Consensus structural models for the amino terminal domain of the retrovirus restriction gene Fv1 and the Murine Leukaemia Virus capsid proteins
© Taylor and Stoye; licensee BioMed Central Ltd 2004
Received: 03 September 2003
Accepted: 12 January 2004
Published: 12 January 2004
The mouse Fv1 (friend virus) susceptibility gene inhibits the development of the murine leukaemia virus (MLV) by interacting with its capsid (CA) protein. As no structures are available for these proteins we have constructed molecular models based on distant sequence similarity to other retroviral capsid proteins.
Molecular models were constructed for the amino terminal domains of the probable capsid-like structure for the mouse Fv1 gene product and the capsid protein of the MLV. The models were based on sequence alignments with a variety of other retrovirus capsid proteins. As the sequence similarity of these proteins with MLV and especially Fv1 is very distant, a threading method was employed that incorporates predicted secondary structure and multiple sequence information. The resulting models were compared with equivalent models constructed using the sequences of the capsid proteins of known structure.
These comparisons suggested that the MLV model should be accurate in the core but with significant uncertainty in the loop regions. The Fv1 model may have some additional errors in the core packing of its helices but the resulting model gave some support to the hypothesis that it adopts a capsid-like structure.
Keywordsvirus capsid protein models threading Fv1 MLV
The Fv1 gene is one of a series of mouse genes that control the susceptibility of mice to murine leukaemia virus (MLV) [1–3]. The gene acts in the cell to restrict virus replication  through a mechanism that is still uncertain. Genetic studies suggest that the target for the Fv1 gene product is the capsid protein (CA) of MLV [5, 6] and it is thought to interact with CA after entry of the virus into the cell but before integration and formation of the provirus.
When cloned and sequenced , the Fv1 gene was found to have sequence similarity to endogenous retroviruses of the HERV-L and MuERV-L families [7, 8]. Based on its position within the Gag gene of these endogenous elements, it appears that Fv1 encodes a capsid-like protein. This structural assignment of the Fv1 gene is consistent with its function as it can be postulated that the gene product might act as a dominant negative mutation and interfere with the MLV capsid function . Sequence alignments have been made between Fv1 and other retroviral capsid proteins  but besides one region of clear similarity, called the Major Homology Region (MHR), there is otherwise little that is conserved across the full family of retroviral (and related lenti-virus) CA sequences.
There are now several known structures for retroviral capsid proteins in the Protein Databank (PDB). While some of these are only fragmentary, a selection can be extracted that gives a reasonable phylogenetic spread across the retroviruses, with examples from three out of six genera of orthoretroviruses. In all the known structures, the CA protein has an all-α type structure consisting of two domains: a larger N-terminal and smaller C-terminal domain with a short extended linker-region between them. As this linker enters the C-terminal domain it incorporates the MHR. There is considerable variation in the orientation of the domains and in the conformation of the loop-regions between α-helices, particularly in the N-terminal domain.
In this work, we have exploited these multiple structures to construct consensus molecular models using threading methods both for the Fv1 gene product (FV1) and its target protein, the MVL CA. As threading takes known and predicted structure into account, it should provide better alignments for the regions that lie outside the MHR. However, as these methods are still far from perfect, we have constructed a model based on each known structure and the degree to which these agree has been used to assess the confidence of different parts of the model. As the threading method we have used has some 'free' parameters (such as the gap-penalty) we have introduced a novel modelling strategy in which the parameters are varied to give maximum agreement among the resulting models.
Results and Discussion
Template sequences selected for alignment. The Ψ-BLAST/QUEST search strategy (Methods Sect n .) when started with the probe sequence of the PDB structure indicated by "SEED" selected the sequences indicated in each subtable: (a) EIAV [1eia], (b) RSV [1d1dA], (c) HIV-1 [1e6jP] and (d) HTLV-I [1qrjA]. The sequences are identified by their gene identification (gi) number (first column) and their local source databank identifier. The sequence fragment (automatically extracted by QUEST) is given as a range of residue numbers.
The databank search using the MLV sequence as a probe provided a useful collection of six sequences (Table 1(a)) which, with the MLV probe sequence itself, were passed through the PsiPred secondary structure prediction protocol (Methods Sect n .). When the predicted secondary structures were viewed on the aligned sequences, several distinct α-helices were apparent, consistent with the protein having an all-α type structure similar to the other capsid proteins of known structure (Figure 1).
The multiple structure alignment of the four models showed good agreement. In the N-terminal domain there were two extensive regions over which all models were aligned in register, covering helices N2-N3 and N5 (including the preceding short helix in the loop region). Two alternate registers were observed for helices N1 and N4, with relative shifts of 3 and 4 residues, respectively. The models based on 1qrjA (HTLV-I) and 1d1dA (RSV) were in complete agreement and the summed F w score indicated that 1d1dA provided the best consensus model.
