Mycobacterium tuberculosis (Mtb) strains resistant to traditional anti-tuberculosis drugs has alarmed health services worldwide. The search for new therapeutic targets and effective drugs that counteract the virulence and multiplication of Mtb represents a challenge for the scientific community. Several studies have considered the erp gene a possible therapeutic target in the last two decades, since its disruption negatively impacts Mtb multiplication. This gene encodes the exported repetitive protein (Erp), which is located in the cell wall of Mtb. In vitro studies have shown that the Erp protein interacts with two putative membrane proteins, Rv1417 and Rv2617c, and the impairment of their interactions can decrease Mtb replication. In this study, we present five nicotine analogs that can inhibit the formation of heterodimers and trimers between these proteins. Through DFT calculations, molecular dynamics, docking, and other advanced in silico techniques, we have analyzed the molecular complexes, and show the effect these compounds have on protein interactions. The results show that four of these analogs can be possible candidates to counteract the pathogenicity of Mtb. This study aims to combine research on the Erp protein as a therapeutic target in the search for new drugs that serve to create new therapies against tuberculosis disease.The increasing emergence of  Mycobacterium tuberculosis (Mtb) is the bacterium causative of tuberculosis (TB), the second most infectious disease after COVID-19 . Th. ThMtb pomplexes ,70. The omplexes , which uomplexes ,61. The Various calculations of molecular dockings were performed for the different analyses presented in this work. As mentioned above, one of them consisted of a local coupling between the different drugs and the hot spots of the Rv1417 and Rv2617c proteins. From our calculations, ten protein–drug complexes were obtained, and the one with the highest binding energy was chosen for the MD simulations. On the other hand, two types of coupling were carried out to evaluate whether the drugs inhibited the interactions between the RVPs and the Erp protein. The first consisted of evaluating complementarity and penetration between protein structures in the presence of the membrane. These calculations were performed using PatchDock software ,73. AfteMtb proteins and nicotine analogs, respectively. In addition, we used the bootstrap analysis to calculate the average binding energy, which is included in the g_mmpbsa tools. All calculations were obtained at 309.65 K, and default parameters were used to calculate molecular mechanics potential energy and solvation-free energy [Binding free energies (BFEs) were used to evaluate the protein–protein and protein–ligand affinities. Molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) calculations were performed using the g_mmpbsa methodology ,77. The gmx sham module, while the RMSD and RG were considered as the atomic position variables with respect to their average structure; figures were constructed using Wolfram Mathematica 12.1 [Statistical results, root mean squared deviation (RMSD), root mean squared fluctuation (RMSF), radii of gyration (RG), solvent accessible surface area (SASA), hydrogen bonds (HBs), binding free energies (BFEs), structures, trajectories, and B-factor maps were obtained using Gromacs modules. Analysis of structure properties was performed using the MD trajectories of the last 200 ns of each simulation, which were then visualized using Visual Molecular Dynamics (VMD) software  and UCSFica 12.1 . The phyica 12.1 .An essential part of analyzing protein–drug interactions at the molecular level is that electrostatic forces are well represented. Much of the success of a drug in anchoring to the active site of the protein may depend on these types of interactions. As part of the methodology used in this work and to better depict protein–drug interactions, the five studied nicotine analog molecules (NAMs) were optimized using DFT calculations. Thus, the results made it possible to obtain the quantum atomic charges and incorporate them into the MD simulations in order to improve the protein–ligand interactions. Analysis of quantum ESPs shows that all five NAMs have similar electrostatic behaviors. In general, these molecules have an electrophilic character at the ends of their structures with a central zone of high electron density. The high density occurs on the nitrogen of the pyridine ring and the oxygen molecules of the carbonyl groups. At the same time, the electropositive zones were found on the methyl group of the pyrrolic ring and the hydrogen atoms of the dichlorophenyl group. It is important to note that in the case of chlorine atoms, a slight electronegative character can be observed on their surface, which is increased or decreased depending on the position of these atoms. Thus, this electronegative character is increased for NIB and NIC compounds due to the continuous positions of the chlorine atoms on the ring.The MM-ESP surfaces obtained by APBS calculations using the Hirshfeld atomic charges adequately represent the electrostatic character of the NAMs. However, in the area corresponding to the methyl group of the pyrrole ring, a slight increase in its electronegative character can be observed due to its proximity to the carbonyl oxygen. These results are key in the subsequent analyses, since they ensure a better representation of electrostatic protein–ligand interactions in calculations where it is not possible to deal with reactive systems. This methodology has been used in various works by our research group and also other groups, with excellent findings ,88,89,90In order to assess the pharmacokinetics and toxicity of NAMs, their ADMET  properties were evaluated using the ADMETlab 2.0 server . ResultsBased on the drug-like soft rule, the physicochemical properties evaluation of the NAMs was positive. Among these properties, several are shared due to their intrinsic structure. For example, the molecular mass was 348.04 amu . For hydrogen bonds, the number of acceptors was four , and there were no donors . The number of rotatable bonds was three , while there were 19 rigid bonds . NAMs had three rings in their structure , with a maximum number of six atoms in the biggest rings . Furthermore, NAMs only had six heteroatoms , which were electrically neutral . Moreover, they only had one stereocenter, and their polar surface area was 50.27 . In addition, the five compounds were found to be soluble in water (logS between −4∼0.5 log mol/L). Despite the logP values exceeding the limit value (0∼3), the values are within the permissible values at physiological pH (1∼3).According to their medicinal chemical properties, NAMs are in the range of not-so-attractive molecules to design (low QED score). However, they are easily designable molecules (high SAScore), novel (MCE-18), with good absorption or permeability (according to the Lipinski rule), and good ADMET profiles (GSK rule and Golden Triangle). In addition, the five NAMs obtained excellent evaluations in absorption properties: high permeability in Caco-2 and MDCK cells; they are not P-glycoprotein (Pgp) inhibitors, nor are they Pgp substrates; and high absorption in the human intestine.TB [There is evidence of the interaction between the Rv1417 and Rv2617c proteins (RvPs) with the Erp protein, one of the main virulence factors of Mtb. Klepp et al. consider that these proteins can form complex transient structures, and that their possible disruption could be used against TB . StudiesTB ,92. FurtTB .Keeping in mind these proteins as targets, new MD simulations of the Rv1417, Rv2617c, and Erp systems were performed to obtain the structures and sites with the highest probability of interaction. The results confirm the high structural stability of the RvPs and the tendency of the Erp proteins to form globular structures in polar environments, as was described in our previous work .z-axis.Although greater fluctuation was observed in the RMSD graph for the new structures, the values of the RMSF, the radius of gyration, and H bonds were very similar in both RvPs . The mosAn essential aspect of elucidating the interaction sites is the exposed area of the proteins. For this reason, solvent accessible surface area SASA, a calculaA possible reason for the high fluctuation of the RvP structures could be their displacement through the membrane. When performing mean square displacement (MSD) calculations, both proteins shifted an order of magnitude faster than in previous studies. The calculated coefficients were DBefore the MD simulations with the NAMs, the electrostatic surfaces and per-residue fluctuations of the RvPs were analyzed to find possible interaction sites. Both proteins exhibit extensive hydrophobic areas (white color), characteristic of membrane proteins regarding the electrostatic surfaces b. HoweveFrom the vibrational analysis c,d, it csolvate tool. Due to the fact that this tool randomly places the molecules inside the simulation box, care was taken that there were at least five molecules above and below the membrane in all initial configurations. Finally, all the complexes obtained by both methodologies were subjected to new 100 ns MD simulations.One of the main challenges in studying protein–drug interactions is finding sites with the highest binding probability. Unfortunately, experimentally, there are inherent limitations in studying active sites in membrane-associated proteins that make it difficult to locate these sites. Such is the case for the RvPs studied in this work, where information is scarce, making it difficult to find structural sites as starting points to analyze RvP–NAM interactions. In order to solve this issue, two different methodologies were followed to assess the affinity of the NAMs with the RvP structures. The first consisted of previously obtaining the interaction sites through pocket conservation analysis during the different MD trajectories . For thius work. a. The reus work. c. The seOn the other hand, for the Rv2617c structure, the main pocket is located in the extracellular region above the four 0.94 nm3 b. The reThese results reveal important information regarding the RvP structures. First, the Rv1417 protein is more susceptible to interactions in the cytoplasmic region under these simulation conditions, as its main pocket is located in this area. This putative active site is bulky and has an electrophilic character. However, its location makes it inaccessible to large structures, such as peptides or other proteins. However, recent studies on membrane-associated proteins have addressed the importance of occluded, cryptic, and hidden pockets as new therapeutic targets in drug design ,95,96.Second, in the case of the pocket found in the Rv2617c protein structure, it can be seen that it is located at a possible channel formed by the four For a better understanding of the following analyses, the complexes formed using the active sites will be named ASC14–NIx and ASC26–NIx, where the