Marc Joyeux
email: marc.joyeux@univ-grenoble-alpes.fr

Impact of self-association on the architectural properties of bacterial nucleoid proteins

Impact of self-association on the architectural properties of bacterial nucleoid proteins

INTRODUCTION

Bacteria lack a nucleus, but their chromosomal DNA is nevertheless folded into a compact body called the nucleoid, which is markedly different from the rest of the cytoplasm.

The nucleoid is composed essentially of DNA (≈80%), RNA (≈10%), and a number of different proteins (≈10%) (1,2). These proteins act as regulators of gene expression (3)(4)(5) and influence the organization of the nucleoid by bridging, bending, or wrapping the DNA (5)(6)(7)(8).

There are at least 12 different species of nucleoid proteins (9), among which HU (10), IHF (11), H-NS (12), Fis (13) and Lrp (14) have been extensively studied. It has been shown that the abundance of many of the nucleoid proteins varies dramatically in response to changes in the growth rate of the cell (15). Their occupancy landscape in the nucleoid (16) and along the genome (17) has also been investigated.

The mechanisms by which nucleoid proteins shape the DNA are still poorly understood. This is due, in part, to the fact that architectural properties are specific to each protein. For example, H-NS, ParB and SMC form bridges between two DNA segments, but these bridges are qualitatively different and affect chromosome organization and gene regulation in contrasting ways (18). Moreover, several proteins exhibit dual architectural properties, depending on several factors, like the concentration of proteins and the DNA binding sequence (8). For example, HU is essentially known for its DNA-bending capabilities and Lrp for its DNA-bridging capabilities, but both of them are also able to wrap the DNA (8). Finally, subtle variations of the cytosol may alter dramatically the architectural properties of certain proteins. For example, an increase in the concentration of divalent cations in the cytosol causes H-NS to switch from the DNA-stiffening mode (characterized by rigid DNA/H-NS complexes) to the DNA-bridging mode (characterized by more compact DNA coils) (19).

Through the development of coarse-grained models and Brownian dynamics simulations, we recently showed that the switch of H-NS proteins from the DNA-stiffening to the DNA-bridging mode may be due to the fact that an increase in the concentration of multivalent cations provokes an increase in the screening of electrostatic charges along the DNA backbone, which leads in turn to a decrease in the strength of DNA/protein interactions compared to protein/protein interactions (20). As a consequence, for concentrations of multivalent cations smaller than a certain threshold, proteins form filaments which stretch along the DNA molecule. In contrast, for larger concentrations of multivalent cations, proteins form clusters which connect genomically distant DNA sites (20). We argued that these two types of DNA/protein complexes may correspond to the DNA-stiffening and DNA-bridging modes of H-NS, respectively. Unfortunately, the model was not precise enough for protein filaments to increase the effective stiffness of the DNA chain and for protein clusters to reduce significantly the radius of the DNA coil. Moreover, this first study left an important question unanswered, namely: To what extent do the self-association properties of proteins influence their nucleoid architectural properties ? In the model proposed in (20), proteins self-associate in the form of 3-dimensional clusters and the final conformation of DNA/protein complexes is actually driven by the relative strength of DNA/protein interactions compared to protein/protein ones. The question we address in the present work is different, in the sense that we consider two proteins, which interact identically with the DNA chain but self-associate differently, and we want to determine whether such a difference in self-association properties is sufficient to induce large changes in the organization of the DNA coil. In addition to its obvious relevance in prokaryotes, this question may also be of fundamental importance in eukaryotes, since it has recently been shown that slightly different variants of histone proteins form central tetramers with rather different properties, which may potentially influence drastically nucleosome assembly and disassembly [START_REF] Zhao | The oligomerization landscape of histones[END_REF]. In order to answer this question, we developed two new coarse-grained models of proteins, which interact identically with the DNA chain but self-associate differently. In the absence of DNA, Model I proteins form spontaneously clusters, whereas Model II proteins form filaments. We showed through Brownian dynamics simulations that self-association of the protein chains increases dramatically their ability to shape the DNA coil. Moreover, we observed that Model I proteins compact significantly the DNA coil (similar to the DNA-bridging mode of H-NS), whereas Model II proteins increase instead significantly the stiffness of the DNA chain (similar to the DNA-stiffening mode of H-NS). This work consequently suggests that the knowledge of the DNA-binding properties of given proteins is in itself not sufficient to understand their architectural properties. Rather, their self-association properties must also be investigated in detail, because they might actually drive the formation of different DNA/protein complexes.

METHODS

Model

The two coarse-grained bead-and-spring models developed in the course of the present study are described in detail in Model and Simulations in Supporting Material. In brief, they consist of a long DNA chain and 200 short protein chains enclosed in a confinement sphere.

Each chain is composed of beads of equal size connected by springs. Concentrations of nucleotides and proteins are of the same order of magnitude as in vivo ones. For DNA, each bead represents 7.5 base pairs (bp) and the chain contains 2880 beads, equivalent to 21600 bp, as in (20,[START_REF] Joyeux | Bacterial nucleoid: Interplay of DNA demixing and supercoiling[END_REF][START_REF] Joyeux | Requirements for DNA-bridging proteins to act as topological barriers of the bacterial genome[END_REF]. Each protein chain contains 7 beads with index m (1

Simulations

The dynamics of the models was investigated by integrating numerically Langevin equations of motion with kinetic energy terms dropped and time steps of 1.0 ps. Temperature T was assumed to be 298 K throughout the study. The value of the Debye length used in the simulations ( 1.07 D r = nm) corresponds to a concentration of monovalent salt of 100 mM, which is the value that is generally assumed for the cytoplasm of bacterial cells. After each integration step, the position of the centre of the confining sphere was slightly adjusted so as to coincide with the centre of mass of the DNA molecule, in order that compact DNA/protein complexes do not stick to the wall of the confinement sphere and results are as little as possible affected by the interactions with the wall [START_REF] Joyeux | Preferential localization of the bacterial nucleoid[END_REF] ε may be characterized by plotting ( ) q s , the probability distribution for a protein chain to bind to s other protein chains. For this purpose, it was considered that two protein chains are bound if the interaction between at least two of their isomerization beads is attractive and of magnitude larger than

Complexes of DNA and protein chains.

Let us now consider complexes formed by the DNA chain and Model I and II protein chains. These complexes were obtained by first allowing the DNA chain to equilibrate inside the confinement sphere. The 200 protein chains were then introduced at random nonoverlapping positions in the confinement sphere and the system was allowed to equilibrate again for values of LJ ε ranging from The two models differ in that the fraction of cis-bound protein chains increases up to

≈35% at LJ B 12 k T ε =
for Model II, whereas it remains nearly constant at ≈25% for Model I.

However, this discrepancy merely reflects different organizations of DNA/protein clusters, because the plot, as a function of LJ ε , of the average fraction of protein chains which belong to clusters that bridge genomically distant DNA beads indicates that the onset of protein selfassociation is accompanied in both models by an increase in the fraction of such protein chains from about 6% to 100% (see Fig. S6).

Finally, it may be worth noting that the maximum number of bridging proteins (right column of Fig. S8), as is also the case without the DNA (Fig. S4). The plots of ( ) p s , the probability distribution for a DNAbinding protein bead to bind to s DNA beads, are also shown in Fig. S8. As for ( ) q s , it was considered that a protein bead and a DNA bead are bound if their interaction is attractive and of magnitude larger than

Architectural properties of Model I and II protein chains differ widely.

Let us now examine in more detail the extent to which the self-association of Model I and II protein chains impact their architectural properties. To this end, we studied the evolution of two quantities which describe the geometrical properties of the DNA, namely the mean radius of the coil, R , and the persistence length of the DNA chain, ξ.

The mean radius of the DNA coil, R , is defined according to CM 1

1 n k k R n = = -  r r , (1)
where Conclusion is therefore that neither Model I nor Model II protein chains are efficient in compacting the DNA coil when in the monomer form. In contrast, Model I protein chains compact significantly the DNA coil as soon as they self-associate (a similar result was already obtained with a different model ( 29)). This is not the case for Model II protein chains (except

for LJ B 9 k T ε =
), in spite of the fact that all protein chains interact similarly with the DNA chain.