Given that the alignment of the capsid protein sequences is ambiguous, the superposition of the models on the structures from which they were derived provides a better way to assess whether there is any significant sequence similarity that could be used as a basis on which any one model might be preferred over the others. The PSId values were: 5.6, 18.5, 10.5, 13.0 for 1d1dA, 1qrjA, 1eia and 1e6jP, respectively. (No differences were observed whether using the standard version or the sequence-biased version of SAP).
It can be seen from Figure 3(a) that there is good location of the predicted and model helices with deviations occurring only at the ends of some helices and into the loop regions at the 'top' of the molecule. The ends of helices N4 and N5 and the loops at this end of the molecule also encompass the mutations identified as affecting the sensitivity of the virus to Fv1 .
With its relatively unambiguous MHR, all the models of the C-terminal domain were in complete agreement over the first half of the domain. The more C-terminal half, however, was less consistent due to a combination of its generally less conserved nature combined with uncertainty in the location of the terminus in some of the sequences.
As the C-terminal domain has been shown to be less important in determining the property of virus susceptibility, further effort was not expended to try and refine the alignment at the carboxy terminus of the molecule, especially in the more difficult alignment of the FV1 sequence described below.
The databank search using the FV1 sequence as a probe provided a less useful collection of only two distinct sequences (Table 1(b)). Although other sequences were found these were rejected by QUEST as being too similar to those retained.
Target sequences selected for alignment. The Ψ-BLAST/QUEST search strategy (Methods Sect n .) when started with the two target sequences indicated by "SEED", selected the sequences indicated in each subtable: (a) MVL and (b) FV1. The sequences are identified as in Table 1. (* the C-terminal domain of this sequence appears to be replaced by an oncogene.)
Control model similarity. The similarity of the capsid protein domains are tabulated as: uRMSd/PSId, both as calculated by the SAP program. The proteins are identified as in Table 4. The column "params" gives the MST parameter values S, G at which the four models in each row had maximum agreement as measured by the combined agreement score F v (Methods Sect n.).
str \ seq
Based on relative degrees of sequence similarity among the control models (Table 3(b)) and the MLV and FV1 sequences (MLV:1qrj = 18.5%, FV1:1e6jP = 15.9%), it would be expected that the models constructed for the MLV sequence would fall in the mid-range of the spread in quality observed in Figure 7 – typically, a good core model (3 Å RMS over 80 residues) with increasing divergence in the more variable loop regions. This is where the majority of the control models lie which were all constructed from sequence similarities that are generally lower than either of the above relationships used to model the MLV or FV1 sequences.
While a similar confidence might be hoped for the FV1 model, given its overall lower sequence similarity to the proteins of known structure and the less consistent nature of its secondary structure prediction, it is more likely that it will be of lesser quality – corresponding more to the poorer models constructed among the control proteins (Figure 7). Typically, this would include shifts in the core helices (giving the characteristic immediate rise in the traces in Figure 7) with further shifts in the loop regions. Despite this, as with all of the control models, it is likely that the core fold of the protein should remain unaltered.
This study has shown that reasonable models can be constructed for both the FV1 and its target MLV protein based on other retroviral capsid proteins. Although this result was suggested by the existence of the MHR in both sequences, the fluid nature of retroviral genomes does not necessarily constrain the preceding domain to remain constant in structure or even remain at all. Despite only weak sequence similarities in this region, the addition of multiple sequences with predicted secondary structure has allowed plausible models to be constructed.
These models can now be used in the interpretation of experimental studies on the mode of action of retroviral susceptibility. As will be reported in more detail elsewhere , a series of amino acids in CA affecting the CA – FV1 interaction have been identified in the loops at the 'top' of the N-terminal domain (Figure 3). Based on the model, they suggest a potential FV1 binding domain in the MLV CA. Experiments are currently under way to test this prediction by crystallographic studies.
For many years, the Fv1 gene has been the only known intracellular non-immune natural defense against a retroviral infection. Recently, two additional genes, Ref1 and Lv1, with antiviral activity have been described in human cells [30, 31]. Phenotypically, they resemble Fv1  but the genes themselves remain to be characterised. Understanding the mechanism of Fv1 action will provide insights into how natural defences to retroviral infection might be deployed against HIV.
Methods and Data
All sequences were extracted from (and searches were made over) the non-redundant protein sequence databank (NRDB) at the National Centre for Biotechnology Information (NCBI) as it was found on 28 th of January 2003.
The sequence of the MLV used was the gag protein AAD55051 (GI:5881091)  and a region was extracted from residues 215–432, corresponding to the CA protein.
