Structure prediction and validation
Using three computational tools, namely Modeller, Swiss-model, and AlphaFold, we analyzed the structural characteristics of PGIP (Polygalacturonase-Inhibiting Protein) and PG (Polygalacturonase) proteins. Modeller and Swiss-model employ a comparative modeling approach, leveraging existing experimental structures of similar proteins as templates to predict the structure of the target protein. These tools optimize the alignment between the target sequence and the template structures, facilitating the generation of a 3D model based on this alignment. On the other hand, AlphaFold utilizes advanced neural network architectures and training techniques, integrating evolutionary, physical, and geometric properties of protein structures to predict their 3D structures. In our analysis, we found that the Swiss-model server provided more accurate models of PGIP and PG proteins compared to Modeller and AlphaFold. The predicted structures of cotton (G. barbadense) PGIPs, consisting of 330 amino acids, and PG proteins from X. citri pv. malvacearum (463 amino acids) and A. macrospora (379 amino acids), are depicted in Fig. 1. Upon structural assessment, we confirmed the validity of the predicted structures, indicating their suitability for further investigation and functional studies. This comprehensive analysis highlights the effectiveness of computational modeling tools in predicting protein structures, with Swiss-model demonstrating superior accuracy in this context.
The stereochemical properties of the predicted gbPGIP and PGs were thoroughly investigated. Analysis of the Ramachandran plot revealed that approximately 77.4% of the residues' phi/psi angles in the modelled structure were situated within the most favored regions, while 22.2% of residues resided in additional allowed regions. For xcPG and amPG, a notable percentage of PG residues, 88.3% and 80.8% respectively, occupied the most favored areas of the Ramachandran plot (Fig. 1d-f). The PROCHECK G-factor values for all predicted structures fell within the ideal range of -0.4 to 0.5, indicating their high quality.
Various evaluation methods were employed to assess the overall quality of the models. The ERRAT and PROSA Z-score metrics indicated the consistency of the models, with negative values signifying their reliability (Wiederstein and Sippl, J.2007). Further validation from the VERIFY 3D server confirmed the quality of the models, as a significant proportion of residues scored above the threshold of 0.1. The WHATCHECK results supported the accuracy of the models, while VADAR analysis provided smaller chi-square standard deviations, suggesting a better fit and higher confidence (Willard et al., 2003). Additionally, ProQ utilized MaxSub and LGscore to evaluate the protein model's accuracy, where an LGscore above 4.0 indicated a very good model (Wallner and Elofsson, 2003). Taken together, these validations underscore the high level of structural quality exhibited by the modelled structures. Further details on the quality assessments are provided in Table 1.
Table 1
Quality measures of the predicted structure
| Protein | Overall Quality Factor | Z-score | LGScore | MaxSub |
| gbPGIP | 86.913 | -7.47 | 10.134 | -0.662 |
| amPG | 89.058 | -7.17 | 9.250 | -0.409 |
| xcPG | 86.555 | -5.76 | 8.997 | -0.416 |
Sequence alignment and active site residues
The alignment of PGIP sequences with their respective templates and PG proteins from pathogenic organisms was conducted using ESPript (Robert and Gouet, 2014), and the results are depicted in Fig. 2. The secondary structures such as α-helices, η-helices, β-sheets, and strict β-turns were annotated as α, η, β, and TT, respectively. Conserved residues were highlighted in red boxes, while similar residues were represented by white boxes. Additionally, the alignment accounted for insertions and deletions of residues, particularly in the loop regions.
The PGIP sequence from G. barbadense exhibited significant alignment and conservation with the PGIP template from P. vulgaris (PDB: 1OGQ), as illustrated in Fig. 2(a). However, notable mutations were observed at the active residues Val152 (V152) and Gln224 (Q224) in the crystal structure of P. vulgaris PGIP, where G. barbadense displayed Glu169 (E169) and Phe242 (F242), respectively. Due to distinct evolutionary histories between the two species, these changes most likely result from evolutionary processes.