Let us now consider the persistence length of the DNA chain, ξ, which is related to the directional correlation function ( ) ( ). ( )

C x x ∆ = + ∆ t t
, where ( ) x t denotes the unit vector tangent to the DNA chain at curvilinear position x, according to

( ) exp( ) C ξ ∆ ∆ = - . (2)
Practically, the directional correlation function can be estimated from

0 , , 1 1 1 ( )
.

N n c k c k L c k C Ll nN + = = ≈  t t , (3)
where N is a large number of DNA conformations spanning a large time interval, and , Conclusion is consequently that neither Model I nor Model II protein chains are efficient in altering the persistence length of the DNA coil when in the monomer form. In contrast, when Model II protein chains self-associate, the persistence length of DNA segments localized in the thick bundles formed by DNA/protein complexes is twice as large as that of free DNA segments. This is however not the case for Model I protein chains, in spite of the fact that all protein chains interact similarly with the DNA chain.

c

Discussion

In this work, we studied the properties of two models describing non-specific interactions between circular DNA and nucleoid proteins. The DNA/protein interaction potential is the same for the two models and was kept constant in all simulations. In contrast, when the strength of protein/protein interactions is large enough, Model I proteins selfassociate in the form of clusters, whereas Model II proteins form filaments. The strength of protein/protein interactions was varied systematically in the simulations, in order to check the impact of protein self-association on the geometrical and mechanical properties of DNA/protein complexes. The two models display characteristic features:

-for the two models, binding of the proteins to the DNA increases strongly when proteins self-associate, although the strength of DNA/protein interactions is kept constant, -when in the monomer form, neither Model I nor Model II proteins are efficient in compacting the DNA coil or increasing the rigidity of the DNA, -clusters of Model I proteins compact significantly the DNA coil, but this is not the case for filaments of Model II proteins, although all proteins interact similarly with the DNA, -filaments of Model II proteins increase significantly the rigidity of the DNA, but this is not the case for clusters of Model I proteins, although all proteins interact similarly with the DNA.

These models consequently suggest that the self-association of nucleoid proteins may have a rich and profound impact on their architectural properties. This claim and the models proposed here are supported by a set of experimental results:

First, many of the nucleoid proteins can self-associate and are present in cells in polymeric forms. For example, H-NS proteins form dimers at low concentrations but assemble into larger multimers at higher concentrations [START_REF] Ceschini | Multimeric self-assembly equilibria involving the histone-like protein H-NS. A thermodynamic study[END_REF][START_REF] Smyth | Oligomerization of the chromatin-structuring protein H-NS[END_REF][START_REF] Leonard | Investigation of the self-association and hetero-association interactions of H-NS and StpA from Enterobacteria[END_REF]. Other members of the H-NS family, like StpA, can also self-associate [START_REF] Leonard | Investigation of the self-association and hetero-association interactions of H-NS and StpA from Enterobacteria[END_REF][START_REF] Suzuki | Oligomerization mechanisms of an H-NS family protein, Pmr, encoded on the plasmid pCAR1 provide a molecular basis for functions of H-NS family members[END_REF]. As for the models proposed here, cooperative binding of H-NS to DNA is related to protein-protein interactions [START_REF] Giangrossi | Probing the relation between protein-protein interactions and DNA binding for a linker mutant of the bacterial nucleoid protein H-NS[END_REF]. The resulting filaments of H-NS proteins bound to the DNA substrate are clearly seen in crystallographic experiments [START_REF] Arold | H-NS forms a superhelical protein scaffold for DNA condensation[END_REF]. It is believed that such protein filaments block DNA accessibility and are the structural basis for gene silencing [START_REF] Lim | Nucleoprotein filament formation is the structural basis for bacterial protein H-NS gene silencing[END_REF][START_REF] Lim | Gene silencing H-NS paralogue StpA forms a rigid protein filament along DNA that blocks DNA accessibility[END_REF], which is one of the main roles of H-NS in the cells. It has however been shown that the simple coverage of the DNA substrate by H-NS proteins at high concentrations is not sufficient, and that the capacity of proteins to self-associate is crucial for the regulation of gene expression: Derivatives of H-NS that are unable to oligomerize fail in silencing genes [START_REF] Spurio | The oligomeric structure of nucleoid protein H-NS is necessary for recognition of intrinsically curved DNA and for DNA bending[END_REF][START_REF] Winardhi | Higher order oligomerization is required for H-NS family member MvaT to form gene-silencing nucleoprotein filament[END_REF][START_REF] Winardhi | H-NS regulates gene expression and compacts the nucleoid: Insights from single-molecule experiments[END_REF][START_REF] Yamanaka | Dimerization site 2 of the bacterial DNAbinding protein H-NS is required for gene silencing and stiffened nucleoprotein filament formation[END_REF].

Moreover, two nucleoid proteins present in the stationary phase, Dps and CbpA, can also self-associate and experiments have shown that their aggregation and the compaction of the DNA are parallel phenomena [START_REF] Ceci | DNA condensation and self-aggregation of Escherichia coli Dps are coupled phenomena related to the properties of the N-terminus[END_REF][START_REF] Cosgriff | Dimerization and DNA-dependent aggregation of the Escherichia coli nucleoid protein and chaperone CbpA[END_REF]. Unlike H-NS ( 26), Dps molecules do not align in filaments in co-crytals, but are rather packed in pseudohexagonal layers [START_REF] Dadinova | Protective Dps-DNA co-crystallization in stressed cells: an in vitro structural study by small-angle X-ray scattering and cryo-electron tomography[END_REF]. The layers slide along the DNA direction and enable the formation of grooves for DNA accommodation [START_REF] Dadinova | Protective Dps-DNA co-crystallization in stressed cells: an in vitro structural study by small-angle X-ray scattering and cryo-electron tomography[END_REF].

Similarly, partition proteins ParB in vivo first bind to the specific parS site and then spread, that is, they simultaneously self-assemble stochastically and bind to the DNA away from the parS site, thus bridging the DNA [START_REF] Sanchez | Stochastic self-assembly of ParB proteins builds the bacterial DNA segregation apparatus[END_REF][START_REF] Broedersz | Condensation and localization of the partitioning protein ParB on the bacterial chromosome[END_REF][START_REF] Funnell | ParB partition proteins: Complex formation and spreading at bacterial and plasmid centromeres[END_REF]. Spreading ability is required, as ParB mutants which lack this ability are also defective in partition [START_REF] Graham | ParB spreading requires DNA bridging[END_REF].

According to this short digest of experimental results, it appears that Model I captures adequately the main features of Dps, CbpA and ParB, which must assemble in clusters to bind to and compact the DNA molecule. The case of H-NS is more complex. Owing to the crystallographic structure in [START_REF] Arold | H-NS forms a superhelical protein scaffold for DNA condensation[END_REF], which displays H-NS filaments aligned parallel to the DNA molecule, as well as the experimental observation that the persistence length of DNA/H-NS complexes may be as large as 130 nm at low divalent cations concentration [START_REF] Amit | Increased bending rigidity of single DNA molecules by H-NS, a temperature and osmolarity sensor[END_REF], it is tempting to conclude that Model II provides a correct description of DNA/H-NS interactions in this salt regime. However, the fact that H-NS proteins form filaments when bound to the DNA is not a proof that they also do so when the substrate is lacking (20). The switch from the DNAstiffening mode to the DNA-bridging mode of H-NS at higher divalent cations concentrations (19) may also be tentatively interpreted as an indication that the self-association properties of H-NS switch from Model II-type to Model I-type. In this respect, we note with interest that it has recently been shown that environmental variations have a direct effect on the selfassociation properties of H-NS [START_REF] Hameed | H-NS uses an autoinhibitory conformational switch for environment-controlled gene silencing[END_REF]. More work is however clearly needed to ascertain whether the switch from the DNA-stiffening mode to the DNA-bridging mode of H-NS is due to a decrease in the strength of DNA/protein interactions, as proposed in (20), a variation in the self-association mode of proteins, as suggested by the present work, or variations in the geometry of the H-NS molecule [START_REF] Qin | Structural basis for osmotic regulation of the DNA binding properties of H-NS proteins[END_REF].

CONCLUSION

In this work, we used coarse-grained modeling to investigate the impact of the selfassociation of nucleoid proteins on their architectural properties. The simulations suggest that this impact is probably strong and that different modes of self-association may result in different architectural capabilities of the proteins. Self-association is therefore a property of the proteins that is worth considering when trying to understand how they shape the DNA coil.