number indicates the protein and the letter x means the analogous nicotine molecule. The complexes formed by the solvation of NAMs will be named Sol14-NIx and Sol26–NIx, respectively. All RvP–NAM molecular complexes were subjected to MD simulations for 100 ns in NPT assemblies at 309.65 K and 1 bar pressure.On the other side, no significant changes in secondary structures or unfoldings that could cause protein denaturation was observed during molecular simulations . In thisIn a first evaluation to understand the nature of the interactions between RvPs and drugs, the simulation trajectories were analyzed to identify those residues in contact with each NAM. In this sense, H bonds and their percentage occupancy were determined using the last 50 ns of each simulation. In the case of the hydrophobic interactions, these were determined by analyzing the minimum energy structures of each molecular complex. In addition, calculations of the RvP–NAM interaction radius were performed to assess the solvating capacity of the drug molecules .x-, y-, and z-axis, respectively, and interactions were registered in all of them. However, the drug molecules did not interact with the proposed active site. In fact, only residues M123 and I125 with high pocket density interacted with the NIC drug. These results, together with the fact that this site is located in a region that is not easy to access, would suggest that it could be an elusive binding site requiring extensive movements to be exposed [On the other hand, significant findings were found in systems solvated with NAM. Initially, one of the reasons for studying these systems was to verify the feasibility of NAMs interacting with found active sites without the need to place them directly on said sites. Under this scoop, trajectory analysis showed that the drug molecules interacted with the protein at various sites and remained close to the proteins by measuring the radius of gyration of RvP–NAM complexes . The mea exposed .The results could also suggest a second hypothesis: this pocket could be an allosteric site. This assumption is proposed because the region of greatest interaction was between residues M1–D7, G78–S83, I92–S96, G106–W109, and L143–R154, located in the cytoplasmic region. All of these residues are found outside and on the periphery of the active site, so any interaction within this site could affect its function. This could also be a reference since, among the results of our previous work , this reMoreover, the results showed that the NAMs also interacted with the Rv1417 protein in the extracellular region. Specifically, in Sol14–NIA Sol14–NIC complexes, the drugs mainly interacted with residues H32, Q49, Q53, and V54. Furthermore, interactions were observed in residues close to the secondary pocket of this protein .Regarding the systems formed with the Rv2617c protein, the analyses confirm that the region of greatest interaction is found in the extracellular region on the proposed active site. In addition, the interactions with the NAMs are distributed among the four main helices of the protein. Among the residues, Q50, H51, N53, M54, A57, N107, and P112 stand out because they provide H bond interactions, in addition to the fact that they were present in almost all the systems, including those that were built from the active site. In these systems, significant interactions were also observed on residues M1–P9 and residues H139, I143, and R145, all located in the cytoplasmic region.In order to assess the energetic affinity of NAMs with RvP structures, MMPBSA calculations were performed using the MD trajectories. The trajectories were saved every 100 ps, and the last 50 ns of each simulation were analyzed. In addition, these analyses were complemented by studying the vibrational energies per residue through the b-factor and obtaining the electrostatic maps of each complex. Finally, for their discussion, the results were divided into the systems built from the active sites and those solvated with NAMs.The calculation of the BFEs of the RvPs complexes is shown in In the case of ASC26–NIx complexes, the drug with the best interaction energy was NIC, which was almost 93% higher than the ASC26–NIA system, whose energy was −79.59 kJ/mol. Furthermore, of all the active site systems, this complex had the highest BFE in the MMPBSA calculations with only 5% electrostatic energy (−7.75 kJ/mol). Together with the contact analysis results, these findings suggest that the dominant interactions at this site are the hydrophobic character. Furthermore, the residues with the more significant contributions to the BFE had hydrophobic interactions: M54 and F114 for the ASC26–NIA complex  and M54 and P112 for the ASC26–NIE complex. (−75.89 and −70.19 kJ/mol). In the graph of the contributions to the BFE, the other two energetic minima correspond to residues Q50 (−54.41 kJ/mol) and A57 (40.57 kJ/mol) of the complexes with the drugs NIA and NIE, respectively.As mentioned above, the vibrational movement of residues can be related to the ability of a protein to interact with other structures. Therefore, what was sought in the B-factor analyses, was a stabilizing effect of the NAMs toward the higher vibration residues in the RvPS. The results are shown in Since previous analyses showed that at active sites, the electrostatic character plays a vital role in the interactions and retention of NAMs, the ESP surfaces of all complexes were calculated. In order to be able to compare the electrostatic effect of NAMs on protein surfaces, all ESPs were calculated without and with the presence of molecule drugs. The results shown in While slight changes can be seen on the ESP surfaces, these changes are primarily local. In ASC14–NIx complexes, the surfaces change mainly due to the presence of nitrogen, oxygen, and chlorine atoms, where the attraction is stronger given the electrophilic nature of the pocket. In contrast, in ASC26 systems, the local effect is more evident in the interactions with the NAMs. In both systems, the presence of the drug seems to reinforce the electrostatic character of the sites since, in the areas where there is no direct contact with the NAMs, the potential values decrease (ASC14–NIx) or approach zero (ASC26–NIx).Following the same methodology to analyze the active site systems, the results in the solvated systems show significant energetic effects due to the free diffusion of the NAMs in the systems. First, the BFE calculations favored the RvP–NAM interaction . The lowAnother point to highlight in the BFE analysis of the solvated systems is to observe that several residues interact with the drug molecules. That is, the contribution to the protein–drug binding energy is not only local but is distributed over the surface with which the drugs interact. To exemplify these observations, To ensure that the behavior of membrane molecules did not affect the results due to possible unpacking or phase changes, density, diffusion, and area per lipid calculations were performed . The res−7 cm2/s ,98. Finacm2/s [2 . Figure 6 cm2/s) ,101.The local effect of the NAMs with the interacting residues can be observed by analyzing the contributions per residue to the BFE and the electrostatic surfaces. On the other hand, in the Sol26–NIA system, although three NIA interaction sites were also observed with the Rv2617c protein, they are energetically less intense than those of the Rv1417 protein. As in the Sol14–NIA system, there was one interaction in the extracellular region and two in the cytoplasmic region. Located in the lateral area of the protein, extracellular binding is only favored by three residues: L119, L124, and V126 . Instead, the interactions in the cytoplasmic region are favored by residues with high values of BFE: R4, P5, S8, and P9 . Only the H130 residues had repulsive energies in this region (14.16 kJ/mol). The electrostatic effect of this drug is similar to that of the Sol14–NIA system; contact causes the area to take on an electronegative character instead of its hydrophobic character observed in the simulations without the presence of the drug. In addition, for the Sol26NIx systems, the highest favorable energies were reached with residues P112 and M54 (−147.88 and −103.23 kJ/mol) with the drug NIE, R4 (−102.01 kJ/mol) with NIA, and F114 (−100.43 kJ/mol) with NIB . FurtherA particular case is Sol26–NIE, in which eight NIE molecules were observed in interaction with the Rv1617c protein. Six molecules interacted in the extracellular region and two in the cytoplasmic region, although laterally. In this complex, the electrostatic effect of the area with the most significant contact is notorious since it changes the electrostatic properties of the protein, making it highly electronegative. In addition, due to the number of NIE molecules, this site was the one that generated the most attractive interaction.In order to assess the results obtained, the final structures of all the systems were evaluated through molecular docking calculations. For these couplings, the protein–NAM–membrane systems were taken as the receptor, and the Erp minimum energy structure as the ligand. The results are shown in Our previous work showed that the RvP–Erp dimers have better interaction energies when the RvPs are anchored in the membrane . FurtherOn the other hand, remarkable effects were observed in systems with the Rv2617c protein as the receptor. In this sense, Mycobacterium tuberculosis bacterium, interacts with the Rv1417 and Rv2617c proteins to form various molecular complexes. One of the theories suggests that the disruption in forming these complexes could help treat TB. In the present work, we show the results of our search for interaction sites in the Rv1417 and Rv2617c proteins that could be potential therapeutic targets. In addition, we have used five nicotine analog molecules (NAMs) to determine whether they can be used to neutralize these sites and prevent the interaction of these proteins with the Erp protein.There is evidence that the Erp protein, one of the virulence factors of the The analyses reveal that there are two pockets with the potential to be active sites, one located in the cytoplasmic region of the Rv1417 protein, and the second in the extracellular region of Rv2617c. The first one could be an allosteric or elusive binding site, since an excellent relative affinity with the drugs used was observed. A decrease in dimers with favorable energies could lead in this direction. Furthermore, under solvation conditions, no drug interacted with residues at this site. In contrast, the active site of Rv2617 is confirmed to be occupied by NAMs in both simulation conditions. The low number of Rv2617c dimers with favorable energies suggests that it could be a good candidate as a therapeutic target.More in-depth calculations and experimental tests are necessary to support the findings presented herein. However, we believe that the methodology can be used as a tool in searching for active sites in systems where there is limited experimental information.


