Monkeypox viral infection is an emerging threat and a major concern for the human population. The lack of drug molecules to treat this disease may worsen the problem. Identifying potential drug targets can significantly improve the process of developing potent drug molecules for treating monkeypox. The proteins responsible for viral replication are attractive drug targets. Identifying potential inhibitors from known drug molecules that target these proteins can be key to finding a cure for monkeypox. In this work, two viral proteins, DNA-dependent RNA polymerase (DdRp) and viral core cysteine proteinase, were considered as potential drug targets. Sixteen antibiotic drugs from the tetracycline class were screened against both viral proteins through high-throughput virtual screening. These tetracycline class of antibiotic drugs have the ability to inhibit bacterial protein synthesis, which makes these antibiotics drugs a prominent candidate for drug repurposing. Based on the screening result obtained against DdRp, top two compounds, namely Tigecycline and Eravacycline with docking scores of − 8.88 and − 7.87 kcal/mol, respectively, were selected for further analysis. Omadacycline and minocycline, with docking scores of − 10.60 and − 7.51 kcal/mol, are the top two compounds obtained after screening proteinase with the drug library. These compounds, along with reference compounds GTP for DdRp and tecovirimat for proteinase, were used to form protein–ligand complexes, followed by their evaluation through a 300 ns molecular dynamic simulation. The MM/GBSA binding free energy calculation and principal components analysis of these selected complexes were also conducted for understanding the dynamic stability and binding affinity of these compounds with respective target proteins. Overall, this study demonstrates the repurposing of tetracycline-derived drugs as a therapeutic solution for monkeypox viral infection. This zoonotic viral infection caused by the double-stranded DNA monkeypox virus (MPXV) belongs to the Orthopoxvirus genus of the Poxviridae family, which includes Variola, Camelpox, Cowpox, Akhemeta, and Alaskapox viruses3. Monkeypox viral infection shows considerable similarity to smallpox due to smallpox-like exanthema. However, monkeypox mortality rate and clinical symptoms are lower than smallpox4. Mammals like squirrels, dormice, rodents, rabbits and various primates are some natural reservoirs of MPXV. Viral transmission is caused by direct contact with an infected animal’s bite, scratches, or consumption of bush meat. In humans, viral transmission occurs through contact with body fluids, respiratory droplets, and lesions of the infected person or animals5.Monkeypox viral infection has become a worldwide issue since many incidences in several non-endemic nations were recorded in May 2022. The outbreak was first reported in 11 countries in Central and West Africa, then exported to other parts of the world, affecting 110 countries6. The genes are closely packed, and those present in the terminal end encode proteins responsible for pathogenesis. Housekeeping genes are present in the conserved central regions and contribute to the transcription and replication of the virus8. Even though the genome is arranged uniformly, the current viral outbreak is due to high mutation, especially the increase in DNA viruses; therefore, MPXV is under microevolution during human-to-human transmission10. The proteolytic development of the MPXV core proteins is necessary for generating infectious virions. Unlike other DNA viruses, MPXV replication cycle occurs in the cytoplasm of infected cells using a range of virus-encoded proteins, such as DNA-dependent RNA polymerase (DdRp) and cysteine proteinase11. DdRp is required by the infected host cell to transcribe the genome in its cytosol, and cysteine proteinase, also known as core protease, cleaves the precursor polyprotein13. These factors display the poxvirus cysteine protease and DdRp as potential pharmacological targets for producing novel therapeutic antivirals  structure protein structures of monkeypox are not predicted through experimental validation. Hence, the targets proteins, i.e., Monkeypox virus DNA-dependent RNA polymerase subunit rpo147 (Accession No: UTG36891.1) and viral core cysteine proteinase (Accession No: NP_536495.1) collected from GenPept database of GenBank gene products for computational modelling28. Both the modelled proteins were prepared with the help of the protein preparation wizard module of the Schrodinger suite. Using the system-building tool, an orthorhombic box was built to simulate the modelled proteins. The salt and the ion around the ligand binding site of the modelled proteins at 20 Å were removed. To the whole system, transferable intermolecular potential with 4 points (TIP4P) and sodium ions was added as counter ions, followed by immersion of the entire system into a virtually created water bath. The constant pressure was maintained throughout the MD simulation by maintaining a 0.002 ps time step for the anisotropic diagonal scaling. Also, the system’s temperature was gradually increased to 310 K, followed by a 20 psi NPT at 1 atm pressure, and the entire system was compressed to 1 g/cm3. The optimised potential for liquid simulation (OPLS-2005) force field present in the academic version of Desmond software was applied to the system for the MD simulation calculation29. To obtain suitable confirmations of the system, the Desmond Trajectory Clustering tool present in the Maestro platform was used to cluster the frames from each trajectory based on the obtained root mean square deviation (RMSD). The most recurring structures from the cluster were selected and used for binding site prediction and virtual screening.The modelled structures of both target proteins were validated by performing molecular dynamic simulation for 300 ns using the free academic Desmond tool30. This server provides an online platform for predicting the geometric and topological characters of the protein structure, along with visualising the active pocket of the protein structure30. The binding site identified using this server was utilised for grid box generation. The dimension of the generated grid box in the x,y and z axis is 60 Å × 60 Å × 60 Å, and in the center, the coordinates are at 23.95 Å, − 7.71 Å, and − 11.58 Å. These coordinates were utilised for docking the MPXV proteins with tetracycline antibiotic compounds.The selected monkeypox viral proteins' binding site was predicted using the Computed Atlas of Surface Topography of proteins (CASTp) server31. These drugs were combined to generate the compound library that will be utilised for virtual screening. These drugs or ligand molecules were prepared using the LigPrep tool of Schrodinger suit33. Ligand preparation using the Ligprep tool reduces the Lewis structure formation of the ligand molecules and removes defective traits of ligands to reduce the chances of computational error. It also helps to optimise the ligand molecules by stabilising and expanding the tautomeric structures, ionisation state and ligand structure confirmations20. For further analysis, these prepared compounds were taken for high-throughput virtual screening (HTVS) against the predicted MPXV protein structures.Sixteen drug molecules belonging to the class of tetracycline antibiotics were retrieved from the PubChem database35. Herein, the compounds are screened out at three different levels based on precision and accuracy, i.e., high-throughput virtual screening (HTVS), standard precision (SP) and extra precision (XP). This hierarchical screening helps filter out the potential ligand poses using an advanced scoring function that preciously binds to the protein's active site36. Based on the result obtained from the three-level screening process of MPXV DdRp and cysteine proteinase proteins against the tetracycline antibiotics, the top compounds with the highest docking score for each protein were selected and underwent molecular docking.A hierarchical level of structure-based virtual screening was performed using the Glide module of the Schrödinger suit28. The parameters for the MD simulation of the modelled protein were kept the same as for the MD simulation analysis of the protein–ligand complex. A detailed explanation of the procedure followed is mentioned in section "The top protein–ligand complexes are further analysed through 100 ns MD simulation performed using the Desmond module37. MM/GBSA method calculates the various components, such as ΔGBind, ligand strain energy e.t.c., for protein, ligand and protein–ligand complex.The free binding energy of the protein–ligand complex is calculated using Molecular mechanics with a generalised Born and surface area solvation (MM/GBSA) method. The Prime module of the Schrodinger suite was used for this calculation, combined with the OPLS-2005 force field under default parametersThe MM/GBSA binding free energy is derived from the equation.38. This statistical technique reduces and identifies all the significant fluctuations experienced by the protein residues through covariance matrix analysis of the alpha-carbon (Cα) atoms in the protein. The eigenvectors with highest egienvalues are considers as principal components. Therefore, three major principal components are considered for this analysis. This method gives an insight about the dynamics of the protein by calculating these principal components.Principal component analysis or PCA of each complex extracted from the MD simulation trajectories was performed using the bio3d R packageThis article does not contain any studies involving human participants or animals performed by any of the authors.The most recurring cluster obtained after the 100 ns MD simulation of the modelled DdRp and cysteine proteinase proteins of MPXV was used to obtain the middle structure of these proteins. Both of these proteins were virtually screened against tetracycline antibiotics to identify potential inhibitory drug molecules based on their binding affinity. A complete set of 16 tetracycline antibiotics were retrieved from the PubChem database for screening against the two selected MPXV proteins. Stereoisomers of these drugs were generated using the LigPrep tool, which was then filtered out with the help of the QikProp filter. Based on the docking score obtained after the three-tire screening, i.e., HTVS, SP and XP, the top two compounds for each protein were selected for further analysis. Two reference ligands were also selected and docked against the selected MPXV protein. GTP was considered as one of the reference ligand, which was docked with the DdRp MPXV protein, and in the case of MPXV proteinase, tecovirimat antiviral drug were used as reference compound for docking with cysteine proteinase.39. Eravacycline is significantly used to treat gram-negative, gram-positive and facultative bacterial diseases. This also suppresses bacterial growth by inhibiting protein synthesis40.The top two compounds that showed the highest docking score when screened against MPXV DNA-dependent RNA polymerase are Tigecycline (PubChem CID54686904) and Eravacycline (PubChem CID54726192), having docking scores of -8.88 and -7.87 kcal/mol, respectively Fig. . Tigecyc41. Minocycline is an antibacterial antibiotic drug derived from tetracycline. They are active against active gram-negative and gram-positive bacteria42.Omadacycline (PubChem CID 54697325) and Minocycline (PubChem CID 54675783) are two antibiotics drugs having the highest docking score of −10.60 and −7.51 kcal/mol after the screening of the drug library against MPXV cysteine proteinase Fig. . Omadacy415 (2), Asp417 (2), Asp419 and Arg287, whereas in DdRp-Eravacycline docked complex Asp415, Gly418, Glu420, and Gln318 residues are involved for hydrogen bond formation (4 H-bond). Moreover, when the intermolecular interaction of MPVX DdRp docked with reference compound GTP was observed, it showed a significant amount of hydrogen bond formation (6 H-bond). Apart from the hydrogen bond, these complexes exhibited salt bridge formations and hydrophobic, polar, positive and negative charge interactions. Based on this analysis, it can be concluded that tigecycline shows better binding stability among the two selected drugs with the DdRp of MPXV compared to reference molecule GTP (Table The selected top two complexes and the MPXV protein reference molecules were studied to find the interaction between the protein–ligand complex. MPXV DdRp- Tigecycline complex significant hydrogen formation (6 H-bonds) with residues Asp35. Except for the reference complex, all the selected docked complexes show salt bridge formation. Moreover, hydrophobic interaction, polar bond, and positive and negative charge interaction were also observed in all the selected complexes, including