To conclude, we would like to mention that models similar to those discussed in the present work have recently been proposed to study the formation of the bacterial nucleoid through the demixing of DNA and non-binding globular macromolecules [START_REF] Joyeux | Compaction of bacterial genomic DNA: Clarifying the concepts[END_REF][START_REF] Joyeux | In vivo compaction dynamics of bacterial DNA: A fingerprint of DNA/RNA demixing ?[END_REF][START_REF] Joyeux | Coarse-grained model of the demixing of DNA and non-binding globular macromolecules[END_REF][START_REF] Joyeux | A segregative phase separation scenario of the formation of the bacterial nucleoid[END_REF], the preferential localization of the nucleoid inside the cell ( 26), the mechanism of facilitated diffusion, by which proteins search for their targets along the DNA sequence [START_REF] Florescu | Description of non-specific DNA-protein interaction and facilitated diffusion with a dynamical model[END_REF][START_REF] Florescu | Dynamical model of DNA-protein interaction: effect of protein charge distribution and mechanical properties[END_REF][START_REF] Florescu | Comparison of kinetic and dynamical models of DNA-protein interaction and facilitated diffusion[END_REF], and the requirements for DNA-bridging proteins to act as topological barriers of the bacterial genome [START_REF] Joyeux | Requirements for DNA-bridging proteins to act as topological barriers of the bacterial genome[END_REF]. All these models are compatible and it is possible to combine two (or more) of them to get a more complete and realistic description of bacterial cells [START_REF] Joyeux | Bacterial nucleoid: Interplay of DNA demixing and supercoiling[END_REF]. This point is crucial, since the effects of different processes taking place simultaneously in living cells are not simply additive and the outcome may be difficult to predict when considering only the effects of each mechanism taken separately [START_REF] Joyeux | Bacterial nucleoid: Interplay of DNA demixing and supercoiling[END_REF][START_REF] Dias | Role of protein self-association on DNA condensation and nucleoid stability in a bacterial cell model[END_REF]. For example, both DNA/macromolecules demixing and DNA supercoiling contribute to the compaction of the bacterial DNA, but the total compaction of the DNA coil is the sum of the two contributions only in a limited range of values of macromolecular concentration and superhelical density, whereas their interplay is much more complex outside from this range [START_REF] Joyeux | Bacterial nucleoid: Interplay of DNA demixing and supercoiling[END_REF]. In this respect, it will certainly be instructive in future work to use the models discussed in the present work to investigate the interplay of nucleoid proteins and macromolecular crowders [START_REF] Dias | Role of protein self-association on DNA condensation and nucleoid stability in a bacterial cell model[END_REF][START_REF] Joyeux | In vivo compaction dynamics of bacterial DNA: A fingerprint of DNA/RNA demixing ?[END_REF][START_REF] Joyeux | Coarse-grained model of the demixing of DNA and non-binding globular macromolecules[END_REF][START_REF] Joyeux | A segregative phase separation scenario of the formation of the bacterial nucleoid[END_REF], transcription factors [START_REF] Florescu | Description of non-specific DNA-protein interaction and facilitated diffusion with a dynamical model[END_REF][START_REF] Florescu | Dynamical model of DNA-protein interaction: effect of protein charge distribution and mechanical properties[END_REF][START_REF] Florescu | Comparison of kinetic and dynamical models of DNA-protein interaction and facilitated diffusion[END_REF], or DNA supercoiling and topological insulators [START_REF] Joyeux | Requirements for DNA-bridging proteins to act as topological barriers of the bacterial genome[END_REF].

SUPPORTING MATERIAL

Model and Simulations section. Figures S1 to S9.

SUPPORTING CITATIONS

E V V V V = + + + . ( S1
)
The stretching and bending contributions write

DNA 2 s 0 1 ( ) 2 n k k h V l l = = -  (S2) DNA 2 b 1 2 n k k g V θ = =  , (S3)
where k r denotes the position of DNA bead k (with the convention that n k k

+ = r r ), 1 + - = k k k l r r
the distance between two successive beads, and

1 1 1 1 arccos(( )( ) / ( )) k k k k k k k k k θ - + - + = - - - - r
r r r r r r r the angle formed by three successive beads. The stretching energy DNA s V is a computational device without biological meaning, which is aimed at avoiding a rigid rod description. The stretching force constant h is set to

2 0 B / 100 l T k h =
, which ensures that the variations of the distance between successive beads remain small enough (1). In contrast, the bending rigidity constant is obtained from the known persistence length of the DNA, 50 = ξ nm, according to

T k l T k g B 0 B 20 / = = ξ .
Moreover, it is assumed that the repulsion between DNA beads that are not close neighbours along the chain is driven by electrostatics and can be expressed as a sum of repulsive Debye-Hückel potentials with hard core

4 DNA 2 e DNA 1 4
( 2 )

n n k K k K k V e H a - = = + = - -   r r , ( S4
n k k V k T f = =  r , (S6)
where the function f is defined according to

0 6 0 6 0 ( ) 0, if ( ) 1, if . f r r R r f r r R R = ≤ = - > (S7)
In addition to the DNA chain, the confining sphere contains 200 P = DNA-binding protein chains, which corresponds to a protein concentration approximately twice the concentration of H-NS dimers during the cell growth phase and six times the concentration during the stationary phase (4). The number of protein chains was chosen to be as small as possible, but still large enough for the effects discussed in the present paper (compaction and/or stiffening) to be clearly seen in the simulations.

j j j j j E V V V V = + + + , ( S8
)
where the stretching, bending, and confinement contributions are very similar to their DNA

j jm jM m M m V k T F r = = + = -   R R , ( S12
)
where 0 3 r = nm and 0 ( | ) F r r is defined according to

6 3 0 0 0 0 6 3 0 0 ( | ) 2 1, if ( | ) 0, if . r r F r r r r r r F r r r r = - + ≤ = > (S13)
The repulsive interaction between two protein beads belonging to the same chain is shown as a thin red solid line in Fig. S1.

Interactions between the DNA chain and protein chain j, DNA/ j E , are taken as the sum of (attractive or repulsive) Debye-Hückel potentials with hard core, which are complemented The fact that DNA/protein interactions are mediated uniquely by effective electrostatic charges placed at the center of each bead is certainly a strong approximation, because it is known that proteins and cationic counterions compete for binding to the DNA, and that binding of a protein is consequently accompanied by the release of counterions in the buffer (12)(13)(14). Since the released counterions regain translational entropy, the net energy balance for the binding of ligands to the DNA results from subtle enthalpy-entropy compensations (15,16), a point which is overlooked in the present models. There is however no reason, why this approximation should affect the validity of the results discussed in the main text, because these results depend essentially on the ability of the proteins to bind to the DNA and on the bond energy, not on the nature of their interactions.

n j k jm k m V k T G s = = = -   r R . ( S16
The two models discussed in the main text differ only in the expression of the interaction / j J E between two protein chains j and J. The two models agree on the fact that each protein chain contains two isomerization sites, but the properties of these sites differ from Model I to Model II.

In Model I, each isomerization site is made up of a single bead, namely bead 2 m = for one site and bead 6 m = for the other one (see Fig. 1). The isomerization beads of protein 17). For Model II, two protein chains cannot bind simultaneously to the same isomerization site of a third protein chain, which ensures that protein chains isomerize in the form of filaments rather than clusters.

As for DNA/protein interactions, it is stressed that modeling protein/protein interactions as sums of Lennard-Jones potentials is a strong approximation, because protein self-association is governed by a variety of factors, including electrostatic interactions, hydrogen bonds, geometric frustrations, and hydrophobic interactions (18)(19)(20). There is however again no reason, why this approximation should affect the validity of the results discussed in the main text, because these results depend essentially on the geometry of protein assemblies and on the strength of the bonds, not on the nature of protein/protein interactions.