The novel coronavirus SARS-CoV-2 infects human cells using a mechanism that involves binding and structural rearrangement of its Spike protein. Understanding protein rearrangement and identifying specific amino acids where mutations affect protein rearrangement has attracted much attention for drug development. In this manuscript, we use a mathematical method to characterize the local topology/geometry of the SARS-CoV-2 Spike protein backbone. Our results show that local conformational changes in the FP, HR1, and CH domains are associated with global conformational changes in the RBD domain. The SARS-CoV-2 variants analyzed in this manuscript  show differences in the local conformations of the FP, HR1, and CH domains as well. Finally, most mutations of concern are either in or in the vicinity of high local topological free energy conformations, suggesting that high local topological free energy conformations could be targets for mutations with significant impact of protein function. Namely, the residues 484, 570, 614, 796, and 969, which are present in variants of concern and are targeted as important in protein function, are predicted as such from our model. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the COVID-19 global pandemic that has taken over 6 million lives. Stopping the spread of the highly infectious virus requires disruption of the infection process and has become the focus for many scientists. Furthermore, the ability to control and stop a global pandemic in the future is of great interest. Therefore, understanding the mechanisms of viral infection is a pressing problem. Fusion of the membranes of both a host cell and a viral cell is necessary for infection ,5,6,7,8.Folded proteins are defined by their primary, secondary, and tertiary structure . The secpolymers ,28,29,30polymers ,34,35,36polymers ,41,42,43polymers , the wrihttps://www.who.int/en/activities/tracking-SARS-CoV-2-variants/, accessed on 1 January 2022) are in or in the vicinity of high local topological free energy conformations. These results could be very helpful not only in understanding the mechanisms of protein rearrangement using rigorous mathematical tools, but also for predicting mutations with impact on the 3-dimensional structure of proteins.In this paper, using a training set of more than 13,000 protein structures as in , we applIn this section, we give the definitions of measures of conformational complexity and the local topological free energy of proteins.We represent proteins by their  protein ,53,54. ADefinition 1.(Writhe). For an oriented curve ℓ with arc-length parameterization Writhe is a measure of the number of times a chain winds around itself and can have both positive and negative values.The total torsion of the chain describes how much it deviates from being planar and is defined as:Definition 2.The torsion of an oriented curve ℓ with arc-length parameterization Writhe and torsion have been applied successfully to study entanglement in biopolymers and proteins in particular ,61,62,63For a polygonal curve, the writhe has a simpler expression that does not require numerical integration, as in . The torlocal writhe, local torsion, respectively.An important property of the writhe and torsion, which makes them useful in practice, is that they can be applied to polygonal curves of any length to characterize 3-dimensional conformations at different length scales. The writhe and the torsion can be applied to any polygonal curve of four or more vertices. In proteins, the writhe and the torsion have been applied at the length scale of the entire protein backbone in ,65, wherDefinition 3.We define the local writhe (resp. torsion) of an amino acid, represented by the CA atom i to be the writhe (resp. torsion) of the protein backbone connecting the CA atoms The local writhe is a measure of the local orientation of a polygonal curve and a measure of its compactness. For example, a very tight right-handed turn (resp. left-handed) will have a positive (resp. negative) writhe value close to 1  and let p in the folded ensemble (the curated ensemble described above). Let m denote the maximum occurrence value for writhe (resp. torsion). To any value p of writhe (resp. torsion), we associate a purely topological/geometrical free energy:Namely, let We denote T) for n consecutive amino acids at a time. In this manuscript, we will focus on n that can be used to define writhe and torsion, and we call it the local topological free energy (LTE). Notice that our sample of more than 13,000 proteins gives rise to more than 1,100,000 local conformations from the PDB. Thus, even if the values of LTE in a protein depend on the original sample used to represent the PDB, we do not expect this choice to influence our model.We note that the above definition of topological free energy can be applied to different lengths of the protein, by measuring The total local topological free energy in writhe (resp. torsion) of a protein is defined as the sum of local topological free energies in writhe along the protein backbone, or along a protein domain. The calculation of the total local topological free energy of a protein follows a sliding window approach, where the local topological free energy of each local conformation of four consecutive CA atoms is added. Similarly, we define the total local topological free energy of a domain of a protein. Notice that in a sliding window approach, there is a partial overlap of conformations and a residue can belong to up to four different conformations, which partially overlap. Even though conformations may overlap, they are not identical and they can have a different LTE.rare or in high local topological free energy (high LTE) in writhe (or torsion) if its value p is such that w is a threshold corresponding to the 95th percentile of We will say that a local conformation is ion, see . We streWe note that although the LTE does not have physical units, it has been associated with experimental proteins . In addiproteins . Howeverproteins , it was proteins  for illuThe SARS-CoV-2 Spike protein is composed of two main domains, the S1 and S2 domains, which are, in turn, composed of other sub-domains. The S1 domain of the protein is composed of the domains NTD, RBD, CTD1, and CTD2. The S2 domain is composed of the domains FP, HR1, CH, CD, and HR2. In between these domains in S2 there are other parts of the protein, which we call A-S2, B-S2, and C-S2.In the following, we present results on the LTE of SARS-CoV-2 and its domains. In the figures shown in this manuscript, the local topological energy along the protein is shown in the y-axis, and the x-axis represents the backbone of the protein with its domains indicated in the order they are on the protein, namely, NTD, RBD, CTD1, CTD2, A-S2, FP, B-S2, HR1, CH, CD, and C-S2. We present the normalized values of the LTE by the length of the domain. These are the values of the LTE per amino acid in a domain. Note that the  12 (see ) and, thWe note that some of the structures analyzed have missing residues. We analyze the LTE of the existing residues in the PDB files only. If there are missing residues in a domain, the LTE of the local conformations existing in the PDB file is normalized by the length of the domain after we subtract the missing residues.In this section, we analyze the distribution of the total LTE of the Spike protein of SARS-CoV-2 among its domains for different pre-fusion states and for different variants. We note that the SARS-CoV-2 variants are obtained from different laboratories or different studies. To avoid any inaccuracy, will not aim at comparing the LTE of those structures quantitatively, but rather look at qualitative trends that are apparent in the structures from all studies and across laboratories.In pre-fusion, the SARS-CoV-2 glycoprotein may be in uncleaved closed, cleaved closed, cleaved open, and intermediate states. Structures of the protein in each one of these stages were obtained in ,68,69. Asion see . This mamentally ,52,70. SIn this section, we compare the distribution of the total LTE in domains of the WT protein to that of the variants G614, alpha variant, beta variant, gamma variant, delta variant, Mink variant, N501, and HexaPro mutants . The preIn The results in torsion show minor local changes in the entire protein (with the maximum at the FP), except in CH, which is invariant for all variants. This suggests a different effect of mutations on the local torsion and local writhe in the open conformations, which is discussed in the The high LTE conformations in the SARS-CoV-2 Spike protein at various stages of pre-fusion, post-fusion, and for its variants are given in We report the high LTE conformations for the G614, the alpha, beta, gamma, delta, Mink, and N501 variants. Although these structures are obtained from different experiments and they can have local differences, we find that some of the high LTE conformations are conserved in all the variants of SARS-CoV-2. More precisely, we notice that amino acids 110, 122, 197, 281, 308–338–365, 480, 543, 569, 601, 612–614, 667, 743, 796, 797, 855, 908–913, 963–964, and 969 are in high LTE conformations in writhe or in torsion in almost all the structures of the Spike protein analyzed in this manuscript. We notice that the majority of those conserved high LTE conformations are outside the RBD.https://www.ecdc.europa.eu/en/covid-19/variants-concern, accessed on 1 January 2022) are in a high LTE conformation; see We compare the observed high LTE conformations to the locations of the actual natural mutations of the Spike protein. We find that only We also compare our results to those of engineered mutations. Engineered mutations are obtained from controlled systematic experimental substitutions of amino acids one or multiple at a time, whose observed effect on the protein structure and function was reported in ,71,72 d. When foOur analysis thus predicts conserved high LTE residues to be advantageous locations for mutations. Indeed, the novel variant of concern, the omicron variant , which became dominant in January 2022, has mutations at two additional conserved high LTE residues compared to the previous variants. Summarizing, thus, out of the conserved high LTE residues, 480, 543, 569, 612, 796, 855, and 969 are related to natural mutation lineages that involve the mutations 614, 570, and 484, whereas 796 and 969 are related to mutations in the omicron variant. Therefore, we find that even though most natural mutations do not occur at high LTE residues, high LTE conformations have higher probability of containing a natural mutation and that most conserved high LTE residues are associated with mutations see . In addiWe used a novel method to analyze the local structure of proteins to detect subtle local differences in the structure of SARS-CoV-2 in various pre-fusion stages and across different variants. More precisely, using a training set of more than 13,000 proteins, we obtained a parameter associated with 3-dimensional protein structure, the local topological free energy, which enables a novel characterization of proteins. We apply this method to characterize the SARS-CoV-2 Spike protein backbone. We find that the total local topological free energy of the Spike protein pre-fusion decreases in the steps leading to protein fusion. The distribution of the local topological free energy in the domains of the Spike protein changes from closed to open states. Interestingly, most changes are observed in the sub-domains of the S2 domain  and not in the RBD domain of S1, which is known to undergo a major global rearrangement. This reveals a coupling between local conformational changes in S2 and major global rearrangement in S1.All variants analyzed in this manuscript  show that the total LTE in writhe is invariant in RBD, and most differences are instead found in the FP, HR1, and CH domains of S2. This shows that all the mutations of concern have higher impact on the local conformation of S2 and not that of the RBD, even though mutations are known to impact the propensity of RBD-up conformations. This further corroborates that local conformational changes in S2 can have a big impact on the global 3-dimensional conformation of the protein.The local topological free energy also enables us to identify locations on the protein backbone where high LTE conformations occur. These are conformations with writhe or torsion outside the 95th percentile of local conformations in the PDB. Our results show that, even though most natural mutations do not occur in high LTE conformations, mutations of concern occur disproportionally to high LTE conformations, compared to the frequency of high LTE conformations in the SARS-CoV-2 backbone. In addition, the most important mutations (associated with variants of concern) occur in or in the vicinity of a high LTE conformation. Thus, high LTE conformations provide an evolutionary advantage for mutations. Compared to experimental data of engineered mutations, we find that most mutations reported to alter the global protein conformation are at amino acids in high LTE conformations. High LTE conformations are conserved overall among variants, suggesting that mutations at these locations do not impact the local structure. This further corroborates that mutations at high LTE conformations provide stability in the local structure of the protein, yet have significant effects on its global conformation. Our results thus point to high LTE amino acids as targets of advantageous mutations with impact on protein folding and function.These results not only help us understand the mechanisms of viral function, but also predict the effect of mutations at specific locations along the protein. For example, the recently discovered omicron variant has two new mutations in the S2 domain, at two conserved high LTE residues. Our results could be used by experimentalists to examine how mutations at the proposed locations that were identified using mathematics alone may impact viral function. Further applications of this topological analysis to viral proteins could lead to our better understanding and controlling viral protein function.


