z. W.; Marambaud, 29



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Figure legends Figure 1. Comparison of protective effects of soluble proteins and heat-stable proteins on LDH. The heat-stable proteins showed significantly better protective effect on LDH against freeze-thaw cycles than soluble proteins. (***p

Intrinsically Disordered Proteins as Important ... - ACS Publications

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