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Guillon, C.; Robert, X.; Gouet, P. HTLV-1 Tax Structure Models. Encyclopedia. Available online: https://encyclopedia.pub/entry/56152 (accessed on 15 April 2024).
Guillon C, Robert X, Gouet P. HTLV-1 Tax Structure Models. Encyclopedia. Available at: https://encyclopedia.pub/entry/56152. Accessed April 15, 2024.
Guillon, Christophe, Xavier Robert, Patrice Gouet. "HTLV-1 Tax Structure Models" Encyclopedia, https://encyclopedia.pub/entry/56152 (accessed April 15, 2024).
Guillon, C., Robert, X., & Gouet, P. (2024, March 12). HTLV-1 Tax Structure Models. In Encyclopedia. https://encyclopedia.pub/entry/56152
Guillon, Christophe, et al. "HTLV-1 Tax Structure Models." Encyclopedia. Web. 12 March, 2024.
HTLV-1 Tax Structure Models
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Human T-cell Leukemia Virus type 1 (HTLV-1) is a human retrovirus responsible for leukaemia in 5 to 10% of infected individuals. Among the viral proteins, Tax has been described as directly involved in virus-induced leukemogenesis. Tax is therefore an interesting therapeutic target. However, its 3D structure is still unknown and this hampers the development of drug-design-based therapeutic strategies. Several algorithms are available that can be used to predict the structure of proteins, particularly with the recent appearance of artificial intelligence (AI)-driven pipelines. However, Tax seems to be resistant to such predictions.

HTLV-1 Tax structure prediction model zinc finger

1. Introduction

Human T-Leukemia virus type 1 (HTLV-1) was the first oncogenic retrovirus discovered in humans [1]. It is estimated that 5 to 10 million people are infected with HTLV worldwide, in areas of high endemicity [2]. HTLV is the etiological agent of adult T-cell leukaemia (ATL) and Tropical Spastic Paraparesis (TSP) which occur in 5 to 10% of infected people [2]. Interestingly, only HTLV type 1 virus (HTLV-1) but not its type 2 homolog (HTLV-2) induces ATL in humans [3]. Among HTLV-1 proteins, Tax plays a central role in viral replication and HTLV-1–related pathologies [4]. Tax is a 353-residue-long viral protein (~40 kDa), in which several functional domains have been described [5] (Figure 1) which confer numerous functions to the protein.
Figure 1. Functional domains of Tax. NLS: nuclear localization signal; E: nuclear export signal, P: PDZ-binding motif; ZnF: zinc finger; LZR1 and LZR2: leucine zipper regions 1 and 2.
Indeed, this viral effector recruits cellular proteins such as RNA polymerase II, CREB transcription factor and p300/CBP coactivator on the viral promotor located in the 5′ LTR of the provirus to allow efficient transcription of the HTLV-1 genome [5][6]. In addition to its function as a viral transactivator, this pleiotropic oncoprotein is able to interact directly with a large panel of cellular proteins, from transcription factors [7][8] to proteins involved in cell signalling, cell cycle or apoptotic pathways [8][9] or mRNA quality control [10], thereby playing a central role in HTLV-1 oncogenesis [4][8][11]. In particular, the expression of Tax is necessary for the proliferation of primary T-cells in ATL patients [12]. Thus, Tax represents an interesting therapeutic target for treatment against ATL, and deciphering its 3D structure would be a significant breakthrough towards the development of anti-HTLV-1 drugs. Unfortunately, the experimental solving of the 3D structure of Tax remains elusive. To date, the only published structures concerning HTLV-1 Tax are that of short peptides in complex with HLA molecules [13][14][15][16][17][18][19][20][21][22][23] or structures of the last eight residues of the C-terminal extremity of Tax, forming a PDZ-binding motif, in complex with PDZ proteins [24][25][26][27].
In the absence of experimental data on the structure of a complete Tax protein, it is tempting to consider modelling this 3D structure de novo. Until recently, the algorithms for the prediction of protein 3D structures were based on homology modelling: schematically, the algorithm will compare the sequence of the protein of interest (query sequence) with sequences of proteins for which experimental structural data are available in protein structure databases, extract its predicted secondary structures, and compare with those of the sequences of proteins that were the closest homologues in the multiple sequence alignment. Then, based on these sequence/secondary structure alignments, it models the structure of the protein of interest using the 3D scaffold of the identified model(s) and a final energy minimization step. With the emergence of artificial intelligence (AI), new structure prediction pipelines have been described. Schematically, these algorithms are based on neural networks and deep learning that are aggregating the physical and geometric constraints that are present in stretches of sequences present in published protein structures as well as global constraints to generate 3D models. These recent algorithms appear to perform with high efficacy in the yearly critical assessment of protein structure prediction (CASP, https://predictioncenter.org, accessed on 3 February 2024).