Furthermore, the PG protein sequences from selected plant pathogens, such as A. macrospora and X. citri pv. malvacearum, were aligned with sequences from other crystal structures. Specifically, PG sequences from F. verticillioides (PDB ID: 1HG8) and A. aculeatus (PDB ID: 1IA5) served as templates for fungal pathogens, while P. carotovorum PG protein (PDB ID: 1BHE) served as a template for bacterial pathogens. Although complete sequence conservation was not observed, active residues were conserved across all five plant pathogens (Fig. 2(b)). These findings underscore the dynamic nature of protein interactions in plant-pathogen systems and highlight the evolutionary adaptations that occur in response to selective pressures.
Docking
The crucial residues comprising the active sites of Polygalacturonase-Inhibiting Proteins (PGIPs) and Polygalacturonases (PGs) were highlighted in red within the sequence alignment and Supplementary file S1. To ascertain the optimal conformation of the gbPGIP receptor proteins with the amPG and xcPG effectors, protein-protein docking simulations were executed using HADDOCK. The structural models of ghPGIP, amPG, and xcPG served as inputs for the docking process. Clusters of generated models were assessed, with preference given to conformations exhibiting the most negative Z-score, as outlined in Table 2. Each selected cluster comprised four protein complex models, from which the model demonstrating lower binding energy and suitable interactions at active site amino acids was chosen for subsequent analysis using the Hawkdock server. The calculated binding energies (in kcal/mol) for the selected models were − 92.1 (gbPGIP-amPG) and − 48.1 (gbPGIP-xcPG), indicative of their stability. Following this, doubly mutated complex structures of PGIP-PG were generated using the MutaBind2 server and subsequently submitted to the HADDOCK server. In-depth intermolecular analysis uncovered the presence of hydrogen bonds within the complexes. By scrutinizing the active sites and evaluating binding energies, the study elucidates critical insights into the stability and specificity of these interactions.
Table 2
HADDOCK parameters for PGIP-PG complexes
| Protein complexes | Structures | Clusters | Water refinement | Z-score |
| gbPGIP-amPG | 121 | 12 | 60% | -1.4 |
| gbPGIP-xcPG | 131 | 11 | 65% | -1.1 |
The gbPGIP-amPG and gbPGIP-xcPG complexes displayed varying hydrogen bond counts, with 7 and 3, respectively, along with several hydrophobic interactions, as illustrated in Fig. 4(a) and 4(c). Specifically, the amino acid Glu169 of gbPGIP formed a hydrogen bond with amPG and engaged in a hydrophobic interaction with xcPG. Additionally, the Phe242 residue of gbPGIP participated in hydrophobic interactions with both amPG and xcPG. These findings suggest a nuanced interaction pattern between gbPGIP and its interacting partners, indicating potential differences in binding affinity and specificity between amPG and xcPG.
Maulik and Basu (2013) conducted research on the PGIP protein of P. vulgaris, where they found that site-directed double mutagenesis at residues Val152 and Qln224, replacing them with Gly and Lys respectively, led to significant structural alterations in the protein's concave structure. This alteration ultimately hindered the binding of PG protein from the pathogen F. phyllophilum. The mutations caused subtle changes in the concave face of the PGIP protein, affecting its functionality. Interestingly, in the PGIP protein of G. barbadense, evolutionary processes have led to mutations at similar positions: Val152 mutated to Glu169 and Gln224 mutated to Phe242 (as depicted in Fig. 3). Inspired by these findings, we conducted a similar study on the PGIP protein of G. barbadense. Utilizing MutaBind2, we performed double mutations to observe the effects of replacing Glu169 and Phe242 with Gly and Lys respectively.
Figure 4(a)
Inter-molecular analysis of ghPGIP-amPG complex, pre and post MD simulations. A is used for PGIP and B for PG protein. 2-D and 3-D images are generated by LigPlot and PyMol tools respectively.
Figure 4(b)
Inter-molecular analysis of mutated ghPGIP with amPG complex, pre and post MD simulations. A is used for PGIP and B for PG protein.