The total potential energy of the system, pot E , is the sum

  7 represent the two DNA-binding sites of each protein, whereas beads 2 m = and 6 (for Model I) or 2 m = , 3, 5 and 6 (for Model II) represent the isomerization sites of the protein (Fig. 1).The overall potential energy of the system (Eq. (S24)) is the sum of four terms, which describe the internal energy of the DNA chain (Eq. (S1)), the internal energy of protein chains (Eq. (S8)), DNA/protein interactions (Eq. (S14)), and protein/protein interactions (Eq. (S18)). The first three terms are common to Model I and Model II. In particular, for both models, the two terminal beads of each protein chain ( 1 m = and 7 m = ) can rotate without energy penalty around beads 2 m = and 6 m = , respectively (Eq. (S10)), and can bind to the DNA chain with a maximum binding energy of B 7.8 k T -(Eqs. (S15) and (S16), Fig. S2(a)). Because of the free rotation of terminal beads, protein chains are significantly less rigid than the DNA chain, as is usually the case in vivo. Moreover, the DNA/protein binding energy is comparable to experimentally determined values for complexes of DNA and H-NS ( ). Model I and II protein chains differ only in their isomerization properties. Indeed, for Model I, beads 2 m = and 6 m = of one protein chain may bind to beads 2 m = and 6 m = of other protein chains, whereas, for Model II, beads 2 m = and 6 m = of one protein chain may bind to beads 3 m = and 5 m = of other protein chains (Eqs. (S19), (S21) and (S23), Figs. 1 and S2(b)). As a result, Model I protein chains form spontaneously clusters, whereas Model II protein chains form filaments. The binding interaction between two protein isomerization beads is modeled by a Lennard-Jones 3-6 potential of depth LJ ε (Eq. (S19)). The isomerization binding energy for Model I is LJ ε -, whereas it varies with a slope close to LJ 2ε -for Model II (Fig.S3). For comparison, it is reminded that the experimentally determined value of the enthalpy change upon forming a complex between two H-NS dimers is

B 3 k

 3 T . The choice of the B 3 k T threshold is somewhat arbitrary, but the principal features of the distributions shown in Fig. S4 do not depend critically thereon. For Model I (left column of Fig. S4), protein chains do not associate significantly 5 other protein chains, which results in clusters like the ones shown in the top left vignette of Fig. 2. For Model II (right column of Fig. S4), protein chains do not associate significantly up to LJ B protein chain binds at maximum to 2 other protein chains, which results in filaments like the ones shown in the top right vignette of Fig. 2.

.

  . Typical equilibration times range from 1 ms to 20 ms, depending on the model and the value of LJ ε . For the lowest values of LJ ε , equilibrated conformations display few protein/protein contacts and a limited number of DNA/protein contacts, whereas huge DNA/protein complexes are observed for larger values of LJ ε . Representative snapshots of equilibrated conformations obtained with large values of LJ ε are shown in the bottom row of Fig. 2. For Model I (bottom left vignette of Fig. 2), the DNA chain wraps around the protein clusters, which are quite similar to those obtained without the DNA. In contrast, for Model II (bottom right vignette of Fig. 2), the protein filaments and the DNA chain form thick bundles, in which they align parallel to each other.As schematized in Fig.S5, protein chains in thermodynamic equilibrium with a DNA chain can be described either as free (no contact with the DNA chain), dangling (only one extremity of the protein chain binds to the DNA chain), cis-bound (the two extremities of the protein chain bind to genomically close DNA beads), or bridging (the two extremities of the protein chain bind to genomically distant DNA beads). The evolution with increasing values of LJ ε of the average fraction of the four types of protein chains is shown in Fig.3for Model I (open symbols) and II (filled symbols). Because the two models of protein interact similarly with the DNA chain, the curves for Model I and II remain superposed as long as selfassociation of protein chains remains negligible, that is, up to LJ In this regime, ≈50% of the protein chains are free, ≈22% are dangling, ≈22% are cis-bound, and only ≈6% are bridging the DNA chain. However, the onset of protein self-association is accompanied in both models by a strong decrease in the number of free proteins (by a strong increase in the number of bridging proteins (≈35% at LJ B 12 k T ε = ). Evolution with increasing values of LJ ε is sharper for Model I than for Model II.

  is due to the fact that, for this value of LJ ε , which is the smallest one that leads to protein self-association for Model I, the protein chains still display some ability to escape and rearrange after binding to an existing assembly. a single regular (nearly 2D) sheet, which apparently maximizes the number of bridges, whereas they assemble in more irregular 3D clusters for larger values of LJ ε . This can be checked in Fig.S7, which shows typical conformations obtained for Model I and LJ of bridges remains constant within computational uncertainties.The plots of the probability distribution for a protein chain to bind to s other protein chains, ( ) q s , are shown in Fig.S8for equilibrated DNA/protein systems. These plots show that the presence of the DNA chain does not alter significantly the self-association of protein chains. Indeed, Model I protein chains bind on average to 4 or 5 other protein chains starting of Fig.S8), whereas Model II protein chains bind at maximum to 2 other protein chains starting from LJ B 9 k T ε =

B 3 k

 3 T . A first conclusion concerning the impact of the selfassociation of protein chains on DNA/protein complexes can be drawn from the comparison of the plots of ( ) p s and ( ) q s in Fig.S8. This figure reveals that the binding of protein chains to the DNA chain is boosted by protein self-association. Indeed, for both models, only ≈30% of the DNA-binding protein beads bind to a DNA bead ( (0) 0.7 p ≈ ) for values of LJ ε corresponding to weak protein self-association, that is, up to LJ B Model II. In contrast, ≈70% of the DNA-binding protein beads bind to at least one DNA bead ( (0) 0.3 p ≈ ) for larger values of LJ ε , for which strong protein self-association is observed.

  k r denotes the position of the center of DNA bead k and CM r the position of the center of mass of the DNA coil. In the absence of protein chains, the average value 82.1 R = nm results from the balance of the compressive forces exerted by the confinement sphere and the expansive forces arising from the bending rigidity of the DNA chain and the electrostatic repulsion between DNA beads. As long as protein chains self-associate only weakly, addition of 200 of them inside the confinement sphere has little effect on the mean radius of the DNA coil, as can be checked in the top plot of Fig. 4 which shows the evolution of R with increasing values of LJ ε . Indeed, R remains close to 80 nm for small values of LJ ε . This is a direct consequence of the fact that only ≈6% of the proteins bridge the DNA chain, which is insufficient to compact significantly the DNA coil. In contrast, R decreases rapidly below 70 nm for Model I and values of LJ ε larger than B 7 k T . This indicates that the ≈35% of protein chains, which bridge the DNA chain (bottom left vignette of Fig. 2), are quite efficient in compacting the DNA coil. However, this is not the case for Model II and LJ B 9 k T ε > , although approximately 35% of the protein chains also bridge the DNA chain. The reason is that most of these bridges localize in thick bundles similar to the one shown in the bottom right vignette of Fig. 2, where they essentially work to maintain DNA segments parallel to each other. Such distributions of bridges are apparently not as efficient for compacting the DNA coil as the distributions of bridges for Model I proteins. An exception occurs for Model II and LJ B 9 k T ε = , for which significant compaction of the DNA coil ( 73 R ≈ nm) is observed in the top plot of Fig. 4. The reason is that this value of LJ ε is the smallest one that leads to protein self-association for Model II, and DNA/protein complexes are different from the thick bundles observed for larger values of LJ ε . They resemble more the conformations obtained with Model I, as can be checked in Fig. S9, which shows typical conformations obtained for Model II and LJ B

k t is the unit vector

  tangent to the DNA chain at the center of bead k in conformation c. The persistence length ξ is obtained from an exponential fit of the evolution of 0 ( ) C Ll as a function of L over a certain interval of values of L. By using this procedure for 0 DNA chain enclosed in the confinement sphere without protein chains. This value is somewhat smaller than the value estimated from the bending rigidity of the DNA chain ( 50 ξ = nm). This is due to the fact that the confinement sphere imposes non- negligible additional curvature to the DNA chain, because its diameter ( 0 2 240 R = nm) is only ≈5 times larger than the persistence length of unconstrained DNA. For equilibrated DNA/protein complexes, it is interesting to discriminate between the persistence length of DNA segments which are not bound to any protein chain ( free ξ ) and the persistence length of DNA segments bound to at least one protein chain ( bound ξ ). This is easily achieved by testing at each step of the averaging procedure whether any bead of the DNA segment comprised between beads k and k L + binds to a protein chain or not, and using this segment adequately to compute either free ξ or bound ξ . The evolution of free ξ and bound ξ with increasing values of LJ ε is shown in the bottom plot of Fig. 4. Not surprisingly, free ξ remains close to 40 nm for all values of LJ ε . Moreover, addition of 200 protein chains inside the confinement sphere has little effect on bound ξ as long as protein chains do not self-associate significantly. This indicates that the ≈22% of protein chains which bind to the DNA chain in cis do not increase significantly its rigidity. In contrast, boundξ increases rapidly up to ≈80 nm for Model II and values of LJ ε larger than B 10 k T . This confirms that the thick bundles composed of DNA segments maintained parallel to each other and bridged by protein segments are quite rigid, as could be anticipated from their almost rectilinear shape (bottom right vignette of Fig. 2). No increase in bound ξ is however observed for Model I, even for large values of LJ ε , as can be checked in Fig. 4. Although the network of protein chains formed for LJ B 7 k T ε ≥ is quite efficient in compacting the DNA coil, it is flexible enough for the numerous cross-links not to alter significantly the persistence length of DNA segments bound to protein chains.