2. Predicting the Structure of HTLV-1 Tax

2.1. Predictions Using Homology Modelling

Swiss-Model (https://swissmodel.expasy.org, accessed on 25 January 2024) predicts a β-stranded structure, which includes only 41 residues from the N-terminus of Tax (Figure 2A, residues 27–67). The predicted fragment is homologous to the nitrite reductase small subunit from Vibrio parahaemolyticus (PDB ID 3C0D [28]) and its confidence score QMEANDisCo is of 0.31 ± 0.12. Thus, this partial model appears poorly reliable.
Figure 2. Prediction of Tax 3D structure using (A) Swiss-Model, (B) Phyre2 with defaults settings, (C) Phyre2 with “intensive” settings and (D) I-Tasser. Models are coloured from N- to C-terminal from dark blue (residue 1) to red (residue 353), as in Figure 1. Therefore, a single residue will have the same colour on all models, including the partial models (A,B).
Phyre2 (http://www.sbg.bio.ic.ac.uk/phyre2, accessed on 29 January 2024) predicts a structure of a 60 residue-long fragment (Figure 2B, residues 27 to 96), which encompasses the Swiss-Model structure and is homologous to the ferrodoxin component of a bacterial toluene-4-monooxygenase complex (PDB ID 1VM9 [29]).
The Phyre2 server can also be used with an “intensive” option to force the modelling of the complete protein through a multiple template modelling (i.e., using several model structures based on local sequence homologies). The predicted region 27–96 is unchanged and the appended modelled parts are constructed from several other template proteins, such as a plant ferrodoxin reductase (PDB ID 1FND [30], Tax residues 207–250). The resulting predicted structure is modular with the N- and C-terminal domains separated by a flexible linker (Figure 2C) but the C-terminal part, which is not modelled with the default settings, appears to be loosely folded, with few secondary structure elements. 
I-Tasser (https://zhanggroup.org/I-TASSER/, accessed on 26 January 2024) is based on the assembly of PDB templates from local homology domains. The server was able to generate a structure prediction for the full protein (Figure 2D) and the first threading template was the human S-phase kinase-associated protein 2 (PDB ID 1FQV, chain A [31]). Because of this new template, the N-terminal domain of Tax is predicted to contain α-helices instead of the previous β-strands. The modelled central region (residues 100–200) and C-terminal extremity (residues 300–353) contains more secondary structure elements than the “Phyre2 intensive” model (Figure 2C,D) but the predicted tertiary structure is still loosely folded. The calculated QMEANDisCo score is also low with a value of 0.35 ± 0.05. 

2.2. Predictions Using AI-Based Pipeline

The difficulties in predicting the 3D structure of proteins that have no homologues in structure databases, as described above for Tax, is a problem which has been encountered for years. The recent appearance of AI-based algorithms, which all appeared to perform with high efficacy in international protein structure prediction competitions, has given new hopes for the deciphering ab initio of structure function relationships. Because they are based on different AI-driven processes, there are four of them to predict the structure of Tax: AlphaFold 2 [32], RoseTTAFold [33], ESMFold [34] and D-I-Tasser [35] (Figure 3). AlphaFold 2 and ESMFold binaries were installed and run on an in-house server, while RoseTTAFold and D-I-Tasser were run through their primary webservice (https://robetta.bakerlab.org/submit.php accessed on 25 January 2024 and https://zhanggroup.org/D-I-TASSER/ accessed on 26 January 2024, respectively).
Figure 3. Prediction of Tax 3D structure using (A) AlphaFold 2, (B) RoseTTAFold, (C) ESMFold and (D) D-I-Tasser. Colour scheme is identical to Figure 2.
AlphaFold 2 is using neural networks based on evolutionary, physical and geometric constraints of protein structures [32]. The model generated by this algorithm (Figure 3A) shows a two-domains protein. The N-terminal domain is composed of β-strands while the central domain, which is rather compact, contains both β-strands and α-helices.
RoseTTAFold is using a “three-track network” in which information at the sequence, the secondary structure, and the 3D level are successively integrated [33]. Based on the Tax sequence, RoseTTAFold is also predicting a two-domain protein, separated by an isolated α-helix (Figure 3B). Both the N- and C-terminal domains are containing a mixture of β-strands and α-helices. However, by opposition to the AlphaFold 2 model, the C-terminal region of Tax is predicted here as containing two α-helices. The N-terminal region (residues 20–74) is predicted to contain helical motifs that are absent from the AlphaFold 2 model (Figure 3A).
ESMFold adopts a different approach, as it uses language models trained on protein sequences and therefore does not depend on multiple sequence alignments [34]. ESMFold, like the other AI-based algorithms, predicts that the Tax protein is composed of two domains (Figure 3C). 
Finally, scholars used D-I-Tasser which is an evolution of I-Tasser (see above) that includes a deep neural-network predictors analysis coupled to the I-Tasser force fields (Figure 3D). D-I-Tasser predicted a model for the whole protein and the first threading template is, this time, a protein from the drosophila apoptosome (PDB ID 1VT4 [36]). As a consequence, the predicted topology is different from the I-Tasser one and the D-I-Tasser model has more α-helices (Figure 2D and Figure 3D). 