the reference complex. The interaction analysis shows that omadacycline shows better binding stability towards MPXV cysteine proteinase than minocycline and reference molecule tecovirimat , and Root mean square fluctuation (RMSF). Also, protein–ligand contact mapping of these complexes from the MD simulation trajectories of both target proteins were also analyzed to find the interaction fractions of major residues during bond formation responsible for the stability of the protein–ligand complexes.RMSD analysis calculates the conformational changes the protein and ligand undergo in the complex during the entire simulation period. During the protein RMSD analysis of the DdRp-tigecycline complex, the DdRp protein displayed a substantial deviation of 6 Å from 50 to 100 ns of simulation, which further increased to ≈ 9 Å at 150 ns, and by the end of 300 ns the protein deviated up to 8 Å. The protein RMSF analysis of this complex further confirmed this finding. The DdRp RMSF showed significant peaks having RMSF values of 6 Å between 200–400 residue index, followed by a 5 Å residual fluctuation between 600 and 800 residues, and finally two higher elevation (9 Å) between 1000 and 1200 residues. The fluctuations in the C-terminal and N-terminal ends of the protein are not considered. Based on the RMSD and RMSF analysis of the protein, it was found that the DdRp protein may have insignificant structural confirmational changes due to the binding of the drug molecule in the DdRp-tigecycline complex. However, the RMSD analysis of the tigecycline displayed a considerable deviation of 6 Å at 100 ns, but the ligand gradually attained stable equilibrium (4-5Å) after 100 ns till the end of simulation. RMSF analysis of tigecycline also exhibited low and stable fluctuation (< 3 Å) during the simulation. This concludes that the ligand molecule remained stable when attached to the binding site of the DdRp protein. In the case of DdRp- Eravacycline complex, the RMSD of the protein showed an insignificant deviation (> 5 Å) between 5 to 50 ns, which remained in stable equilibrium (5–6 Å) till the end of the simulation. Contrary to the protein, the ligand eravacycline displayed a very high deviation of 10 Å within the initial 20 ns and further increased up to 14 Å till 100 ns and the RMSD value fall to 12 Å by attaining equilibrium until the end of the simulation. Ligand RMSF analysis confirms this finding. The RMSF graph of the eravacycline showed multiple peaks of 6 Å value between 1 and 40 atoms of ligand molecule. On the basis of RMSD and RMSD analysis of the DdRp- Eravacycline complex, it was concluded that protein remain stable without any confirmation even though the drug exhibited significant deviation when docked with DdRp protein. However, the protein RMSD analysis of the reference DdRp-GTP complex showed an acceptable deviation (6 Å), by attaining a stable equilibrium state till the end of the simulation. The protein RMSF analysis also exhibited fluctuations of < 5 Å between 200 and 800 residual index and between 100 and 200 residual index the RMSF value of 6–7 Å were also noted during the trajectory analysis of the reference complex. The RMSD of the GTP showed a significant deviation between 6 and 8 Å till the end of the simulation, and the RMSF of all atoms of GTP showed 3 Å. Based on the RMSD and RMSF analysis of the DdRp-drug complex, it was found that, when tigecycline drug binds with the active site it induces confirmational change in the DdRp protein, whereas in case of Eravacycline, there is no confirmational changes are observed and the drug molecule remains bounded to the target protein, when compared with the reference compound GTP Fig.  and S2.FSimilarly, the RMSD graph of the proteinase in the proteinase-drug complex was also analysed. Based on the protein RMSD of these complexes, it was found that the MPXV proteinase remained in a stable state (< 4 Å) in all the protein–ligand complexes. This condition was also seen in the proteinase-tecovirimat reference complex. The protein RMSF analysis of the docked complexes also supports this information. The graph only shows a significant peak at residue number 150 and then drops to a stable state with an RMSF value below 2 Å. Likewise, during the RMSD analysis of the drug molecule in proteinase- omadacycline complex it was observed that the drug remain is stable sate (< 5 Å) with an insignificant fluctuation (8 Å) between 200 and 250 ns till the end of total simulation. The RMSF graph of omadacyclin displayed continuous peaks between 0 to 40 atoms, but the ligand RMSF value was in the acceptable range i.e., < 4 Å. The RMSD analysis of Minocycline showed stability (2 Å) till 60 ns and showed a substantial deviation of 6 Å after 60 ns and the gradually attains stable equilibrium till the end of the simulation. The RSMF analysis of minocycline supports this observation as the ligand shows acceptable fluctuations with an RMSF value of < 4 Å. However, the RMSD of reference molecule Tecovirimat remained stable during the initial 30 ns, followed by a significant rise of 10 Å, and then gradually attained the equilibrium stage (8 Å) at 100 ns simulation. At 200 ns the RMSD value of the reference molecule was further increased to12 Å and by the end of 300 ns simulation the RMSD value seen to in between 10 and 8 Å. The tecovirimat RMSF also exhibits two significant peaks with an RMSF value of > 4 Å, which gradually declined to 2 Å. Finally, it can be concluded, based on the RMSF and RMSD analysis of the proteinase-drug complex, that both the omadacycline and minocycline showed better stability compared to reference drug tecovirimit Fig.  and S4.F415 and Glu420 residues display hydrogen bonds for 100%, whereas Glu675 exhibited hydrogen bond for 50% of the total simulation time. Herein, Asp415, Asp417 and Asp419 showed ionic bond formation for 100% of the interaction fraction. Asp419 and Glu420 also exhibited water bridge formation for 100% of the total interaction fraction. Furthermore, the protein–ligand contact mapping of DdRp-Eravacycline complex showed ionic interaction with Asp415, Asp417 and Asp419 for 100% of 300 ns simulation. In this complex Asp415 and Lys579 exhibited hydrogen bond formation for 60% and 100% of the total simulation fraction, respectively. In the case of the reference complex also, Asp415, Asp417 and Asp419 displayed ionic bonds for 100% of the total interaction fraction, and Asp415 and Lys670 involved hydrogen bond formation for 100% until the end of the simulation. Some other residues in all these complexes also form hydrophobic bond formation. Ligand–protein contact schematic representation was also analysed, displaying significant residues involved in the interaction for more than 30% of the simulation time. During the schematic interaction diagram analysis, Asp415, Asp417, and Asp419 exhibited metallic bond formation with Magnesium ions in all three DdRp-ligand complexes. Also, in the reference ligand contact diagram, Sodium ions were responsible for metallic bond formation. Apart from the metal coordinate bond, these residues are also involved in hydrogen bond formation for more than 30% of the simulation time , Asn33 (40%), Ile34 (80%), Tyr393 (60%) and Lys401 (60%) exhibited hydrogen bond formation of the total interaction fraction. In this complex water bridges were also formed with the residues Ser26 (30%), Ser32 (30%), Asn33 (40%), and Ile34 (100%) during the MD simulation. Also in this complex, His23 and Phe368 residues participates in hydrophobic and ionic bond for 40% of the 300 ns simulation time. Moreover, the reference complex exhibited hydrogen bond formation for 10% and 15% of the total simulation time with residues Val36 and Lys401. Glu397 and Lys401 displays water bridge formation for 35% and 25% of the total interaction fraction. Hydrophobic bonds were also formed with residue Leu40 for 10% of the simulation time. The schematic diagram of the ligand contact also shows the involvement of active site residues that make contact with the ligand for more than 30% of the simulation time  serves as a pivotal method for dimensionality reduction in complex datasets, with a core objective of preserving essential information. Through this technique, a triad of linear combinations, referred to as principal components, is extracted from the data. These principal components are meticulously crafted to encapsulate the most significant variability present within the dataset, thus ensuring a comprehensive representation of the data's inherent patterns and trends. Three essential components or principal components named PC1, PC2 and PC3 were retrived from the MD simulation trajectories having maximum amount of data are taken for PCA analysis. The PCA analysis of DdRp- Tigecycline complex exhibited a total of 66.68% during the summation of summation of three principal component (PC1 + PC2 + PC3). The cluster analysis of this complex showed there is no overlapping of three colours  and finally the scree plot also exhibited a partial slope formation. Based on these observations, it was concluded that the protein may have undergone certain structural confirmation change due to the binding of the ligand molecule. Similarly in case of DdRp- Eravacycline complex, the total percentage obtained by calculating the three principal components was 56.47%. In this complex also the clusters are partially overlapped and scattered and in the scree plot shows fall of eigen values in steep pattern. In this also complex, it was protein tends be flexible in nature due to the binding of ligand. Similarly, in the reference complex, DdRp-GTP also, the total PCA value was 66.68 percentage similar to the tigecycline complex. And the clusters are scattered and a sharp fall was seen in the scree plot, stating the fact the protein molecule have undergone confirmational changes during the simulation analysis. Based on the observation made during the PCA analysis of DdRP-drug complexes it was concluded that the protein may undergo confirmation changes due to the binding of drug molecule similar to the reference molecule Fig. .Figure 1Likewise, the PCA analysis of the cysteine proteinase-drug complexes were also observed. Herein, the cysteine-proteinase-omadacycline complex, exhibited a total value of 73.72% during the total calculation of the three essential components. Also, the cluster analysis of this complex exhibited overlapping of colours present in clusters and the scree plot shows a sharp elbow shaped slope formation. These observations explains that the protein remain in stable state with minor to no confirmational changes. Similarly, in the Minocycline complex also, the cluster overlapping can observe, having total PCA value of 78.91% and the scree plot also shows sharp slope formations. This also states that chances of protein undergoing confirmational changes are less. Finally, during the PCA analysis of the reference molecule the clusters are overlapped. The total PCA value obtained in this complex, was 61.12% lesser than selected drug molecule. The scree plot showed a slight cure by the end of plot, stating that here also the protein molecule remains in rigid state with minor to no confirmational changes. Overall, it can be concluded that the cysteine proteinase does undergoes any kind of confirmational changes due to the binding of drug molecules by may tend to inhibit the function of the protein Fig. .Figure 1Serological and molecular testing is currently used to detect MPXV infection, a recently re-emerged pathogen. MPXV can spread via zoonotic reservoirs from animal to human and then from human to human. DdRp and viral core cysteine proteinase of MPXV play a significant role in viral replication cycle. Therefore, these two proteins are primary targets for drug development. Herein, the 3D structure of both proteins were modelled using the homology modelling technique due to the unavailability of experimentally proven 3D structures. The protein models of both proteins, DdRp and cysteine proteinase, were screened against the 16 tetracycline groups of antibiotics. Tigecycline and Evaracycline showed the highest docking score when screened against DdRp protein. MD simulation analysis of these compounds showed that tigecycline has better binding stability towards DdRp than tetracycline and reference compound GTP. The RMSD, RMSF and protein–ligand contact mapping analysis of each selected complex proved this. Similarly, while screening drugs against proteinase, the top two compounds, omadacycline and minocycline, displayed the highest docking score. The MD simulation analysis of these complexes showed that omadacycline has a better binding affinity towards proteinase. These two drug molecules show potential inhibitory properties against MPXV DdRp and proteinase protein.Supplementary Information.


Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a +sense single-strand RNA virus. The virus has four major surface proteins: spike (S), envelope (E), membrane (M), and nucleocapsid (N), respectively. The constitutive proteins present a high grade of symmetry. Identifying a binding site is difficult. The virion is approximately 50–200 nm in diameter. Angiotensin-converting enzyme 2 (ACE2) acts as the cell receptor for the virus. SARS-CoV-2 has an increased affinity to human ACE2 compared with the original SAR strain. Topological space, and its symmetry, is a critical component in molecular interactions. By exploring this space, a suitable ligand space can be characterized accordingly. A spike protein (S) computational model in a complex with ACE 2 was generated using silica methods. Topological spaces were probed using high computational throughput screening techniques to identify and characterize the topological space of both SARS and SARS-CoV-2 spike protein and its ligand space. In order to identify the symmetry clusters, computational analysis techniques, together with statistical analysis, were utilized. The computations are based on crystallographic protein data bank PDB-based models of constitutive proteins. Cartesian coordinates of component atoms and some cluster maps were generated and analyzed. Dihedral angles were used in order to compute a topological receptor space. This computational study uses a multimodal representation of spike protein interactions with some fragment proteins. The chemical space of the receptors  suggests the relevance of the receptor as a drug target. The spike protein S of SARS and SARS-CoV-2 is analyzed and compared. The results suggest a mirror symmetry of SARS and SARS-CoV-2 spike proteins. The results show thatSARS-CoV-2 space is variable and has a distinct topology. In conclusion, surface proteins grant virion variability and symmetry in interactions with a potential complementary target . The mirror symmetry of dihedral angle clusters determines a high specificity of the receptor space. Designing an antibody against the SARS-CoV-2 spike protein S is challenging due to its nonspecific nature, mainly conferred by each spike protein monomer . The spiRecent research and protein sequences designed to fit spike protein S failed to bind. A lack of specificity is a result of high mobility and flexibility combined with additional maximum solvent exposure of the monomer domains . A mass effect should virtually resolve this lack of specificity; in other words, by increasing the mass of the desired paratope, that will interact with the epitope (S protein), an alternative unavailable from the receptor–ligand interaction point of view . Judging from the antigen–antibody point of view, epitope amino acid (Aa) sequences located on the antigen must fit complementarily with the Aa chain  of the paratope (antigen variable (VL) and constant regions (CR)) .f) of one argument from a given domain is a polynomial function if a polynomial exists. Like any other functions, polynomial functions can be represented by a graph  in H2n. Stated precisely, this is a bilinear form. This is a symmetric form for n even , in which case the signature of M is defined to be the form’s signature, and an alternating form for n odd . These can be referred to uniformly as ε-symmetric forms, where ε = (−1)n = ±1, respectively, for symmetric and skew-symmetric forms . These fθ is the determinant, and cosθ represents the bound angle value in Å, and if cosθ11, cosθ22, cosθ33, and cosθ44 are considered zero, the determinant matrix can be written as follows  × [−165 − (−130)] = (−0.35) × (−35) = 1225. The convergence of the two functions is determined via the following relationship:On the function interval −0.5 ≤ x ≤ −0.15, function f1 = −14x6) and (f1(x))/(−x6) is computed: (f1(x))/〖−x〗6 =10 + 8/x + 6/x2 − (0.0002)/x3 − (0.0556)/x4 − (0.146)/x5 + (110.3)/x6 also (f2(x))/(−x6) = 14 + 10/x − 7/x2 +5/x3 + (0.0265)/x4 − (3.0432)/x5 + (165.5)/x6. Because A is a constant, the limits of the two functions are 10 and 14, respectively. While the limits have positive values , both of the functions have the following convergence domain: −∞ < x < +∞.The Riemann theorem shows that via permutation of a series of terms, the sum can be made equal to any number given before. In the case of functions f1(x) and f2(x), these numbers are 10 and 14. So, the functions are convergent.y-axis is described by = (x(t), θ, y(t)). In Cartesian coordinates, these becomes (y(t)cos(θ), y(t)sin(θ), z(t) and (x(t)cos(θ), x(t)sin (θ), y(t)) [The surface of revolution in Euclidean space is a surface created by a rotating curve around an axis of rotation . Here, t), y(t)) ,46. ProtRamachandran plots are a tool for visualizing energetically allowed domains for backbone dihedral angles ψ against ϕ of amino acid residues in proteins. Dihedral angle values are annular (0 degrees is the same as 360 degrees). Ramachandran plots warp from right to left and bottom to top . The ω aThe property space of both spike proteins demonstrates similar characteristics of both molecules. Solvent exposure of both molecules shares common characteristics. Spike protein SARS and SARS-CoV-2 solvent exposure are similar .Binding site identification of potential therapeutic targets is crucial in every drug design process as the expected spike protein has various binding sites. Also, many binding sites in various spike protein regions are described in the literature. An efficient vaccine can be designed by identifying the energetically and structurally favorable binding site. In the present study, the ligand’s interaction with its receptor (spike protein) is a general process of applying various binding sites.Furthermore, the mirror symmetry of SARS-CoV-2 compared with SARS can potentially suggest potential ligands that can be used as vaccines. Symmetric molecules of SARS-proven ligands can be considered in such a way. Also, as seen in The design of an appropriate paratope that can lead to a COVID-19 antibody in in silico methods was used together with secondary data derived from the literature. In order to find a suitable paratope, a set of very low (VL) random sequences were used to assess the chemical space of epitope–paratope interactions see also .Firstly, a target for a potential antibody was established. Spike protein S was chosen as a target due to its volume, shape, and frequency at the external membrane of COVID-19 compared to proteins E, M, and N, respectively. Computational studies and published data show that spike S protein is a valid target ,50,51,52Also, an Aa sequence was chosen as an epitope. The Aa sequence was chosen from the literature  ,54.A set of 47 PDB VL chains was chosen from the literature to explore the paratope space by evaluating the epitope–paratope interactions using distinct computational techniques.https://www-cohsoftware.ch.cam.ac.uk, accessed on 1 July 2023). For example, the following sequence was obtained for 1A8J using Paratome: SYEGSDF. After checking that the sequence was of the outer external region and could contact the antigen, the probabilistic contribution of each Aa was computed using Parapred, in this case yielding the following values: S 0.1649, Y 0.486472, E 0.713605, G 0.32135, S 0.469778, and F 0.0840889. In order to take into account the epitopes’ first light chains, interactions with the light chains were studied. The PSOPIA online server was used [Firstly, a light chain of 47PDB structures was used. Paratopes were detected for each structure using the Paratome online engine . Resultswas used . PSOPIA was used .iFrag, a protein–protein interaction prediction server, was used to compute and quantify the interaction between epitopes and paratopes . First, Spike protein monomer:AYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVNCTFEYVSFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKGNYNYLYRKPFERDIYFPLQSYGFQPTNVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTSQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDPPEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSACEII monomerSTTEELAKTFLETFNYEAQELSYQSSVASWNYNTNITEENVQNMNNAGDKWSAFLKEQSTLAQTYPLQEIQNLTVKLQLQALQQNGSSVLSEDKSKRLNTILNTMSTIYSTGKVCNPDNPQECLLLEPGLNEIMANSLDYNERLWAWESWRSEVGKQLRPLYEEYVVLKNEMARANHYEDYGDYWRGDYEVNGVDGYDYSRGQLIEDVEHTFEEIKPLYEHLHAYVRAKLMNAYPSYISPIGCLPAHLLGDMWGRFWTNLYSLTVPFGQKPNIDVTDAMVDQAWDAQRIFKEAEKFFVSVGLPNMTQGFWENSMLTDPGNVQKAVCHPTAWDLGKGDFRILMCTKVTMDDFLTAHHEGHIQYDMAYAAQPFLLRNGANEGFHEAVGEIMSLSAATPKHLKSIGLLSPDFQEDNETEINFLLKQALTIVGTLPFTYMLEKWRWMVFKGEIPKDQWMKKWWEMKREIVGVVEPVPHDETYCDPASLFHVSNDYSFIRYYTRTLYQFQFQEALCQAAKHEGPLHKCDISNSTEAGQKLFNMLRLGKSEPWTLALENVVGAKNMNVRPLLNYFEPLFTWLKDQNKNSFVGWSTDWSPYADPFGEVFNATKFPSVYAWERKKISNCVADYSVLYNSTFFSTFKCYGVSATKLNVYADSFVVKGDDVRQIAPGQTGVIADYNYKLPDDFMGCVLAWNTRNIDATSTGNYNYKYRYLRHGKLRPFERDISNVPFSPDGKPCTPPALNCYWPLNDYGFYTTTGIGYQPYRVVVLSFEIn order to characterize the VL sequences, a cluster analysis was performed using molecular descriptors. The properties, after the cluster was computed, are the following: sum of atomic polarizabilities, number of hydrogen bond acceptors, number of acidic atoms, number of aromatic atoms, number of H donor atoms, the sum of the number of H bond acceptors and donors, number of heavy atoms, information content, medium information content, number of carbon atoms, number of hydrogen atoms, number of nitrogen atoms, number of oxygen atoms, Balaban index, difference in bonded atom polarizabilities, number of rotable single bonds, number of aromatic bonds, number of bounds, number of heavy bounds, number of single bounds, atomic connectivity index, atomic valence connectivity index, number of chiral centes, number of unconstrained chiral centers, density, diameter (most considerable vertex eccentricity in a graph), SlogP, molar refractivity, topological polar surface area, vertex adjency information, volume surface area, van der Waals acceptor surface area, molecular weight, winner path, Weiner polarizability, Zagreb index, surface rugosity, total energy, angular energy, electronic energy, non-bonded energy, solvation energy, strain energy, van der Waals energy, and globularity. As seen in The following PDB models were used: 6VXXfor SARS-CoV-2spike protein, 6CVR for SARS spike protein, 5X29for SARS-CoV-2 envelope protein, and3I6Gfor SARS-CoV-2 spike protein. Furthermore, the PDB models were energetically minimized, charges corrected, and protonated at pH 7.4, a salt concentration of 0.9 nmol/L, and at 315 K. The model preparations were performed using Schrodinger2009 and MOE 2009software packages ,58. ShapIn order to reduce the amount of data and to simplify the results, monomers for each protein were prepared computationally, as stated before. The trimers and pentamer PDB structures were energetically minimized, and charges were corrected using the AMBER force field available in MOE 2009 software package. For each monomer structure, the energy was minimized, charges corrected, and structures protonated at a physiological pH. Dihedral (torsion angles) for each monomer were computed using