Figure 4 (a-d) clearly illustrates that residues Gly169 and Lys242 of gbPGIP do not participate in the interaction with amPG. However, when xcPG interacts with gbPGIP, it disrupts the hydrophobic interaction with Gly169 and instead forms a hydrogen bond with the Lys242 residues of gbPGIP. The green and red dotted lines indicate the hydrogen bonds and hydrophobic interactions between amino acids of gbPGIP interact with amPG or xcPG residues. While the mutation does affect the flexibility and interactions of active residues in the docked complexes, its impact appears to be minimal at this stage. To comprehensively assess the effects of the mutations in gbPGIP on interactions with amPG and xcPG, molecular dynamics simulations were conducted in the subsequent step. These simulations provide deeper insights into the dynamic behavior and stability of the protein-protein complexes, shedding light on the conformational changes and energetic profiles underlying the interaction dynamics.
Analysis of Trajectories
The docked complexes underwent molecular dynamic simulations lasting 100 nanoseconds to observe their behavior and evaluate their structural stability. Various metrics, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA), were used to assess the stability of the complexes during the simulations.
RMSD served as a quantitative measure to gauge the stability of the complexes throughout the 100 ns MD simulation, measuring the variation in the positions of the protein's backbone from the beginning to the end of the simulation. Lower RMSD values indicated greater stability in the docked complexes. In the case of the gbPGIP-amPG complex, consistent stability was observed, maintaining an average RMSD of ~ 0.39 nm over the entire 100 ns simulation, as depicted in Fig. 5. Conversely, the mgbPGIP-amPG complex showed a maximum deviation of ~ 4.6 nm. Notably, this complex displayed an average deviation of ~ 1.29 nm, with significant fluctuations of ~ 2.4 nm during the time interval of 62–67 ns and ~ 1.89 nm during 74–80 ns. After 80 ns, it consistently exhibited high fluctuation, peaking at ~ 3.9 nm.
The gbPGIP-xcPG complex also demonstrated stable dynamic behaviour with a RMSD of ~ 0.48 nm throughout the simulation. The mgbPGIP-xcPG complex showed an initial RMSD of ~ 0.5 nm up to 50 ns. Subsequently, it slightly decreased to ~ 0.47 nm for the remainder of the simulation.
The results suggest that the three trajectories—gbPGIP-amPG, gbPGIP-xcPG, and mgbPGIP-xcPG—demonstrate consistent structural stability throughout the simulation, maintaining RMSD values around 0.5 nm. These structures remain close to their initial conformations, indicating minimal structural changes. This could be due to the interaction between gbPGIP and xcPG which is stabilized by a hydrogen bond with Lys242 and possibly other interactions with Gly169 as observed in Fig. 4(d). This shows that these interactions do not induce substantial structural changes in the PGIP, allowing it to remain stable and presumably functional in inhibiting the polygalacturonase activity of the pathogen. Conversely, the mgbPGIP-amPG trajectory exhibits initial stability similar to the other trajectories but shows significant fluctuations starting around 60 ns which could be due to lack of interaction between Gly169 and Lys242 residues of gbPGIP and amPG as evident in Fig. 4(b). This instability culminates in a dramatic increase in RMSD values, reaching up to 5 nm after 80 ns. This behavior indicates a substantial structural transition or instability in the latter part of the simulation for mgbPGIP-amPG. This is not observed in the other systems, suggesting it undergoes critical structural changes. Such instability suggests that the mutation disrupts the ability of PGIP to maintain a stable complex with amPG, likely impairing its inhibitory effectiveness. Consequently, this could make the host more susceptible to infection by A. macrospora, highlighting the critical role of specific amino acids in maintaining the structural integrity and function of PGIP. For Xanthomonas citri, both the wild-type and mutated PGIPs (gbPGIP-xcPG and mgbPGIP-xcPG) show stable interactions with xcPG, suggesting that the structural changes induced by the mutation do not significantly affect the ability of PGIP to interact with xcPG. This indicates that the key binding residues for xcPG are not substantially impacted by the mutation, allowing both wild-type and mutated PGIPs to effectively inhibit the polygalacturonase activity of pathogen. Thus, the host resistance to X. citri is not compromised by the mutation in PGIP.
Furthermore, a superimposition analysis of wild-type and mutant PGIP structures was conducted to assess the extent of structural deviation induced by mutations. The RMSD values were calculated for both pre-and post-MD simulation structures. The RMSD of the pre-MD superimposed structures was determined to be ~ 0.11 nm, indicative of minimal structural variance between the initial conformations of the wild-type and mutant PGIP. However, following MD simulations, the RMSD increased substantially to ~ 2.82 nm, suggesting a notable divergence in structural alignment between the wild-type and mutant PGIP structures (Figure S3).
