Conceived and designed the experiments: JR YF ZQ YS. Performed the experiments: WC KW JR JT. Analyzed the data: WC KW JR JT. Contributed reagents/materials/analysis tools: WC KW JR. Wrote the paper: JR JT.
The authors have declared that no competing interests exist.
CR6261 was found in 2008 and F10 was found in 2009. In 2010 Friesen et al experimentally showed that Oseltamivir/Zanamivir may improve the therapeutic efficacy of CR6261. As a result, the use of CR6261 combined with a drug to provide an antibody-based therapy against all influenza A viruses was proposed. Although CR8020 may neutralize group 2 influenza viruses and FI6 may neutralize both group 1 and group 2 influenza viruses as determined in 2011, the insight of Friesen et al is still interesting. Here, we address the following questions: how to uncover the molecular mechanism of a drug, which improves the therapeutic efficacy of mAbs and how to find drugs that enable CR6261 (CR8020, F10) to become a universal mAb.
Using the 3D structures of 3 gbn, 3 gbm, 3 ztn, 3 ztj, 3 fku and 3 sdy, we separate the 3D structures of CR6261, F10, CR8020 and FI6, and the 3D structures of trimer HAs of H3N2 and H5N1. Based on the experimental result of Friesen et al, we have found many clues, which reveal the molecular mechanism of action for a drug and an HA-mAb complex.
Oseltamivir/Zanamivir may congruously improve the therapeutic efficacies of CR6261, F10, CR8020 and FI6 by providing an additional affinity to compensate for the loss of affinity between HA and mAb resulting from mutations. However, Oseltamivir or Zanamivir are not expected to generally widen the spectrum of these mAbs. In order to enhance CR6261, CR8020, or for F10 to become universal, we may select Azichromycin, Oseltamivir, or the combination of Azichromycin and Oseltamivir, respectively.
Since the discovery of the human monoclonal antibody CR6261 published by Throsby et al (
Although a universal mAb FI6 has already been found, the insight for the use of a drug in a complex with CR6261 to neutralize all influenza A viruses is still worth pursuing, because it can provide a general method to enhance a wide spectrum of mAb and enable them to become a universal antibody. This can also show the way to enhance a universal mAb and avoid drug resistance. Therefore, this approach may lead to multiple choices for antibody-based therapies. The use of mAb in a combination with a drug will be easier and cheaper relative to the cocktail method that is based on two types of mAbs. Therefore, one of the objectives of this study is to provide a new insight regarding the utilization of mAbs. With an increasing number of mAbs becoming available, selectivity of mAbs in combination with drugs offers an opportunity to construct better mAb-drug combinations.
In this paper, we first determine the molecular mechanism by which Oseltamivir and Zanamivir improve the therapeutic efficacy of an mAb. Then, we look for the drugs which enhance CR6261, F10 or CR8020 to become a universal mAb, respectively. To perform the latter task, we must first deal with the hard problem of demonstrating the relationship between mAbs and the trimer HAs while being fully aware of the fact that mAbs cannot neutralize influenza viruses. For example, since we know that CR6261 cannot neutralize all group 2 influenza viruses, we should show that CR6261 and group 2 HAs may be combined first.
The fact that Oseltamivir or Zanamivir in complex with CR6261 improves the therapeutic efficacy of CR6261 to treat group 1 influenza viruses is a crucial piece of evidence in support of the assumption that Oseltamivir must directly act on either 3 gbn or 3 gbm. In fact, we cannot use the cocktail idea to explain this enhancement of the therapeutic efficacy of CR6261 by adding Oseltamivir. This is because Oseltamivir is ineffective against H5N1 when it binds to the NA protein of H5N1 and Oseltamivir does not bind to the trimer HA alone. Therefore, the complexed protein (CR6261 with the trimer H5 HA) must be the molecular target for the action of Oseltamivir. For the same reason, we know that the CR6261-trimer HA (for all group 1 HAs) must be the target protein for both Oseltamivir and Zanamivir. In order to uncover the mechanism by which a drug may improve the therapeutic efficacy of CR6261, we should look for a clue from the complex CR6261-trimer HA for all group 1 HAs. The result that Oseltamivir may improve the therapeutic efficacy of CR6261 in treatment of H5N1 should also lead to additional clues. For example, the docking poses between the drug and its benchmark pockets are known to be very diverse.
Other clues and additional information will be gradually extracted. For example, based on CR8020, we find that the footprint of mAb containing an epitope is not a necessary condition for mAb to neutralize influenza viruses. This clue leads us to focus on the issue of affinity. However, it is clear that an antibody is ineffective if its footprint is located on the head of a trimer HA. To look for underlying clues, one should use both computational tools and experimental methods. Since this is not an experimental project, we will use computational modeling to the greatest extent possible.
Based on all clues deduced from the well-known complexes of mAbs and HAs, we determine a general molecular mechanism to explain why a drug may improve the therapeutic efficacy of CR6261, CR8020, F10 and FI6. In order to look for a drug used in combination with CR6261, F10 or CR8020 to enable it to become a universal mAb, or enhance the effect of FI6 to prevent drug resistance, we need additional insights. They can be used to show that CR6261 and F10 bind to group 2 HAs and to predict the location of the footprints of CR6261 (F10) on H3 HA.