Recently the E protein of SARS-CoV-2 has become a very important target in the potential treatment of COVID-19 since it is known to regulate different stages of the viral cycle. There is biochemical evidence that E protein exists in two forms, as monomer and homopentamer. An in silico screening analysis was carried out employing 5852 ligands (from Zinc databases), and performing an ADMET analysis, remaining a set of 2155 compounds. Furthermore, docking analysis was performed on specific sites and different forms of the E protein. From this study we could identify that the following ligands showed the highest binding affinity: nilotinib, dutasteride, irinotecan, saquinavir and alectinib. We carried out some molecular dynamics simulations and free energy MM–PBSA calculations of the protein–ligand complexes (with the mentioned ligands). Of worthy interest is that saquinavir, nilotinib and alectinib are also considered as a promising multitarget ligand because it seems to inhibit three targets, which play an important role in the viral cycle. On the other side, saquinavir was shown to be able to bind to E protein both in its monomeric as well as pentameric forms. Finally, further experimental assays are needed to probe our hypothesis derived from in silico studies. Coronaviridae family, subfamily Coronavirinae. They are RNA single-stranded and enveloped viruses. The subfamily Coronavirinae are divided in four genera: alpha, beta, gamma and delta coronavirus. The CoVs are able to infect different species of mammals, including human beings, mainly the beta coronavirus [The coronavirus (CoV) belongs to the onavirus . The fironavirus ,3.Some of the most known endemic CoVs in the last two decades include: (a) SARS-CoV that emerged in 2002 with about 8000 cases all over the world and with a mortality rate of 9.6%, (b) MERS-CoV that emerged in 2012 and infected about 2500 over the world and with a mortality rate of 40%, and (c) SARS-CoV-2 which shows a mortality of 6.9% [The CoV genome codifies four main structural proteins: spike protein (S), nucleocapsid (N), membrane protein (M) and envelope protein (E). All these proteins are necessary to assemble the virus and to release new viruses in the human cells ,8. TheseThe E protein of the CoV is an integral membrane protein which consists of 76 to 109 amino acids and has a molecular size within 8.4 to 12 kDa . This prThis E protein is viroporin. These viroporins are known to form membrane pores ,18,19. I+ and K+. Moreover, E protein is essential and known for its role in the activation of the inflammatory NF-kB pathway [2+) channel in the endoplasmic reticulum Golgi apparatus intermediate compartment (ERGIC)/Golgi membranes [The E protein of CoV is selective to cations which is related to its ionic channel properties, and it shows its preference for the cationic monovalent Na pathway . The E pembranes . The chaembranes .2+ transportation in SARS-CoV, which triggers activation of the inflammasome [The activation of the inflammasome for the E protein of SARS-CoV was identified for the first time in porcine reproductive and respiratory syndrome virus (PRRSV). The blockage of the activity of the ionic channel with amantadine significantly inhibited the activation of the inflammasome which suggests the role of the E protein in the inflammation processes . Additioammasome . Interesammasome ,17,22.Additionally, the biochemical evidence proposes that the capability of the E protein of the CoV to form homo-oligomeric conformers depends on its TMD ,23. In aIt has been determined that the N-terminal segment of the E protein is responsible for the activation of TLR2 in macrophages, and thus in the inflammatory signaling pathways ,25,26. FThe C-terminal region of E protein interacts with several other proteins:(1) Interaction with PALS1:The E protein by PDZ-Binding Motif (PBM) interacts with syntenin proteins which triggers the activation of the p38 MAPK and leads to the overexpression of inflammatory cytokines ,28. Addi(2) Interaction with BET:Of worthy interest is that Gordon and collaborators found that the transmembrane E protein, which is likely resident on the endoplasmic reticulum-Golgi intermediate compartment and Golgi membranes, binds to bromodomain-containing protein 2 (BRD2) and bromodomain-containing protein 4 (BRD4) proteins, which are members of the bromodomain and extra-terminal (BET) domain family . The C-tOn the other hand, it has been suggested that the interaction between BET with E protein can cause important changes in the genetic expression within the host cell, and thus, is also important for the viral cycle. BET proteins are also known to regulate immunity and inflammation mechanisms ,37.(3) Interaction with M protein:The C-terminal segment of the E proteins is known to be important for the interaction with the C-terminal domain of the M protein, which is located at the cytoplasmic side of the endoplasmic reticulum-Golgi intermediate compartment, the budding compartment of the host cell . The menCOVID-19 is characterized by an excessive production of proinflammatory cytokines, yielding in some cases to acute pulmonary damage which is highly associated with the mortality of the patients. Even though innate immunity cells produce multiple inflammatory cytokines during the SARS-CoV-2 infection, Karki and collaborators found that the combination of TNF-α and IFN-γ induces inflammatory cellular death . The SARDue to the strong evidence that the E protein is crucial in the modulation of diverse processes, in this work we propose diverse in silico approaches to study the binding of potential inhibitors on this protein. Moreover, here we propose to investigate different forms of the E protein, such as monomer as well as pentamer. We also considered different potential sites of inhibition (1) ionic channel (pentamer), (2) N-terminal (pentamer), (3) C-terminal (monomer).It has been stated in the literature that the C-terminal region is a very flexible segment. Two conformations of the monomer have been denominated: the first one is where the C-terminal region shows the shape of a curve or harpin, and it is embedded in the membrane ,42; the Quaternary structures of the homopentamer and monomeric E protein of SARS-CoV-2 were modeled. We screened 5852 compounds from the Zinc database through ADMET analysis, discarding the compounds that did not fulfill the safety requirements (3697 ligands). Furthermore, docking analysis was performed with the remaining 2155 compounds.Different docking analyses were carried out, considering different conditions: (a) homopentameric form as an ion channel itself and the N-terminal segment of this form, (b) monomer form which includes two binding sites: C-terminal solvent-exposed and hairpin. Four molecular docking studies were carried out, which are described .As can be seen in the The amino acids of the N-terminal region of E protein in the homopentamer form interact with alectinib  while shThe drugs irinotecan, nilotinib, and saquinavir, bind to the E monomer in the hairpin conformations showing affinities of −8.2 kcal/mol, −8.2 kcal/mol and −7.7 kcal/mol respectively. After analyzing the molecular docking results, some compounds were selected based on the affinity criteria and considering critical residues at the site of interest. Pharmacokinetic properties of the compounds mentioned in Sixteen molecular dynamics (protein–ligand complexes) of 150 ns in total were performed, in which six are MD simulations of the homopentamer with compounds targeting the ion channel, which were named with the following nomenclature: lumacaftor (E-IC_lumacaftor), nilotinib (E-IC_nilotinib), dutasteride (E-IC_dutasteride), naldemedine , zafirlukast (E-IC_zafirlukast) and saquinavir (E-IC_saquinavir), a MD simulation of the homopentamer without ligand (E-IC-Nt). Alectinib was selected for MD simulation targeting the N-terminal site of the E protein homopentamer . For the case of dockings directed to the C-terminal site, simulations were explored in the two conformations. For the solvent-exposed C-terminal conformation, four MD simulations were performed: saquinavir (E-CtS_saquinavir), irinotecan (E-CtS_irinotecan), dutasteride (E-CtS_ dutasteride), and the MD simulation without ligand (E-CtS). Finally, the dynamics performed for the hairpin include: irinotecan (E-CtH_irinotecan), nilotinib (E-CtH_nilotinib), saquinavir (E-CtH_saquinavir) and the MD simulations without ligand (E-CtH). The results of the different MD simulations are described below. The binding energy of E protein and the analyzed compounds were calculated. From these results, we could observe that saquinavir, nilotinib, dutasteride and alectinib bind with high affinity to the homopentamer form .In addition, dutasteride, irinotecan and saquinavir also bind to the monomer with the highest Gibbs free energy value in the C-terminal region . MoleculDiverse molecular interactions were tracked along the different MD simulations.In the E-CtS_dutasteride complex we can observe different molecular interactions which include: Pi-alkyl interactions between residue Tyr59 and the ligand, additionally, this residue interacts with different rings of the ligand and the atoms C39 and C19. Furthermore, the residue Tyr59 shows a Pi-Sigma interaction with the atom HC, which is conserved in the MD simulation. Another alkyl interaction is formed between the atom CB of Val70 and the atom C39 of dutasteride, although this interaction is unstable. The interaction between irinotecan and the hairpin is towards one saline bridge formed between Val 75 (OT1) and H15 of the irinotecan; such an interaction is mostly conserved along the trajectory. On the other hand, in the binding of the ligand irinotecan on the hairpin, we could detect some important interactions which include: Leu74 (VdW), Leu73 (CHB) and Val75 (SB).For the case of the complex E-CtH_saquinavir we could identify some hydrogen bonds that are stable along the trajectory, one of which is formed between Val75 (OT1) and the H89 of the drug. In addition, a hydrogen bond interaction was formed between the atom HG1 of Ser55 and the atom O43 of the ligand; such interaction is observed in almost half of the simulation. Another hydrogen bond that is formed between the atom HH of Tyr59 (E protein) and the atom O43 of saquinavir which is conserved half of the of the trajectory and it showed some other VdW interactions with the residues: Ile46, Tyr42 and Val58 Additionally, we could observe a saline bridge between the saquinavir and Val75 which is conserved mostly along the trajectory.In the complex E-IC_dutasteride it could be observed an alkyl interaction between Val17A (CB) of the pentameric form of the E protein and the atom C32 of the ligand dutasteride, which is mostly conserved along the trajectory. Additionally, other alkyl interaction is formed between Val12A (CB) and the atom C32 of the ligand, which is also conserved along the trajectory. Regarding the interactions between the compound alectinib and the homopentaric form of E protein, in which we could observe a saline bridge between the residue Glu8A, Pi-cation