The RMSF serves as a tool to discern regions of proteins that undergo variations by gauging the flexibility of specific residues over time. A lower RMSF score indicates stability within protein complexes, while a higher score implies increased flexibility and instability. To assess the flexibility of individual residues relative to their average position during simulations, we computed the mobility of Cα atoms across all systems. Consistent fluctuations were observed across the systems, as illustrated in Fig. 6.
In the gbPGIP-amPG complex, RMSF values for residues ranged from ~ 0.05 to ~ 0.29 nm, averaging at ~ 0.12 nm. The mgbPGIP-amPG complex displayed a wider range of RMSF values, spanning from ~ 0.05 to ~ 0.30 nm, with an average of ~ 0.43 nm. Similarly, in the gbPGIP-xcPG complex, RMSF values ranged from ~ 0.05 to ~ 0.33 nm, averaging at ~ 0.13 nm. Conversely, the mgbPGIP-xcPG complex exhibited RMSF values ranging from ~ 0.05 to ~ 0.31 nm, with an average of ~ 0.12 nm.
These findings indicate distinct patterns of residue flexibility within the complexes. Particularly, the mgbPGIP-amPG complex showed higher overall RMSF values, suggesting increased flexibility in specific residues compared to the non-mutated gbPGIP-amPG complex. Conversely, the mgbPGIP-xcPG complex displayed similar RMSF values to its non-mutated counterpart, indicating comparable flexibility in residues. These observed variations in RMSF values may denote altered dynamics and structural adaptability induced by mutations in the PGIP.Figure 6: RMSF of Cα during 100 ns MD simulation
To further evaluate the structural stability of the docked complexes, additional analyses were conducted using measurements of the Radius of Gyration (Rg) and Solvent Accessible Surface Area (SASA). SASA quantifies the extent of the protein surface exposed to the solvent, while Rg provides insights into the compactness of the system and the dimensions of protein-protein complexes, crucial for their proper interactions.
The Rg values for gbPGIP-amPG, gbPGIP-xcPG, mgbPGIP-amPG, and mgbPGIP-xcPG were approximately 2.86, 2.72, 3.48, and 2.76 nm, respectively (as depicted in Fig. 7a). Notably, mgbPGIP-amPG exhibited major fluctuations during the 62–67 ns and 74–80 ns intervals, followed by consistent higher fluctuations after 80 ns, averaging at 5 nm. These significant fluctuations in Rg for the mgbPGIP-amPG complex during the 62–67 ns and 74–80 ns intervals, followed by consistently higher fluctuations after 80 ns, averaging at 5 nm, indicate substantial structural instability. This instability suggests that the mutation disrupts the compactness of the PGIP when interacting with amPG.
For the gbPGIP-amPG complex, the SASA values remained stable with an average of 299 nm², suggesting a consistent and stable exposure to the solvent. For the gbPGIP-xcPG complex, a decreasing trend in total SASA was observed, averaging at 316 nm², which might indicate slight compaction or reduced exposure to the solvent over time, reinforcing the stability of this complex. In contrast, the SASA for the mutated complex mgbPGIP-amPG increased from 267 nm² to 295 nm², highlighting structural changes that lead to greater solvent exposure. This increased SASA further supports the notion of instability and conformational changes observed in the mgbPGIP-amPG complex. For the mgbPGIP-xcPG complex, the SASA change from 308 nm² to 315 nm² (illustrated in Fig. 7b) suggests minor adjustments in solvent exposure but overall maintains stability, consistent with the stable Rg and FEL results for this complex.
Both wild-type and mutated PGIPs (gbPGIP and mgbPGIP) demonstrate stable interactions with xcPG, maintaining consistent Rg and SASA values. In contrast, mgbPGIP-amPG complex exhibits significant structural instability, as evidenced by fluctuating Rg and increasing SASA values. These structural changes likely impair the ability of mgbPGIP to effectively inhibit amPG, making the host more susceptible to infection by A. macrospora.