Figure 1 :

 1 Figure 1 : Diagrams of protein chains for Models I and II. Index m is indicated for each bead. Red circles represent DNA-binding beads (index 1 = m and 7 ), which rotate freely around beads with index 2 m = and 6, respectively. Green circles represent isomerization beads (index 2 m = and 6 for Model I, index 2 m = , 3, 5 and 6 for Model II). In Model I, beads 2 m = and 6 m = of one protein chain may bind to beads 2 m = and 6 m = of other protein chains. In Model II, beads 2 m = and 6 m = of one protein chain may bind to beads 3 m = and 5 m = of other protein chains. All other features are common to the two models. Note that the two chains shown in the figure have minimum internal energy.

Figure 2 :

 2 Figure 2 : Representative snapshots extracted from simulations with 200 protein chains and

Figure 3 :

 3 Figure 3 : Plot, as a function of LJ ε , of the average fraction of free (circles), bridging (lozenges), cis-bound (triangles), and dangling (upside-down triangles) protein chains for Model I (open symbols) and II (filled symbols). Each set of 4 open or closed symbols with the same value of LJ ε was obtained from a single simulation with the DNA chain and 200 protein chains, by averaging the relevant quantity over time intervals of at least 2.5 ms after equilibration.

Figure 4 :.FIGURE 1 FIGURE 3 FIGURE 4 Impact

 4134 Figure 4 : Plot, as a function of LJ ε , of the mean radius of the DNA coil (top) and the persistence length of the DNA chain (bottom), for Model I (open symbols) and II (filled symbols). In the bottom plot, lozenges represent the values of free ξ and triangles the values of

  Eq. (S4), DNA e denotes the electric charge placed at the centre of each DNA bead. e is the absolute charge of the electron, is the product of 0 l and the net linear charge density along a DNA molecule immersed in a buffer with monovalent cations derived from Manning's counterion condensation theory length, whose value corresponds to a concentration of monovalent salt of 100 mM, which includes the (implicit) cationic counterions that are required for the global electroneutrality of the investigated systems. included in Eq. (S4) because it is considered that they are already accounted for in the stretching and bending terms. The repulsive interaction between two DNA beads is shown as a blue short-dashed line in Fig.S1. Note that the equilibrium distance between two successive beads ( 0 2.5 l = nm) is small enough to ensure that different DNA segments do not cross in spite of the small value of D r .

  200 P = turns out to be an adequate choice in this respect. We note in passing that most experiments dealing with the architectural properties of nucleoid proteins were similarly performed at protein concentrations much larger than physiological ones (5-7). Each DNA-binding protein j is modeled as a chain of 7 beads (indexes 1 m = ,2,..,7), where the two terminal beads 1 m = and 7 m = represent the DNA-binding sites (see Fig. 1). Protein beads have the same radius 0 . 1 = a nm as DNA beads and are separated at equilibrium by the same distance 5 . 2 0 = l nm. The internal energy of each protein chain j, j E , consists of 4 terms s b ev w

R

  denotes the position of bead m (1 7 m ≤ ≤ ) of protein chain j (1 j P ≤ ≤ ), jm L the distance between beads m and 1 m + of protein chain j, and jm Θ the angle formed by beads 1 m -, m, and 1 m + of protein chain j. The values of the stretching force constant h and the bending force constant g are identical to those of the DNA chain. Note, however, that the sum in the expression of b rotate without energy penalty around beads 2 m = and 6 m = , respectively. The free rotation of terminal beads mimics the flexible linkers, which connect the C-terminal DNA-binding domains of H-NS to the main body of the dimer (8).The excluded volume term ev j V ensures that beads belonging to the same chain j repel each other at short distances and do not overlap. ev j V is expressed in terms of the repulsive part of a Lennard-Jones 3

  the centre of the other beads ( 2 6 m ≤ ≤ ). The values of these effective charges are compatible with those obtained from a naive counting of the number of positively and negatively charged residues in published crystallographic structures of H-NS(9), except that H-NS is globally neutral, whereas the protein chains have here a total slightly negative charge of 1.2 e -, in agreement with the fact that most proteins encoded in the genome of E. coli are anionic(10). In Eq. (S16), and DNA beads is shown as a thick green long-dashed line in Fig.S1and the repulsive interaction between other protein beads ( 2 6 m ≤ ≤ ) and DNA beads as a thin green long-dashed line. The most stable DNA/protein complex is shown in Fig.S2(a) : each DNA-binding protein bead binds simultaneously to two successive DNA beads with a total binding energy of B 7.8 k T -, which is comparable to experimentally determined values for complexes of DNA and H-NS ( ). It is seen in this figure that the terminal protein bead and the two DNA beads overlap to some extent for the most stable DNA/protein complex. The reason is that the radius Langevin equations (see Eq. (S25) below). Nonetheless, the distance between the centers of a DNA bead and a protein bead can become smaller than 2a , which reflects the fact that proteins may insert loops in the major or minor groove of the DNA molecule. The sum of the attractive Debye-Hückel potential with hard core at distance a (Eq. (S15)) and the repulsive part of a Lennard-Jones 1-2 potential without hard core (Eq. (S16)) results in a maximum binding energy of B 3.9 k T at a center-center distance of 1.60 nm (thick green long-dashed line in Fig. S1), which is indeed smaller than 2 2.0 a = nm and is responsible for the overlaps in Fig. S2(a).

{{2ε.

  is the only variable parameter of the model. It was varied from B 4 k T to B 12 k T , in order to check the influence of the isomerization binding energy on the equilibrium properties of the system. As already mentioned, for Model I the isomerization binding energy is just LJ ε -. For Model II, the isomerization binding energy of two protein chains was estimated by letting two chains frozen in minimum energy conformations (Π-shaped) slide parallel to each other and computing their interaction energy as a function of the coordinates of the central bead of one chain relative to the central bead of the other one. The Π-shaped conformation of protein chains was used for the calculation of the isomerization binding energy, because terminal protein beads rotate without energy penalty around beads 2 m = and 6 m = (Eq. (S10)) and the 90° angle ensures minimum repulsion between these beads. As shown for LJ B 8 k T ε = in Fig. S2(b), the interaction energy displays a sharp minimum around 7.6 x = nm and 1.1 y = nm, whose depth is taken as the isomerization binding energy. As illustrated in Fig. S3, this energy varies almost linearly with LJ ε For the sake of comparison, it is reminded that the experimentally determined value of the enthalpy change upon forming a complex between two H-NS dimers is B 10.2 k T -

  dynamics of the model was investigated by integrating numerically the Langevin equations of motion with kinetic energy terms neglected. Practically, the updated position

Figure S1 :

 S1 Figure S1 : Plot, as a function of the distance r between the centers of the beads, of the various bead-bead interaction potentials of the model. Blue short-dashed line : DNA-DNA repulsive interaction (Eqs. (S4)-(S5)). Thin green long-dashed line : repulsive potential between a DNA bead and a protein bead with 2 6 m ≤ ≤ (Eq. (S15)). Thick green longdashed line : binding potential between a DNA bead and a DNA-binding protein bead with 1 m = or 7 m = (Eqs. (S15)-(S17)). Thin red solid line : repulsive potential between two protein beads, which do not belong to the ensemble ℑ (Eqs. (S20), (S21) and (S23)). Thick red solid line : binding potential between two protein beads, which belong to the ensemble ℑ (Eqs. (S19), (S21), (S22) and (S23)). r is expressed in nm, energy values in units of B k T .

