Y Schuurmana

V C H Krow

P Ferreira-Apariciob

C Mirodatosa

Use of transient kinetics techniques for studying the methane reforming by carbon dioxide

Keywords:

State

-of-the-art transient kinetic techniques have been used for studying the mechanism of methane reforming into syngas. Steady-state isotopic transient kinetics (SSITK) and temporal analysis of products (TAP) experiments are reported for Ni-and Ru-silica supported reforming catalysts. Qualitative and quantitative data obtained from the transient kinetics are discussed and mechanistic conclusions are proposed.

Introduction

In heterogeneous catalysis, the knowledge of the kinetics of chemical reactions, i.e., of the way in which the rates of reactions depend upon process conditions, forms the basis for the design and optimisation of both catalysts and chemical reactors. Within the frame of a study confined to the steady-state kinetics, only lumped kinetic parameters can be estimated from a regression of steady-state kinetic data.

By generating transient input signals, such as step functions in the gas concentration, one observes sig nals out of the steady-state (transient decreases of the product yield, thermodesorption), the relaxation of which may be related to the time scale on which the corresponding global reaction occurs at the steady-state [1]. One unique way to get these time constants pertaining to steady-state conditions is to generate isotopic step functions, which let the overall *Corresponding author.

surface occupancy unperturbed and therefore give access to true time constants of the reactive inter mediates and to their concentration. This technique is referred as steady-state isotopic transient kinetic ana lysis (SSITKA) [l,2].

Another way to generate transient kinetics was recently developed by means of the temporal analysis of product (TAP) reactor which consists pulsing small and known amounts of reactants through a catalyst bed maintained under vacuum and analysing the pulse expansion and relaxation at the reactor exit with a time resolution below the millisecond [START_REF] Gleaves | [END_REF].

These transient techniques were applied in our laboratory to various reactions among which the methane reforming into syngas by carbon dioxide over Ni-and Ru-based catalysts. This paper presents some selected transient kinetics data obtained for this reaction aimed at determining the rate determining steps (RDS) and the coverage of the most significant reacting intermediates, in view of a better understand ing of the reaction mechanism. 1

Experimental

Steady-state isotopic transient experiments were carried out in an atmospheric fl ow system using either a tubular quartz microreactor or a DRIFT cell from Spectratech as a micro reactor, allowing the gases to fl ow through a fi xed bed of catalyst pellets and able to be heated up to 900°C. The gas composition was monitored at the reactor outlet by on-line mass spec trometry.

Transient pulse experiments were performed in a TAP-2 reactor system. In a TAP experiment a narrow gas pulse of reactants is introduced in a microreactor which is evacuated continuously. The response of this pulse is detected by a quadrupole mass spectrometer at the reactor exit. The shape of the response refl ects diffusion, adsorption, desorption and reaction, as extensively described in [START_REF] Gleaves | [END_REF]. The TAP-2 reactor sys tem used in this lab was first commercially sold unit by Mithra Technologies. Its main features are: (1) four high-speed pulse valves able to deliver 0.25 ms FWHH pulses, (2) a microreactor of 25.4 mm in length and 4.1 mm in diameter, (3) a high-throughput liquid nitrogen trapped vacuum system, (4) a slide valve containing a needle valve to switch between high-pressure experiments and vacuum experiments, (5) a quadrupole mass spectrometer located directly underneath the reactor exit, ( 6) a gas and liquid vapour feed panel to accurately make up blends and (7) a PC based system for data collection and pulse valve control. Typically, a catalyst charge of 25 mg with a particle size of 0.3-0.4 mm was placed in the centre of the reactor between two layers of 0.3-0.4 mm size quartz particles.

Results

The mechanism of the carbon dioxide reforming of methane was investigated over a 4 wt% Ni/Si02 cat alyst, which was shown to be stable under reaction conditions [4]. Typically the reaction was carried out at 700°C and at atmospheric pressure with a reacting mixture CH.JC02/He=7.5:7.5:85 and a total fl ow rate of 31 hl. The reaction rate of methane and carbon dioxide conversion measured after 1 h on stream was 327 and 345 mmol/h per g cai. respectively, with a selectivity towards CO and H2 of 95 and 92%, respec-tively [4]. Some additional data obtained on the TAP reactor with a 0.7 wt% Ru/Si02 catalyst will also be presented in order to determine if the mechanistic conclusions obtained on nickel are also valid for a noble metal such as ruthenium. For the latter, the rate of methane and carbon dioxide conversion measured at 550°C with a reacting mixture CH.JC02/ Ar/ He=10:10:10:70 and a total fl ow rate of 31 h -1 was 51 and 59 mmol/h per g cai. respectively. Carbon tracing transients were generated by switch ing from the reacting mixture 1 2 CH/ 2 C02/He to the labelled mixture 13 CH.J 1 2 C02/He/Ar, while maintain ing the overall steady-state conditions (Fig. la andb).

3.1.

The isotopic composition of the unconverted C02 and of the produced CO was similar and almost equili brated (56 and 44%, respectively) after the switch 1 2 CHJ 1 2 C02/He--+ 13 CH.J 1 2 C02/He/Ar (Fig. la). In contrast less isotopic scrambling was observed for the unconverted methane (86% 13 CH.i and 14% 1 2 CH4) at the reactor outlet.

Only a very small delay was observed between the response of the labelled methane and one of the argon tracer, representing the reference signal (Fig. lb). At variance with methane, a significant delay was observed between the reference Ar curve and the CO and C02 signals indicating an accumulation on the catalyst of reacting intermediates I, precursors of the two latter compounds. Without making any hypothesis about the nature of these adspecies, an evaluation of their amounts can be obtained from the product of the observed delays (time units) by the steady-state flow rates of the considered gases (mol per time unit) at the reactor outlet [START_REF] Mirodatos | Catalyst Characterization[END_REF]. On the basis of the measured dispersion of metallic nickel, i.e., the coverage of the surface by these active intermediates I/Ni� (Ni� being a metallic surface nickel atom) was estimated to 0.10 and 0.43 for the C02 and CO precursors, respectively [ 4].

The hydrogen tracing transient under steady-state conditions was generated by switching from CH.J C02/He to CDJC02/He/Ar mixture. This experiment revealed some HID exchange from the observation of slight amounts of CH3D, CH2D2 and CHD 3 during the transient period. The hydrogen pathways in the reforming were also studied under steady-state conditions by feeding the reactor with the mixture C�+CD,JC02/Ar. Table 1 reports the isotopic composition of the non reacted methane and of the produced hydrogen at the reactor outlet at 700°C and the calculated isotopic composi tion which would be obtained if the H and D atoms in the methane molecules were randomly distributed [START_REF] Mirodatos | Catalyst Characterization[END_REF].