the Chemoffice 2008 software package.In probing the chemical space, some structural information, from simple dihedral angles to complex tertiary and quaternary organizations, was obtained computationally using Schrodinger 2009 software packages. Dihedral angles were represented using a scatter plot for each monomer . While an abrupt cut-off was observed in data, and an abrupt variation in data was noted, a logarithmic scale was used. Also, as discussed in the introductory part, by normalizing the data, clusters are more easily observed . A logarhttps://www.wolframalpha.com/, accessed on 1 July 2023) interface was used to perform a cluster analysis of all dihedral angles. To compare their function domains, the function table dn/dxn (f(x)) for n = 1 … 4 was calculated for all four monomers. Surface plots were computed using the radius of gyration of each logarithmic trend line. Here, r2, while exploring a trend and not a correlation between two phenomena, is not significant. A histogram was computed using dihedral angle values on 300 intervals for all three monomers. Using logarithmic trendline equations, a derivate equation was computed for all three protein monomers to represent the dynamic of dihedral angles as a function graphically.In order to expand data dimensionality, a Ramachandran plot diagram was computed for all four monomers in order to characterize the dihedral angles of the structures using the Schrodinger 2009 software package. The Wolfram Alpha software (n/dxn (−0.059 log(x) + 6.1779) for n = 1 … 4; for M (COVID-19) protein monomer table: dn/dxn(−1.51011 log(x) + 6.1779) for n = 1 … 4; for E (COVID-19), protein monomer table: dn/dxn(−0.842 log(x) + 62.544) for n = 1 … 4; and for S (COVID-19) protein monomer table: dn/dxn(−0.443 log(x) + 9.7581) for n = 1 … 4.Radar plots were used to emphasize the dihedral angles of protein monomers. Furthermore, the surface of revolution for protein S for SARS and for M, E, and S for SARS-COV-2 were computed to retrieve the 3D dimensionality of monomers’ dihedral angles. Surfaces were generated using the following formulas: for S (SARS) protein monomer table: dThe dimensionality of the COVID-19 interaction space is defined by its interaction with its core receptor. The interaction space has a finite dimensionality defined quantitatively by the polynomial discriminant. Judging by its 2D dimensionality, the interaction space has a negative and positive domain through which epitope–paratope interactions are defined.SARS and SARS-CoV-2 spike proteins have similar solvent exposure and mirror symmetry regarding their dihedral angle chemical space. Both proteins have a similar solvent exposure even if their dihedral angle conformational space has mirror symmetry. Mirror symmetry explains to some extent the chemical inaccessibility of the spike protein SARS-CoV-2 as a receptor and the evasive but permanent chemical bonding in which it is involved.


Mycobacterium tuberculosis (MTB) is the world’s first cause of mortality tied to a single disease. This study aims to characterize and anticipate potential antigen characteristics for promising vaccine candidates for the hypothetical protein of MTB through computational strategies. The protein is associated with the catalyzation of dithiol oxidation and/or disulfide reduction because of the protein’s anticipated disulfide oxidoreductase properties. This investigation analyzed the protein's physicochemical characteristics, protein-protein interactions, subcellular locations, anticipated active sites, secondary and tertiary structures, allergenicity, antigenicity, and toxicity properties. The protein has significant active amino acid residues with no allergenicity, elevated antigenicity, and no toxicity.Adaptation of infections and hosts has resulted in several metabolic mechanisms adopted by intracellular pathogens to combat the defense responses and the lack of fuel during infection. Human tuberculosis caused by  Mycobacterium tuberculosis (MTB) as the causative microorganism responsible for human tuberculosis (TB) by Robert Koch, numerous scientific advances have been made to help cope with this significant pathogen. However, despite this progress, MTB still holds many unresolved secrets. Much work remains to be done to translate the essential findings from recent research into novel strategies against the pathogen [Pathogenic mycobacteria are significant sources of illness in humans and animals. Despite the accessibility of antibiotics and chemotherapeutic candidates that are efficient against some mycobacteria, the emergence of drug-resistant strains necessitates the development of new active molecules and intervention strategies -3. Withipathogen -7. Tuberpathogen -10.MTB has an irregular, highly recurrent life cycle that encompasses a varied and heterogeneous spectrum of habitats and physiologic states, many of which are unique from other infections. It is not unanticipated that MTB has conceived a specific set of metabolic capacities to facilitate its adaption to and movement across hosts, given its peculiar, if not distinctive, environment -13.Numerous advancements in computational biology have made it possible to construct diverse technologies and strategies for predicting protein structure and the identification of sequence commonalities for active investigation and the analysis of active site residue relationships -16. A biThe amino acid sequence of the protein was obtained from the NCBI database in FASTA format. The physicochemical properties were determined using ProtParam (ExPASy) and SMS v.2.0 programs. Afterwards, the subcellular location of the selected protein was determined. This study also anticipated the protein family, superfamily, domain, coil, and folding pattern of the protein. The STRING program was used for protein protein interaction determination. Moreover, secondary structural documentation was performed using the SOPMA, DISOPRED (v. 3.0), and SPIPRED (v. 4.0) programs. The tertiary structure was predicted using the Modeller program with the HHpred database and validated by the PROCHECK, Verify3D, and ProSA-web tools. Furthermore, the CASTp server was used for active site determination of the selected protein present in MTB. Additionally, the antigenicity, allergenicity, and toxicity properties of the protein were determined.https://www.bioinformatics.org/sms2/index.html, accessed on September 10, 2022).The physicochemical parameters of the protein were evaluated by the ProtParam assessment tool of the ExPASy server program  and the The CELLO (v.2.5) ,22, PSORThe NCBI CD tool was used to anticipate the conserved domain . The GenThe STRING program (v.11.5)  was usedThe SOPMA program was used following the default parameters similarity threshold (8), window width (17), and the number of states (4) . DISOPREThe tertiary structure of the protein is generated by using the Modeller program . The HHpThe CASTp program was used to determine the active sites in the protein .The VaxiJen (v2.0) program  was usedThe protein’s amino acid sequence was retrieved from the NCBI database in FASTA format. The protein contains 173 amino acids .in vitro), >20 h , and >10 h . The demonstrated isoelectric point (pI), the total number of atoms, and molecular weight as of 5.19, (4.98*), 2,572, and 18,382.98 Dalton  is the most abundant amino acid in the protein sequence . There i8 Dalton .Moreover, the total number of positively (Arg + Lys) and negatively charged residues (Asp + Glu) are 10 and 11 in the protein. The instability index (29.40) demonstrates protein stability, whereas the aliphatic index (86.42) denotes protein balance over a broad temperature scale. The grand average of hydropathicity  determines the enhancement of thermostability .The computerized estimation of the subcellular location of bacterial proteins is essential for proteome categorization and for selecting novel therapeutic targets and vaccination candidates. Various subcellular localization predictors have been created in recent years, including both generic localized and feature-based predictors -52. The Moreover, transmembrane helix identification in integral membrane proteins is an essential bioinformatics component. In addition to predicting individual transmembrane helices, the most effective approaches to date strive to predict the complete topology of the protein, including the entire count of transmembrane helices and their direction related to the membrane ,53. The The Conserved Domain Database (CDD) intends to annotate biomolecular sequences with the evolutionarily conserved protein domain placement. A repository of pre-computed domain identification is kept for NCBI’s Entrez database-tracked proteins, and real-time search facilities are provided. CDD also facilitates comparative analysis of protein families employing conserved domain architectures, and a new curation effort focuses on giving functional categorization of various subfamily structures -60. The -15), Redoxin , thioredoxin , thioredoxin-2 , thioredoxin-8 , and thioredoxin-9 . The Pfam program [-30). Thioredoxins are small proteins composed of around one hundred amino acid residues that participate in numerous redox processes. Thioredoxins operate by reversibly oxidizing an active center disulfide bond. An intramolecular disulfide bond connects the two cysteine residues in reduced or oxidized forms [The GenomeNet program identified six different motifs, including AhpC-TSA  and secondary  linkages -72. The The trxC, Rv2876, dipZ, Rv1929c, mpt63, Rv1676, Rv2968c, Rv1929c, Rv2969c, and Rv1138c are the thioredoxin that participates in multiple redox reactions through the reversible oxidation of its active center dithiol to a disulfide and catalyzes dithiol-disulfide exchange reactions, possible conserved transmembrane protein, cytochrome c-type biogenesis protein, conserved hypothetical protein, immunogenic protein mpt63 (antigen mpt63/mpb63), uncharacterized protein, probable conserved integral membrane protein, conserved hypothetical protein (uncharacterized), possible conserved membrane or exported protein, and possible conserved membrane or exported protein, respectively -76. The Static high-resolution structures have contributed significantly to our protein structure and molecular activity knowledge. As structural biology has progressed, it has become evident that high-resolution structures alone cannot adequately represent the molecular basis for the structure and the action of proteins in solution -80. The The SPIPRED (v.4.0) and the DISOPRED (v.3.0) programs were used to determine the secondary structure, sequence plot, and transmembrane topology .Homology modeling is a technique for constructing the three-dimensional structures of proteins based on their primary sequence and using existing information from structural matches to other proteins. Sequence/structure compatibility is improved in the homology modeling procedure, a framework is constructed, and side chains are appended ,86. The -22, and target length of 136.The HHpred is a robust technology for remote homology