Figure S3 :

 S3 Figure S3 : The blue and red lines show the evolution, as a function of LJ ε , of the binding energy of protein/protein complexes for Models I and II, respectively. For comparison, the green dashed line shows the bond energy of DNA/protein complexes ( B 7.8k T -). LJ ε and

Figure S5 :

 S5 Figure S5 : Diagram showing the different modes of interaction between protein chains and a DNA chain. DNA beads are shown in brown and protein beads in red, green or black, according to the same color code as in Fig. 1.

Figure S6 :

 S6 Figure S6 : Plot, as a function of LJ ε , of the average fraction of protein chains which belong to a cluster bridging different DNA segments, for Model I (open symbols) and II (filled symbols). Each point was obtained from a single simulation with the DNA chain and 200 protein chains, by averaging the relevant quantity over time intervals of at least 2.5 ms after equilibration.

RESULTS AND DISCUSSION Self-association of Model I and II protein chains.

	conformations are shown in the top row of Fig. 2. Evolution of protein complexes with
	increasing values of LJ
				. Simulations were run for both models
	and values of LJ ε ranging from	B 4 k T to	B 12 k T , in order to check the impact of self-
	association of the protein chains on the equilibrium properties of the system. The upper limit
	was fixed to	B 12 k T , because the probability for Model II proteins to form clusters instead of
	filaments becomes non negligible for this value of LJ ε and increases rapidly for larger values.
	Model I and II protein chains interact identically with the DNA chain but self-
	associate differently. The goal of this work is to determine whether the difference in self-
	association might result in different architectural properties of the proteins, that is, in
	DNA/protein complexes with substantially different conformations. A preliminary step
	consists in characterizing in some detail the complexes that protein chains form spontaneously
	in the absence of the DNA chain. To this end, 200 protein chains were introduced at random
	non-overlapping positions in the confinement sphere and the system was allowed to
	equilibrate for values of LJ ε (the depth of the Lennard-Jones 3-6 potential that governs
	protein/protein interactions) ranging from	B 4 k T to	B 12 k T . Typical equilibration times range
	from 1 ms to 50 ms, depending on the model and the value of LJ ε .
	For the lowest values of LJ ε , thermal noise is strong enough to prevent association of
	the protein chains, as was also the case for a previous model of H-NS mediated compaction of
	bacterial DNA (27,28). In contrast, for larger values of LJ ε , Model I proteins form clusters,
	whereas Model II proteins form filaments. Representative snapshots of equilibrated

chain j may bind to the isomerization beads of chain J but repel all other beads through an excluded volume term. The other beads of chain j ( 2 m ≠ and 6 m ≠ ) repel all the beads of chain J through the same excluded volume term. More explicitly, for Model I the interaction energy between protein chains j and J, / j J E , writes / / / iso ev

where In Model II, each isomerization site is instead made up of two beads, namely beads 2 m = and 3 m = for one site and beads 5 m = and 6 m = for the other one (see Fig. 1). An isomerization site of one protein chain can bind to an isomerization site of another protein chain, but only in a "head-to-tail" fashion. This is obtained by imposing that beads 2 m = and 6 m = of one chain can bind to beads 3 m = and 5 m = of the other chain, but repel all other beads. More explicitly, the interaction energy between protein chains j and J, / j J E , has the same expression for Model II as for Model I, except that the ensemble ℑ is defined according to vector for each bead (whether DNA or protein),

, was computed from the current position vector, ) (n j r , according to


Cnn Model

Uyen Trang

Tran

H A Thanh

Thi Hoang

Hoai Phuong

Dang

Michel Riveill

Multitask Aspect_Based Sentiment Analysis with Integrated Bidirectional

Keywords: CCS CONCEPTS, Machine Learning, Machine Learning approachs, Neural Networks Aspect-Based Sentiment Analysis, Multitask, BiLSTM, CNN, Word embedding, POS

Sentiment analysis or opinion mining used to understand the community's opinions on a particular product. Sentiment analysis involves building the opinion collection and classification system. Aspect-based sentiment analysis focuses on the ability to extract and summarize opinions on specific aspects of entities within sentiment document. In this paper, we propose a novel supervised learning approach using deep learning techniques for multitask aspect-based opinion mining system that support four main subtasks: extract opinion target, classify aspect-entity (category), and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of entity. Using extra POS layer to identify morphological features of words combines with stacking architecture of BiLSTM and CNN with word embeddings achieved by training GloVe on Restaurant domain reviews of the SemEval 2016 benchmark dataset in our proposed method is aimed at increasing the accuracy of the model.

Experimental results showed that our multitask aspect-based sentiment analysis model has extracted and classified main above subtasks concurrently and achieved significantly better accuracy than the state-of-the-art methods.

INTRODUCTION

The sentiment analysis [START_REF] Liu | Sentiment Analysis and Opinion Mining[END_REF] is a field of research on the ability to extract and categorize opinions of the community about entities and attributes related to these entities such as products and services…that support for tracking the mood of the community. Therefore, the need to exploit the ability to detect and understand the views automatically is increasingly interested in research. The standard opinion mining focus on sentiment of the overall document or sentence. However, in case of customer reviews, they always discuss about many aspects of each entity in the sentence or document and present the sentiment on every single aspect of entity rather than expressing the sentiment on whole document. Because of this reason, we focus on Aspect-Based Sentiment Analysis (ABSA) with the purpose to identify aspect of entity in sentiment text and the opinion presented on each aspect. The innovations of proposed ABSA models, the existing aspect extraction and opinion specification techniques are absolutely the core research directions of sentiment analysis.

In this paper, we propose to apply the deep learning approach for multitask ABSA using an integrated CNN-BiLSTM architecture with word embeddings [START_REF] Mikolov | Efficient Estimation of Word Representations in Vector Space[END_REF] obtained by training GloVe [START_REF] Pennington | GloVe: Global Vectors for Word Representation[END_REF] on reviews of the SemEval 2016 dataset. The reasons for our choosing model are that: (i) CNN with various-size convolution kernel mechanism can support effectively to extract local features (ii) LSTM can well process long sequences based on the ability to keep long-term memory and control the gradient backpropagation through time to solve the gradient vanishing and exploding issue. The experimental results show that our proposed model used for ABSA proved to be more effective than previous state-of-the-art approaches.

In the rest of this paper, we organize as follows. Section 2 presents the related works for ABSA. Our approach for proposed model with BiLSTM and CNN integrated architecture is described in Section 3. Section 4 presents our experiments using a benchmark dataset and discussion about received results. Finally, Section 5 focuses on our conclusion and future research direction.

RELATED WORKS

Most of recent sentiment analysis researches have focused on two main approaches [START_REF] Liu | Sentiment Analysis and Opinion Mining[END_REF]: (i) lexicon-based approach with two sub classifications dictionary-based and corpus-based using class of measures on various models to calculate the opinion orientation for the document from the opinion orientation of words in the opinion text. This approach identifies the sentiment polarity of words based on the correspondence between sentiment words in opinion lexicon and the data (ii) machine learning-based approach using classification techniques to classify text, identify opinion on training and testing data with supervised learning or on unlabeled data with unsupervised learning.

In lexicon-based approach, [START_REF] Hu | Mining opinion features in customer reviews[END_REF][20] used a list of seed words in the dictionary to identify semantic orientation for adjectives in the document. [START_REF] Peter | Measuring praise and criticism: Inference of semantic orientation from association[END_REF] considered the relationship between phrases and a set of positive or negative seed word to correct the semantic of these words with the two statistical measurements PMI and LSA for calculating the average semantic orientation of all phrases to receive the sentiment. WordNet was used in [START_REF] Kamps | Using WordNet to measure semantic orientation of adjectives[END_REF] to determine the shortest route between words in the document and the two seed words "good" and "bad" to predict the sentiment orientation of the words. [START_REF] Li | Application of a clustering method on sentiment analysis[END_REF] applied the k-means clustering algorithm and TF-IDF to cluster opinion texts into one of two groups: positive or negative group. [START_REF] Ding | A holistic lexicon-based approach to opinion mining[END_REF] identified the orientation of opinion document based on the context dependent technique. [START_REF] Qiu | Opinion word expansion and Target extraction through Double Propagation[END_REF] used the syntactic relationship between opinion and aspect with a small set of opinion seed words to extract concurrent opinions and aspects. [START_REF] Taboada | Lexicon-Based Methods for Sentiment Analysis[END_REF] developed thesauruses dictionary of words to build the Semantic Orientation Calculator for labeling the positive or negative orientation to the sentiment document. [START_REF] Liang | Opinion Mining on Social Media Data[END_REF] applied the Mutual Information and Chi square for feature extraction and used Unigram Naive Bayes model to identify the polarity of the tweet.