The sequence of the Fv1 gene  was taken from FV1_MOUSE (GI:3913713). The region corresponding to the CA was identified as residues 100/120–340/360 where the inner numbers represent the probable core of the protein. This range corresponds to the region of highest similarity to the MuERV-L sequence . The leading 100 residues of the polyprotein may correspond with a relic matrix protein. Perhaps because of this, there is no obvious protease cleavage motif  to give any indication of the true terminus. However, in other situations this has not always been an accurate guide .
The structures of capsid proteins were extracted from the PDB  with the aid of the FSSP structure comparison database http://www.ebi.ac.uk/dali/fssp/fssp.html. Of the six structures in the FSSP alignment, only four extended over the full length of the two structural domains. There were as follows, with their PDB code (and chain delimiter, if any) shown in brackets: Rous Sarcoma Virus (RSV) [1d1dA] , Human T-cell Leukemia Virus (HTLV-I) [1qrjA] [16, 17], Equine Infectious Anemia Virus (EIAV) p26 [1eia]  and Human Immunodeficiency Virus (HIV-I) p24 [1e6jP] .
The common core of the N-terminal domains of these proteins (in the numbering of the PDB structure) was defined as: 1d1dA 15–148, 1qrjA 16–129, 1eia 16–145 and 1e6jP 16–146. These fragments will be distinguished below as: 1d1dAn, 1qrjAn, 1eia-n and 1e6jPn and each terminates 8 or 9 residues before the conserved glutamine of the MHR. The N-terminal domain can be described as having five α-helices (N1...N5) with a long 'disordered', partly helical, loop between helices N4 and N5. For ease of reference below, this region will be called the 'top' of the molecule and its representation in the Figures will preserve this orientation.
The C-terminal domains were defined as: 1d1dA 152–224, 1qrjA 132–204, 1eia 149–220, 1e6jP 149–220 and were distinguished by the suffix "c". The common core of this domain consists of an extended strand leading into the MHR region followed by four helices designated C1...C4.
Despite their different sizes, both the N and the C domains have the same fold, perhaps suggesting an ancient gene duplication. This is most obvious in the HIV structure [1e6jP] where the domains can be superposed with 4.6 (2.0) unweighted (weighted) RMSd over 68 residues.
Capsid protein similarity. The similarity of the capsid protein domains are tabulated as: (a) RMSd (unweighted) and (b) PSId, both as calculated by the SAP program. Upper-right triangle: over the common core of the C-terminal domain and Lower-left triangle: over the common core of the N-terminal domain. The proteins are identified as: D1D = RSV [1d1dA], QRJ = HTLV-I [1qrjA], E6J = HIV-I [1e6jP], EIA = EIAV [1eia ].
N \ C
N \ C
Sequence Databank Searches
Initially, each probe sequence was compared against the sequence databank using the Ψ-BLAST program  with a significance level set at 0.001 and 5 cycles of iteration. When the probe is a retroviral sequence, the number of hits found by Ψ-BLAST can be large (typically over 1000). These were reduced to manageable numbers by the use of the search program QUEST which is similar to Ψ-BLAST but incorporates a multiple sequence alignment stage in its iterations to exclude redundant sequences as well as excluding poorly related or incomplete sequences . The alignments produced by QUEST typically contain between 6–12 sequences (including the probe sequences), none of which have more than 60% sequence identity (PSId) with each other.
The sequences retained by QUEST are selected on the basis of associated biological information, with those including useful annotation and structural data being given preference over those with no annotation or keywords such as "hypothetical". The filters are part of the MULTAL sequence alignment program  which are fully described in Ref. .
Secondary Structure Prediction
The multiple alignments resulting from the Ψ-BLAST/QUEST search protocol were passed to the program PsiPred http://bioinf.cs.ucl.ac.uk/psipred/, Version 2.3) . This program normally performs its own databank searches using Ψ-BLAST to build-up an alignment. Given the problems described above that arise when searching with retroviral sequences, the PsiPred program was used locally to search only a database consisting of the sequences that had already been selected by QUEST.
Each sequence in the alignment was taken in turn and used as a probe against this small local database. As the Ψ-BLAST parameters used by PsiPred were more restrictive than those used in the full search (only 3 cycles) and there are fewer sequences in the databank, each sequence may only find those to which it is more closely related. This introduces some variation into the predictions which provides a useful indication of the confidence of each predicted secondary structure element (SSE).
Multiple Sequence Threading
The alignment gathered on the probe sequence was then aligned with a protein structure using the multiple sequence threading MST program . This program uses multiple sequence and structural information to automatically construct an α-carbon molecular model for the probe sequence with some limited remodelling in regions of insertion and deletion.