determination and structure prediction, first constructed as hidden Markov models as well as popularized by the first pairwise comparison study of homologous protein patterns. It permits several repositories, such as PDB, CDD, Pfam, SMART, SCOP, and COG . It acceMoreover, the PROCHECK program of the SAVES (v.6.0) tool was used for the Ramachandran plot assessment . The amiCASTp is a database platform capable of locating zones on proteins, outlining their outlines, determining the dimensions of the regions, and calculating their area. This incorporates pockets on the surface of proteins and hidden vacuums within proteins. The computation includes a pocket and volume spectrum or vacuum, which are mathematically calculated by a solvent-accessible surface (Richards surface) and molecular surface model (surface of Connolly). CASTp could be used to study surface characteristics and protein functioning regions. CASTp delivers a graphical, user-interface-flexible, dynamic display and on-the-fly assessment of user-submitted constructions . The CASin silico assessment of genome data, with the most likely defensive antigens anticipated instead of the cultivation of pathogenic bacteria. Despite the apparent benefits of this method, such as speed and cost-effectiveness, its success is contingent on the precision of antigen prediction. Antigens are identified using sequence alignment in most cases [Vaccine development in the post-genomic age often commences with the st cases -93. Thisst cases -96. ConsAllergy overreaches the immune function to a formerly exposed, normally innocuous chemical, leading to skin rash, mucous membrane swelling, sneezing or wheezing, or other aberrant symptoms. The rising prevalence of altered proteins in food, commercial items, laundry detergent, medical therapies, and diagnostics renders anticipating and detecting possible allergies a significant social concern. Using bioinformatics, allergen prediction has been extensively studied, and several tools have been created over the past decade; many are accessible on the complimentary internet ,98. FurtAdaptation between pathogens and their innholders has resulted in several metabolic strategies employed by intracellular infections to cope with the defense responses and nutritional insufficiencies throughout infection. Comprehending how proteins act is essential for explaining how they operate, and this protein contains disulfide oxidoreductase, a crucial enzyme associated with the oxidation of dithiol and/or the reduction of disulfide in target sites. This study reveals the fundamental characteristics of the protein of MTB. Moreover, the protein-protein interactions, active amino acid residues, allergenicity, antigenicity, and toxicity uncover the protein potentiality of MTB infection.


Since the etiology of numerous human diseases has been associated with protein aggregation, and it has become a field of increasing importance in the biopharmaceutical industry, the biophysical characterization of protein misfolded states and their aggregation mechanisms continues to receive increased attention. Mass spectrometry (MS) has firmly established itself as a powerful analytical tool capable of both detection and characterization of proteins at all levels of structure. Given inherent advantages of biological MS, including high sensitivity, rapid timescales of analysis, and the ability to distinguish individual components from complex mixtures with unrivalled specificity, it has found widespread use in the study of protein aggregation, importantly, where traditional structural biology approaches are often not amenable. The present review aims to provide a brief overview of selected MS-based approaches that can provide a range of biophysical descriptors associated with protein conformation and the aggregation pathway. Recent examples highlight where this technology has provided unique structural and mechanistic understanding of protein aggregation.Protein aggregation is now recognized as a generic and significant component of the protein energy landscape. Occurring through a complex and dynamic pathway of structural interconversion, the assembly of misfolded proteins to form soluble oligomers and insoluble aggregates remains a challenging topic of study, both  Proteins rely on a high degree of conformational flexibility to fold into their native states and facilitate the extensive interactions with a wide variety of binding partners that underlie biological function. Reaching and maintaining such native structures is enabled by a downhill-folding pathway on a free-energy surface. However, the near cancellation of enthalpic and entropic contributions to free energy during the folding process, and the low energy barriers to structural interconversion, mean that the flexibility required for a protein to function often places them at the brink of stability . ProteinProtein aggregation refers to the multistep process by which abnormal association of proteins gives rise to larger aggregate structures, which tend to be insoluble. This process commences with the self-assembly of misfolded monomers to produce a range of intermediates termed soluble-misfolded oligomers. Further assembly from such early oligomeric states leads to formation of insoluble structures in the forms of amorphous aggregates or amyloid fibrils with ordered β-sheet conformations ,8 Figur. This caGiven increasing recognition of the importance of protein aggregation in biology, significant research effort has been focused on enabling detailed structural and mechanistic understanding of the aggregation pathway. However, this is a particularly complex and challenging task given the heterogeneous and dynamic nature of the aggregation process. Many biophysical methods have been employed for the study of protein misfolding and aggregation . Importam/z) [From the most fundamental perspective, an MS experiment involves the conversion of molecules into gas-phase ions, where they are separated in a mass analyzer according to their mass-to-charge ratios (m/z) . For prom/z) . A numbem/z) . Perhapsm/z) .m/z as a basis of protein identification from biological samples. The measurement of molecular weight for intact proteins and their assemblies can enable the identification of stoichiometry to provide the number of monomeric units in an oligomeric structure. Furthermore, as native ESI-MS allows for preservation of noncovalent-binding interactions, the association of small-molecule cofactors, ligands, or chaperones with aggregating proteins may also be observed. Beyond simple molecular weight measurement however, extensive structural information can be provided through a range of coupled techniques, with key examples further described below. On this basis, MS has emerged as a highly sensitive technique for protein aggregation studies, offering complementary insights into the onset of amorphous aggregation and particularly amyloid fibril formation, including monomer and oligomer structural characterization, aggregation mechanisms, and inhibitor screening.In the context of protein aggregation, if the protein/oligomer is enzymatically digested before MS analysis, then the resulting peptides can be separated according to their m/z or a single species has different conformational states, multidimensional separation is critical. Ion mobility (IM) coupled to MS provides a means for further gas-phase separation of ions based on the rate at which they migrate through a neutral carrier gas under the influence of an external electric field. Importantly, the mobility of an ion can be related to rotationally averaged collision cross-section (CCS), and therefore can be used to infer information regarding biomolecular structure [While native MS serves as a key method for the detection of intact protein assemblies, in cases where oligomeric complexes may overlap at the same tructure . IM-MS tn or ‘product-ion’) spectra, which contain rich structural information [Tandem MS allows selected ions to be isolated based on a first MS or IM analysis step, then submitted to activation and/or fragmentation. The resulting products are subsequently analysed to give MS/MS  coupled with MS provides an additional method to characterize 3D conformations of proteins and identify protein-binding interfaces. In a basic CX-MS workflow, bifunctional small-molecule cross-linking reagents are used to covalently link two functional sites on the protein, typically with some defined reactivity toward specific amino acids. Since the links are made by a spacer with distinct length, localizing these linkage sites using proteomics methods provides an upper distance constraint to probe tertiary and quaternary structures . As CX-Min situ are based on limited proteolysis (LiP) and stability profiling. LiP-MS identifies structural changes in different cellular states or under different conditions by quantifying the relative sensitivity of proteins to a given protease through structure-dependent proteolytic patterns [The advantages of MS that enable investigation of complex protein mixtures mean that it is increasingly being exploited to study structural and biophysical properties of proteins at a whole proteome scale. Of note, two additional MS approaches that probe proteome structural alterations patterns ,34. In cpatterns ,36. In tDue to their dynamic, polydisperse, and polymorphic nature, aggregating proteins are particularly challenging to characterize using traditional structural biology methods. Here, recent studies are selected from among the many of this expansive field, to showcase the unique ability of MS in characterizing the underlying assembly pathway for a broad range of amyloidogenic and amorphously aggregating proteins. Furthermore, these studies outline the potential of MS as a tool for the screening of aggregation inhibitors and quality control monitoring.The mechanisms by which proteins fold and misfold in solution remain the subject of significant study and debate. Disruption of native state(s) can occur because of many influences, and the early conformational changes from native structure have traditionally monitored in solution using a variety of techniques. However, IM-MS has proven invaluable to investigate the conformational landscape of many proteins ,38 and tA range of IM-MS studies have focused on early conformational changes of amyloidogenic proteins, showing a structural reorganization from extended structures of the intrinsically disordered proteins clinically implicated in neurodegenerative disorders to more compact states which precede aggregation ,43. SimiPulse-labeling HDX-MS also enables direct monitoring of rapid events such as the earliest stages of protein misfolding, with high structural and temporal resolution. For example, this technique has provided enhanced understanding of the mechanism of aggregation for the clinically important proteins amyloid-β, α-synuclein, and tau . By moni105–115), describing oligomeric states and aggregation over time. For example, the NNQQNY peptide was shown to form extended, likely β-sheet-rich