Sentiment analysis based on machine learning-based approach is also recently interested. [START_REF] Jin | A novel lexicalized HMM-based learning framework for web opinion mining[END_REF] extracted aspects and opinion phrases by learning patterns with lexicalized HMM model. [START_REF] Li | Structure-aware review mining and summarization[END_REF] extract both features and opinions by combining two CRF variations: Skip-CRF and Tree-CRF. [START_REF] Kiritchenko | Detecting aspects and sentiment in customer reviews[END_REF] trained SVM classifier to correct aspect terms and also identify the sentiment expressed on these aspects of reviews. [START_REF] Lakkaraju | Aspect specific sentiment analysis using hierarchical deep learning[END_REF] applied a hierarchical deep learning framework with Recursive Neural Network, Matrix-Vector Recursive Neural Network and Recursive Neural Tensor Network to extract aspects and detect opinion polarities that expressed on them. [START_REF] Thien | Phrase recursive neural network for aspect-based sentiment analysis[END_REF] presented PhraseRecursive Neural Network to predict the sentiment of the aspect in document. This model is the extention of RNN that use dependence and constituent trees of each sentence in the text to get the syntactic information. [START_REF] Tang | Effective LSTMs for Target-Dependent Sentiment Classification[END_REF] proposed Target-Dependent LSTM model to detect the sentiment polarity towards the target based on the target word and context word. [START_REF] Zhang | Neural networks for open domain targeted sentiment[END_REF] expanded CRF model using an adding neural layer between input and output nodes to apply a continuous word embedding mechanism for extracting both relevant aspects and opinions. [START_REF] Wang | Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis[END_REF] used an integrated model of recursive neural network and CRF to extract aspects and opinions. [START_REF] Wang | Attention-based LSTM for Aspect-level Sentiment Classification[END_REF] proposed an attention-based LSTM model for ABSA having ability to capture the key part of sentence concerning to each given aspect. A deep memory network model was applied by [START_REF] Tang | Aspect Level Sentiment Classification with Deep Memory Network[END_REF] to pick up the worth of context word with a few computational layers to calculate the important degree of context word with each of these layers is a neural attention network. [START_REF] Ma | Interactive Attention Networks for Aspect-Level Sentiment Classification[END_REF] used an Interactive Attention Networks including two attention networks that focus on the important parts in the aspect and context to model the aspect and context interactively. [START_REF] Chen | Recurrent Attention Network on Memory for Aspect Sentiment Analysis[END_REF] used a Recurrent Attention Network on memory to apply multiple attention mechanism for synthesizing dispersed opinion features in the sentence for finding the sentiment of aspects in customer reviews.

Liu et al. [START_REF] Liu | Content Attention Model for Aspect Based Sentiment Analysis[END_REF] proposed an attention based model by combining content attention mechanism and context attention mechanism to gain the key information of aspects and take both the order of words and their relations for ABSA. [START_REF] He | Effective Attention Modeling for Aspect-Level Sentiment Classification[END_REF] proposed two solutions for enhancing the productivity of attention mechanism on attention-based LSTM model: one for better capturing the aspect semantics of target word by a new encoding technique for target and one for applying the syntactic information to built a syntax-based attention model. Gated Convolutional Networks with the integration of gating mechanisms and CNN was proposed by Xue and Li [START_REF] Xue | Aspect Based Sentiment Analysis with Gated Convolutional Networks[END_REF].

The authors presented parallel computations on training model based on time independency of convolutional layers and gating units independent action mechanism. Trang et al. [31][32] proposed two integrated BiGRU-CRF and Bi-IndyLSTM-CRF approachs for extracting aspects in opinion document. They took advantage of the forward and backward mechanism of Bi-IndyLSTM and BiGRU for their models using the previous and the next token to extract features with higher accuracy. Based on deep learning approach, we propose a multitask ABSA and have achieved better accuracy on both the F1-score and Accuracy-score than the previous approaches.

METHODOLOGY

In this paper, we apply two stacked models with CNN and BiLSTM for multitask ABSA. The integrated approach with the extra POS tag layer allow optimal use of the capabilities of CNN and BiLSTM. CNN has ability to get the local feature around a mentioned word in input sentence but it is not possible to combine distant information of the current word. Nevertheless, BiLSTM with its capacity can combined locally extracted features by the CNN layer around each word at current position with extracted features of words at distant previous and next position.

In addition, the BiLSTM on top of CNN also serves as a classifier to infer other components that need to be extracted simultaneously. This integrated approach is aimed at increasing model accuracy in multitask execution : opinion targets, categories (entities-aspects) and sentiments extraction.

Convolutional Neural Network (CNN)

Although CNN were originally useful for image processing, they have been recently applied to several natural language processing tasks and got achievement [START_REF] Huang | Parameterized Convolutional Neural Networks for Aspect LevelSentiment Classification[END_REF][27] [START_REF] Kim | Convolutional Neural Networks for Sentence Classification[END_REF]. When working with sequences of words, convolutions apply the ability to extract local features around each word. The input sentence with length N (𝑤 1 , 𝑤 2 , … , 𝑤 𝑁 ) after going through the embedding layer composed of POS tag layer to define the morphology of words (𝑒 1 , 𝑒 2 , … , 𝑒 𝑁 ) will be feed to CNN for extracting high level features. The feature output is as input of the next layer_BiLSTM.

Bidirectional Long Short-Term Memory (BiLSTM)

LSTM is one of the variants of Recurrent neural network (RNN)_a deep learning technique that has been recently used for sequential learning tasks. RNN has ability to model sequences of arbitrary length. This capacity is due to repetition of a recurrent unit along tokens in the sequence. However, RNN is limited because of the issues involved in vanishing and exploding gradients [START_REF] Bengio | Learning long-term dependencies with gradient descent is difficult[END_REF] that made it is not sufficient for learning longterm dependencies. Because of that, LSTM was applied to solve these weak points and it can be depicted with three gates mechanism as follows: forget gate layer 𝑓 𝑡 for filtering and deciding which information to forget from the cell state 𝐶 𝑡 , input gate layer 𝑖 𝑡 for deciding which values to update and output gate layer 𝑜 𝑡 .

A main issue of unidirectional LSTM is that it allows learning representations from previous time steps. Thus, it only preserves information of the past because the only inputs it has seen are from previous time steps. In some cases, we should use Bidirectional LSTM (BiLSTM) to learn representations from future time steps as well to better understand the context and eliminate the ambiguity incurred by learning one way.

Data Preprocessing

Data cleaning: In this task, we remove unusual characters and emoticons; convert all words to lowercase, remove numbers (i.e. '8') and empty sentences; find and replace slang or abbreviated with word substitutions (i.e. 'u' -> 'you'); lemmatize all words into their base form (e.g. 'computing', 'computed' -> compute).

Word embedding:

We use pre-trained word vectors from GloVe [START_REF] Pennington | GloVe: Global Vectors for Word Representation[END_REF], an unsupervised learning technology, for learning word representation. The purpose of training is to use statistical information to find similarities among words.

Part-of-Speech (POS) tags:

We generate POS tags for words by Stanford POS Tagger to assign parts of speech to each word in input review.

IOB2 tags: Generating IOB2 tags on the top of our model to label I/O/B to words in review is intended to extract opinion targets. In particular, we consider the opinion target extraction as a word labeling task. According to IOB2, each word in review can get one of three tags B/I/O and can be confirmed as following rules: the first word label in review should start with "B-" or "O", not "I-", the valid patterns should be "O B-label", "B Ilabel" but "O I-label" is invalid. The first label of one opinion target, maybe word or phrase, should start with "B-", not "I-".