First, we use computational methods, namely protein-protein docking algorithms to show that CR6261 also bind to group 2 HAs. Clearly, it would be ideal if all spatial structures of group 2 HAs were well known. However, only 3D structures of H3 HA and H7 HA are available in the Protein Data Bank (PDB). This small number of structures is obviously insufficient. Fortunately, the conservation of footprints of antibodies on each of the group 2 HAs ensures that we may be able to infer a general conclusion based on some representative cases.
The available crystal structures of HAs in a complex with an antibody are 3 gbn (the H1 HA in complex with CR6261,
The conservation of a footprint helps us find the common conserved area on the surface of a stem of group 1 HAs or group 2 HAs. This conserved area is larger than the union of the footprints from a wide spectrum of mAbs on group 1 HAs or group 2 HAs. For this purpose, we need to use multiple sequence alignment algorithms and the benchmark dataset of all available HA sequences. Since public algorithms
In order to look for the binding pocket of Oseltamivir/Zanamivir on the target protein 3 gbn, we use AutoDock
Although CR6261 was validated experimentally to neutralize group 1 influenza A viruses, its molecular mechanism of action has not been uncovered yet. To determine the mechanism, we analyze all 3D structures of the HA-CR6261 complex for all group 1 HAs. This is because we can use it to obtain a critical affinity for CR6261 neutralizing group 1 influenza viruses. Nevertheless, we have only used 3 gbn with 3 gbm, which are partial crystal structures of HA-CR6261 complexes for H1 HA and H5 HA, respectively. As additional support, we have used all crystal structures 3 gbn, 3 gbm, 3 fuk, 3 sdy, 3 ztn and 3 ztj as a test panel to estimate the critical affinity for an mAb neutralizing the influenza viruses. Then, we have used the Ligand Explorer software and the molecular dynamic simulation (MD) software (e.g., GROMACS) to estimate the upper bound of the binding free energy for an antibody to unbind from an HA. As an introduction to MD simulations, we refer the reader to ref.
As approximate models of the crystal structure of CR6261 or F10 in complex with H3 HA, we use the predicted 3D structures. For this purpose, we need to use the protein-protein docking software (RosettaDock). As an introduction to the topic of protein-protein docking, we recommend refs.
Before we discuss the results obtained, we need to explain the benchmark dataset in more detail. We first downloaded all sequences of influenza A viruses from the Uniprot database updated on Dec.6, 2010. We then retrieved all HA sequences. All A-type sequences were classified into 16 types, denoted by H1, H2, etc., up to H16. To avoid confusion, we simply denoted C-type HA sequences and B-type sequences as HC and HB, respectively. The total number of HA sequences in the benchmark dataset is 36,051 (some sequences of a mixed or unidentified type have not been deleted from this dataset) and we have used these 36,051 sequences as the benchmark dataset in this paper. Section 3 of
Since we have used the unpublished multiple sequence alignment software MCABMSA, it is worth explaining the reason for it. Numerous software packages are readily available for use, for example, BLAT
It will be very convenient to directly use the dendrogram of H1–H16 used in refs.
HC | HB | H1 | H2 | H3 | H4 | H5 | H6 | H7 | H8 | H9 | H10 | H11 | H12 | H13 | H14 | H15 | H16 |
54 | 3002 | 9837 | 315 | 14235 | 467 | 4301 | 739 | 989 | 56 | 1354 | 201 | 145 | 63 | 69 | 7 | 10 | 24 |
A convenient method to reconstruct the dendrogram of H1–H16 is to group all available sequences into a few clusters using MCABMSA under each given parameter
Compute
Transform the cluster into the vector
Analyzing the components of each vector, we readily recognize which Hk sequences are grouped into the same cluster. For example, MCABMSA may output 7 clusters after being aligned under
C | B | H1 | H2 | H3 | H4 | H5 | H6 | H7 | H8 | H9 | H10 | H11 | H12 | H13 | H14 | H15 | H16 | |
HA_1 |
|
0.0000 | 0.0003 | 0.0000 |
|
|
|
0.0000 | 0.0010 | 0.0000 | 0.0185 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
|
0.0000 | 0.0000 |
HA_2 | 0.0000 |
|
|
|
|
0.0306 |
|
|
|
|
|
|
|
|
|
|
|
|
HA_3 | 0.0000 | 0.0000 |
|
0.0519 |
|
|
0.0780 |
|
|
0.0000 | 0.0542 |
|
0.0207 | 0.0339 | 0.0000 | 0.0000 |
|
0.0417 |
HA_5 | 0.0000 | 0.0000 |
|
|
0.0000 | 0.0087 | 0.0019 | 0.0027 | 0.0000 | 0.0200 |
|
0.0000 |
|
0.0000 | 0.0152 | 0.0000 | 0.0000 | 0.0000 |
HA_6 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
|
0.0066 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
|
0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
In
To distance H1–H16 from HB and HC, we need to gradually enlarge the parameter
The main idea to reanalyze the conservation of the residues on the surface of HA using the five-group method based on all available sequences is stated as below:
Rebuild datasets of HA1 and HA2 based on the benchmark dataset. The key to doing this is to use the palindrome peptide FGAIAGF as the maker to divide all H1–16 sequences into HA1 and HA2. Coincidentally, peptide FGAIAGF has already been used earlier as a marker to divide HA1 and HA2 (
Rebuild HA1A and HA1B based on the datasets of HA1. The key to doing this is to use the highly conserved peptide WGIHHP as the marker found by MCABMSA with
Rebuild HA2A and HA2B based on the datasets of HA2. The key to doing this is to use the highly conserved peptide YNAELLV as the marker found by MCABMSA with
HA1B is a longer segment and all amino acids on the head of a spike are contained in the first region of this segment. Using the peptide C***C****G which is located at the head of a spike but links the stem of the spike as a marker, we can further divide HA1B into HA1B1 and HA1B2.