Template Sequence Alignments
The MST program can incorporate multiple aligned sequences along with both the probe sequence and the template structure. The latter were gathered in an identical manner to the probe sequence using the Ψ-BLAST/QUEST search protocol described above. Each search against the NRDB was started with the sequence of the protein of known structure and the resulting multiple alignments examined 'by-eye' in the light of the known secondary structures. If any large insert had been made in a secondary structure element (SSE) then it was assessed whether the gap could be shifted outside the SSE without significant loss of residue matches. Similarly, if a large insert (more than 6 residues) was made by any sequence other than the probe sequence (of known structure) then the insert was reduced to six residues by removing the positions with most gaps.
The MST program has parameters that allow different weights to be attached to the matching contribution of the sequences, their secondary structures, residue exposure and the residue packing in the resulting model. There is also a gap-penalty. The best values for these weights depends on the number and degree of relatedness among both the probe and the template sequences . Rather than vary all these parameters individually, the weights on the structural components (secondary structure, exposure and packing) were 'ganged' together into a single parameter reflecting the contribution of structural terms relative to the sequence matching component. This gave two parameters: S (for structure) and G (the gap-penalty). Previously, the structural parameters had all been scaled into the same range so a value of S = 3 corresponds to a value of 3 for each individual weight. Although the gap-penalty is correlated with S, it cannot be linked in the same way without the risk of missing good alignments.
In the current application, there was more than one available template structure and advantage was taken of this by constructing models based on all available templates and choosing the MST parameters such that the agreement among the models was greatest. The parameters were varied over the ranges: S = 0→9 (in steps of one) and G = 10→90 (in steps of 10).
Measuring Model Agreement
Whatever the parameters for MST, all the models constructed from the same probe have an identical sequence. These might therefore be compared using the α-carbon RMSd based on a one-to-one (100 PSId) sequence equivalence. However, using this simple measure, a 'trivial' shift in space in which, say, an α-helix shifts by one turn relative to another α-helix might result in a large RMSd between what are, topologically, similar models. It is better to allow a local relative shift in sequence of four residues to restore the spatial equivalence at the expense of residue identity.
To implement this trade-off between RMSd and PSId, the models constructed for each parameter combination were compared against each other using the program SAP http://mathbio.nimr.mrc.ac.uk/ftp/wtaylor/sap/. This program calculates both a weighted (Rw) and unweghted (Ru) RMSd for the two structures being compared and reports the percentage sequence identity of the alignment. The weighted RMSd down-weights regions of weak similarity which are mainly loop regions that can have large relative displacements. Despite its origins , in its current implementation the SAP program http://mathbio.nimr.mrc.ac.uk/ftp/wtaylor/sapid/ does not include a sequence matching component and this was restored (for sequence identity only) by doubling the local residue pair score for identical residue types and otherwise halving all other residue match scores in the initial score matrix.
A score reflecting match quality (f) was calculated as: f = M/(1+R), where M is the PSId measured over the positions aligned by SAP and R is one of the RMSd measures. Identical structures would score 100. For a set of N models, a sum was calculated over the (N2 - N)/2 pair combinations giving an overall measure of agreement (F) among the set. For a set of four models that align perfectly (100 PSId) with 2 Å RMSd, the overall score obtained would be 200. This score was calculated for both the wRMSd and uRMSd values (giving F w and F u , respectively) and a combined score (F v ) as the product of F w and F u .
While this procedure provides a general method for choosing parameter values, in the current application to a multi-domain protein it was not meaningful to calculate the RMSd over the full atomic model (because of relative domain movements). Instead, the agreement was calculated over the more distantly related N-terminal domain.
Selecting a consensus model
Although any model in the set could be taken as a representative, it is best to try and select one that, by some criteria, can be considered to be the most representative. To do this, we compared each pair of models usuing the structure comparison protocol described in the previous section. This provides a pairwise alignment based on structure, and even though each model has an identical sequence, the structural alignment may not match-up identical residues. The pairwise alignment were then combined into a multiple structure alignment  and as the models all have an identical sequence, their relative shifts can be seen easily. Rather than use a pure structure or sequence based measure of similarity between the proteins, the score F was devised in the previous section (Methods Sect n .) that combines both a sequence and a structural component. This was used to find the model with the greatest sum-of-scores to the others.
An alternative selection test was also considered of selecting the model that had greatest sequence similarity when superposed with the template structure from which it was derived. As most of the sequence similarities considered below lie in the 'twilight-zone', the latter option was only used when one model was clearly better than the others. For this, we choose the criterion that it had to be 10 PSId points clear of its 'rivals'.
gene/gene-product of Friend Virus susceptibility locus-1
Murine Leukaemia Virus
Human Endogenous RetroVirus (L family)
Murine Endogenous RetroVirus (L family)
Major Homology Region
National Centre for Biotechnology Information
Multiple Sequence Threading (program)
Secondary Structure Element
Percent Sequence Identity
Root-Mean Square deviation
weighted Root-Mean Square deviation
unweighted Root-Mean Square deviation
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