interaction with the amino acid Phe4B , and hydrogen bonds with the residues: Asn15D, Asn15E, and Asn15A. In the case of the binding of saquinavir on the homopentameric form it is mainly through van der Waal’s interactions with the following residues: Asn15B, Asn15D, and Phe26C. All the mentioned interactions are shown constantly along the trajectories. Regarding the interactions of nilotinib with E homopentameric, it shows constant interactions with these residues: Phe26B (Pi-Pi stacking interaction), Phe26D , Phe26E  and Phe26A .E protein sequences from SARS CoV NP_828854.1) and SARS CoV-2 (BCA87363.1) have an identity of 94.7% and sequence similarity of 97.4% 28854.1 a such as From the binding energy simulations (MM–PBSA approach), we could identify the best evaluated compounds on the different forms of E protein .Interactions between the monomeric and homopentameric forms are described below:Considering the free binding energy simulations (MM–PBSA approach), we found that the compound dutasteride showed the highest energy on the C-terminal exposed to solvent; and for the hairpin, the most promissory compounds include irinotecan  and saquinavir . On the other side, considering the monomeric form, the compound irinotecan interacts with the residues: Leu73, Leu74 and Val75, and for the compound saquinavir, it shows interactions with Val75 and Leu74. The mentioned residues belong to the PBM of the E protein, which is known to be essential for the binding to the PDZ domain of the PALS1 protein ,33. The On the other hand, there is valuable information (protein interaction maps) about the involvement of C-terminal helical bundle as binding epitope for ligand binding, thus, corroborating our hypothesis regarding the potential role of a C-terminal helical bundle in the mediation of viral replication processes ,53. InteWe have performed diverse in silico studies which include docking analysis, MD simulations and free binding energy calculations (MM–PBSA) from which could depict the energy values of the following ligands: saquinavir −24.9147 kcal/mol, nilotinib −23.0781 kcal/mol, dutasteride −16.9153 kcal/mol and alectinib −116.67 kcal/mol showed the highest binding affinity to the homopentamer. Of worthy interest is that we could depict that alectinib showed the highest binding free energy  from the molecular dynamics simulation studies in comparison to the other compounds. From the binding interactions, it could be observed that the mentioned ligand interacts with the residue Asn15 through hydrogen bond interactions, additionally it interacts with the segment of amino acid residues 7 to 12, which make it a very interesting target to inhibit the ionic channel and N-terminal at the same time. Additionally, it shows interactions with other residues which include: Glu8A and Phe4B.Saquinavir seems to be an interesting ligand because it targets the spike protein and also 3C-like protease, as has been described previously ,56. Of wIt has been reported that nilotinib appears to bind to the receptor binding domain RBD of the SARS-CoV-2 spike protein, and some in silico studies have been carried out to depict the interaction between tyrosine kinase inhibitors and SARS-CoV-2 protein. On the other side alectinib has been prescribed along antiviral treatment however clinical trials are needed in terms of treatment of cancer and treatment of COVID-19. Furthermore, it has been identified that the residue Phe26 is located in a key position better known as “bottleneck region” which is important for the function of the ion channel of the E protein ,57. On tMoreover, the primary sequence of E protein is quite conserved among the different coronaviruses, making it an interesting therapeutic target ,34. DespNowadays, targeted drug repurposing represents a very useful strategy to identify libraries of pre-existing molecules or approved drugs that could prevent COVID-19 ,61.Of worthy interest is that the ligands we proposed in this work could also bind to different SARS-CoV-2 variants as well as another coronaviruses, meaning they can be considered as potential drugs for COVID-19 treatment.Based on the primary sequence of the E protein of SARS-CoV-2, it was possible to build the transmembrane region (TMD) of the E protein of this virus, by employing the crystal structure of E protein from SARS-CoV PDB: 5X29 as template . The ideWe downloaded 5852 molecules from the Zinc database . The molIn the process of virtual screening, software such as: DataWarrior program V5.5.0  and SwisMolecular docking studies of the 2155 ligands were carried out using Autodock Vina (version 1.2) program . The docThe process of selection was carried out by using a Perl script to obtain the binding affinity of each of 2155 compounds in just one file. Once docking simulations were finished, Discovery Studio Visualizer  and ChimThree E protein MD simulations (without a ligand) and sixteen MD simulations of E protein–ligand complexes were carried out by considering the results from docking analysis that showed the highest affinities and showed interactions with key residues. All these complexes were prepared by using Charmm-GUI Software ,79 and eThe method of particle-mesh Ewald (PME) was used for the calculation of the electrostatic potential energy. A no-bonded cutoff of 12 Å, switchdist of 10 Å and pairlistdist of 16 Å were implemented for these long-range interactions. MD simulations were solvated (TIP3 model) and neutralized up to a final concentration of 0.15 M NaCl in the equilibration step. The equilibration protocol consisted in six minimization steps, reaching a total of 2.5 ns of equilibration time; an NTV (constant volume and temperature) protocol was applied. For the production step an NTP (constant temperature and pressure) ensemble was maintained with a Langevin thermostat (310 K) and anisotropic Langevin barostat (1 atm). For these last two steps, an integration time step of 2 femtoseconds (fs) was used, with all the bond lengths involved, and used the CHARMM36 force field . FinallyStructural analysis of the E proteins (from the MD simulations) was carried out by employing the Carma program and considering the alpha atoms of the structure . These sbind) were estimated for the 100 ns MD simulation trajectories. A stride of 10 was considered for the calculations, resulting in about 100 frames for the ΔGbind analysis. The estimations were carried out employing molecular mechanics combined with the Poisson–Boltzmann surface area (MM–PBSA) method. MM–PBSA was applied by the Calculation of Free Energy (CaFE) plugin [E protein–ligand complexes binding free energy (ΔG) plugin  implemen) plugin ,85,86. FIn this work we have employed in silico methods  to identify compounds with potential E protein interaction from SARS-CoV-2. From these studies, we could identify five compounds: irinotecan, alectinib, saquinavir, nilotinib and dutasteride which were best evaluated. Particularly, alectinib could inhibit the functions of the ion channel as well as avoiding the binding of other proteins involved in pro-inflammatory processes that could trigger the cytokines cascade, which in consequence could lead to a serious and mortal illness. On the other side, saquinavir, nilotinib and alectinib were also considered as a promising multitarget ligand because they seem to inhibit three targets, which play an important role in the viral cycle. Additionally, saquinavir was shown to be able to bind to E protein both in its monomeric as well as pentameric forms so it could act in different steps of the viral replication cycle. Finally, further experimental assays are needed to probe our hypothesis derived from in silico studies.We give our consent to publish this manuscript.


The conventional methods to detect homologous protein pairs use the comparison of protein sequences. But the sequences of two homologous proteins may diverge significantly and consequently may be undetectable by standard approaches. The release of the AlphaFold 2.0 software enables the prediction of highly accurate protein structures and opens many opportunities to advance our understanding of protein functions, including the detection of homologous protein structure pairs.In this proof-of-concept work, we search for the closest homologous protein pairs using the structure models of five model organisms from the AlphaFold database. We compare the results with homologous protein pairs detected by their sequence similarity and show that the structural matching approach finds a similar set of results. In addition, we detect potential novel homologs solely with the structural matching approach, which can help to understand the function of uncharacterized proteins and make previously overlooked connections between well-characterized proteins. We also observe limitations of our implementation of the structure-based approach, particularly when handling highly disordered proteins or short protein structures. Our work shows that high accuracy protein structure models can be used to discover homologous protein pairs, and we expose areas for improvement of this structural matching approach.https://doi.org/10.17863/CAM.87873. The code can be accessed here: https://github.com/VivianMonzon/Reciprocal_Best_Structure_Hits.Information to the discovered homologous protein pairs can be found at the following URL: Bioinformatics Advances online. For the last decades the field of similarity searching between proteins has been dominated by sequence similarity methods. This is due to the vast numbers of available protein sequences in databases such as UniProt . With thHomo sapiens (human), Caenorhabditis elegans (worm), Drosophila melanogaster (fruit fly), Saccharomyces cerevisiae (budding yeast) and Schizosaccharomyces pombe (fission yeast) (We used data from the UniProt Reference Proteomes for the following organisms: n yeast) . We seleH.sapiens proteins). We extracted the sequences for each of the available structure models in Fasta format. Thus, our dataset contains 20 296 protein sequences and structure models for H.sapiens, 19 694 for C.elegans, 13 458 for D.melanogaster, 6040 for S.cerevisiae and 5128 for S.pombe.We downloaded the AlphaFold structural models for the five selected organisms from the AlphaFold Protein Database (release v2)  of the ivergent . The fisivergent . We set ivergent . The anaC.elegans, D.melanogaster, S.cerevisiae and S.pombe models in turn and the S.cerevisiae AlphaFold models against the S.pombe models using an E-value threshold of 1e–4. To obtain the reciprocal best hits, we used the same methodology as the one used for RBH, the hits by E-value were sorted and the hit with the lowest E-value was kept as best hit. In case the next hit had the same E-value, the hit or hits with the highest bit score was kept. We kept the reciprocal best structure hits, which passed a coverage threshold of 75%.To detect reciprocal best structure hits (RBSH), we carried out structure comparisons with Foldseek [release 1-3c64211 ], an extremely fast structure comparison tool  for each of the five model organisms  against the organism sequences using the