As can be seen:

(i) a large HID exchange was observed, (ii) the isotopic composition of produced hydrogen was very close to the statistical one, indicating a fast gas/surface exchange for hydrogen, (iii) the ratio of exchanged methane (i.e. the sum of the CH3D, CH2D2, CHD 3 outlet partial pressures) to converted methane into CO/C02 was equal to 0.10 . This value indicated that the probability for an activated methane to be oxidised was much higher than its probability to be recombined into gaseous methane, (iv) qualitative evidence of isotopic scrambling was also obtained for water.

The last key information about the working surface was provided by in situ DRIFT spectroscopy. No adsorbed CO and OH vibrations were detected at 700°C, indicating that neither Ni-CO nor Ni-OH adspecies formed signifi cant pools on the reacting surface under reforming conditions [4].

TAP experiments on Ni/Si02

Methane pulsing

Prior to the methane pulses the catalyst surface was completely oxidised using pure oxygen pulses at atmospheric pressure until a stable oxygen signal was attained. Fig. 2 shows the response of an initial methane pulse. The sharp methane response signifi es a fast combustion into carbon oxides. The conversion of methane was approximately 20%. The carbon mon oxide response almost superimposes with the Ar peak; while the carbon dioxide peak appears signifi cantly delayed. Water was also present at the reactor outlet Experimental and statistical isotopic distribution of gaseous methane and hydrogen measured at 700°C over Ni/Si02, corresponding to an equimolar CHJCD4 inlet mixture with C02 and an overall conversion of methane of 89% but its response time was very large due to strong adsorption phenomena. Fig. 3 shows the integrated surface under the methane, carbon monoxide, carbon dioxide and hydrogen responses as a function of the pulse number. It can be seen that as the pulse number increases the methane signal intensity decreases, i.e., the conversion increases. The carbon dioxide signal decreased very rapidly to zero. At the same time the carbon monoxide intensity went through a maximum, while the hydrogen signal increased constantly. This experiment was continued up to the disappearance of CO at the reactor outlet. It was then observed that the methane signal increased again indicating that its conversion decreased, which corresponded to a decreasing hydrogen production, up to a complete catalyst deactivation. So both carbon monoxide and hydrogen went through a maximum when pulsing methane over a completely oxidised surface occurred.

C02 pulsing

When C02 was pulsed over the previous carbon adspecies loaded surface, carbon monoxide was observed at the reactor outlet. However, the conver sion of carbon dioxide and, so the production of CO, decreasing to zero with increasing the pulse number.

12CH/3C02 pump-probe pulsing

This pump-probe experiment consisted in pulsing in an alternating mode 1 2

Cl!i then 13 C02 pulses over the Ni/Si02 catalyst in a reduced state in order to get information concerning the reactive adspecies. The responses of this experiment are shown in Fig. 4 (a) and (b). The conversion of methane and carbon diox ide amounted to 87% and 96%. Hydrogen was pro duced instantly on the methane pulse. The tailing on the hydrogen curve can be assigned to diffusion either through the reactor or through the vacuum system. Two distinct carbon monoxide responses but with close surface area were observed together with the carbon dioxide pulse: (i) one corresponding to labelled CO, almost concomitant with the unconverted labelled C02 and without tailing, (ii) another one correspond ing to unlabelled CO, markedly delayed and present ing a long tailing. A small peak of unlabelled carbon dioxide was also observed, with a maximum corre sponding to the unlabelled CO. Variations of the time interval between the two pulses did not influence the spectra.

3.3.

TAP experiments on Ru/Si02 catalyst 3.3.1.

12 CH/ 13 C02 pump-probe pulsing

After reduction of the catalyst by a fl ow of hydrogen at 400°C, a similar pump-probe experiment as described above for the Ni/Si02 catalyst was carried out at 550°C on Ru/Si02. The responses of this experiment are shown in Fig. 5. The conversion of methane and carbon dioxide was approximately 20% and 80%, respectively. The responses of the reactants and products from the CHJ Ar pulse and the 13 C02 pulse are shown in Fig. 5. Similar to nickel catalyst an instantaneous hydrogen production was observed on the methane pulse. But, at variance with the nickel catalyst, the 13 CO and 1 2 CO responses, observed on the 13 C02 pulse, are identical in shape and are of similar intensity. A considerable amount of 1 2 C02 was observed on the 13 C02 pulse, with an average resi dence time smaller than that of the CO response. The TAP experiments reported in Figs. 2 and3 show that the initial nickel oxide can be activated under pure methane atmosphere by following a sequential process:

(i) Initially, only combustion products are observed in Fig. 2. Carbon dioxide is mainly formed but delayed with respect to carbon monoxide formation. This could mean that CO is a primary product and carbon dioxide a secondary product of the combustion process. It could also be considered that C02 arises from a combustion process on the NiO phase, invol ving slow steps and numerous surface intermediates (such as methoxy, formyl and/or carbonate species). In contrast, the formation of CO would come from the activation of methane on a very fi rst metallic surface reduced by the initial combustion.

(ii) Additional pulsing of methane causes the dis appearance of C02 formation and a marked increase in CO and H2 formation. This period obviously corre sponds to the development of the metallic surface where methane can activate, producing H2 and surface carbon. The latter reacts in tum with the remaining lattice oxygen atoms to give CO. When the nickel phase begins to be strongly depleted into lattice oxy gen (i.e., reduced), the CO formation decreases before stopping. However, the activation of methane into surface carbon and hydrogen is still possible (as observed by the still increasing hydrogen production) until the reduced nickel phase is saturated into carbon, i.e., poisoned for methane simple cracking. This pro cess of progressive and stoichiometric reduction of the initial oxidised phase by methane into a metallic phase saturated in carbon can schematically be represented by: 4Ni0 + C� --. 4Ni

+ C02 + 2H20 Ni + C� --. NiC + 2H2 NiO + NiC --. CO + 2Ni (1)
Admission of C02 pulses on the reduced Ni satu rated in carbon led to CO formation. There are two possible routes for this carbon dioxide activation. The first one is the direct activation of C02 on reduced nickel and sequentially oxidising the nickel. A second possibility might be the reverse Boudouard reaction in which the carbon adspecies previously deposited may react with carbon dioxide to form CO. These oxidation processes by C02 can be written as follows:

C02 +Ni --. NiO + CO NiO + NiC --. CO+ 2Ni (2)
A precise quantification of the amounts of C02, CO and H2 produced during this sequential process of nickel activation is in progress in order to ascertain the proposed stoichiometries.