Proposed Multitask Aspect-based Sentiment Analysis model

Deep CNN-IOB2 model for extracting opinion target

The data after being cleaned according to the sequence of steps in 3.3 was applied for the GloVe embedding layer with 100-dimension feature vector to encode all properties related to the semantics and syntax of each word in input review. Each input sentence with length l will be fixed to the maximum sentence length of the dataset. We use 0s padding scheme for shorter sentence. This helps ensure all sentence matrices have the same dimensions. At the same time, generating POS tagger to identify the parts of speech of word will make the 34-dimention feature vector. Next, we concatenate the two types of vectors above resulted from GloVe layer and POS tagger and feed for the deep three-layer CNN architecture to extract features.

Multitask Aspect-based Sentiment Analysis (MABSA) model

Experimental results

We perform experiments for our proposed models in the Restaurant domain of the SemEval 2016 dataset and evaluate our models' performance using accuracy value and F1-score, the same as the previous baseline methods. We use pretrained GloVe word embeddings with 100-dimensional feature vector, concatenate with 34-dimensional POS tag vector for the feature extracting CNN layer, the maximum input sequence length is 62, the rmsprop optimizer with learning rate 0.001, dropout 0.25 and a batch size of 128 in the two models. The number of epochs is set to 125 and 300 for the opinion target extracting CNN-IOB2 model and the Multitask Aspect-based Sentiment Analysis model on Restaurant domain respectively. Experiment results of our models are presented in Table 2 and Table 3 below.

Our proposed Deep CNN-IOB2 model for extracting opinion target words or phrases of input reviews is evaluated through comparison with the following baseline models also experimented on the same benchmark dataset_the SemEval 2016, Restaurant domain. Besides, our Multitask Aspect-based Sentiment Analysis model of classifying the remaining subtasks such as category, aspect and sentiment polarity also achieved remarkable results.

Model

Opinion target extraction F1_score Accuracy_score NLANGP(U) [START_REF] Toh | NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features[END_REF] 72.34 -CRF [START_REF] Hamdan | SentiSys at SemEval-2016 Task 5: Opinion Target Extraction and Sentiment Polarity Detection[END_REF] 66.54 -AUEB [START_REF] Xenos | AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis[END_REF] 70.44 -MIN [START_REF] Li | Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction[END_REF] 73.44 -DE-CNN [START_REF] Xu | Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction[END_REF] 74.37 -

Model

Opinion target extraction THA&STN [START_REF] Li | Aspect Term Extraction with History Attention and Selective Transformation[END_REF] 73.61 -BiDTreeCRF [START_REF] Luo | Improving Aspect Term Extraction with BidirectionalDependency Tree Representation[END_REF] 74.49 -Our CNN-IOB2 model 99.96 99.93 Accuracy_score: Our model achieves pretty high result 99.93%. As can be seen clearly from the graph in Figure 4, our proposed model achieves outstanding performance in both F1 and accuracy scores over baseline models. Visually seen from the graph in Figure 6, our MABSA model shows the good performance across all three classification subtasks: category, aspect and sentiment polarity.

CONCLUSION

We have proposed a multitask deep learning approach to aspect based sentiment analysis. integrates with the three-layer CNN architecture mentioned above is used in the our second model. We apply a fully connected layer in the last state of the model and feed the output to the softmax layer to compute propability distribution over the possible categories for three remaining subtasks: category, aspect and sentiment classification. Our proposed models achieved significant improvement in performance over the previous state-of-the-art approaches. As future work, we plan to employ this our multitask learning model in multidomain dataset, the solution for domain adaptation.

Figure 1 :

 1 Figure 1: Deep CNN-IOB2 model for extracting opinion target Using the IOB2 scheme on top of the CNN layers aims to label each token with one of three tags B/I/O with B and I represent the opinion target at the beginning and the next/inside position of the target word phrase respectively and O represents the outside/irrelevant word. By applying this above labeling mechanism we can achieve the opinion target extraction task.

  We apply data preprocessing, GloVe word embedding, POS tag and feature extraction steps like the above deep CNN-IOB2 model and add some more elements to extract the remaining three components in the mentioned multitask extraction process.Using the BiLSTM layer on top of the CNN we can take advantage (i) learning ability from long-term memory in previous time step of LSTM and (ii) information preserve ability from both sides: past and future of the current time step based on bidirectional mechanism. Next, we apply the output of the BiLSTM to the average pooling layer to reduce the number of dimensions of word vectors in sentence review. We use a fully-connected layer (dense) in the final stage of the proposed model and feed the output to the softmax layer to compute propability distribution over the possible categories for completing classifying and extracting what we are looking for: the category (entity-aspect) and sentiment simultaneously. Our proposed model has achieved remarkable accuracy compared to previous state-of-art models.

Figure 2 :

 2 Figure 2: Deep Multitask Aspect-based Sentiment Analysis model for classifying three remaining subtasks

Figure 3 :

 3 Figure 3: F1, Accuracy score and training loss vs. epoch curve of our opinion target extraction model The graph from Figure 3 depicts our opinion target extraction model's validation F1, accuracy measure and loss. Observing the third graph_ model loss in Fig. 3, we notice that after a sharp decline in the first few epochs and a gradual decrease in the next 40 epochs, the loss has a steady downtrend since the 60th epoch and reaches saturation state after the 100th epoch. In particular, the stability is evident from the 100th epoch to the 125th epoch.

Figure 4 :

 4 Figure 4: F1 & Accuracy score of opinion target extraction models

Figure 5 :

 5 Figure 5: Precision, Recall, F1, Accuracy score and Loss of our MABSA model In the graph cluster below from Figure 5, besides illustrating the performance on validation F1 and accuracy measures, we also can see the loss of the proposed model in the last three graphs. With category subtask, after a sharp decline in the first 50 epochs and a slight decrease from the 50th epoch to the 150th epoch, the loss tends to stabilize to the last epoch. The same goes for aspect loss graph, there is also a spike in the first 20 epochs, a smaller drop in the next 80 epochs, a slight drop from the 100th epoch to the 200th epoch and towards stabilized since the 200th epoch. The final polarity loss graph has a slight difference compared to the previous two graphs. After a drop in the first 50 epochs, there is a spike in the next 50 epochs and then stable from after the 100th epoch to the last 300th epoch.

Figure 6 :

 6 Figure 6: Precision, Recall, F1 & Accuracy score of our MABSA model

  Our two models have finished main subtasks in ABSA: opinion target extraction, category classification, aspect classification and sentiment polarity classification used a three-layer CNN architecture that comprises the word embedding layer, GloVe and POS tagger to make the feature vectors for words in input opinion sentence. With the first model, we apply the IOB2 scheme on top of the CNN layers aims to label each token with one of three tags B, I or O for opinion target extraction subtask. Implementing the BiLSTM with one forward LSTM and one backward LSTM to take full advantage of information from both side of each word_the previous and the next tokens our MABSA model Sentiment Polarity classification Aspect classification Category classification

Table 1 :

 1 Using Restaurant domain in the SemEval 2016 dataset for experiment

	Dataset	Domain	Train/Test	Train/Test
			Sentences	opinion_targets
	SemEval 2016	Restaurant	2000/676	1743/622

Table 2 :

 2 Comparison results in F1_score and Accuracy_score on Restaurant domain of the SemEval 2016 dataset

Table 2

 2 shows that our proposed model outperforms the previous state-of-the-art methods for opinion target extraction subtask on F1 score metric and achieves outstanding results on Accuracy score metric. Our proposed model is 27.62% higher than NLANGP(U), 33.42% higher than CRF model, 29.52% higher than AUEB model, 26.52% higher than MIN model, 25.59% higher than DE-CNN model, 26.35% higher than THA&STN model and 25.47% higher than BiDTreeCRF model in the same Restaurant domain of the SemEval 2016 dataset.

	F1_score:

Table 3 :

 3 Experimental results of our MABSA model in Precision, Recall, F1 and Accuracy_score on Restaurant domain

		of the SemEval 2016 dataset
	Our MABSA model Category classification	Aspect	Sentiment Polarity
			classification	classification
	Precision_score	97.97	92.52	97.19
	Recall_score	97.67	92.24	96.58
	F1_score	97.82	92.38	96.88
	Accuracy_score	97.83	92.39	96.58

Similar to our CNN-IOB2 model, our proposed MABSA model for remaining subtasks from Table

3

. also shows good results between over 96% to approximately 98% for the two category and sentiment classification subtasks and over 92% for the aspect classification subtask on Precision, Recall, F1 and Accuracy score metrics.

http://alt.qcri.org/semeval2016/task5/