HMMER tool (version 3.3.2) with the gathering (GA) threshold option. To identify and assess novel homologies that had not been identified by any other orthologue prediction method we used curated inventories of orthologues between To investigate the effect of the disordered protein fraction on the structural alignment method, we applied IUPred (IUPred2a) on the protein sequences of the used model organisms with the IUPred2-type ‘long’ option for predicting long disordered regions.H.sapiens and each of the model organisms as well as between S.cerevisiae and S.pombe. Using Foldseek 4316 reciprocal best structural hits were found between H.sapiens and D.melanogaster, 3837 RBSH between H.sapiens and C.elegans, 1921 RBSH between H.sapiens and S.cerevisiae and 2095 RBSH between H.sapiens and S.pombe  (S.pombe (UniProtKB: Q9P7J0), C.elegans (UniProtKB: Q9TYS7) and D.melanogaster (UniProtKB: Q7JWR4). All of these proteins show a high structural similarity to the S.cerevisiae chaperone although sequence similarity was undetected by RBH or any sequence-based orthologue predictor.The detected RBSH include known homologs such as the human proteasome assembly chaperone 4 (PAC4) (UniProtKB: Q5JS54), which was described as homologous to the POC4 protein in S.cerevisiae (UniProtKB: P39543) using BLAST and pHMMer (S.pombe (UniProtKB: O94694), which was previously unconnected to S.cerevisiae Sop7p.The RBSH approach also confirms divergent connections made previously based on homology-search algorithms. Wideman connected the human ER-membrane protein complex Emc7 subunit (UniProtKB: Q9NPA0) to the Sop4p protein in d pHMMer . The strWe validated the detected RBH and RBSH using the PANTHER classification system . We calcOverall, we see that in each case there are more putative closest homologous pairs found with the structure-based method compared to the sequence-based method. However, we see that not all of the pairs detected are shared between the RBH and RBSH methods. The fraction of matches in common between the RBH and RBSH methods was not as high as we expected, with many homologs differing between the two approaches. We further analysed the matches missed by each method to understand how much of the difference is based on discrepancies between the two approaches. We observed that many of the matches found as the best hit with the RBSH method were indeed found by the RBH method as a lower scoring match and vice versa. Thus the differences are not due to the ability of each method to find homologous sequences or structures, but more likely due to different pairs of proteins being the best match when viewed through the lens of sequence comparison or structure comparison. We are showing such an example in S.pombe (UniProtKB: O13650)  (UniProtKB: Q9UKN8), which was before only based on interaction partners connected to the Sfc9 protein in  O13650) .S.cerevisiae nuclear pore localized protein PML39 (UniProtKB: Q03760), a nuclear peripheral protein required for the nuclear retention of unspliced mRNA (S.pombe Rsm1 (UniProtKB: O94506). Fission yeast Rsm1 is a poorly characterized C3HC protein with defects in RNA export and a G2/M cell cycle transition (elongated at division) phenotype  identification of their protein families using the PANTHER classification system and (ii) comparison of their annotated protein domains using the Pfam database . The fra S.pombe . For S.cit is 24 . It shouWe further analysed the RBSH without a common Pfam domain annotation nor PANTHER family classification. These matches would be the potentially most important, representing completely novel homologs identified by the use of structural matching, which can help to understand the function of uncharacterized proteins. One example is the human epithelial membrane protein 3 (UniProtKB: P54852), which was found by the RBSH approach to be homologous to an uncharacterized worm protein (UniProtKB: G5EBZ7) . AnotherC.elegans (UniProtKB: G5EBZ7) and S.cerevisiae (UniProtKB: P53326) proteins, no orthologous proteins were found in DIOPT. For the H.sapiens epithelial membrane protein (UniProtKB: P54852) seven orthologous proteins in C.elegans were predicted . None of them yielded a better RMSD (Root Mean Square Deviation) value compared to the protein detected with the RBSH method (UniProtKB: G5EBZ7), when superposing the protein structure models using pymol (H.sapiens acid phosphatase type 7 protein (UniProtKB: Q6ZNF0) two orthologous proteins in S.cerevisiae were predicted by DIOPT , which indeed yielded a better RMSD score than the protein found by the RBSH method (UniProtKB: P53326), when superposing the structure models.We searched for known orthologues for the four described proteins using the orthologue prediction tool DIOPT (version 8.5)  and S.cerevisiae PEX8 (UniProtKB: P53248)  . No famiS.pombe Mug174 (UniProtKB: O74434) as structural homologue of human coilin (UniProtKB: P38432)  . Mug174  P38432) . The mouS.pombe SPAC19D5.02c (UniProtKB: Q1K9B6) with S.cerevisiae EMC10, a subunit of the ER membrane complex involved in the insertion of newly synthesized proteins into the ER membrane (UniProtKB: Q12025) (S.pombe SPAC19D5.02c and S.cerevisiae Pex22 (UniProtKB: P39718) (S.pombe SPAC19D5.02c. Jackhammer using human EMC10 retrieves both S.cerevisiae EMC10 and fission yeast SPAC19D5.02c. This finding increases the EMC complex complement in S. pombe to eight.The third RBSH is  Q12025) . This fi P39718)  (Lock etWithin the set of RBSH without a common Pfam domain or PANTHER family classification we observed RBSH that are unlikely to be homologous and were most likely found by the RBSH method due to a high fraction of protein being disordered. Indeed, the percentage of exclusive RBSH matches, but also exclusive RBH matches, slightly increases with a higher disordered protein fraction . One exaThe disordered protein regions largely overlap with low confidence scoring regions .In addition to RBSH exclusively found with the structural matching approach, there are also RBH exclusively found by sequence similarity . These mProtein sequences encode the information about protein structures and functions, and the use of protein sequences for the detection of closest homologous protein pairs is widely established . The relIn this study, we assess the detection of homologous protein pairs using protein structures in comparison to protein sequences. We used the structure aligner Foldseek to detect RBSH and the sequence aligner BLASTP to detect RBH . A largeWe used the PANTHER classification system to validate the RBSH and RBH approaches. Even though we found a higher number of RBSH compared to RBH, the validation showed that the conventional RBH approach yields a higher precision score compared to the RBSH approach. This outcome can be explained by limitations of the RBSH approach, but also possibly due to the bias that homologs were so far mainly detected using the protein sequences and protein pairs with similar sequences are therefore better studied and more prevalent in the PANTHER database. It is likely that the PANTHER database aligns well with RBH because it is also a sequence-based method (We encountered different limitations of the RBSH approach while investigating the protein pairs exclusively detected by the RBH approach. The structure of highly disordered proteins turned out to be very challenging to align as the RBSH approach on one hand aligned protein structures solely based on the disordered regions, but on the other hand missed homologous proteins due to their disordered regions. When removing the structure regions with a low AlphaFold confidence score, the results improved with regard to single false positive RBSH, which were no longer detected, but overall, the precision score worsened. A possible explanation is that the AlphaFold confidence scores are not homologous between the potential RBSH and consequently removing different parts of the two proteins forming a true positive RBSH pair, can lead to a less significant E-value and lower structure coverage. Furthermore, homologous protein pairs were missed by the RBSH approach due to different domain structure orientations or due to a small protein size, so that the structural matches are not able to achieve statistical significance.S.pombe a species with extensive coverage of curated orthologues the RBSH approach provided 39 novel S.cerevisiae and 11 novel human homologs, which were added to the PomBase database (When investigating the protein pairs detected with the RBSH approach, we detected potential novel homologs, which can be particularly useful for describing uncharacterized proteins and to draw new functional connections between different species. Model organisms are generally well studied and hence the majority of proteins of their proteomes are already characterized to some degree (database  (Harris We have introduced a novel extension of the reciprocal best matching method to use structural models. Our work shows that such a method works in principle and has a similar set of results to sequences based on RBH detection. Interestingly the structure-based search when faced with many choices in large paralogous families often selects a different pair of best hits compared to the sequence-based approach. We also find that there are a small set of unique matches of potential distant homologs that can be discovered by the RBSH method. We do find some deficiencies in our implementation of the RBSH method. For example, the method seems to be confused by highly disordered proteins and also often fails to find obvious homologs for short proteins.We hope that our work will encourage others to investigate whether structural models can be used for tasks that have traditionally been the preserve of sequence-based methods. We also see many directions for the refinement of the RBSH method through the use of alternative structural comparison methods and filtering procedures. For example, flexible structure matching may enable RBSH identification even when interdomain orientations have been incorrectly predicted. We also envisage combined methods that make use of both sequence and structural information to infer homology across longer evolutionary distances.S.pombe and S.cerevisiae and between S.pombe and H.sapiens. A.B. conceived and supervised the study and contributed to the assessment of detected RBSH. T.P.-L. and V.W. contributed to the writing of this manuscript. V.M. and A.B. were major contributor in writing this manuscript. All authors read and approved the final manuscript.V.M. performed the search for RBH and the verification of detected RBH and RBSH. T.P.-L. conducted the search for RBSH and contributed to the assessment of detected RBSH. V.W. conducted the extensive assessment of detected RBSH and identification of novel homologs between V.M., T.P.-L. and A.B. were supported by core EMBL funding. V.W. was supported by Wellcome Trust [218236/Z/19/Z].Conflict of Interest: A.B. is Editor-in-Chief of Bioinformatics Advances but was not involved in the editorial process of this manuscript.vbac072_Supplementary_DataClick here for additional data file.