Mechanistic scheme under steady-state conditions fo r Ni/Si02 catalyst

From the above statements and from the SSITKA and TAP experiments carried out with labelled mole cules, several mechanistic steps can be proposed, in relation to the surface occupancy:

(i) Hydrogen was released on a reduced surface on the methane pulse together with the inert pulse in the TAP experiment (Fig. 4). This indicates that the methane activation proceeds via a reaction between gas phase and surface (assuming sites S1), forming adsorbed carbon and gaseous hydrogen:

(step 1) XPS data, temperature programmed hydrogenation and magnetic measurements reported in [ 4] suggested that a carbide phase NhC or Ni3C formed on the surface (and possibly subsurface) of the nickel parti cles under reaction conditions. From the SSITKA experiments reported here (Fig. 1), a surface accumu lation of intermediates leading to CO formation was found to represent a ratio l/Nis=0.43. Assuming that this intermediate species is the Cads monomer in step 1, the latter ratio agrees with the above stoichiometry of a surface carbide layer. Note, however, that within the step 1, the intermediate formation of hydrogenated species CHx and a fast gas/surface hydrogen equili brium have to be considered in order to account for the observed HID exchange reactions. Thus, step 1 can be considered under the present conditions as a fast step, largely reversible at 700°C and leading to the accu mulation of dehydrogenated carbon monomers.

(ii) The SSITKA and TAP experiments clearly revealed that the produced carbon monoxide derived both from the direct and fast activation of C02 and from the oxidation of the carbon monomers issued from methane activation. The two corresponding steps could therefore be written as:

C02 + S2 +:±CO + O ads ( step 2)
C ads + Oads-> CO+ S1 + S2 (step 3)

In step 2, the dissociative adsorption of C02 (assuming a site S2) would lead to surface oxygen and CO. The latter would be immediately released into the gas phase, as indicated by the IR study and the absence of tailing for the corresponding TAP response. This step has to be considered as highly reversible and fast in order to explain the isotopic equilibrium established between C02 and CO. It can therefore be considered that surface oxygen atoms are precursors of gaseous C02• Their concentration can be estimated from the SSITKA measurements which indicated an accumulation of C02 precursors corre sponding to a ratio l/Nis=0.10. Thus, it comes beside the carbide-like species already quantified, around 10% of the surface is occupied by 0 species under steady state conditions.

Step 3 corresponds to the reaction of surface carbon monomers (arising from step 1) with surface oxygen atoms (arising from step 2) into CO slowly released into the gas phase, as revealed by the important tailing of the related TAP response. In contrast with step 2, step 3 cannot be considered as fast, since it leads to the accumulation of surface intermediates identified by various independent techniques (SSITKA, tempera ture programmed oxidation and hydrogenation [4]). It cannot either be considered as highly reversible since only a limited 1 2 C/ 13 C isotopic scrambling was observed for this pool of active surface carbons. Moreover, no isotopic effect was observed for the overall methane and carbon dioxide conversion, indi cating that the RDS of the process do not involve C -H bond cleavage or formation. Accordingly, step 3 can be proposed as rate limiting for the reforming process over Ni/Si02 catalyst. The relatively slow process of surface migration for the adsorbed 0 and C species which is necessarily involved in this step could account for this kinetic behaviour.

(iii) For all the transient experiments, a kinetic behaviour of H20 close to the one of C02 was observed. This strongly suggests that water interacts reversibly with the catalytic surface via an adsorption/ desorption equilibrium m a similar way as carbon dioxide:

(step 4)

The combination of steps 2 and 4 represents the water gas shift (WGS) equilibrium, which was shown to be achieved under the present reaction conditions Note that this first step could be much less reversible for ruthenium than for nickel, as observed from pre liminary SSITK experiments, not reported here, show ing the almost complete absence of methane isotopic scrambling.

The identical responses of 1 2 CO and 1 3 CO indicate the formation of these species in the same step. Hence, in contrast with nickel, the gaseous C02 reacts directly with the adsorbed carbon species to give two mole cules of CO:

(step 2')
No oxygen is left on the surface. This reversible reaction step also explains the formation of 1 2 C02. The residence time of the 1 2 C02 response indicates a consecutive reaction of 1 2 C02 back to CO. The low methane conversion and high 1 3 C02 conver sion are due to a high carburation of the ruthenium surface.

Thus a quite different mechanistic scheme seems to prevail for the Ru/Si02 catalyst. Within this scheme, the rate determining step would be now step l'. The lower operating temperature and/or the nature of the active metal phase could account for these differences. Further KIE measurements are in progress to ascertain this point.

5.

Conclusions

A detailed mechanistic proposal could be deduced from various transient kinetics, leading to an advanced description of the working surface for the case of Nil Si02 catalysts. A modelling of the SSITKA and TAP responses is in progress to determine precisely the kinetic parameters of the reaction and the physical meaning of the active sites S 1 and S2• For the case of Ru/Si02 catalysts, a quite distinct scheme seems to prevail, as revealed by preliminary TAP experiments. Further transient kinetics are in progress in order to reach a full description of the working ruthenium surface as for the case of nickel.

Fig. 1 .

 1 Fig. 1. Normalised responses corresponding to the transients: 12 CIIJC02/He -> 13 CHJC02/He/Ar -> 1 2 CHJC02/He at 700°C over Ni/Si02.

Fig. 2 .

 2 Fig. 2. Methane TAP pulse at 600°C over Ni/Si02 oxidised surface.

Fig. 3 .

 3 Fig. 3. Integrated surface of methane carbon monoxide and hydrogen as a function of the methane TAP pulse number at 600°C over Ni/Si02-

Fig. 4 .

 4 Fig. 4. (a) TAP pump-probe pulse responses using 1 2 CRi and 13 C02 at 600°C over Ni/Si02, (b) Normalised responses.

  Activation of the Ni!Si02 catalyst

Fig. 5 .

 5 Fig. 5. (a) TAP pump-probe pulse responses using 12 C� and 13 C02 at 550°C over Ru/Si02 (b) Normalised responses.

3 .