HA1A, HA1B2, HA2A and HA2B are four segments containing the regions 1–58 and 302–344 on HA1 and the regions 3–87 and 94–196 on HA2, respectively.
These four regions form the stem of HA. For more details regarding how to divide the stem and head the reader is referred to Section 6 of
Using 1rd8 and 1 mql as the models of group 1 HAs and group 2 HAs, we read off the common conserved area on group 1 HAs and group 2 Has, respectively based on Tables S13 and S14. Furthermore, we find the footprint of CR6261 on H1 HA, the footprint of F10 on H5 HA and the footprint of FI6 on 1rd8, respectively. For clarity, we highlight the conserved area with yellow, the footprint of CR6261 with red, the footprint of F10 with blue, and the footprint of FI6 with green. In the same way, the footprint of CR8020 on H3 and the footprint of FI6 on H3 are found on 1 mql, respectively. Shown in yellow is a conserved area, the red area means the footprint of CR8020 and the green area means the footprint of FI6. This is shown in
(A) The conserved area (green), the footprint of CR8020 (red), and the footprint of FI6 (purple) on group2 HA. The overlap between footprint of CR8020 and footprint of FI6 is yellow. (B) The conserved area (green), the footprint of CR6261 is Cambridge blue and yellow, the footprint of F10 is navy blue and yellow, and the footprint of FI6 is completely covered by yellow on group 1 HA.
In
Since Oseltamivir and Zanamivir may enhance the therapeutic efficacy of CR6261 to treat group 1 influenza viruses, we infer that 3 gbn and 3 gbm must be the target proteins for Oseltamivir and Zanamivir. To explore where on 3 gbn or 3 gbm the binding pocket of Oseltamivir and Zanamivir is located, we use AutoDock to search for the pocket using computational methods because we have no experimental means to do so. How to use it with a panel of drugs and a panel of target proteins to enhance the reliability of AutoDock has been mentioned in Section 1.2 of the Introduction. Typically, we explore the location of the binding pocket as follows.
Based on the panel of drugs and the panel of target proteins, we use AutoDock to blindly dock, one by one, the 9 drugs with the 6 target proteins.
Within the output of the drug and protein pair, we choose the pose ranked number 1 in the first cluster to be the optimal pose for each drug and protein pair.
On each target protein, we use PyMOL to show all of these optimal poses with negative values of MFE at the same time.
The cave/groove is called a binding pocket if it contains the union of these optimal poses with a negative value of MFE.
Using the above method of exploration, we obtain six pockets shown in
(A) The pocket of drugs on 3 gbn is the fork. (B) The pocket of drugs on 3 gbm is the fork. (C) The pocket of drugs on 3 fku is the fork. (D) The pocket of drugs on 3 sdy is the fork. (E) The pocket of drugs on 3 tzn is the fork. (F) The pocket of drugs on 3 tzj is the fork.
Let the pose of the drug bound to the pocket with the globally minimum free energy be the optimal pose. Then slight differences between the angles will result in variations of the optimal pose. Intuitively,
In order to quantitatively analyze the additional affinity provided by a drug in the optimal pose, we use Ligand Explorer to compute all hydrogen bonds and all hydrophobic interactions for each optimal pose. For simplicity, we do not distinguish hydrogen bonds and hydrophobic interactions and both are called by a joint name, non-covalent bonds. Then all non-covalent bonds can be classified into two sets. The non-covalent bonds between the ligand and HA are classified into the left set and the non-covalent bonds between the ligand and mAb are classified into the right set. We denote the numbers of left and right sets by a, b respectively. It is clear that a+b is proportional to the total binding affinity of a drug docked to the given pocket in the optimal pose. It is logical to expect that min{a, b} rather than a+b is the key parameter for improving the affinity between HA and mAb. Therefore, we infer that the therapeutic efficacy of CR6261 will be improved by a drug if min{a, b}>0.
Specifically, for the 9 optimal poses produced by the 9 drugs docked to pocket_3 gbn, we find the values a, b, a+b and min{a, b} for the 9 optimal poses on pocket_3 gbn, which are shown in
H1 | H5 | H3 | |
CR6261 |
|
*98–111 | |
CR8020 | *126–136 | – |
|
F10 | – |
|
*170–180 |
FI6 |
|
– |
|
Here, a is the number of non-covalent bonds between a drug and HA; b is the number of non-covalent bonds between a drug and CR6261; a+b is the total number of non-covalent bonds between a drug and the complex HA and CR6261; and min{a, b} is the minimum number between a and b. This table shows that Azithromcyin is best overall; Oseltamivir and Zanamivir also enhance the prophylactic and therapeutic efficacy of the CR626 antibody. Aspirin and Heroin appear to be ineffective. Both Isosorbide and Amantadine are almost ineffective.
We further compute the values a, b, a+b and min{a, b} of the 9 optimal poses for pocket_3 gbm, pocket_3 ztn, pocket_3 ztj, pocket_3 fku and pocket_3 sdy, respectively. The results are shown in Table S16 of Section 9 of
For the simplicity of computation, we will not distinguish between the pose and the coordinates. This is because each pose is determined by recalculated coordinates of the drug obtained through shifting or rotating its original 3D coordinates. Conversely, renewed 3D coordinates of the drug also uniquely determine a pose.
