We noticed important deviations in the activity and structure of the CIB1 in the current investigation as a result of five newly developed peptide inhibitors of the CIB1 target, which interacted with the CIB1 receptor interface and were reported to influence CIB1 activity. Overall, there was a considerable impact on CIB1 activity.
3.1 Interface Analysis of Mutant UNC10245092/CIB1 complex
The complex structure of UNC10245092/CIB1 was downloaded from a protein data bank with PDB Id (6OD0). The calcium integrin-binding protein1 (CIB1) consists of 164 residues which are bound to 12 amino acids long peptide. The structural and biological analysis showed the CIB1 involved in the interaction with the UNC1024509 peptide. The Interface before MD simulation and the Hydrogen bond analysis was done by using Pymol are shown between the UNC10245092/CIB in Figure2. Sixty peptide complexes were designed (peptide/CIB1) in comparison with wild-type peptides (UNC10245092/CIB1). Amongst these four (peptide/CIB1) were taken based on binding affinity. All four (peptide/CIB1) complex is single residues substituent e.g. S5R, S10F, S5F, S5Y are shown in table1. The UNC10245092/CIB1 complex was exposed to Molecular dynamic simulation to better understand its structure and stability in the dynamical state in a hypothetical brine situation. A molecular dynamic simulation was run for Wild-type and four mutant systems to examine the interface after MD simulation. The total number of H-bond reduced after the MD simulation is shown in Figure4. Various studies were carried out, including for stability we calculated RMSd, for residual flexibility we calculated RMSf, Total energy, principal components analysis (PCA) for protein movements, DCCM for correlated and anti-correlated residues, and binding free energy. These investigations considerably improved our understanding of the peptide-protein relationship.
3.3 Residues Scan methodology
To check the role of UNC10245092 peptide in the CIB1 protein receptor residue scanning methodology was applied by using MOE software. In a wild-type protein-peptide complex the hotspot residues are Gln151, Phe175, Ser177, Trp179, and Asn67 are in the interface and these residues are involved in the interaction and the rest of the residues are found less important to make interaction. Alanine scanning methodology is the most important computational technique for choosing promising residues for mutation. All the 12 peptides residues were passed through the alanine scanning method and among the 12 residues only four have a high dAffinity –ve value and the rest of the residues have a positive value which is less important for interaction. Then the residue scanning methodology was adopted and the total number of ten novel peptides is processed for the per-mutation analysis various combination of mutated residues of the peptide inhibitor was generated. Here a library of four mutated peptides (12 a.a long) inhibitor was generated by mutating SFWYGFMKALYG (exact numbering from 5 to 16) of the wild-type peptide Table1. In this case, dAffinity was noticed as a difference in the binding energies of the wild type and mutant amino acid residues. Among the sixty mutated peptides, the top four were selected based on Affinity and dAffinity scores. The less Affinity and negative value showed more stability and high interaction between mutated peptide and CIB1.
The biochemical study revealed that altered residues like serine, arginine, and aspartate have a key role in limiting CIB1 target biological activity, and hence this procedure is called residue scan analysis.
All Atom Molecular dynamics simulation
To assess the stability of mutants during the simulation period, a comparison of MD characteristics on variations and wild-type protein complexes was done. Each simulation run was conducted three times. After 100 ns, the trajectory was examined, and RMSds were determined. Figure23indicates that the wild-type system fluctuates between 20 and 80 ns but remains steady during simulation between 80 and 100 ns. For all Mutants, the wild type UNC10245092/CIB1 complex can be used as a standard. It can be seen that the wild Type of acceptable fluctuation has stabilized, and a straight graph is constructed from 100 ns onwards, reporting the wild type system's steady behavior. The RMSd increased during the first 35 ns in the mutant-1 scenario, but then stayed steady for the duration of the simulation. Mutant-2, on the other hand, has exhibited overall stability across the system. Mutant-3, which forms multiple interactions with a UNC10245092 peptide, significantly reduces the system's overall stability. It can be seen in the graph that there was a lot of convergence at different intervals. During the simulation, periods of 10–20 ns, 50–60 ns, and 70–85 ns showed considerable variation. Moreover, during the simulation, the Mutant-4 system showed substantial variance. Specifically, until the end of the experiment, Mutant-2 demonstrated significant convergence instability. Significant convergence was seen at various intervals. The influence of alanine substitution on Mutant-4 did not favor the stability modification. Mutant-3, on the other hand, had a huge impact on the system. The system stability was influenced by stability shifts of 10–20, 50–60, and 70–80 ns. Overall, our findings reveal that when compared to Wild-Type protein, the mutant-1, 2, 3, and mutant-4 variants have higher deviation. Even at the end of the simulation period, mutant-3 and mutant-4 appeared unstable, with maximal RMSd of 2.8 and 2.5, respectively, when compared to wild type using the red line threshold. There are no substantial differences to be found, and all simulation findings are significant.