 3 Changes in mechanistic scheme fo r Ru/Si02 catalyst The production of hydrogen on the methane pulse in the TAP experiment points to a methane activation step over ruthenium close to the one proposed in the reaction scheme over nickel:(step l ')

Table 1

 1


Naveen Kumar
email: naveen.kumar4@icar.gov.in

Rahul Kaushik

Chandana Tennakoon

Vladimir N Uversky

Sonia Longhi

J Zhang

Sandeep Bhatia

Kam Y J Zhang

Comprehensive Intrinsic Disorder Analysis of 6108 Viral Proteomes: From the Extent of Intrinsic Disorder Penetrance to Functional Annotation of Disordered Viral Proteins

Keywords: Intrinsically disordered proteins, Viruses, Gene ontology, Post-translational modifications, Subcellular localization

published or not. The documents may come

INTRODUCTION

Proteins are among the most important biological macromolecules that govern the majority of cellular processes required for the sustenance of life. To carry out those diverse and essential functions, many proteins acquire unique well-defined structures. This phenomenon is known as the classic protein structure-function paradigm, which states that a unique protein function is tied to its unique three-dimensional (3D) structure, which is encoded in the unique amino acid sequence [START_REF] Anfinsen | Experimental and theoretical aspects of protein folding[END_REF][START_REF] Redfern | Exploring the structure and function paradigm[END_REF] . Nevertheless, many biologically active proteins or protein regions fail to acquire distinctive 3D structures under physiological conditions either completely or partially. Such proteins and regions are known as intrinsically disordered proteins/regions (IDPs/IDRs) [START_REF] Habchi | Introducing protein intrinsic disorder[END_REF] .

Importantly, such proteins/regions are not "broken", since the lack of a specific 3D structure does not prevent them from performing a broad spectrum of crucial and diverse biological functions. Instead, intrinsic disorder defines the multi-functionality of these proteins and makes them essential for the control of cellular signaling networks, regulation of various biological processes, and disease-related pathways [START_REF] Dunker | Identification and functions of usefully disordered proteins[END_REF][START_REF] Dunker | Flexible nets. The roles of intrinsic disorder in protein interaction networks[END_REF][START_REF] Dunker | Intrinsically disordered protein[END_REF][START_REF] Dunker | The protein trinity--linking function and disorder[END_REF][START_REF] Dunker | Function and structure of inherently disordered proteins[END_REF][START_REF] Dyson | Making Sense of Intrinsically Disordered Proteins[END_REF][START_REF] Iakoucheva | Intrinsic disorder in cell-signaling and cancer-associated proteins[END_REF][START_REF] Oldfield | Flexible nets: disorder and induced fit in the associations of p53 and 14-3-3 with their partners[END_REF][START_REF] Uversky | Natively unfolded proteins: a point where biology waits for physics[END_REF][START_REF] Uversky | Understanding protein non-folding[END_REF][START_REF] Uversky | Why are "natively unfolded" proteins unstructured under physiologic conditions?[END_REF][START_REF] Uversky | Showing your ID: intrinsic disorder as an ID for recognition, regulation and cell signaling[END_REF][START_REF] Uversky | Intrinsically disordered proteins in human diseases: introducing the D2 concept[END_REF][START_REF] Wright | Intrinsically disordered proteins in cellular signalling and regulation[END_REF] . Indeed, IDRs are abundant in different domains of life and proteomes have been extensively analyzed in terms of the abundance of IDPs/IDRs. Such studies include the bioinformatics characterization of six archaeans, 13 bacterial, and five eukaryotic proteomes [START_REF] Ward | Prediction and functional analysis of native disorder in proteins from the three kingdoms of life[END_REF] , 332 prokaryotic proteomes [START_REF] Burra | Reduction in structural disorder and functional complexity in the thermal adaptation of prokaryotes[END_REF] , 53 archaean proteomes [START_REF] Xue | Archaic chaos: intrinsically disordered proteins in Archaea[END_REF] , as well as disorder-focused analysis of 965 proteomes including 59 archaean, 110 eukaryotic, 471 bacterial, and 325 viral proteomes [START_REF] Peng | Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life[END_REF] . Besides, in 2012, Xue et al. had carried out the most comprehensive study on disorder abundance in 3,484 proteomes including 73 archaeans, 951 bacterial, 67 eukaryotic, and 2,393 viral proteomes [START_REF] Xue | Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life[END_REF] . The trend of disorder abundance is quite similar in all these studies, viz., eukaryotes are steadily predicted to have much higher disorder content than prokaryotes.

Conversely, viruses are ubiquitous parasites capable of infecting representatives of all domains of life. Despite having a much smaller proteome size, they easily hijack the complex cellular processes of their hosts across all the domains of life. Due to the very limited size of the viral proteome, and to achieve a steady and successful perpetuation, proteins encoded by viruses are destined to be multi-functional. Therefore, studying IDPs/IDRs in viruses is of great interest.

Previous studies have established that IDPs/IDRs in viruses play diverse roles [START_REF] Uversky | Flexible Viruses: Structural Disorder in Viral Proteins[END_REF][START_REF] Xue | Protein intrinsic disorder as a flexible armor and a weapon of HIV-1[END_REF][START_REF] Fan | The intrinsic disorder status of the human hepatitis C virus proteome[END_REF][START_REF] Xue | Disordered interactome of human papillomavirus[END_REF][START_REF] Dolan | Intrinsic disorder mediates hepatitis C virus core-host cell protein interactions[END_REF][START_REF] Meng | Unstructural biology of the Dengue virus proteins[END_REF][START_REF] Giri | Intrinsically Disordered Side of the Zika Virus Proteome[END_REF] , such as adaptation to their host [START_REF] Charon | First Experimental Assessment of Protein Intrinsic Disorder Involvement in an RNA Virus Natural Adaptive Process[END_REF] , oncogenicity [START_REF] Xue | Disordered interactome of human papillomavirus[END_REF][START_REF] Dyson | How Do Intrinsically Disordered Viral Proteins Hijack the Cell?[END_REF][START_REF] Tamarozzi | Understanding the Role of Intrinsic Disorder of Viral Proteins in the Oncogenicity of Different Types of HPV[END_REF][START_REF] Uversky | Protein intrinsic disorder and human papillomaviruses: increased amount of disorder in E6 and E7 oncoproteins from high risk HPVs[END_REF] , binding to host cells [START_REF] Rieder | A Novel Mechanism for Zika Virus Host-Cell Binding[END_REF] , and virus replication and pathogenesis [START_REF] Bourhis | Structural disorder within the replicative complex of measles virus: functional implications[END_REF][START_REF] Habchi | Structural disorder within paramyxovirus nucleoproteins and phosphoproteins[END_REF][START_REF] Nagano | The Measles Virus V Protein Binding Site to STAT2 Overlaps That of IRF9[END_REF][START_REF] Prates | Potential pathogenicity determinants identified from structural proteomics of SARS-CoV and SARS-CoV-2[END_REF][START_REF] Song | The Ntail region of nucleocapsid protein is associated with the pathogenicity of pigeon paramyxovirus type 1 in chickens[END_REF][START_REF] Sungsuwan | Nucleocapsid proteins from other swine enteric coronaviruses differentially modulate PEDV replication[END_REF][START_REF] Tsimbalyuk | The Intrinsically Disordered W Protein Is Multifunctional during Henipavirus Infection, Disrupting Host Signalling Pathways and Nuclear Import[END_REF] . Although previous studies systematically analyzed specific structural and functional features of intrinsic disorder in viral proteins [START_REF] Brocca | Liquid-Liquid Phase Separation by Intrinsically Disordered Protein Regions of Viruses: Roles in Viral Life Cycle and Control of Virus-Host Interactions[END_REF][START_REF] Xue | Structural disorder in viral proteins[END_REF][START_REF] Xue | Viral disorder or disordered viruses: do viral proteins possess unique features?[END_REF] , and in spite of the obvious importance of intrinsic disorder for the function of many viral proteins, comprehensive analyses of IDPs/IDRs encoded by viruses and their functional annotations are relatively limited.