Analysis of intermolecular hydrogen bonds
Intermolecular hydrogen bonds play a crucial role in maintaining molecular stability and facilitating recognition processes. To assess the dynamic stability of four complexes (gbPGIP-amPG, gbPGIP-xcPG, mgbPGIP-amPG, and mgbPGIP-xcPG), H-bond analysis was performed during 100 ns MD simulations as shown in Fig. 8 and Fig. 4(a-d).
Within the gbPGIP-amPG complex, a consistent and average of 4.8 hydrogen bonds was observed throughout the simulation. In the gbPGIP-xcPG complex, an increasing trend in the number of hydrogen bonds between the interacting proteins was noted, with an average of 8.5 over the course of the simulation.
Nevertheless, the mgbPGIP-amPG complex exhibited a decreasing trend in the number of hydrogen bonds between the interacting proteins, with an average of 2.6. After 70 ns, the number of hydrogen bonds continuously decreases and at the end of the simulation process the complex is separated, represented by the green colour in Fig. 8. The mgbPGIP-xcPG complex displayed a pattern similar to gbPGIP-xcPG, with a slight decrease in the number of hydrogen bonds. The average number of hydrogen bonds for mgbPGIP-xcPG was 8.2. The difference of hydrogen bond interactions in gbPGIP-xcPG (red) and mutated gbPGIP-xcPG complex (blue) reflects higher variation between 80–100 ns. At the end of the simulation, gbPGIP-PG shows more hydrogen interaction and site directed mutations in gbPGIP inhibit the formation of hydrogen bonds and their stability with xcPG.
These results suggest that while the non-mutated complexes (gbPGIP-amPG and gbPGIP-xcPG) maintain stable hydrogen bond interactions, the mutated complexes (mgbPGIP-amPG and mgbPGIP-xcPG) exhibit alterations in the dynamics of intermolecular hydrogen bonding. The decreasing trend in hydrogen bond numbers in mgbPGIP-amPG and the slight decrease in mgbPGIP-xcPG may indicate potential disruptions in the stability of these complexes.
Clustering Analysis
The cluster analysis of the four complexes (gbPGIP-amPG, gbPGIP-xcPG, mgbPGIP-amPG, and mgbPGIP-xcPG) reveals significant insights into their structural stability and conformational dynamics. The superimposition of the top two representative structures, obtained using the Jarvis-Patrick clustering method, shows that the gbPGIP-amPG (a) and mgbPGIP-amPG (c) complexes exhibit significant deviations between the structures, indicating notable conformational flexibility and instability. This is contrasted by the gbPGIP-xcPG (b) and mgbPGIP-xcPG (d) complexes, where the structures are closely aligned, reflecting structural stability and minimal conformational changes (Fig. 9).
For the gbPGIP-amPG complex, the range of RMSD values was 0.05–4.5 nm, with an average RMSD of 2.1 nm, resulting in 25 clusters. The mgbPGIP-amPG complex showed a similar pattern with a range of RMSD values from 0.05–4.6 nm, an average RMSD of 2.2 nm, and 40 clusters. These high RMSD ranges and large number of clusters indicate significant structural deviations and instability for the amPG complexes. Conversely, the gbPGIP-xcPG complex had a much narrower RMSD range of 0.05–0.3 nm, an average RMSD of 0.2 nm, and formed only 4 clusters. Similarly, the mgbPGIP-xcPG complex exhibited an RMSD range of 0.05–0.45 nm, an average RMSD of 0.2 nm, and 7 clusters. The small RMSD ranges and fewer clusters in the xcPG complexes suggest a higher degree of structural stability and minimal conformational changes.
The RMS distribution further supports these observations (Fig. 10). For the gbPGIP-amPG (black) complex, the distribution shows a broad peak, indicating a range of conformations and higher RMS values, which suggests structural instability. Similarly, the mgbPGIP-amPG (green) complex exhibits multiple peaks, with one significant peak at higher RMS values, reinforcing the idea of considerable conformational flexibility and instability. In contrast, the RMS distributions for the gbPGIP-xcPG (red) and mgbPGIP-xcPG (blue) complexes are narrow and focused at lower RMS values, implying that these complexes maintain similar conformations and exhibit structural stability.