structures, for oligomers beyond octamers, representing a distinct transition toward the amyloid pathway from that point [Growing evidence suggests that the soluble oligomers on pathway to large aggregates are primary contributors to cell toxicity, particularly in the context of the diverse range of amyloid disorders . Once agat point . In compat point . Amyloidat point . One sugat point .Kinetic-pulsed variations of HDX have also been used to characterize early amyloidogenic intermediates , enablinm/z and charge [Given extensive characterization by X-ray crystallography, nuclear magnetic spectroscopy and, more recently, cryoelectron microscopy, the structure of mature amyloid fibrils is largely well understood. Nevertheless, while gas-phase analysis of amyloid fibrils is particularly challenging due to their enormous size, MS is still able to further probe the properties of such mature assemblies. For example, molecular-weight determination is the most direct way of determining the aggregation state of a protein. Recent applications of charge-detection MS, a single-molecule method where the mass of each ion is directly determined from individual measurements of d charge , demonstd charge ,60. Thisd charge ,62. As td charge .A range of MS-based methods are able to observe and interrogate binding interactions of protein–ligand complexes . Often, While much focus in the protein aggregation field has been directed toward amyloid aggregation due to impacts in human health, amorphous aggregation is also of significant note in a biopharmaceutical context. Here, aggregates must be strictly controlled as they are not only less efficacious but resultant particulates may cause serious immunological responses when administered . Liquid Although there are many different approaches to study protein aggregation, MS has firmly established itself as critical to this pursuit based on the diversity of information afforded. It is clear from the proliferation of protein aggregation studies reported in recent decades, which employ MS methodologies that it can reveal many previously hidden aspects of protein misfolding and aggregation across the entire structural landscape. Importantly, a key strength of MS is its ability to further combine with orthogonal techniques. Continued advancements in MS technology and an ongoing drive toward integrated structural biology, where multiple methods combine to contribute to a deep understanding of protein structure and dynamics, will lead to a rich knowledge of the fundamental factors that modulate aggregation. We might ultimately see MS-driven technologies routinely describe protein aggregation states across the whole proteome and under a wide diversity of conditions, providing further opportunities to intervene to the advantage of human health and biotechnology.Protein aggregation occurs via a complex, multistep pathway involving aberrant assembly of misfolded proteins.MS offers unique advantages for the biophysical analysis of heterogeneous and dynamic processes involved in protein misfolding and aggregation.Utilizing MS-based detection with various experimental approaches, information regarding structural changes, stability, self-assembly, and other molecular interactions and dynamics can be captured along the entire protein aggregation pathway.MS-based understanding of protein aggregation can have broad and impact across areas of health and biotechnology.


GRIN2A is a gene that encodes NMDA receptors found in the central nervous system and plays a pivotal role in excitatory synaptic transmission, plasticity and excitotoxicity in the mammalian central nervous system. Changes in this gene have been associated with a spectrum of neurodevelopmental disorders such as epilepsy. Previous studies on GRIN2A suggest that non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein’s structure and function. To gain a better understanding of the impact of potentially deleterious variants of GRIN2A, a range of bioinformatics tools were employed in this study. Out of 1320 nsSNPs retrieved from the NCBI database, initially 16 were predicted as deleterious by 9 tools. Further assessment of their domain association, conservation profile, homology models, interatomic interaction, and Molecular Dynamic Simulation revealed that the variant I463S is likely to be the most deleterious for the structure and function of the protein. Despite the limitations of computational algorithms, our analyses have provided insights that can be a valuable resource for further in vitro and in vivo research on GRIN2A-associated diseases. The goal of human genetics research is to comprehend how frequent genetic variations affect the probability of getting common illnesses . Single in silico approach is an efficient way to determine which SNPs are harmful and which are not, using particular processes [Various genetic tools and related genomics have been used for the large-scale extraction of SNPs over the years. Over 10 million SNPs have been found, covering the most common types of genetic variations . Since trocesses . Besidesrocesses –9.GRIN2A gene by evaluating their impact on the structure and function of the GluN2A protein.SNPs that occur in the coding region can be either synonymous, meaning they do not cause any change in the amino acid sequence, or non-synonymous (nsSNPS), meaning the amino acid sequence is altered . It is uGRIN2A gene encodes for the GluN2A subunit of the N- methyl-D-aspartate (NMDA) glutamatergic receptor that is expressed throughout the brain and is important in the function of all neuron types. This receptor is activated through simultaneous binding by Glu and glycine and is formed by combinations of NR1, NR2, and NR3 subunits, with NR1-NR2 heterodimers forming the basic functional structure. The NR1 subunit has glycine binding sites, while the NR2 subunit has glutamate binding sites. NR3 subunits are regulatory subunits that decrease ionic currents generated by NR1/NR2 heteromers and are probably involved in activating silent NMDA-alone synapses. NMDA receptors are essential for synaptic transmission, learning, and memory as they function as ion channels that allow positively charged ions to flow through the neuron’s membrane [GRIN2A gene weaken ion flow through the receptor, resulting in abnormal neuron function, epilepsy, and related developmental differences [membrane . Any mutferences ,18.GRIN2A-related speech disorders and epilepsy affects individuals from a young age. All affected ones display some degree of speech disorder, with severe cases including dysarthria and speech dyspraxia, and both receptive and expressive language delay or regression. In addition to speech disorders, about 90% of affected individuals also experience epilepsy, with seizure onset typically occurring between the ages of three and six. The seizures associated with GRIN2A mutations can take various forms, including seizures with an aura of perioral paresthesia, focal or focal motor seizures, and atypical absence seizures. Different epilepsy syndromes including Landau-Kleffner syndrome, epileptic encephalopathy, childhood epilepsy, autosomal dominant rolandic epilepsy and infantile-onset epileptic encephalopathy have been linked to GRIN2A mutations [the GRIN2A pathogenic variants is yet to be identified. The abundance of SNPs present in the GRIN2A makes laboratory experimentations challenging, seeking to investigate the functional effects of these variations, as such experiments can be both expensive and time-consuming. Therefore a computational screening of SNPs to minimize the number of potential pathogenic ones is a must before experimental mutation analysis. Considering this fact, in the current study we aimed to predict the consequences of the most damaging nsSNPs that occur in the GRIN2A coding region as reported in the dbSNP database (https://www.ncbi.nlm.nih.gov/snp/).utations . HoweverThe complete workflow employed in this study is outlined in GRIN2A gene were retrieved from NCBI dbSNP [https://www.uniprot.org/) (UniProt ID: Q12879) [Single Nucleotide Polymorphism (SNP) variants recorded for the human BI dbSNP . Only mi Q12879) .GRIN2A. These tools include PolyPhen-2, PANTHER, SNPs & GO, PhD-SNP, and PredictSNP2.Five different tools were used to predict the impact of nsSNPs on the http://genetics.bwh.harvard.edu/pph2/) server, which is used to predict the effects of human nsSNPs on the function of protein molecules. This server, which is based on sequence homology, can help identify potentially damaging missense mutations and analyze the potential effects of an amino acid substitution on the structure and function of a human protein [The functional and structural effects of certain missense variants were analyzed using several online tools. At first, the variants were submitted to the PolyPhen-2 ( protein . The rsIhttp://pantherdb.org/tools/csnpScoreForm.jsp) is a software that is used to predict non-synonymous genetic variants that may be related to human disease. This tool uses position- specific evolutionary conservation (PSEC) scores to make these predictions [PANTHER (dictions .https://snps.biofold.org/snps-and-go/pages/method.html) is an online server that combines data from protein sequences, evolutionary information, and functions encoded in Gene Ontology terms to predict the effects of SNPs. This server is considered to be more accurate than other similar methods available [SNPs&GO (vailable .https://snps.biofold.org/phd-snp/phd-snp.html) is a tool for predicting the effects of deleterious single nucleotide polymorphisms in humans, which is based on a Support Vector Machine (SVM) classifier. This tool can take the protein sequence, position of the mutation, or the mutated residue as input query. PMut (http://mmb.irbbarcelona.org/PMut/) is another web- based tool for predicting the effects of nsSNPs on protein function. A score greater than 0.5 obtained from this tool indicates that the nsSNPs have a damaging impact on protein function [PHD-SNP (function .https://loschmidt.chemi.muni.cz/predictsnp2/) is an online interface that provides easy access to predictions from five different tools and their consensus scores in a user-friendly format. It is tailored to the specific features of various categories of variations. The predictions are also accompanied by annotations from relevant databases to enable a comprehensive evaluation of variants [PredictSNP2  were used to find out the effect of SNPs on the protein structure.https://services.healthtech.dtu.dk/service.php?NetSurfP-2.0) is a relatively new sequence-based tool that can accurately predict important local structural features of proteins with fast computation time. This server uses a neural network architecture that includes convolutional and long short-term memory layers, trained on previously solved protein structures [NetSurfP-2.0  utilizes both Support Vector Machines and Neural Networks to calculate the impact of point mutations on protein stability. Similarly to NetSurfP2.0, it requires the FASTA format of the protein sequence as input [With a prediction