To fill this gap, we performed a comprehensive and detailed analysis of 283,000 viral proteins from 6,108 reference viral proteomes, utilizing the two most commonly used and accurate intrinsic disorder predictors (IUPred2A [START_REF] Mészáros | IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding[END_REF] and Espritz [START_REF] Walsh | ESpritz: accurate and fast prediction of protein disorder[END_REF] ). We gathered the proteome-level analyses to describe intrinsic disorder abundances and differences across the different types of viral genomes, diverse proteome sizes, and distinct cellular replication sites in the taxonomically divergent hosts. Furthermore, we classified viral proteins by Charge-Hydropathy (CH) versus Cumulative Distribution Function (CDF) plot, and subsequently, we evaluated the correlation between the disorder content of viral proteins and their functional annotations (i.e. gene ontologies, and post-translational modifications (PTMs)), and subcellular localization.

MATERIALS AND METHODS

Reference virus proteome dataset

In this study, we retrieved proteomes of a total of 6,108 reference viruses from the UniProt database [START_REF]UniProt: a worldwide hub of protein knowledge[END_REF] that include more than 283,000 viral proteins. On the basis of the Baltimore Classification, the genomes of these reference viruses were classified into ten groups based on the nature of their nucleic acids viz., dsDNA, (n = 2,596), ssDNA (n = 741), dsRNA (n = 189), ssRNA(-) (n = 413), ssRNA(+) (n = 597), dsDNA-RT (n = 76), ssRNA-RT (n = 55), satellite (n = 59), virophage (n = 7), and unclassified (n = 1,375) viruses. The host information of these reference viruses were retrieved from virus-host DB [START_REF] Mihara | Linking Virus Genomes with Host Taxonomy[END_REF] and categorized into eight taxonomically distinct groups, Animalia (n = 1,882), Archaea (n = 150), Eubacteria (n = 2,227), Fungi (n = 79), Plantae (n = 757), Protista (n = 41), Environment (n = 59), and Unclassified (n = 916) (Table S1).

Prediction of Intrinsically Disordered Regions (IDRs)

It is largely recommended to employ more than one disorder predictor for the identification of IDRs to improve the accuracy and reliability of predictions. Hence, we used two different disorder predictors namely, IUPred2A [START_REF] Mészáros | IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding[END_REF] and Espritz [START_REF] Walsh | ESpritz: accurate and fast prediction of protein disorder[END_REF] , where the former estimates total pairwise interaction energy from the amino acid compositions, and the latter relies on bi-directional recursive neural networks, a sequence-based machine learning algorithm, for the prediction of IDRs. The additional advantage of using IUPred2A is that it can predict two different types of IDRs, namely short (at least 10 consecutive residues) and long disorder (at least 30 consecutive residues). Using Espritz on a dataset of reference viral proteome, we predicted IDRs by a shortdisorder prediction method with a 5% false-positive rate [START_REF] Walsh | ESpritz: accurate and fast prediction of protein disorder[END_REF] . Both disorder predictors offer a per residue intrinsic disorder (ID) probability score -that ranges from 0 to 1-for each viral protein sequence. Residues with an ID probability score ≥ 0.5 are classified as disordered. The total content of ID was estimated for each viral protein sequence as inputs as the fraction of the total number of residues with an ID probability score ≥ 0.5 out of the total residues in that protein sequence. A similar methodology was then used for the estimation of the total content of ID in a virus proteome. In addition, the viral proteins were classified depending on their ID content (as the fraction of disordered residues over the total residues) as ordered (ID content < 0.1), moderately disordered (ID content, 0.1 to 0.3), and highly disordered (ID content > 0.3) proteins.

After doing so, the fraction of disordered proteins (ID content > 0.3) over total proteins for each of the viral proteome was also calculated as a metric to determine the ID content.

Sub-classification of structure-disorder tendencies of viral proteins by the CH-CDF plot

All the proteins (n = 283,160) of 6,108 reference viral proteomes were subjected to binary classification into ordered (structured) proteins and IDPs using Charge-Hydropathy (CH) [START_REF] Uversky | Why are "natively unfolded" proteins unstructured under physiologic conditions?[END_REF][START_REF] Oldfield | Comparing and combining predictors of mostly disordered proteins[END_REF] , and Cumulative Distribution Function (CDF) classifiers [START_REF] Oldfield | Comparing and combining predictors of mostly disordered proteins[END_REF][START_REF] Xue | CDF it all: consensus prediction of intrinsically disordered proteins based on various cumulative distribution functions[END_REF] . Both the classifiers provide a binary classification of a protein sequence as ordered or disordered, which leads to four possibilities when implemented in combination [START_REF] Xue | CDF it all: consensus prediction of intrinsically disordered proteins based on various cumulative distribution functions[END_REF][START_REF] Huang | Subclassifying disordered proteins by the CH-CDF plot method[END_REF][START_REF] Huang | Improving protein order-disorder classification using chargehydropathy plots[END_REF][START_REF] Mohan | Intrinsic disorder in pathogenic and non-pathogenic microbes: discovering and analyzing the unfoldomes of early-branching eukaryotes[END_REF] . These four possibilities are usually indicated as four quadrants of the CH-CDF plot. Quadrant-I (Q-I) corresponds to proteins that are classified as disordered by the CH classifier (CH > 0) and ordered by the CDF classifier (CDF > 0).

Quadrant-II (Q-II) represents the proteins that are classified as ordered by both the CH and CDF classifiers (CH < 0 and CDF > 0). Quadrant-III (Q-III) represents proteins that are ordered according to the CH classifier (CH < 0) and disordered as per the CDF classifier (CDF < 0).

Quadrant-IV (Q-IV) accounts for proteins classified as disordered by both the classifiers (CH > 0 and CDF < 0).