A minor flaw of AutoDock which has been mentioned in Section 1 of
It is not hard to imagine that a drug docked to a benchmark pocket will exhibit multiplicity of conformers (multiformity) because there is no means to ensure it must exactly bind to its benchmark pocket according to the designed pose. In fact, based on the available experimental results measured by x-ray crystallography, we can explore this multiformity of experimentally determined poses. For example, ADP is a well-known ligand which binds to many hundreds of proteins and has many benchmark pockets on each target protein. For example, the first group contains 12 proteins which are 1×88_A, 1 yrs_A, 2 fky_B, 2fl2_A, 2fl6_A, 2g1q_B, 2q2y_A, 2q2z_B, 2 wog_A, 2×7d_A, 2×7e_B and 3 cjo_A. The second group contains 11 proteins which are 1 bmf_D, 1e1q_D, 1e1r_D, 1efr_D, 2ck3_D, 2jiz_D, 2jj1_D, 2jj2_K, 2v7q_F, 2w6e_D and 2 wss_M. All of these proteins may be bonded by ADP. We assume that 12 poses are the poses of ADP docked to the same protein 1×88_A by independently repeating it 12 times and that 11 poses are the poses of ADP docked to the same protein 1 bmf_D by repeating it independently 11 times. Gathering the 12 poses on 1×88_A and the 11 poses on 1 bmf_D, we find that in practice ADP docks to the same benchmark pocket on 1×88_A or 1 bmf_D with multiple poses. A detailed validation is stated in Section 10 of
Generally, it is believed that the molecules of a drug randomly collide with the benchmark pocket with no control over the exact docking mode to the benchmark pocket. Therefore, the real binding poses span a manifold within a neighborhood of a given pose. A drug is effective if most of the actual poses play the same effective role in binding to the target. Conversely, a pose is ineffective if all poses in the neighborhood of the pose play the same ineffective role.
For convenience, we define the quotient of these associated poses in the neighbor of the given pose playing the same role as the given pose versus the total number of poses in the neighbor as the tolerance of the pose. Following from the definition of tolerance, we easily find that a drug has high efficacy if the designed pose of the drug binding to its benchmark pocket has high tolerance. As mentioned before, the number of associated poses within the neighborhood of a given pose is huge. Therefore, the computation of the tolerance of a pose in practice should be approximated by a simplified method. For n associated poses, if m associated poses play the same role as the given pose, then we use the ratio m/n as the estimation of the tolerance of the given pose.
Specifically, for the optimal pose of Oseltamivir or Zanamivir binding to the pocket_3 gbm, we have randomly selected many associated poses in the neighborhood of the optimal pose and computed the numbers a and b and we find that min{a, b} = 0 for all of these selected associated poses. Therefore, the optimal pose of Oseltamivir or Zanamivir binding to the pocket_3 gbm is ineffective. This unexpected result further leads us to look for the real cause that Oseltamivir or Zanamivir improves the therapeutic efficacy of CR6261 to treat H5 influenza viruses.
Analyzing the size of pocket_3 gbm, we find it is large enough to be filled with more than two molecules of Oseltamivir. Therefore, we classify pockets into two classes according to a given drug. For a given drug, we say the pocket is small relative to the drug if the pocket cannot be filled with two or more molecules of the drug. Otherwise, we say that it is a large pocket relative to the drug. In other words, a small pocket may only have one neighborhood of the optimal pose, while a large pocket may contain at least two neighborhoods of two poses without an overlap. For small pockets, we frequently do not find an associated pose in the neighborhood of the optimal pose such that min{a1, b1}> min{a, b} through shifting and rotating the optimal pose. However, for a large pocket, we frequently find an associated pose so that min{a1, b1} > min{a*, b*} and that the neighborhoods of these two poses do not overlap. Here, min{a1, b1} is the contribution of the associated pose and min{a*, b*} is the contribution of the optimal pose.
Specifically, the existence of an associated pose in a large pocket such that min{a1, b1}> min{a, b} for all 6 pockets and 9 drugs is shown in Table S17 of Section 9 of
It appears that the essential cause that Oseltamivir and Zanamivir may improve the therapeutic efficacy of CR6261 to treat group 1 influenza viruses has finally been found. However, there is still a gap that should be closed. That is, we need to answer whether the drug’s molecule may arrive in the neighborhood of the associated pose? We answer the question by stating the following property:
If a drug molecule in a large pocket has two poses such that their neighborhoods do not overlap, then the neighboring pose may be adopted by the drug molecule.
The proof of this property is not hard to arrive at. In fact, we regard a pocket just as the union of the two neighborhoods. Then this pocket is regarded as a bag with two boxes and each box can be packed into one ball. Since more than two molecules of the drug may enter into the pocket, it implies that more than two balls may enter into the bag. Therefore, it is certain that each box binds one ball.
Based on the above computational analysis, we summarize the clues found and propose the molecular mechanism of action as follows:
For CR6261 (F10) which may neutralize group 1 influenza viruses and their footprints which contain the epitope, a drug may enhance the therapeutic efficacy of CR6261 (F10) if and only if it has a high tolerance pose satisfying min{a, b}>0 for group 1 HAs.