3.2 Per Residues Fluctuation Analysis
To understand the impact of residual motion we calculated RMSf to give us better information about the flexibility and residual fluctuation. During the MD simulation, RMSf helps to identify the flexible and disordered regions, as well as system heterogeneity. During the MD simulation (0 to 100ns), the computational design peptide mutant structure was employed and the UNC10245092/CIB1 was taken as a reference complex. The alignment of the structures derived from the equilibrium phase was done to improve the accuracy of the analysis. In the trimming trajectory of 0 100 ns, the RMSf was determined Figure5. The RMSf analysis is used to determine how much each peptide/complex residue travels through the trajectory. The RMSf analysis revealed that each residue replacement in each proposed peptide had a diverse impact on the overall structure. After the RMSf analysis, the overall designed peptide was compared with the wild type complex UNC10245092/CIB1 complex, the second peptide mutant S10F substitution the active site of the CIB1 protein is stabilized, resulting in fewer fluctuations toward active (1-65 residues) Figure5. The second peptide produced solid H-bond connections with the CIB1 receptor's active residues and hindered the CIB1 protein interactions with the UNC10245092 peptide (reference). Based on total binding free energies (PB and GB) the 2nd one peptide/CIB1 showed PB (-14.9865) and GB (-45.7079) respectively Table2. Good Except for the 3rd peptide/complex (mutant1) Figure 5A and 4th peptide-complex in (mutant2) Figure5B, the other peptide-complexes had the same RMSf as the top inhibitor. i.e. S10F substitution peptide, and overall, the designed peptide-complexes have fewer fluctuations than the reference-complex (mutant4). The fluctuations in some peptides-complexes are due to hydrophobic residues, such as Glycine and Tryptophan, opposing the negatively charged residues of the CIB1 receptor surface, or it might be due to the carbonyl of the glutamine, which resists carbonyl moiety residues, such as serine, but some regions of the peptides-complexes also showed higher fluctuations.
3.3 PCA Analysis
To understand the collective motion of the wild-type complex and designated 1-4 mutants. PCA analysis is the computational statistical method used to reduce sizable data without any important information. Principal components analysis for the wild type complex and designated mutants are shown in Figure6. The PCA analysis was used to obtain information on the conformational states of the UNC10245092/CIB1 (wild type) and designed peptide/complexes using the 0-100ns MD simulation trajectory. The eigenvectors of the covariance matrix were used to determine the total combined motion of the peptide-complexes, which was supported by the eigenvalues. Throughout the 100 ns simulation time, the blue to wine color continuous representation shows the shift from one conformation to another. Each frame is represented by a dot that ranges from blue to red. The Wild Type Peptides-CIB1 and its five mutants in both the situation showed a clustering motion in both the wild-type UNC10245092/CIB1 and the intended mutants, whereas the second peptide mutants-complex were observed in compact motion. The wild type UNC10245092/CIB1 and the design mutant’s peptide-complex or second peptide complex were observed in compact motion covering an area of (-50, +50) in principal components1 and principal components2 (PC1, PC2) (-40 and +40), respectively, in the wild type UNC10245092/CIB1 state (Wild-Type in Figure6), whereas the PCA plots for designed peptide-complex showed the more compact type of motion along PC1 (-50, +50) and PC2 (-40, +40). (Mutant-2 in Figure6).
3.4 DCCM Analysis
To determine functional Displacement in protein atom interaction we analyzed DCCM. During the Simulation from 0-100 ns trajectory, the wild type protein-peptide showed high positive correlations of residue movements in protein systems Figure7. The number of correlated movements among the residues in the mutants UNC10245092/CIB1complex, induced by binding of the chosen peptides, was investigated using inter-residue correlation research. Above and below the diagonal illustrated in the Plots, all matrices were symmetrical. Positive correlations indicate that residues are moving, in the same way, i.e. parallel direction, whereas negative correlations indicate that residues are moving in the opposed direction i.e. anti-parallel correlation. The DCCM analysis found positive and negative correlations, which were represented by the regions dark-green to light green and black to light brown, respectively. The deeper the color, the stronger the positive link, and vice versa. The generated altered peptide/complex systems were compared to the DCCM plot of UNC10245092/CIB1complex (Wild Type).
3.5 Hydrogen Bond Analysis
The UNC10245092 peptide is linked to the active residues of the CIB1 receptor in the 3D crystal structure. The results of a time-dependent hydrogen bond analysis revealed that all five proposed peptide variants exhibited the finest and strongest hydrogen-bonding networks with the CIB1 protein Figure8. In comparison to the wild-type complex, computationally designed peptide-complex systems feature several hydrogen bonds, for example, the 2nd peptide-complex system Figure6. The five developed peptides exhibit a strong good binding potency with CIB1, implying that they could operate as effective peptide inhibitors, according to our findings.