Correlation of disorder contents with functional annotations and subcellular locations of viral proteins

We first extracted the gene ontology terms (GO Terms), post-translational modifications (PTMs), and subcellular locations of individual viral proteins from the UniProt database with the help of bash shell scripts in an automated mode. A further data mining was performed on the raw information to derive the metadata and thereafter, the viral proteins were segregated in accordance with the CH-CDF plots (unusual/rare, structured, mixed, and disordered viral proteins). The average/baseline disorder content for each category of CH-CDF classified viral proteins were calculated and significant differences in the ID content of a given GO term/PTMs/subcellular location from corresponding baselines were designated as ID enriched or depleted.

Statistical analyses

Statistical analyses were performed using GraphPad Prism 7.01 (GraphPad Software, San Diego, CA, USA). We used Dunn's multiple comparisons test, a non-parametric test, to compare the difference in the ID content among the viruses. In addition, one-way analysis of variance (ANOVA) with the Bonferroni method for multiple comparisons was used to assess the difference between the mean IDRs content of viruses that replicate in the nucleus and that of viruses replicating in the cytoplasm. In all the statistical analyses, a p-value less than 0.01 was considered statistically significant. All the graphs were generated by using GraphPad Prism 7.01.

RESULTS

ID content in the viral proteomes is governed by both virus genome types and host species

On comparing the two different types of ID predictions (namely, short and long disorder) implemented in IUPred2A on the virus proteome dataset, an extremely high significant positive correlation was observed (r = 0.980, p < 0.0001) (Figure 1A). Likewise, we also detected a highly significant positive correlation between Espritz and IUPred2A short disorder (r = 0.758, p < 0.0001) (Figure 1B), and Espritz and IUPred2A long disorder (r = 0.743, p < 0.0001) (Figure 1C) indicating that the observations and interpretations of results based on these two different ID predictors/types are comparable. Notably, DNA viruses were found to have a significantly higher mean ID content (0.233-0.264) compared to RNA viruses (0.183-0.199) (p < 0.0001), except for ssRNA-RT viruses (Figure 1D). In addition, on classifying the viral proteins depending on their ID content (as the fraction of disordered residues over the total residues) as ordered (ID content < 0.1), moderately disordered (ID content, 0.1 to 0.3), and highly disordered (ID content > 0.3), we found that most of the viral proteomes are enriched in moderately disordered proteins (Table S2). On investigation of the fraction of disordered proteins over total proteins as a metric to the ID content in virus proteomes, the finding that DNA viruses carry high mean ID content than that of RNA viruses remains consistent (p < 0.01 -0.0001) (Figure 1E). Besides, of all virus genome types, ssRNA-RT viruses have an exceptionally high fraction of disordered proteins (p < 0.0001). However, viruses sharing the same genome type show a marked variation in their mean ID content as a function of their host (Table S3). While investigating the influence of cellular sites of replication that different viruses prefer, we observed that the members of dsDNA viral families that use the nucleus as a site of viral genome replication show a higher mean ID content (0.179 -0.318), as compared to those that use cytoplasm (such as Poxviridae = 0.138±0.044) (p < 0.01 to < 0.0001) (Figure 2A). Of note, ssRNA(-) family members that replicate in the nucleus (especially Orthomyxoviridae) show a significantly higher mean ID content compared to those that replicate in the cytoplasm (p < 0.0001) (Figure 2B). This finding is consistent even after comparing pooled ssRNA(-) family members that replicate in the nucleus against those of the cytoplasm (p < 0.0001) (Figure 2C). In general, viruses of either DNA or RNA genome type that prefer the nucleus as a site of replication show a higher mean content in IDRs. These results suggest that virus genome type and host species together govern the IDRs contents in viral proteomes.

Relationships between ID contents and viral proteomes size

We next investigated the relationships between ID contents and virus proteomes size. A significant negative correlation between ID content and virus proteome size was observed for dsDNA (r = -0.229, p < 0.0001), dsRNA (r = -0.268, p = 0.0002), ssRNA(+) (r = -0.518, p < 0.0001) and unclassified viruses (r = -0.229, p < 0.001), while a significant positive correlation was found for ssRNA-RT (r = 0.271, p = 0.045) and satellite viruses (r = 0.465, p = 0.0002) (Table S4). In order to ascertain whether the observed varying relationships between ID content and virus proteome size could be tied to differences in hosts, we performed a host-wise correlation analysis. Interestingly, a significant negative correlation between ID content and virus proteome size was observed for dsDNA viruses infecting Animalia (r = -0.521, p < 0.0001) and Plantae (r = -0.452, p = 0.023), but a significant positive correlation was observed for Archaea (r = 0.370, p < 0.0014) and Eubacteria (r = 0.070, p < 0.0022) (Figure 3A). It may be noted that the different viral genome types infecting Animalia exhibit both a positive (ssDNA, ssRNA(-), and ssRNA-RT) and a negative (dsDNA, dsRNA, and ssRNA(+)) correlation between the ID content and virus proteome size (Figure 3A-F and Table S4).

Importantly, double-stranded viral genomes infecting Animalia have a tendency to show a negative correlation while single-stranded viral genome infecting Animalia show a positive correlation tendency between ID content and proteome sizes, except for ssRNA(+). Conversely, in the case of viruses infecting Plantae, a significant negative correlation between ID content and virus proteome size was maintained by most of the viruses irrespective of their genome types.

Concordance between ID content and position proteins within the CH-CDF plot

CH-CDF plot [START_REF] Huang | Subclassifying disordered proteins by the CH-CDF plot method[END_REF][START_REF] Huang | Improving protein order-disorder classification using chargehydropathy plots[END_REF][START_REF] Mohan | Intrinsic disorder in pathogenic and non-pathogenic microbes: discovering and analyzing the unfoldomes of early-branching eukaryotes[END_REF] has been recently employed for the sub-classification of vertebrate host proteins into structured, disordered, mixed, and rare or unusual proteins. To this end, viral proteins (n = 283,160) encoded by the diverse viral types were sub-classified into structured, disordered, mixed, and rare proteins by using CH-CDF plots (Figures 4A-J). In the CH-CDF plots, the viral proteins that fit in quadrants, I, II, III, and IV are classified as unusual/rare proteins, structured, mixed (having both ordered and disorder properties or native molten globules), and disordered, respectively. Furthermore, quantification of ID within the viral proteins (in terms of ID %) was correlated (based on a set of ID thresholds) to the distribution of proteins across the four quadrants of the CH-CDF plot (Figure 4K and Table S5). The CH-CDF plot classifies 8.2% (23,439 out of 283,160) of viral proteins as disordered proteins, where all of them showed more than 10% ID content. While considering viral proteins with an ID content of more than 50%, 81.8% of the resulting viral proteins (3,804 out of 4,648) occupy quadrant of highly disordered proteins indicating a sharp rise in concordance between these two methods (Table S5). The concordance between these two methods has further improved to more than 91.0% by increasing the ID content threshold to more than 60% (Figure 4K). Therefore, an ID threshold of >50% can be used as a supplement with the CH-CDF plot to improve the reliability of prediction results.