For CR8020 which may neutralize group 2 influenza viruses but its footprint is located on HA2 although it does not contain the epitope, a drug may enhance the therapeutic efficacy of CR8020 if and only if it has a high tolerance pose satisfying min{a, b}>0 for group 2 HAs.
For F16 which may neutralize both group 1 and group 2 influenza viruses and their footprints contain the epitope, a drug may enhance the therapeutic efficacy of FI6 if and only if it has a high tolerance pose satisfying min{a, b}>0 for all HAs.
Following from the above mechanisms and using the data in Table S17, we deduce that both Oseltamivir and Zanamivir may enhance the therapeutic efficacy of CR6261, CR8020, F10 and FI6 within their original spectrum. The other drugs do not lead to stable associations with Oseltamivir and Zanamivir having a high tolerance pose.
To identify an antibody-drug complex without drug resistance, we should estimate the affinity between HA and mAb. We should also determine how much affinity would be lost if the HA is changed and how much additional affinity would be provided by the drug. Moreover, it is useful to know whether or not the added affinity depends on the specific HA. We answer these questions one by one in the subsection that follows.
The affinity between an mAb and an HA is determined by multiple factors (i.e., hydrogen bonds, hydrophobic interactions, van de Waals forces, etc.), therefore we can hardly compute its value precisely. In practice, we use GROMACS to directly estimate the affinity between antibodies and HAs based on 3 gbn, 3 gbm, 3 fku, 3 sdy, 3 ztn and 3 ztj. Typically, we may use a large constant force (2,500 kJ/mol nm) to pull the CR6261, CR8020, F10 and FI6 far away from an HA along a fixed direction. We plot the distance-versus-time function and look for the time interval corresponding to a distance of 1A. We then trace it back to find the output energies corresponding to the given time interval. We use the energy corresponding to the left terminal of the interval and the energy corresponding to the right terminal of the interval as the lower and upper estimates of the affinity between HAs and mAbs, respectively. Typically, we estimate the range of affinities between HAs and mAbs as shown in
mAb | HAs | Potential contributed amino acids |
CR6261 | H1 | A_38_H A_40_V A_41_N A_42_L A_291_S A_292_L; B_19_D B_20_G B_21_W B_38_Q B_41_T B_42_Q B_45_I B_46_D B_49_T B_52_V B_53_N B_56_I |
CR6261 | H5 | A_38_H A_40_Q A_41_D A_42_I A_291_S A_292_M A_293_P A_318_T; B_19_D B_20_G B_21_W B_38_C B_41_T B_42_Q B_45_I B_46_D B_49_T B_52_V B_53_N |
F10 | H5 | A_32_H A_34_Q A_292_S; B_18_V B_19_D B_20_G B_21_W B_38_K B_41_T B_42_Q B_45_I B_49_T B_52_V B_53_N |
CR8020 | H3 | A_21_P A_325_E; B_15_E B_16_G B_17_M B_18_I B_19_D B_25_R B_26_H B_30_E B_31_G B_32_T B_33_G B_34_Q B_35_A B_36_A B_38_L B_146_N B_150_E B_153_R |
FI6 | H1 | A_28_H A_29_S A_289_S A_316_T; B_18_V B_19_D B_20_G B_21_W B_38_L B_39_K B_41_T B_42_Q B_43_N B_45_I B_46_D B_49_T B_53_N B_56_I B_57_E |
FI6 | H3 | A_38_N A_277_C A_278_I A_318_T; B_18_I B_19_D B_20_G B_21_W B_38_L B_39_K B_41_T B_42_Q B_43_A B_45_I B_46_D B_48_I B_49_N B_53_N B_56_I B_57_E |
Here, the boldfaced data are estimated based on the real crystal structures, and the data marked with a star are estimated based on the predicted structures. The symbol “-” means that structures corresponding to the trimer HA and mAb are absent.
To estimate the maximum loss of affinity between CR6261 (F10, FI6) and HA as HA ranges for group 1 or the maximum loss of affinity between CR8020 (FI6) and HA as HA ranges for group 2, we alternatively estimate the maximum loss of the non-covalent bonds between CR6261 (F10, FI6) and HA in group 1 or between CR8020 (FI6) and HA in group 2. Therefore, we should know which amino acids in the footprint of CR6261 (F10, CR8020, FI6) are the potential contributors towards non-covalent bonds. Since the lengths of the hydrogen bonds or hydrophobic interactions are less than 4 angstroms, we readily find the potential contributors within the footprint for each case. This is shown in
Target | Left contributors on HA | Right contributors on mAb |
3 gbn | 289N, 290S | 70T, 72D, 79Y |
3 gbm | 289N, 290S, 291S | 23K, 75A, 79Y |
3 fku | 35D, 38S, 40K, 293S | 75S, 76T |
3 sdy | 18H, 20V, 17M, 18I, 20G, | 56T |
3 ztn | 286I, A287N, 288T, 289S | 28T, 29F, 30S, 31T, 73N, 74S, 76N |
3 ztj | 55P, 278I, 280E, 288I, 289P, 290N | 28T, 29F, 30S, 76N |
Obviously, there is no strict obligation to contribute non-covalent bonds. We use Ligand Explorer to filter these potential contributors which give no actual contribution from the set if we regard it as a ligand. Based on the conservation analysis mentioned in Section 2.2, we know that the loss of non-covalent bonds is due to the mutations of these real contributors which come from HA1 (A-chain) because the contributors which come from HA2 (B-chain) are invariant. That is, we only care about the mutations of these contributors on HA1.