3. Comparison of HTLV-1 Tax Structure Models

Two of the four AI-generated models (RoseTTAFold and ESMFold) exhibited the best local confidence scores for the N-terminal domain of Tax, which is the zinc finger domain which was also modelled by Swiss-Model and Phyre2. Thus, scholars compared the secondary structures elements of all these models with respect to Tax functional regions (Figure 4).
 
Figure 4. Depiction of the secondary structure elements from the different models with the functional domains of the Tax protein. Underlined in red is the sequence of Tax used for the modelling (Query). Above the sequence: arrow: β-strand. Squiggles: α-helix. T: turn. The boundaries of the partial models are depicted by red brackets. Under the sequence, blue rectangles mark the functional domains of Tax depicted in Figure 1; the dotted line together with the leucine zipper regions depict the dimerization domain of Tax. NES: Nuclear Export Signal; PBM: PDZ-binding motif. The figure was generated by ESPript 3.0 [37].
It appears that the Nuclear Export Signal and the centre of the dimerization domain are predicted as being in an α-helical region by all predictors that modelled this region (residues 175–205). Notably, it is the region which had the best local confidence score in the D-I-Tasser model. For the rest of the protein, none of the models are convergent (Figure 4). 
As there are a lot of different topologies for zinc fingers that have already been described in the literature [37], this observation could suggest that the Tax protein harbours another, yet undescribed, zinc finger topology that the algorithms do not identify, especially as they do not support the prediction of metal coordination. Indeed, although not superposing, both ESMFold and RoseTTAFold predicted three cysteines (C29, C36 and C49) and one histidine (H52) in close vicinity, which could coordinate a zinc ion. Such zinc fingers with three cysteines and one histidine (CCCH) have been described and are involved in RNA metabolism [38]. Their consensus sequence is C-(X4–15)-C-(X4–6)-C-(X3–4)-H (with X for any amino-acid) [39]. Thus, this putative CCCH zinc finger in Tax, with the sequence C-X6-C-X12-C-X2-H, would be non-canonical and marked by a particularly longer distance between the second and third cysteines (12 instead of 4 to 6). Of note, this zinc finger is also predicted by AlphaFold 2 but not by D-I-Tasser, nor by any other homology modelling method. 
Another possibility is that this region of Tax is intrinsically disordered and that the zinc finger is only forming through induced folding when Tax interacts with a biological partner. The formation of the zinc finger of Tax could also require trans-complementation with domains or residues of the interacting partner, as it contains only seven cysteines or histidine residues while eight are needed to complete two zinc fingers. Such an induced folding and trans-complementation for the formation of the zinc finger have been described for the HIV-1 Tat protein: this regulatory protein, which is intrinsically disordered [40][41], contains seven cysteine residues and uses a residue from its interacting partner, Cyclin T1, to complete its two zinc fingers that are then folded as α-helices [42].
A third possibility comes from the fact that Tax can undergo several post-translational modifications, such as phosphorylation, acetylation, SUMOylation and/or poly-ubiquitination which are important for its function [1] and may influence its conformation. However, there is no algorithm to date which includes this parameter during protein structure prediction

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