Functional annotations of viral proteins and association with their disorder contents

Given that the viral proteins sub-classified into the four quadrants of the CH-CDF plot have different characteristics, we assessed whether these viral proteins show distinctive functional properties or specific subcellular locations. The specific GO terms (n = 29,813) of viral proteins were retrieved and sub-classified into the four quadrants of the CH-CDF plot. Since several GO terms were overlapping the four quadrants of the CH-CDF plots, we selected only those GO terms explicitly located in a particular quadrant of the CH-CDF plot (Figure 5). Of note, disordered proteins are primarily involved in key virus-host interactions, such as regulation of viral and host transcription, key steps in viral morphogenesis, and down-regulation of the host antiviral immune response. Proteins having both structured and unstructured regions (or behaving as native molten globules) affect virus replication, and modulate signaling pathways to down-regulate the host adaptive immune response, while ordered proteins are characterized by the GO terms featuring primarily metabolic and biosynthetic processes. Similar to the GO terms, the viral proteins with known PTMs (n = 3,397) and subcellular localization (n = 29,001) were sub-classified into the four quadrants of the CH-CDF plot followed by a measurement of correlation with the ID contents. Figure 5 shows that the PTMs found in structured viral proteins (phosphorylation, sumoylation, and myristoylation), mixed/molten globular proteins (methylation, and ubiquitination), and disordered proteins (phosphorylation, and ADP-ribosylation) were associated with disorder enriched proteins. Of note, the ordered viral proteins having glycosylation, and disulfide bonds as PTMs were depleted in disorder. In addition, the majority of subcellular locations in each of the viral protein categories were not found to be tied to disorder enrichment/depletion except for disorderdepleted structured viral proteins that were found to be located in the virion membrane and apical cell membrane.

DISCUSSION

A substantial variation in ID distribution has been observed among the main kingdoms of life.

Previous studies have shown that with the increase in the complexity of organisms, there is a notable increase in the length and frequency of IDRs [START_REF] Dunker | The protein trinity--linking function and disorder[END_REF][START_REF] Ward | Prediction and functional analysis of native disorder in proteins from the three kingdoms of life[END_REF][START_REF] Peng | Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life[END_REF][START_REF] Xue | Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life[END_REF][START_REF] Oldfield | Comparing and combining predictors of mostly disordered proteins[END_REF][START_REF] Schad | The relationship between proteome size, structural disorder and organism complexity[END_REF] . In fact, long IDRs (> 30 consecutive residues) are more common in eukaryotic proteins (45-50%) than in prokaryotic (7-30%) proteins [START_REF] Dunker | The protein trinity--linking function and disorder[END_REF][START_REF] Ward | Prediction and functional analysis of native disorder in proteins from the three kingdoms of life[END_REF][START_REF] Peng | Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life[END_REF][START_REF] Xue | Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life[END_REF][START_REF] Oldfield | Comparing and combining predictors of mostly disordered proteins[END_REF][START_REF] Schad | The relationship between proteome size, structural disorder and organism complexity[END_REF] . The broad variability of ID levels in different viruses found in our study is consistent with the results of previous studies [START_REF] Peng | Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life[END_REF][START_REF] Xue | Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life[END_REF] . We analyzed whether disorder predictions are affected by the length of IDRs. To this end, we have compared the results of disorder prediction provided by the IUPred2A long method designed to identify IDRs longer than 30 amino acids, to those provided by the IUPred2A short method (developed for the identification of IDRs shorter than 10 amino acids). The strong correlation between the short and long disorder prediction types supports the conclusion that the length of IDRs does not affect the findings. Consistency in results is maintained even when using a different IDRs predictor, namely Espritz, which uses bi-directional recursive neural networks.

The propensity of viruses to co-evolve with their hosts or on the other hand, their propensity to undergo regular host-switching (cross-species transmission) can be measured by co-phylogenetic analysis, which compares the topology of virus and hosts phylogenetic trees [START_REF] Geoghegan | Comparative analysis estimates the relative frequencies of co-divergence and cross-species transmission within viral families[END_REF] . Such studies have shown that dsDNA viruses exhibit typical virus-host co-evolution, contrary to RNA viruses that have a significant tendency for frequent host-switching [START_REF] Geoghegan | Comparative analysis estimates the relative frequencies of co-divergence and cross-species transmission within viral families[END_REF][START_REF] Kumar | Label-free peptide nucleic acid biosensor for visual detection of multiple strains of influenza A virus suitable for field applications[END_REF][START_REF] Kumar | Molecular characterization of unusual bovine rotavirus A strains having high genetic relatedness with human rotavirus: evidence for zooanthroponotic transmission[END_REF] . This result is consistent with the fact that RNA viruses frequently cause acute (but transient) infections, while DNA viruses are mostly associated with chronic infections. These studies, therefore, support the notion that the higher levels of IDRs in proteins of DNA viruses relative to the RNA viruses could be the result of long-term co-evolution that would have endowed viral proteins with regulatory functions. Another plausible explanation comes from the observation that disorder-enriched regions are commonly found in those proteins involved in multiple protein-protein interactions and such manifold protein interactions were shown to be negatively correlated with their rate of evolution [START_REF] Bertolazzi | On the functional and structural characterization of hubs in protein-protein interaction networks[END_REF][START_REF] Fraser | Evolutionary rate in the protein interaction network[END_REF] . Since the rate of evolution of DNA viruses is comparatively lower than that of RNA viruses, DNA virus-encoded proteins appear to be more enriched in IDRs. Also, our study has shown that the proteomes of the viruses (either DNA or RNA) that prefer the nucleus as a site of replication are enriched in the IDRs. This may be consistent with previous research, which showed that in eukaryotes, DNA-binding proteins are significantly enriched in ID [START_REF] Wang | Disordered nucleiome: Abundance of intrinsic disorder in the DNA-and RNA-binding proteins in 1121 species from Eukaryota, Bacteria and Archaea[END_REF] . The high disorder levels of proteins in these viruses can lead to more efficient hijacking of numerous and complex cell processes/pathways at the advantage of the virus, which is confirmed by the GO terms analysis of highly disordered proteins.