In particular, we select all actual contributors for 3 gbn, 3 gbm, 3 fku, 3 sdy, 3 ztj and 3 ztn, and obtain the sum of non-covalent bonds contributed by these contributors, and further infer the maximum loss of non-covalent bonds as follows:
The total number of non-covalent bonds between CR6261 and H1 is 36. 11 of 36 are contributed by 291S, 38H, 40V and 42L on HA1. Then, 3 of these 11 are contributed by the atoms on the backbone or by the first level of residues, and therefore the maximum loss is 8 if H1 HA is replaced by other group 1 HAs.
The total number of non-covalent bonds between CR6261 and H5 is also 36. But 7 of the 36 are contributed by 293P, 291S, 38H and 42L on HA1. Then 1 of these 7 is contributed by the backbone and therefore the maximum loss number is 6, if H5 HA is replaced by other group 1 HAs.
The total number of non-covalent bonds between F10 and H5 is 23, and only 2 non-covalent bonds are contributed by the amino acids 292S and 32H on HA1. Then, the maximum loss number is 1 if H5 is replaced by other group 1 HAs.
The total number of non-covalent bonds between CR8020 and H3 is 34. Only 1 non-covalent bond is contributed by the amino acid 325E on HA1. Then, the maximum loss number is at most 1 if H3 HA is replaced by other group 2 HAs.
The total number of non-covalent bonds between FI6 and H1 is 44, 12 of the 44 are contributed by 289S, 28H and 316T on HA1. Then, the maximum loss number of the non-covalent bonds is 7 if we assume that all group 1 HAs are mutated from H1. This is because 5 are contributed by the backbone or the first and second level of the side chains of amino acids.
The total number of non-covalent bonds between FI6 and H3 is 26; 1 of these 26 is contributed by 318T on HA1. We find that maximum loss number of the non-covalent bonds is at most 1 if H3 HA is replaced by other group 2 HAs.
Using the above observations, the real contributors and all potential contributors listed in
Inspecting Table S17 again, we find that Oseltamivir may supplement more than 10 non-covalent bonds to improve the affinities of 3 gbn, 3 gbm, 3 fku, 3 sdy, 3 ztn and 3 ztj. We wish to known if the contributions of Oseltamivir are affected by mutations. We list the contributors in the left and right footprints of Oseltamivir based on the 6 crystal structures.
Following these sites in different proteins, we can easily find where they are located. From
drug | A | B | L | min | drug | A | B | L | min |
Azichromycin |
|
|
|
|
HEM | 10 | 7 | 7 | 7 |
Aspirin | 8 | 3 | 6 | 3 | Heroin | 6 | 0 | 13 | 0 |
Amantadine | 9 | 7 | 0 | 0 | Isosorbide | 11 | 0 | 7 | 0 |
Oseltamivir | 3 | 7 | 0 | 0 | Zanamivir | 9 | 0 | 9 | 0 |
For the H1-H2-H5 and H8-H9-H12 subgroups, C***C****G*****PFQN is conserved and the common form is CDAKCQTPQGAINSSLPFQN. However, it is slightly mutated in H6, H11, H13 and H16. For most HAs in group 2, C***C****G*****PFQN becomes CNSECITPNGSSIPNDKPFQN. It is slightly changed in H4, H14, H7, H10 and H15. Thus, the left contributors of Oseltamivir are conserved for all four mAbs.
In conclusion, the results of Sections 2.2 and 2.3 tell us that Oseltamivir may enhance the therapeutic efficacy of CR6261, F10 and FI6 to treat group 1 influenza viruses without resulting in drug resistance, and it can also enhance the therapeutic efficacy of CR8020 and FI6 to treat group 2 influenza viruses without causing drug resistance.
In order to prove that a drug may enhance CR6261 and F10 to become universal mAbs, we must first confirm that CR6261 and F10 may bind to group 2 HAs. Similarly, in order to prove that a drug may enhance CR8020 to make it a universal mAb, we must first confirm that CR8020 may bind to group 1 HAs. However, this is hard to do in the absence of crystal structures for these possible mAb-HA complexes. As the best recourse, we use RosettaDock to simulate how CR6261 and F10 bind to H3 HA and how CR8020 binds to H1 HA.
Of course, the precondition is whether we can use RosettaDock correctly. Therefore, we use 3 gbn, 3 gbm, 3 fku, 3 sty, 3 ztn and 3 ztj as samples to validate that RosettaDock has the ability to obtain similar results to those of the 6 crystal structures. Unfortunately, the results computed by RosettaDock are quite different from the known 3 gbn structure, if we first separate CR6261 and A-chain+B-chain from 3 gbn, and then input CR6261 and A-chain+B-chain into RosettaDock. In the same way, we find the computed results of RosettaDock for 3g bm, 3 fku, 3 sty, 3 ztn and 3 ztj are also quite different from their crystal structures.
Accidentally, we have found that 3 ztj has complete trimer HA data and we input the trimer HA and FI6 into RosettaDock. Then we use the docking pose with the minimum free energy within 1,000 iterations as the computed docking pose between the trimer HA and FI6. We find that the computed pose is very similar to the original crystal structure (see
(A) The output of RosettaDock at the 338th iteration when the inputs are the trimer HA and FI6 which are separated from 3 ztj. (B) The output of RosettaDock at the 263rd iteration when the inputs are the trimer HA and F10 which are separated from 3 fku.