Antibodies recognize protein antigens with exquisite specificity in a complex aqueous environment, where interfacial waters are an integral part of the antibody–protein complex interfaces. In this work, we elucidate, with computational analyses, the principles governing the antibodies’ specificity and affinity towards their cognate protein antigens in the presence of explicit interfacial waters. Experimentally, in four model antibody–protein complexes, we compared the contributions of the interaction types in antibody–protein antigen complex interfaces with the antibody variants selected from phage-displayed synthetic antibody libraries. Evidently, the specific interactions involving a subset of aromatic CDR (complementarity determining region) residues largely form the predominant determinant underlying the specificity of the antibody–protein complexes in nature. The interfacial direct/water-mediated hydrogen bonds accompanying the CDR aromatic interactions are optimized locally but contribute little in determining the epitope location. The results provide insights into the phenomenon that natural antibodies with limited sequence and structural variations in an antibody repertoire can recognize seemingly unlimited protein antigens. Our work suggests guidelines in designing functional artificial antibody repertoires with practical applications in developing novel antibody-based therapeutics and diagnostics for treating and preventing human diseases. The aromatic residues  are overly populated among polar residues in the CDRs6, and the interface propensities for the CDR polar residues are slightly higher or similar to those on solvent accessible protein surfaces8. The antibody–protein interfaces are complementary in geometrical shape10 and electrostatic/chemical composition13, with water molecules stabilizing the interfaces through water-mediated hydrogen bonds19. The energetic contributions to the spontaneous formation of antibody–protein complexes have been attributed to a combination of diverse energetic origins, including hydrophobic interactions21, direct hydrogen bonding23, interactions involving aromatic sidechains28, electrostatic interactions32, and water mediated hydrogen bonding35. All these interactions need to be considered in the context of solvation by water: firstly, water mediated hydrogen bonding directly or indirectly stabilizing the polar interfacial groups in the combination complexes37; secondly, complementary real charges and electric dipoles, including direct hydrogen bonding, with enhanced electrostatic interactions39 due to increasingly diminishing dielectric screening by the increasingly immobilized water molecules near the complex interfaces17; thirdly, direct van der Waals contacts of aromatic sidechains on a variety of protein functional groups with corresponding contact energies in aqueous solvation environment40; fourthly, hydrophobic effect by releasing non-structured water molecules around nonpolar carbon atom surfaces into the bulk solvent during forming van der Waals interfacial contacts in the combination complexes21. The former three kinds of driving force are mostly enthalpy-driven, while the latter kind of driving force is largely entropy-driven, frequently leading to a process with thermodynamic manifestation of negative change of Gibb’s free energy ΔG, enthalpy ΔH, entropy ΔS and heat capacity ΔCp for the antibody–protein combination41. All these driving forces are relevant to the antibody–protein interactions only in the context of water as solvent. As such, water molecules solvating the antibodies and the protein antigens in their native aqueous environment have been recognized as an integral part of the antibody–protein recognition process44. Although a large body of evidence suggests that only a fraction of the interfacial interactions contribute substantially to the antibody–protein interaction energy46, it has not been clear in terms of general principles as to how these energetic contributions underlie the antibodies’ specificity and affinity towards their cognate protein antigens observed in nature. In particular, the coding of the amino acids on the CDRs determining the epitope location on protein antigens and the roles of the water-mediated and direct hydrogen bonding in the formation of the antibody–protein complex interfaces are not completely understood.The antibodies’ biological functions rely on their spectacular capabilities of recognizing cognate antigens with exquisite specificity in a complex aqueous environment filled with a large variety of biomolecules of diverse chemical properties. It has been well-established that the CDRs (complementarity determining regions) of antibodies recognize cognate protein antigens with standard-size antibody–protein interaction interfacesThis work is aimed to elucidate the shared indispensable elements in the antibody–protein recognitions, so as to understand the essential driving forces underlying the biological functions of antibodies in recognizing their cognate protein antigens. We carried out computational analyses to investigate the principles governing the antibodies’ specificity and affinity towards their cognate protein antigens in the presence of explicit interfacial waters, and then experimentally compared, in four model antibody–protein complexes, the contributions of the interaction types in antibody–protein antigen complex interfaces with the antibody variants selected from phage-displayed synthetic antibody libraries. The results address the phenomenon that natural antibodies with limited sequence and structural variations in an antibody repertoire are capable of recognize seemingly unlimited protein antigens. The insights provide guidelines in designing functional artificial antibody repertoires with practical applications in developing novel antibody-based therapeutics and diagnostics for treating and preventing human diseases.44, and hence need to be taken into consideration in analyzing the antibody–protein interactions in nature. Since the water structural data in PDB (protein data bank) are incomplete47, we predicted interfacial water structures in antibody–protein interaction interfaces of known complex structures with ISMBLab-H2O  encoded with relatively limited amino acid types in comparison with the vast sequence and structural diversities of proteins recognize almost unlimited protein antigens? To address this question, we first analyzed 88 non-redundant antibody–protein antigen complexes from PDB , indicating that the non-polar contacts in the antibody–protein complex interfaces are mostly driven by the interfacial direct hydrogen bonding, rather than by tightly packed hydrophobic sidechains as in protein interiors or permanent protein–protein interaction interfaces. Moreover, the CDR position distribution shown in Fig. 2 = 0.06 , indicating that the CDR residues involving the aromatic interfacial contacts are distributed differently in terms of CDR residue positions from the CDR residues involving direct hydrogen bonds bridging the antibody–protein interfaces. These results indicate that, in the antibody–protein complex interfaces, aromatic interactions involving CDR aromatic residues, in particular tyrosine, are much more prevalent in comparison with the conventional hydrophobic interactions involving tightly packed hydrophobic sidechains.Unexpectedly as shown in Fig. 2 = 0.63 , indicating that the same set of CDR residue positions are used for direct and water-mediated hydrogen bonds bridging the antibody–protein interfaces. This correlation is not due to overlapping of CDR residues involving direct and water-mediated hydrogen bonding simultaneously—only a total of 73 pairs of polar interactions are simultaneously connected by direct and water-mediated hydrogen bonding. Given that an average antibody complex structure in S88 has 17 CDR residue positions contacting its cognate antigen  are more prevalent than interfacial direct hydrogen bonding pairs , emphasizing the importance of water-medicated hydrogen bonding bridging the antibody–protein interfaces. The histograms in Fig. Evidently, the antibody–protein recognitions are essentially dominated by two types of interactions: firstly, the interfacial contacts involving CDR aromatic amino acid types as shown in Fig. 10. To this end, we compared the interfacial polar interactions in S88 dataset with those in a set of randomly generated complex structures, dubbed S880 dataset. We assembled S880 dataset using each of the S88 antibody-antigen pairs to generate 10 randomly docked complex structures with the docking algorithm PatchDock57 by optimizing the geometrical complementarity of the CDRs of the antibody binding to non-native epitopes of the cognate protein antigen , which is the log-odd ratio of pairwise amino acid type contact preference for amino acid type x in antibodies to interact with amino acid type y in the corresponding protein antigens for contact group n: n = 1 for carbon–carbon atomistic contacts; n = 2 for direct hydrogen bonding contacts; n = 3 for water-mediated hydrogen bonding contacts, as defined in Eq. , indicating that the chemical complementarity does contribute to the stereospecificity of the native antibody–protein complex structures to an extent. Many of the large positive Pn values can be understood by amino acid contact pair preferences in the interfaces of protein–protein interactions58. Specifically, positively charged residues (RKH) interact favorably with negative changed residues (DE) through electrostatic interactions  and P3 in Supplementary Fig. 1 and P2 in Supplementary Fig. Native interfacial polar interactions in S88 retain substantial chemical complementarity between the CDR polar sidechains and the interacting sidechains on the cognate protein antigen. The interfacial chemical complementarity can be quantitatively measured with the pairwise amino acid type contact preferences, P9. This conclusion is supported in that PatchDock has been reasonably successful in modelling interfaces in homodimers and permanent protein complexes57 and yet is not likely to predict the epitope locations with the same set of geometrical complementarity criteria proven useful in predicting the interfaces in homodimers and permanent protein complexes. Although the conclusion is derived from the comparison of S88 dataset with S880 dataset as the null hypothesis, which was constructed with rigid body docking as a first approximation, the modeling uncertainty is only to argue for more extensive geometrical complementarity in the artificial complex structures, not less.In summary, the results indicate that the interfacial polar interactions in native antibody–protein complexes are optimized to form direct or water-mediated hydrogen bonding, and these polar interactions retain substantial chemical complementarity among the interacting sidechains, such as electrostatic complementarity. In addition, the native antibody–protein complex interfaces are usually limited to only a small portion (about one third on average) of the potential optimal contacts of the CDRs forming maximal complementarity in geometrical shape as in the artificially generated antibody–protein complexes in S880, likely due to the energetic balance in optimizing interfacial polar/aromatic interactions while avoiding desolvation accompanied with the formation of the antibody–protein complexes. These results suggest that the optimal interfacial polar/aromatic interactions, rather than maximization of the extent of van der Waals contacts in the interfaces, contribute as determinants for the stereospecificity of the natural antibody–protein complex structures, for which the interfaces are less compact in comparison with those in homodimers and permanent protein complexes48. To quantify the statistical significance of the CDR sequence preference profiles, we statistically compared the CDR sequence profiles with null hypothesis datasets, where the amino acid type distribution probabilities for the degenerate codon NNK . The CDR amino acid type preference profiles were attained by first experimentally enumerating the amino acid types of the CDR residues in only one CDR with the NNK degenerate codon to construct phage-displayed synthetic antibody libraries and followed by determining the CDR sequences of the antibody variants in the antibody libraries binding to the cognate antigen. The experimental process was carried for each of the CDRs under consideration. Since only one CDR was altered with the other 5 CDRs unaltered in each of the variants, the binding mode of the antibody variants to the target protein antigen was expected to be unaltered as well, as had been proven with competitive ELISA (Enzyme-Linked Immuno-Sorbent