accuracy of 84%, MUpro (as input .http://mutation3d.org) is a web server that aims to identify driver genes in cancer by detecting clusters of amino acid substitutions within tertiary protein structures [Mutation3D (ructures . The 3D https://www3.cmbi.umcn.nl/hope/about/) uses a comprehensive approach to analyze a specific mutant protein. It gathers data from various sources, including 3D coordinate calculations of the protein. Before conducting analysis, this server requires the native protein sequence and information on the specific location and type of mutation to be studied [Have (y) Our Protein Explained (HOPE) server ( studied .http://consurf.tau.ac.il) was used. This server, which has been in operation for over 15 years, uses evolutionary patterns of amino/nucleic acids to identify regions that are critical for structure and/or function [To unravel the evolutionarily conserved sites of the human GRIN2A protein, the Consurf web server (function .http://pfam.xfam.org/) was employed. This is a widely used tool for analyzing protein function that features a collection of curated protein families, each defined by two alignments and a profile hidden Markov model (HMM). The profile HMMs, generated by aligning a set of family-representative sequences, are statistical models used to search for similarities among proteins. The Pfam website provides several methods to access its database content, including graphical representations and interactive data access [For domain identification the Pfam server (a access .https://toolkit.tuebingen.mpg.de/tools/hhpred) is a web-based tool for detecting remote protein homology and predicting structures that extracts homology information from HH-suite (open-source software for searching sensitive protein sequences). It is one of the first servers to effectuate pairwise comparison of profile hidden Markov models (HMMs). It accepts a single query sequence and multiple alignments as input depending on the user’s preference [https://servicesn.mbi.ucla.edu/PROCHECK/) evaluates the structural integrity of a protein by looking into the geometry of individual residues and the overall structure of the protein [HHpred  server was employed. This is a web-based tool for predicting and refining protein structures along with other related methods. It processes protein structure by performing repeated perturbation and structural relaxation by molecular dynamics simulation. The mechanism starts with the reconstruction of side chains and continues with side-chain repacking and overall structure relaxation [To improve the accuracy of the 3D model for the GRIN2A protein, GalaxyRefine (laxation .https://biosig.lab.uq.edu.au/dynamut2/) is an all-inclusive tool for analyzing protein motion and flexibility. It integrates optimized graph-based signatures with normal mode parameters to estimate the impact of point mutations on protein stability. Not only that but, it also predicts the effect of missense variations on protein stability and dynamics which is critical for understanding the link between protein structure and function and their role in associated diseases [The nsSNPs selected from the upstream analysis were evaluated using the DynaMut2 server. DynaMut2 (diseases .The wild-type GRIN2A and selected mutant protein models were subjected to a 100 ns molecular dynamics simulation using the GROMACS (version 2020.6) simulation software . The forGRIN2A gene (Uniprot ID Q12879) was retrieved from the NCBI database (https://www.https://www.ncbi.nlm.nih.gov/snp/). Out of the 179,447 SNPs covered, 1,320 were missense (nsSNPs).Single nucleotide Polymorphism (SNP) of the human GRIN2A gene product, five tools were used: PolyPhen 2, PANTHER, SNPs&GO, PhD-SNP, and Predict SNP2. Out of the total 1320 nsSNPs, PolyPhen 2 server predicted 832 nsSNPs to be functionally damaging with 490 labeled as "probably damaging" and 275 as "possibly damaging." To further investigate these SNPs, additional analysis was conducted using SNPs&GO, PANTHER, PhD-SNP, and Predict-SNP2. The number of detrimental ones predicted by Sthe NPs&GO server was 154 while PANTHER, PhD-SNP, and Predict-SNP2 predicted 141, 451, and 692 nsSNPs as disease-associated, respectively.To evaluate the functional effects of nsSNPs on Based on the outputs, 16 most highly deleterious nsSNPs were selected that were labeled as damaging, possibly/probably damaging, or deleterious by these tools. These 16 functionally significant nsSNPs were further taken into consideration for the next stage of filtering. The results from PolyPhen 2, PANTHER, SNPs&GO, PhD-SNP, and Predict SNP2 are presented in Sixteen nsSNPs that had been predicted to be damaging by 5 different tools then underwent structural impact analysis. For this purpose, four servers were employed namely, Mutation3D, NetSurfP-2.0, MUpro, and HOPE.GRIN2A on the protein, outputs from the Mutation 3D server were loaded onto the PyMol software. The result was a three-dimensional representation of the human GRIN2A protein , with the mutated residues highlighted in red.For visualizing the locations of 16 nsSNPs of Table 2).Native and mutant protein accessibility and stability for the 16 variants were assessed by NetsurfP-2.0. NetSurfP-2.0 predicts the degree to which a residue is buried or exposed within the protein structure, as a percentage. These predictions were substantiated using multiple distinct test datasets. Out of 16 deleterious nsSNPs, 3  were found to be exposed in the wild type, while the remaining 13  were buried. On the other hand, 2 mutants  remained exposed while the rest of the 14  were buried  were larger than the wild type, while 6 mutant residues  were smaller than the wild type. Out of the 16 nsSNPs analyzed, 14 were predicted to have a probable damaging effect, one as a possibly damaging effect and one as a damaging effect on the protein structure according to the HOPE server.To further examine the potential impact of the shortlisted 16 nsSNPs, the ConSurf web tool was used to calculate the evolutionary conservation of amino acid residues in the GRIN2A protein. The results were presented as a structural representation of the protein sequence, with the putative structural and functional residues highlighted.Fig 3. After careful analysis, we identified 9 out of 16 mutants as having the most significant impact on the structure of the GRIN2A protein. We subsequently selected specific nsSNPs (listed in Table 3) from the GRIN2A protein for further investigation.According to ConSurf output, 12 of the 16 nsSNPs  were found to be highly conserved residues with a conservation score of 9. Three variants  were predicted as moderately conserved (conservation score of 8) while only one variant (R52Q) was predicted as a variable with a conservation score of 3. Variants located in these conserved regions are considered to be highly damaging to the protein, as referred to as those located in non-conserved sites. The deleterious predictions for each SNP by ConSurf are summarized in Three domains, namely- ANF receptor, Lig chan-Glu, and Lig chan were identified within the GRIN2A protein using the Pfam server. The ANF receptor domain contained 1 nsSNP (Q146P), while the Lig chan-Glu domain had 4 nsSNPs  and the Lig chan domain had 4 nsSNPs .S1 Fig).The homology model of the protein was generated in PDB Format using the HHPred server. The quality of the model was validated by inspecting the Ramachandran plot generated by the PROCHECK web server. The findings indicated that over 90% of the residues of the homology model belonged to the most favored regions for both the wild and mutant version (Table 4).The 3D model of the protein of interest was refined using the GalaxyRefne server. The input for the refinement process was in PDB file format. The server provided information on the 5 best models in terms of RMSD, Clash score, Rama favored, and MolProbity. The model with the lowest MolProbity score and the highest Rama favoured score was selected (Table 5). DynaMut2 provides the change in stability (ΔΔG) in terms of kcal/mol. A negative value indicates a destabilizing effect while a positive value indicates a stabilizing effect. The more negative the ΔΔG value the more destabilizing the mutation is for the protein. The analysis identified 8 residues  as destabilizing for the protein structure, while 1 residue (A643T) had a stabilizing effect. The SNP, I463S exhibited the highest destabilizing effect on the protein . Therefore, it was selected for sophisticated molecular dynamics simulation in the next step.The nsSNPs selected so far were analyzed using the DynaMut2 server .Root Mean Square Deviation (RMSD) calculation is performed in to evaluate the stability of the systems. A higher RMSD value corresponds to instability in the protein. The RMSD for the wild GRIN2A residues .Room Mean Square Fluctuation (RMSF) is used to determine the regional flexibility of the protein. The higher the RMSF, the higher is the flexibility of a given amino acid position. Except for a few in the middle, almost all residues in the mutant I463S exhibited higher flexibility compared to the wild-type GRIN2A was relatively more compact than the I463S mutant GRIN2A as the former reached a stable value shortly after 35 ns while the latter reached this state after 65 ns .The radius of gyration is a measure to determine its degree of compactness. A relatively steady value of the radius of gyration means stable folding of a protein. Fluctuation of the radius of gyration implies the unfolding of the protein. The radius of gyration analysis indicated that the wild type GRIN2A remained close. However, the wild type displayed a slightly lower SASA profile compared to the other .Solvent Accessible Surface Area (SASA) is used in MD simulations to predict the hydrophobic core stability of proteins. The higher the SASA value, the higher the chance of destabilization of the protein due to solvent accessibility. SASA values for the wild type and the I463S mutant Almost half of the genetic variations that cause hereditary diseases come from nsSNPs . ThorougGRIN2A, GRIN2B, and GRIN2D) that encode NMDAR subunits that have so far been linked to human disease, GRIN2A appears to have the most extensive and well-characterized range of phenotypic effects [GRIN2A missense variants have revealed various gain- or loss-of-function effects [GRIN2A variant was found in a child with early-onset epileptic encephalopathy [GRIN2A gene in a patient with childhood focal epilepsy and acquired epileptic aphasia. This particular mutation was found to lower NMDAR activation implying that the decreased function of NMDAR could potentially be involved in the development of epilepsy [GRIN2A related nsSNPs is essential before starting targeted treatment options. In this study, we conducted intensive in silico analyses to identify the pathogenic nsSNPs of the GRIN2A gene. For this purpose, a wide range of computational tools was used to get a detailed understanding of the potential impact of these SNPs on the target gene.Among the four genes Click here for additional data file.


Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development. Protein engineering aims to design and discover proteins with desirable functions, such as improving the phenotype of living organisms, enhancing enzyme catalysis, and boosting antibody efficacy . It has Two traditional protein engineering approaches include directed evolution  and ratiractable . As a reRecently, data-driven machine learning has emerged as a new approach for directed evolution and protein engineering , 9. MachThe process of data-driven MLPE typically involves several elements, including data collection and preprocessing, model design, feature extraction and selection, algorithm selection and design, model training and validation, experimental validation, and iterative model optimization. Driven by technological advancements in high-throughput sequencing and screening technologies, there has been a substantial accumulation of general-purpose experimental datasets on protein sequences, structures, and functions , 14. TheData representation and feature extraction are crucial steps in the design of machine learning models, as they help to reduce the complexity of biological data and enable more effective model training and prediction. There are several typical types of feature embedding methods, including sequence-based, structure-based , 17, phyThe designs and selections of MLPE algorithms depend on the availability of data and efficiency of experiments. In real-world scenarios, where smaller training datasets are prevalent, simpler machine learning algorithms such as support vector machines and ensemble methods are often employed for small training datasets, which is often the case in real scenarios. In contrast, deep neural networks are more suitable for larger training datasets. Although regression tasks are typically used to distinguish one set of mutations from another , unsuperMLPE has the potential to significantly accelerate the development of new and improved proteins, revolutionizing numerous areas of science and technology . DespiteIn artificial intelligence, natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally . NLP covSimilar to human language, the primary structure of a protein is also represented by a string of amino acids, with 20 canonical amino acids. The analogy between protein sequences and human languages has inspired the development of computational methods for analyzing and understanding proteins using models adopted from NLP . The selTo train a local evolutionary model, MSAs search strategies such as jackhmmer  and EvCoWith large-size data, global evolutionary models usually adopt the large NLP models. Convolutional Neural networks (CNNs)  models aAlthough global evolutionary models can learn a variety of sequences derived from natural evolution, they face challenges in concentrating on local information when predicting the effects of site-specific mutations in a target protein. To enhance the performance of global evolutionary models, fine-tuning strategies are subsequently implemented. Specifically, fine-tune strategy further refines the pre-trained global models with local information using MSAs or target training data. The fine-tuned eUniRep  shows siWith various language models proposed, comprehensive studies on various models and the strategy in building downstream model is necessary. A study explored different approaches utilizing the sequence embedding to build downstream models . Two othAided by advanced NLP algorithms, sequence-based models have become the dominant approach in MLPE , 11. HowTDA is a mathematical framework based on algebraic topology , 63, whiThe basic idea behind TDA is to represent the data as a point cloud in a high-dimensional topological space, and then study the topological invariants of this space, such as the genus number, Betti number, and Euler characteristic. Among them, the Betti numbers, specifically Betti zero, Betti one, and Betti two, can be interpreted as representing connectedness, holes, and voids, respectively , 77. TheHomology groups are algebraic structures that are associated with topological spaces . They prHomology theory has many applications in mathematics and science. It is used to classify topological spaces in category theory, to study the properties of manifolds in differential geometry and algebraic geometry, and to analyze data in various scientific fields . HoweverPersistent homology is a relatively new tool in algebraic topology that is designed to incorporate multiscale topological analysis of data , 63. ThePersistent homology is the most important approach in TDA  . The motHowever, aforementioned TDA methods are still limited in describing complex data, such as its lack of description of non-topological changes  , its incPersistent topological Laplacians (PTLs) are a new class of mathematical tools designed to overcome the aforementioned challenges in TDA . One of Like persistent homology, persistent Laplacians are limited in their ability to handle directed networks and atomic polarizations. To address these limitations, persistent path Laplacians have been developed . Their hNone of the PTLs mentioned above are capable of handling different types of elements in a molecule as persistent cohomology does. To overcome this limitation, persistent sheaf Laplacians  were desPersistent hypergraph Laplacians enable the topological description of internal structures or organizations in data . PersistFinally, unlike all the aforementioned PTLs, evolutionary de Rham-Hodge Laplacians or persistent Hodge Laplacians are defined on a family of filtration-induced differentiable manifolds . They arSimilar to topological data analysis, graph- and topology-based deep learning models have been proposed to capture connectivity and shape information of protein structure data. Graph neural networks (GNNs) consider the low-order interactions between vertices by aggregating information from neighbor vertices. A variety of popular graph neural network layers has been proposed, such as convolution graph networks (GCN) , graph aWith variety of architectures of GNN layers, self-supervised learning models are widely used for representation learning of graph-based data. Graph autoencoder (GAE) and variational graph autoencoder (VGAE) consist of both encoder and decoder, where the decoder employ a linear inner product to reconstruct adjacent matrix . While mIn applications to learning protein structures, GCNs have been widely applied to building structure-to-function map of proteins , 102. MoTopological deep learning, proposed by Cang and Wei in 2017 , is an ek-GNNs  and simpk-GNNs .Protein engineering is a typical black-box optimization problem, which focuses on finding the optimal solution without explicitly knowing the objective function and its gradient. In protein engineering, the goal in designing algorithms for this problem is to efficiently search for the best sequence within a large search space:4 = 160,000 mutations, 30 local maximum fitness peaks were found [There are two practical challenges in protein engineering. First, the fitness landscape is usually epistatic , 112, whre found . Either re found , making With enriched data-driven protein modeling approaches from protein sequences to structures, recent advanced machine learning methods have been widely developed to accelerate protein engineering in silico  1, 11, , 115. UtUsing a limited number of experimentally labeled sequences, machine learning models can carry out zero-shot or few-shot predictions . The accMLPE methods need to take the experimental capacity into account when attempt to balance the exploitation-exploration. In this section, we discuss different strategies upon the number of experimental capacity. First, we discuss zero-shot strategy when no labeled experimental data is available. Second, we discuss supervised models for performing greedy search . Last, we discuss uncertainty quantification models that balance exploration and exploitation trade-off.First, we review the zero-shot strategy that interrogates protein fitness with an unsupervised manner . This isZero-shot predictions rely on the model’s ability to recognize patterns in naturally observed proteins, enabling it to make informed predictions for new sequences without having direct training data for the target protein. As discussed in Section 3, protein language models, particularly generative models, learn the distribution of naturally observed proteins which are usually functional. The learned distribution can be used to assess the likelihood that a newly designed protein lies within the distribution of naturally occurring proteins, thus providing valuable insights into its potential functionality and stability .VAEs are popular local evolutionary models for zero-shot predictions such as DeepSequence  and EVE nference  providesle works , 55, 5.Transformer is the currently state-of-the-art model which has been used in many supervised tasks . It learsequence , 34 : , the detRecently, researchers have turned to generative models to quantify uncertainty in protein engineering, employing methods such as Variational Autoencoders (VAEs) , 22, 40,In this review, we discuss the advanced deep protein language models for protein modeling. We further provide an introduction of topological data analysis methods and their applications in protein modeling. Relying on both structure-based and sequence-based models, MLPE methods were widely developed to accelerate protein engineering. In the future, various machine learning and deep learning will have potential perspectives in protein engineering.Comparing to sequence data, three-dimensional protein structural data offer more comprehensive and explicit descriptions of the biophysical properties of a protein and its fitness. As a result, structure-based models usually provide superb performance than sequence-based models for supervised tasks with small training set , 120.As protein sequence databases continue to grow, self-supervised models demonstrate their ability to effectively model proteins using large-scale data. The protein sequence database provides a vast amount of resources for building sequence-based models, such as UniProt  databaseTo address the limited availability of structure data, researchers have focused on developing highly accurate deep learning techniques aimed at enabling large-scale structure predictions. These state-of-the-art methodologies have the potential to significantly expand the database of known protein structures. Two prominent methods are AlphaFold2  and RoseBoth AlphaFold2 and RosettaFold are alignment-based, which rely on MSAs of the target protein for structure prediction. Alignment-based approaches can be highly accurate when there are sufficient number of homologous sequences  in the database. Therefore, these methods may have reduced accuracy with low MSAs depth in database. In addition, the MSAs search is time consuming which slows down the prediction speed. Alternatively, alignment-free methods have also been proposed to tackle these limitations . An earlIn building protein fitness model, the structural TDA-based model has exemplified that the AlphaFold2 structure is as reliable as the experimental structure . The zerCurrent MLPE methods are usually designed for limited training set. The ensemble regression is an effective approach to accurately learn the fitness landscape with small but increasing size of training sets from deep mutational scanning .The breakthrough biotechnology, next-generation sequencing (NGS)  largely 1