Encouraged by this finding, we continue our search for the trimer HA-mAb complex. Nevertheless, only for 3 fku (the H5 HA in complex with F10) can we find a complete trimer HA-mAb complex. In the same way we used to process 3 ztj, we obtain the computed pose of F10 docked with trimer HA and we compare it with the original crystal structure of 3 fku shown in
In order to find a trimer HA based on 3 gbn and 3 gbm, we have to use the trimer 1rd8 to replace the trimer HAs corresponding to 3 gbn, and the trimer 2 ibx to replace the trimer corresponding to 3 gbm, respectively. We obtain a docked pose for 1rd8-CR6261 and a docked pose for 2ibx-CR6261 through RosettaDock shown in
(A) The output of RosettaDock at the 200th iteration when the inputs are 1rd8 and CR6261. (B) The output of RosettaDock at the 363rd iteration when the inputs are 2 ibx and CR6261.
Based on
To check whether or not CR62661 (F10) may dock with group 2 HAs, we only show that CR6261 (F10) may dock with the trimer HA of H3 separated from 3 ztj. Similarly, to check whether or not CR8020 may dock with group 1 HAs, we only show that CR8020 may dock with 1rd8. With the same operation, we obtain the computed poses of CR6261 docked with H3 and F10 docked with H3 as shown in
(A) is the predicted structure of CR6261 docking with H3 HA which is separated from 3 ztj, (B) is the predicted structure of F10 docked with H3 HA which is separated from 3 ztj, (C) is the predicted structure of CR8020 docked with 1rd8.
Let CR6261&H3 denote the structure of
(A) is the pocket on the predicted structure CR6261&H3, (B)is the pocket on the predicted structure F10&H3, (C) is the pocket on the predicted structure CR8020&H1.
At first, we suspected this result was wrong because CR6261&H3, F10&H3 and CR8020&H1 are computed structures. Nevertheless, we soon gave up this suspicion since we found that the drug does not come to the fork but to the stem of HA when we use the crystal structures 3 ztj and 3 fku (see
(A) is the pocket on 3 fku, (B)is the pocket on 3 tzj.
This indicates that pockets on CR6261&H3, F10&H3 and CR8020&H1 explored by AutoDock are reasonable. Moreover, we further find that these pockets on the complete 3 fku complex, the complete 3 ztj complex, CR6261&H3, F10&H3 and CR8020&H1 explored by AutoDock are quite different from the pockets explored on the corresponding timer HAs. In fact, if we only use 1rd8, 1 mql, 3 m5 g and 2 ibx as models, then the pockets on each trimer HA are located almost in the same place (see Figures S17–S20 in Section 1 of
The above computational analysis tells us that a combined protein may have many pockets but these pockets should be explored using different subunits as the target proteins. In order to show that clefts formed by CR6261 and H3 HA, F10 and H3 HA, and CR8020 and H1 HA are pockets, we need to use a single strain complex with CR6261, F10 and CR8020 as target proteins. This is shown in
We first note the structure of CR8020&H1, since the footprint of CR8020 on H1 HA is at the stem and it covers the epitope of HA. However, the number of non-covalent bonds between HA and CR8020 is only 4. Furthermore, using GROMACS, we also find that the affinity between H1 HA and CR8020 ranges from 126 to136 kJ/mol (see
Based on the trinity symmetry of the HA-mAb complex, we know that CR8020 in complex with H1 HA has at least nine pockets, which are large enough to be bonded by Oseltamivir and Zanamivir. In fact, three of them locate on the three faces of the stem of trimer HA, three locate on the three forks and three on three identical mAbs. Following from the CR8020&H1 case, we find that Oseltamivir may help CR8020 to neutralize H1 influenza viruses using the molecular mechanism described in Section 2.3. It is important to note that the footprint of CR8020 on H1 HA is located on the stem.
Following from the pose of Oseltamivir docked to the fork formed by CR8020 and HA shown in
(A)is the pocket explored based on one subunit of the predicted structure CR6261&H3, (B)is the pocket explored based on one subunit of the predicted structure F10&H3, (C)is the pocket explored based on one subunit of the predicted structure CR8020&H1.
By comparison, the footprints of CR6261 and F10 on H3 HA both are located on the head of the HA and therefore can at most link to HA1 but cannot cause HA2 being separated from HA. In other words, a drug may enhance CR6261 or F10 to neutralize H3 influenza viruses if it prevents HA2 from dissociating from HA and if it provides additional affinity so that CR6261 or F10 may bind to the head of HA, too.