Analyses of the correlation of the length of the viral proteomes concerning their ID contents showed that small viral proteomes appear to have a high content of disorder, and vice-versa, with a few exceptions. Since the viruses of a particular genome type infect taxonomically divergent hosts, these findings may have confounding effects. Therefore, we have expanded our analyses to the host-level associations. In particular, different viruses infecting Animalia showed both negative and positive correlations between ID content and proteome size; these differential findings may be attributed to the distinct virus-type specific proteome sizes; i.e., viruses having smaller proteome sizes (ssDNA, ssRNA(-), and ssRNA-RT) show a positive correlation, while viruses with larger proteome sizes (dsDNA, and dsRNA) exhibit negative correlation. Our findings are consistent with the previous research, which also showed a negative correlation between large proteome sizes of eukaryotes and bacteria and their disorder contents [START_REF] Peng | Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life[END_REF] . Furthermore, intrinsically disordered protein regions evolve more rapidly than that of ordered regions in both the eukaryotes [START_REF] Brown | Evolution and disorder[END_REF][START_REF] Kastano | Evolutionary Study of Disorder in Protein Sequences[END_REF] , and viruses [START_REF] Gitlin | Rapid evolution of virus sequences in intrinsically disordered protein regions[END_REF] . This high rate of evolution of sequences in disorder regions not only modifies eukaryotic protein functions [START_REF] Kastano | Evolutionary Study of Disorder in Protein Sequences[END_REF][START_REF] Ahrens | Evolution of intrinsic disorder in eukaryotic proteins[END_REF] , but also generates diversity in some viruses that enable them to interact with multiple proteins of different species [START_REF] Ortiz | Rapid evolutionary dynamics of structural disorder as a potential driving force for biological divergence in flaviviruses[END_REF] .

Disorder regions, therefore, impart functional versatility as well as sequence malleability within the same protein.

Functional annotation shows that disordered proteins are primarily involved in key virus-host interactions, such as regulation of viral and host transcription, key steps in viral morphogenesis, and down-regulation of the host antiviral immune response. Besides, disorder-enriched viral proteins are preferentially associated with certain PTMs (phosphorylation, sumoylation, myristoylation, methylation, ubiquitination, and ADP-ribosylation), and these PTMs allow them to perform several regulatory functions and virus-host interactions. For instance, phosphorylation of viral proteins is known to affect various cellular signaling pathways, and viral proteins can be phosphorylated by the multiple cellular kinases, thereby providing an opportunity for a virus to expand its host and cellular tropism with varying kinases profiles [START_REF] Keating | Phosphorylation events during viral infections provide potential therapeutic targets[END_REF] . On the other hand, sumoylation and ubiquitination are needed for the modulation of anti-viral defenses and viral replication [START_REF] Lowrey | Viral manipulation of the cellular sumoylation machinery[END_REF][START_REF] Taylor | Assessing ubiquitination of viral proteins: Lessons from flavivirus NS5[END_REF] ; myristoylation is related to virus entry, assembly, structure, and budding [START_REF] Maurer-Stroh | Myristoylation of viral and bacterial proteins[END_REF] ; methylation for modulation of protein-nucleic acid interaction [START_REF] Hundt | Post-translational modifications of hepatitis C viral proteins and their biological significance[END_REF] ; and ADP-ribosylation for modulation of viral infection through unknown mechanisms [START_REF] Grunewald | The coronavirus nucleocapsid protein is ADP-ribosylated[END_REF] .

Taking together the fact that a high ID content in the proteome of tardigrades (water bears) plays a critical role in survival against extreme desiccation [START_REF] Boothby | Tardigrades Use Intrinsically Disordered Proteins to Survive Desiccation[END_REF] , and that the ID content was found to be related to adaptability to complex and diverse environments [START_REF] Mohan | Intrinsic disorder in pathogenic and non-pathogenic microbes: discovering and analyzing the unfoldomes of early-branching eukaryotes[END_REF][START_REF] Pancsa | Structural disorder in eukaryotes[END_REF] , we explored the relationship between the viral proteomes with a high ID content (>0.4), namely ssDNA (Torque teno virus), dsDNA (Papillomavirus and the Haloarcula hispanica SH1 virus), ssRNA(-) (Deltavirus) and satellite viruses (plant), with the environment of their respective hosts.

Interestingly, the archaeal haloviruses that infect the halophilic archaeon Haloarcula hispanica, do not lose their infectivity even in the presence of high salt concentrations [START_REF] Demina | Vesicle-like virion of Haloarcula hispanica pleomorphic virus 3 preserves high infectivity in saturated salt[END_REF] , making them Table S1. Categorization of reference viral proteomes corresponding to their viral nucleic acid types and hosts; Table S2: Fractions of disordered, moderately disordered, and ordered viral proteins across the diverse reference virus proteomes; Table S3. Mean ID contents across the virus genome types infecting taxonomically divergent hosts; Table S4. Correlation analysis between ID contents and viral proteomes size; Table S5. Quantification of ID contents in the viral proteins (as the fraction of disordered residues over the total residues) and distribution of the viral proteins (n = 283,160) in the four quadrants of the CH-CDF Plot.

Figure 1 .

 1 Figure 1. The diversity of intrinsically disordered (ID) contents across the diverse reference viral

Figure 2 .

 2 Figure 2. Mean IDRs contents within the members of (A) dsDNA and (B) ssRNA(-) virus

Figure 3 .

 3 Figure 3. ID contents diversity in diverse virus genome types as a function of their proteome

Figure 4 .

 4 Figure 4. Charge-Hydropathy-Cumulative Distribution Function (CH-CDF) plots for the viral

Figure 5 .

 5 Figure 5. Functional annotations of the viral proteins sub-classified into the four quadrants of the

capable of surviving under the harsh environmental conditions of their hosts. Likewise, the Torque teno virus, a non-enveloped human DNA virus, is ubiquitously present in the environment due to its high stability 75,76 . We noted that a high ID content in viruses tends to (i) support long-term survival of the virus under extreme environmental conditions, (ii) provide to their proteins multi-functionality that compensates their smaller proteome size (satellite viruses), and (iii) support their long-term co-evolution, especially in the case of DNA viruses.

AUTHOR INFORMATION

Corresponding Author

Naveen Kumar -Diagnostics & Vaccines Group, ICAR-National Institute of High Security

Animal Diseases, Bhopal 462022, India; orcid.org/0000-0002-3326-5465; Email: naveen.kumar4@icar.gov.in; Phone: +91-7552759204; Fax: +91-755-2758842

CONFLICT OF INTEREST

The authors declare no competing financial interest.

ACKNOWLEDGMENTS

All the authors acknowledge and thank their respective Institutes. The authors thank Dr. Christopher J. Oldfield, Virginia Commonwealth University, Richmond, USA for providing technical support in the generation of the CH-CDF plots.

ASSOCIATED CONTENT Supporting Information