The combined analyses from the superimposed structures and RMS distributions indicate that the gbPGIP-amPG and mgbPGIP-amPG complexes undergo significant conformational changes and are less stable. In contrast, the gbPGIP-xcPG and mgbPGIP-xcPG complexes exhibit minimal conformational changes and greater structural stability. These findings suggest that interactions involving xcPG are more stable, whereas those involving amPG lead to considerable conformational flexibility and instability in the complexes.
Principal Component and Free Energy Landscape Analysis
In the study of protein dynamics, PCA was utilized to delve into the conformational variations of PGIP complexes (both wild-type and mutant) with PG. This analysis allowed for the examination of their collective motions using the essential dynamics approach. By employing PCA, we were able to visualize how proteins move in their atomic space to execute specific functions. Eigenvalues were extracted from the covariance matrix, while PCs were identified using tools like gmx anaeig and gmx covar. These eigenvalues provided insights into the atomic contributions to motion, while PCs elucidated the overall direction of motion of the atoms. Our analysis of MD trajectories unveiled distinct behaviors between the wild-type and mutant gbPGIP complexes, particularly when interacting with amPGIP and xcPGIP. Notably, the wild-type gbPGIP demonstrated stability with increased dynamics in structural conformation, whereas the mutant gbPGIP exhibited instability, especially in the complex with amPG.
Additionally, porcupine plots were generated to visualize the global motions in both systems, leveraging the top two principal components PC1 and PC2. These plots provided further insights into the collective motions of the protein complexes, enhancing our understanding of their dynamic behavior.
Porcupine plots was employed to visualize the movement patterns captured by the top PC obtained from PCA. These plots are valuable tools for understanding how protein structures move over time during molecular dynamics simulations. Porcupine plots depict the extreme projections of protein structures onto the PC1, which accounts for the maximum variance in the dataset. The length of the arrows in the plots represents the strength of motion, while the direction indicates the direction of movement. The analysis revealed distinct motion patterns among the protein complexes. For instance, gbPGIP-amPG showed a pronounced inward motion, suggesting a conformational change towards a more compact structure. Conversely, gbPGIP-xcPG and mgbPGIP-xcPG displayed subtle outward motion along PC1, indicating a slight expansion of the structures. These observations were consistent with the RMSF analysis, which identified the terminal ends of the complexes as the regions of greatest flexibility. Interestingly, mgbPGIP-amPG exhibited minimum motion due to lack on interaction.
In the examination of the Gibbs free energy landscapes (FELs), the first two eigenvectors (EVs) were utilized to investigate deeper into the conformational dynamics of the complex. The FELs of the gbPGIP-amPG, gbPGIP-xcPG, mgbPGIP-amPG, and mgbPGIP-xcPG complexes over a 100 ns timeframe were presented in Fig. 13. Within these plots, regions shaded in deeper blue indicated energetically favorable conformational states characterized by lower energy levels, whereas yellow regions denoted less favorable conformations Notably, the gbPGIP-xcPG and mgbPGIP-xcPG complexes exhibited stable, energetically favorable states indicating minimal conformational changes. In contrast, the gbPGIP-amPG and mgbPGIP-amPG complexes showed more pronounced fluctuations and less stable energy states, suggesting significant conformational changes and instability. Further analysis revealed that the presence of amPG influenced both the size and position of the essential space sampled by gbPGIP and mgbPGIP, resulting in unstable configurations and higher energy states.
The FEL analysis complements the RMSD findings observed for the mgbPGIP-amPG trajectory, which reached up to 5 nm after 80 ns, highlighting that specific mutations can have differential impacts on PGIP interactions with different pathogens. The stable interaction with xcPG despite the mutations suggests that the inhibitory function of PGIP against X. citri remains intact. In contrast, the instability observed with amPG indicates that the global structural integrity required for effective inhibition is compromised, emphasizing the critical role of specific amino acids in maintaining both local and global structural stability and function of PGIP.