Assay) in the presence of the parent antibodyi of Eq.  in Suppli of Eq.  were app60 involve mainly a subset of aromatic residues on the CDRs of G648. In the G6-VEGF complex structure60, 6 aromatic residues in CDRs    and water-mediated hydrogen bonding  indicate that these two types of polar interactions involving in the native G6-VEGF complex structure are also a necessary condition for forming the native complex structure, suggesting that the direct/water-mediated hydrogen bonds are not likely to predominantly underlie the specificity of the native G6-VEGF complex structure. Understandably, the direct/water-mediated hydrogen bonding can easily form from the ubiquitous hydrogen bond donors/acceptors in polar sidechains, mainchain peptide groups and solvation water molecules, as demonstrated by the random complex structures in the null hypothesis dataset  complex structureDRs Fig. A contactDRs Fig. C. SequenDRs Fig. B of the DRs Fig. D are higues Fig. D are sub61, for which the indispensable interactions of the antibody F10 binding to HA have been elucidated with the same experimental procedure62. Since the light chain of F10 does not involve in the interface of the native F10-HA complex structure61, only the heavy chain CDR positions were investigated for the CDR amino acid preferences in the F10-HA combination shown in Fig. 2 = 0.28, P-value = 0.007) are indispensable for the specific binding of F10 to HA. The other three types of interactions are not likely to predominantly underlie the specificity of the native complex structure, judging by the similarity of the sequence profile pairs derived from positive and null hypothesis datasets, as shown in comparison of Fig. 2 = 0.66, P-value = 8.2 × 10–7), Fig. 2 = 0.89, P-value = 1.0 × 10–12), and Fig. 2 = 0.53, P-value = 3.8 × 10–5). The conservativeness of serine and proline in VH57 and VH58 in the sequence LOGO  antibody–protein complex structure55 constructed with the scFv framework of human variable domain sequence combination of VH3–23–JH4 for the VH domain and Vκ1–Jκ1 for the VL domain with the CS combination of 1–2–2–1–1 shared by all the antibody structures analyzed in this work. The amino acid type preferences for each of the subset of the CDR positions  in Fig. 66. These antibody–protein interfaces are not necessarily optimally complementary in geometrical shape as in the interfaces of the permanent protein–protein interactions. The interfacial direct/water-mediated hydrogen bonds could stabilize the complex interfaces but without substantial energetic driving force to determine epitope location, because the donors and acceptors involving direct/water-mediated hydrogen bonds in the partially solvated interface are exposed to the aqueous environment, and hence are frequently freely exchangeable with those from solvation water molecules42 and thus are not specific enough to select epitope location. Moreover, water-mediated hydrogen bonds are more extensive than direct hydrogen bonds in the antibody–protein complex interfaces, and the statistical overlapping of the CDR positions suitable for direct hydrogen bonds and water mediated hydrogen bonds suggests that these two types of interfacial polar interactions are interchangeable. Hence, as observed in Figs. 2, as demonstrated experimentally in the guest–host systems in aqueous environment40. Evidently, the specific interactions involving a subset of aromatic CDR residues surrounded with interfacial polar interactions largely form the predominant determinant underlying the specificity and affinity of the stereospecific antibody–protein complexes in nature.With the computational antibody–protein complex analyses in the presence of interfacial waters and experimental investigations identifying indispensable CDR residues for specific antibody–protein complexes from enumerated CDR variants, we concluded that the indispensable interactions dictating the epitope specificity of the natural antibodies are mainly involved with the antibodies’ CDR aromatic sidechains. We found that antibodies recognize cognate protein antigens by forming key contacts involving a subset of aromatic CDR residue sidechains on the exposed CDR residue positions, and in addition, direct and water-mediated hydrogen bonds involving polar CDR residues occupying another set of CDR residue positions in the partially solvated interfaces bridging the CDRs and the cognate protein antigens66, where desolvation of the ubiquitous hydrophilic interfacial groups is prohibitive33 against formation of contiguously tightly packed interface as the predominant determinant for the specificity of paratope-epitope complexes. Although the protein sequences space in nature could be unlimited, aromatic sidechain binding sites composed of backbone atoms and sidechain carbons are common features ubiquitously shared on all protein surfaces2, such that the diversity space of the epitopes on protein antigens mapped by the aromatic sidechain binding propensity and electrostatic complementarity are vastly smaller in comparison with the protein sequence space of the epitopes characterized by amino acid type. Our findings explain why limited amino acid types encoded in the CDRs of natural antibodies can recognize almost unlimited protein antigen sequences. Similarly, although protein surface shape space in nature could be unlimited, aromatic sidechain contacts on the protein antigens do not necessitate large complimentary and continuous interfaces between the combining antibody-antigen pairs. The findings explain why the limited canonical CDR structures in the CDRH1–H2–L1–L2–L3 of natural antibodies54 do not impose limitation on the antibodies from recognizing a large variety of protein surface shapes. From these perspectives, it can be envisaged that antibodies with limited sequence and structural variations in an antibody repertoire are capable of recognizing seemly unlimited protein antigens as observed in nature—a phenomenon underlain by the aromatic and polar amino acids encoded in natural antibody CDRs.These antibody–protein recognition principles described in the previous paragraph provide sensible insights into the antibody–protein complex formation. These principles explain that the antibodies from an antibody repertoire with limited sequence and structural diversity can recognize almost unlimited average protein antigen surfaces intermixed with roughly half hydrophobic and half hydrophilic residues48 and are described in detail in 67 and have been used in this work without modification. Other technical details mentioned in this work are described below.The computational methodologies related to ISMBLab-H2O have been published by our lab48 and the detailed method is shown in 2O) = 1.4 Å, Supplementary Table The ISMBLab-H2O water placement prediction algorithm places the first water molecule at the grid position with the highest probability of water oxygen in the water oxygen probability density map (PDM) around the query protein structure. The construction of the PDM has been published previously49, Fold-X50, 3D-RISM51 and ISMBLab-H2O), removing redundant predicted water molecules by the criterion of water center-to-center distance ≤ 2 × vdW(H2O) + 0.5 Å following the order of ISMBLab-H2O > Dowser++ > 3D-RISM > Fold-X in decreasing priority of surviving the removal of redundant predicted water molecules. Supplementary Fig. The META water molecule placement prediction in Table 2O) + 0.5 Å) water molecule placements within the interfacial spaces of the protein complexes until water placement saturation. The interfacial space in a protein-protein interaction complex is defined as the intersection space from the volumes of the first solvation layer of the interacting proteins, where the volume of the first solvation layer of a protein is the volume in the corresponding solvent accessible surface defined by a probe of radius = vdW(H2O) + 0.5 Å minus the van der Waals volume of the protein; vdW(H2O) = 1.4 Å is the van der Waals radius of a water molecule used throughout this work.Three sets of RANDOM predictions (RANDOM 1–III) in Table –III in Thttp://www.ks.uiuc.edu/Research/vmd/) hydrogen bonding donor (D) and acceptor (A) criteria for both protein main-chain and side-chain: distance cutoff (D-A) ≤ 3.5 Å and angle cutoff (D-H-A) ≥ 150°. Psfgen (http://www.ks.uiuc.edu/Research/vmd/plugins/psfgen/) and CHARMM (http://mackerell.umaryland.edu/charmm_ff.shtml) were used to add hydrogen to protein structure. Some side-chain donors  are flexible in torsion angle rotation, such that for these donors, the angle cutoff was turned off with an additional constraint that a hydrogen must exist between the donor and acceptor. For defining water-mediated hydrogen bonds, the water molecule was treated as a rotating rigid body. For each of the three rotational degree of freedoms of the water molecule, the grid system of 36° interval was used to reduce the infinite conformational space to 500 grid points (10 × 10 × 5), which were enumerated for hydrogen bonding to the hydrogen bond donors/acceptors on the nearby proteins. Hydrogen bonding interaction mediated by an interface water molecule between the donor/acceptor from two separate proteins is defined as water-mediated hydrogen bonding (WMHB) if the interface water molecule is capable of forming hydrogen bonding with the two proteins. Supplementary Fig. Both direct hydrogen bond (DHB) and water-mediated hydrogen bond (WMHB) were defined with VMD (The atomistic contact pair (ACP) of atom x in antibody A and atom y in protein antigen B in the AB complex forms when the center-to-center distant between x and y is less than vdW(x) + vdW(y) + 0.5 Å, where vdW(x) is the van der Waals radius of protein atom x. Supplementary Table 54 with the CDR sequence length of 13–10–11–8–956. These complex structures were collected based on the following sequential criteria: (1) the initial list of antibody–protein complex downloaded from the structural antibody database (http://opig.stats.ox.ac.uk/webapps/newsabdab/sabdab/); (2) complex structures with protein antigen < 35 amino acids removed from the list; (3) antibodies with CDR length of H1, H2, L1, L2, and L3 respectively equal to 13, 10, 11, 8, and 956 were selected; (4) removal of complexes with incomplete structure in any of the CDRs in the antibody variable domains; (5) complexes with CDR canonical structure combination of 1–2–2–1–1 for CDRH1–H2–L1–L2–L3 confirmed were selected; (6) manual selection of antibody–protein complexes with conformational epitope (epitope on the protein antigen composed with more than one discontinuous peptide segments); (7) clustering heavy chain amino acid sequences of the antibody variable domains using CD-Hit (https://github.com/weizhongli/cdhit) with sequence identity cutoff 95%, and constructing the non-redundant antibody–protein complexes list from the center of each of the antibody clusters.The S88 dataset contains the antibody–protein complexes from PDB –VEGF(Ag) complex structure in PDB code:2FJG60. Each of the 244 CDR variants was different from the parent antibody G6 in the amino acid sequence of only one CDR with sequence variation of no more than 5 residues 2FJG as the comparable group for the positive control group S2442FJG. In difference, each of the variants in S1000(qji)2FJG had randomly picked amino acid type based on the distribution probability qji in all 30 residue positions of the 6 CDRs. As such, the variants in S1000(qji)2FJG were different from antibody G6 in 30 CDR residue positions distributed in all 6 CDRs at most, while the variants in S2442FJG were different from antibody G6 in 5 CDR residue positions at most distributed in only 1 CDR.We compared the modelled structures in S2442i in Eq.  in Suppli in Eq.  used for2FJG dataset 2FJG dataset 2FJG dataset 2FJG dataset  indicate that the modelling uncertainties associated with the computational complex structures in S2442FJG and S1000(qji)2FJG datasets are not expected to be significant for the quantitative conclusions from the amino acid sequence profiles calculated based on the computationally modelled structures.To assess the impact of the uncertainty of the computationally modelled structures towards the analysis results of the interacting contacts in the antibody-antigen interfaces of the modelled complex structures, we compared the analysis results with the two datasets side-by-side. If no difference of the statistical analyses were observed, the impact of the structural modelling uncertainty would be deemed as insignificant in terms of interpretating the statistical analyses. We calculated the percentage of the antibody aromatic residue (FWY) at each of the CDR positions involving aACPs with VEGF in S244Supplementary Information.