Pocket on trimer HA-F10 complex | cleft formed by trimer and F10 | ||||||
drug | B | D | F | drug | HA | F10 | min |
Azichromycin | 0 | 0 | 0 | Azichromycin |
|
|
|
Aspirin | 6 | 17 | 0 | Aspirin | 11 | 0 | 0 |
Amantadine | 2 | 2 | 6 | Amantadine | 15 | 2 | 2 |
Oseltamivir |
|
|
|
Oseltamivir | 14 | 3 | 3 |
HEM | 0 | 0 | 0 | HEM | 16 | 0 | 0 |
Heroin |
|
|
|
Heroin | 6 | 8 | 6 |
Isosorbide | 8 | 6 | 0 | Isosorbide | 13 | 4 | 4 |
Zanamivir | 15 | 4 | 8 | Zanamivir | 15 | 4 | 4 |
Vancomycin | 0 | 0 | 0 | Vancomycin | 0 | 0 | 0 |
Following from
The footprint of F10 on H3 HA is also at the head. Moreover, the number of the non- covalent bonds between F10 and HA is also 11 as computed by Ligand Explorer. Furthermore, the affinity between F10 and HA estimated using GROMACS ranges from 170 to 180 kJ/mol (see
ligand | a+b | a | b | Min{a,b} |
Azithromcyin | 36 | 23 | 13 | 13 |
Oseltamivir | 17 | 13 | 4 | 4 |
Zanamivir | 17 | 13 | 4 | 4 |
HEM | 27 | 9 | 18 | 9 |
Aspirin | 24 | 24 | 0 | 0 |
Isosorbide | 21 | 20 | 1 | 1 |
Vancomycin | 19 | 16 | 3 | 3 |
Amantadine | 9 | 8 | 1 | 1 |
Heroin | 18 | 18 | 0 | 0 |
Summarizing the computational analysis in Sections 2.2 and 2.3, we have obtained the following results. We found a molecular mechanism that explains why Oseltamivir and Zanamivir may enhance the therapeutic efficacy of CR6261 for treating group 1 influenza viruses. Based on this mechanism, we further found that Oseltamivir and Zanamivir may also enhance the therapeutic efficacy of F10 or FI6 for treating group 1 influenza viruses and the therapeutic efficacy of CR8020 or FI6 for treating group 2 influenza viruses. Moreover, these drugs may compensate for the loss of affinity between HA and mAb due to some mutations of amino acids within their footprints.
Using the RosettaDock software, we found that the footprint of CR8020 on H1 HA is located in the middle of the stem of HA which is better than the footprint of CR8020 on H3 HA. However, the affinity between CR8020 and H1 HA is too low to keep CR8020 and H1 HA together sufficiently tightly (see
Using the RosettaDock software, we found that the footprint of CR8020 on H1 HA is located in the middle of the stem of HA which is better than its footprint on H3 HA. However, the affinity between CR8020 and H1 HA is too low to keep CR8020 and H1 HA bound sufficiently tightly together. Therefore, we believe we have found a molecular mechanism required in the search for a drug to enhance CR8020 to become a universal mAb.
Using the RosettaDock software, we found an explanation why CR6261 (F10) does not neutralize group 2 influenza viruses, namely we believe this is due to the fact that the footprints of CR6261 and F10 are located at head of the HA. Therefore, a molecular mechanism for using a drug to enhance CR6261 or F10 to become a universal mAb has been formulated. Based on the mechanism and distribution of non-covalent bonds between a drug and a binding pocket, we recommend Azichromycin as a candidate to enhance CR6261 to become an mAb. Moreover, we recommend Azichromycin and Oseltamivir together as a candidate to enhance F10 to become a universal mAb.
We hope that using DrugBank and other medicinal chemistry databases we will be able to find other drugs, which may play the same role as Oseltamivir, Zanamivir or Azichromycin. However, clinical experience shows that Azithromycin is safe (no reported lethal side effects yet). Its half-life is long (about 18 hours) and therefore it is convenient for clinical use. It causes no damage to human proteins unless bacteria residing in the human body are affected. Importantly, it is inexpensive. Hence, Azithromycin is a good candidate to help CR6261 and F10 become universal antibodies. Comparably, Oseltamivir has some side-effects and is much more expensive without offering an advantage over Azithromycin. Zanmivir is also a mature drug to treat influenza viruses aiming at the target protein NA. However, its mode of delivery is not convenient, although the price is not too high.
In summary, this study shows that Oseltamivir may improve the therapeutic efficacy of FI6 to overcome drug resistance. As well, Osetamivir may possibly enhance CR8020 to become a universal mAb. Azithromycin may enhance CR6261 to become a universal mAb and Azichromycin and Oseltamivir taken together may enhance F10 to become a universal mAb. Therefore, we have multiple choices to obtain cheap and safe antibody-based therapies. We should carefully note the order of drug delivery. The correct order should be that mAb docks with HA first, and then a drug should be added. Otherwise, the function of the mAb will be seriously lost. In fact, we have mentioned in Section 1 of
Finally, we wish to comment on the use of MCABMSA software. Over the last three years we have validated it on many test datasets (general and specific) and compared it with MUSCLE and MAFFT. Regarding computational speed, MCABMSA is much fast than MUSCLE but almost the same as MAFFT. For the size of the dataset, MCABMSA is much larger than MAFFT. Regarding the SP-score, MCABMSA is superior to MUSCLE and MAFFT on average. Based on the study presented in this paper, MCABMSA emerges as having greater agility than the publicly available multiple sequence alignment packages. Readers may freely download it from
Finally, we would like to emphasize that AutoDock, Ligand Explorer, GROMACS, and RosettaDock are four types of useful software packages that perform computational drug design and can reliably select antibody and drug complexes. We note that the speeds of AutoDock, Dock and GROMACS are much lower. If their speeds could be increased, they would be more useful in the applications aimed at finding new targets for old drugs. For the current cases studied here, we have to pay more attention to choosing the panel of drugs in order for them to span a wider spectrum of properties.
(DOC)
We wish to thank the anonymous reviewers for their valuable suggestions that resulted in a vastly improved quality of the paper. JR also wishes to thank his graduate students Mr. Xingye Qiu, Mr. Zhang Chen and Mr. Xu Jun, who invested their valuable time to help us complete the data in Table 4, Table 5 and Table S17 of Information S1.