The complex of protein-protein (PGIP-PG) is explained to be important for the plant-pathogen interaction mechanism at atomic level (Misas-Villamil and van der Hoorn, 2008). The crystal structures of PG of various parasitic species, P. carotovorum (1BHE), A. aculeatus (1IA5), E. leycettana (7E56), F. verticillioides (1HG8), A. thaliana (7B8B), C. lupini (2IQ7), A. niger (1CZF), C. purpureum (1K5C), T. maritima (3JUR) and Y. enterocolitica (2UVE), have been resolved and submitted in Protein Data Bank. But, the crystal structure of PGIP protein is available only for PGIP of P. vulgaris in Protein Data Bank. The study of plant-pathogen interaction (PPI) is instrumental in deciphering the intricate molecular interactions during host-pathogen interactions (Murmu & Archak, 2023; Kumar et al., 2020). The PPI of PGIP from G. barbadense with bacterial pathogen, X. citri pv. malvacearum and fungal pathogen, A. macrospora, and effect of double mutations in the PGIP have not been studied yet. Here, we employed a combinatorial approach of structural bioinformatics including structural modelling, active site prediction, protein-protein docking, double mutated complex formation, and molecular dynamic simulation to explore the mechanism of the PGIP-PG complex to explain the plant-pathogen interactions. The mode of interaction of fungal amPG with gbPGIP differs from the mode of interaction of bacterial xcPG with gbPGIP. The results of docking studies are evidence of hydrogen bonding, gbPGIP-amPG complexes have higher number stronger H-bonds and hydrophobic interactions compared to gbPGIP-xcPG.
Several studies have reported that 10 LRRs are associated to make a solenoidal shape of the PGIPs and these LRRs are directly involved in various cellular processes by protein-protein interactions (Figure S2). But, the results of mutation at the selective sites, the structure to be changed and a concave structure to inhibit the interaction with pathogenic PG protein. The studies of MD simulation of complexes, before and after mutation, have been performed for the gbPGIP-amPG, mutated gbPGIP-amPG, gbPGIP-xcPG and mutated gbPGIP-xcPG. The MD simulation results collectively suggest that while the non-mutated complexes exhibit stable structures and interactions, the mutated complexes, particularly mgbPGIP-amPG, undergo alterations in dynamics, leading to potential disruptions in stability. These findings highlight the importance of considering the impact of mutations on the structural and dynamic behaviour of protein complexes, providing valuable insights for further investigations into the functional consequences of these changes. A deep understanding of interaction at the molecular level for cotton (G. barbadense) and its two major fungal and bacterial plant pathogens has been provided by this study and it would be helpful in designing the plant protection and disease management strategies as well as in improving crop production at the commercial level and for storage efficacy too.
The results of the dynamic behavior of the complexes provide compelling evidence that different pathogens interact differently with host PGIPs and mutations in PGIPs can have varying effects on these interactions, ultimately impacting host resistance to pathogen infection. Stable trajectories were observed for interactions between wild-type and mutated PGIPs with X. citri, suggesting that mutations do not significantly impact the ability of PGIP to inhibit xcPG. This finding aligns with previous research where mutations did not compromise PGIP ability to inhibit the polygalacturonase activity of Fusarium phyllophilum (Maulik and Basu, 2013). Conversely, a mutated PGIP interacting with A. macrospora showed significant structural instability, potentially impairing its inhibitory effectiveness. These results emphasize the critical role of specific amino acids in maintaining the function of PGIP. This observation is consistent with previous studies where mutations in PGIPs were shown to affect their ability to inhibit Verticillium dahlia (Liu et al., 2018). These findings highlight the complex interplay between PGIP mutations and pathogen interactions, influencing host resistance to infection.
The observed alterations in protein complex dynamics due to mutations, also present a unique opportunity for crop protection in cotton through genome editing technologies like CRISPR-Cas (Cardi et al., 2023). This approach aligns well with the growing interest in utilizing genome editing tools for crop improvement and disease resistance (Abdallah et al., 2015). The destabilization observed in mutated complexes, as demonstrated in MD simulations, offers insights into potential vulnerabilities which can be targeted for modification, aiming to optimize plant defence mechanisms. This strategy not only holds promise for accelerating breeding programs but also contributes to sustainable agriculture by developing cotton varieties with improved disease resistance, aligning with the broader goals of precision agriculture and crop protection. Further experimental validations will be crucial in confirming the predicted effects of genomic modifications on plant phenotype and performance in real-world agricultural settings.