Drug designing is high-priced and time taking process with low success rate. To overcome this obligation, computational drug repositioning technique is being promptly used to predict the possible therapeutic effects of FDA approved drugs against multiple diseases. In this computational study, protein modeling, shape-based screening, molecular docking, pharmacogenomics, and molecular dynamic simulation approaches have been utilized to retrieve the FDA approved drugs against AD. The predicted MADD protein structure was designed by homology modeling and characterized through different computational resources. Donepezil and galantamine were implanted as standard drugs and drugs were screened out based on structural similarities. Furthermore, these drugs were evaluated and based on binding energy  profiles against MADD through PyRx tool. Moreover, pharmacogenomics analysis showed good possible associations with AD mediated genes and confirmed through detail literature survey. The best 6 drug  further docked and analyzed their interaction behavior through hydrogen binding. Finally, MD simulation study were carried out on these drugs and evaluated their stability behavior by generating root mean square deviation and fluctuations (RMSD/F), radius of gyration (Rg) and soluble accessible surface area (SASA) graphs. Taken together, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar displayed good lead like profile as compared with standard and can be used as possible therapeutic agent in the treatment of AD after in-vitro and in-vivo assessment. Globally, it has been observed that around 35 million people are affected by AD annually and this number is believed to double in next 20 years. In USA, 6.7 million American are living with dementia having age greater than 653. The etiology of AD is highly complex, however, there are some biomarkers including low levels of acetylcholine, β-amyloid (Aβ) deposits, tau-protein aggregation, neurofibrillary tangles (NFTs), oxidative stress, and dys-homeostasis of bio-metals are used for the diagnosis of AD4. These available clinical therapeutics show only partial effectiveness in ameliorating the AD symptoms5.Alzheimer’s disease (AD), a neurodegenerative disorder usually characterized by memory loss in older persons7. Drug repositioning is basic computational approach to predict the therapeutic potential of known drugs against different disease by targeting different receptors9. However, in present time, medical research emphases on the factors that are thought to contribute to AD development, such as tau proteins and Aβ deposits10. MAP kinase-activating death domain (MADD) protein is bulky a protein comprises 1647 amino acids and four different domains such as uDENN, cDENN, dDENN and death domain, respectively. Furthermore, six disordered regions are also present in MADD structure with different residues lengths. MADD protein plays a significant role in regulating cell proliferation, survival, and death through alternative mRNA splicing indifferent disease such as Deeah and AD11. MADD protein also links TNF receptor superfamily member 1A (TNFRSF1A) with MAP kinase activation and important for regulatory role in physiological cell death12.Drug development is time consuming and overpriced process however, computational techniques particularly drug repurposing is being used in the present timeIn the present study, Food and Drug Administration (FDA)-approved drugs were screened against AD by taking double standards donepezil and galantamine through shape-based screening technique. The screened hits underwent molecular docking analysis using PyRx against MADD protein. Moreover, pharmacogenomics analysis was carried out and best drug interacting with AD-medicated genes were shortlisted from the pool of selected drugs. The best-selected drugs were further examined by a docking procedure with AutoDock to check their binding affinities against the MADD protein. Finally, the best-generated docked complexes were further analyzed through molecular dynamics simulations to observe the structural stability through RMSD, RMSF, Rg, and SASA graphs.https://www.uniprot.org/uniprot/Q8WXG6) was accessed from the UniProt database13. MADD protein contains four major domains  and unstructured regions..The amino acid sequence of MADD protein having accession number Q8WXG6 ; therefore, homology modelling based method was used to predict the complete structure of MADD protein. There are different online protein prediction servers, however, trRosetta (transform-restrained Rosetta) is the most recent protein structure prediction server. trRosetta is a web-based platform for fast and accurate protein structure prediction, powered by deep learning and Rosetta (https://yanglab.nankai.edu.cn/trRosetta/). However, to predict the complete MADD protein at once using server, its much difficult therefore, all four domains  and unstructured/disordered regions were modelled separately using trRosetta. After modelling of all four domains and unstructured/disordered regions, The Domain Enhanced MOdeling  was used to assemble all structure using default parameters15. Demo is a method for automated assembly of full-length structural models of multi-domain proteins. Starting from individual domain structures, DEMO first identify quaternary structure templates that have similar component domains by domain-level structural alignments using TM-align. Replica-exchange Monte Carlo simulations are used to assemble full-length models, as guided by the inter-domain distance profiles collected from the top-ranked quaternary templates. The final models with the lowest energy are selected from Monte Carlo trajectories, followed by atomic-level refinements using fragment-guided MD simulations15.The complete three-dimensional (3D) structure of MADD is not available in Protein Data Bankhttps://zlab.umassmed.edu/bu/rama/index.pl) and MolProbity server (http://molprobity.biochem.duke.edu/) were used to check the amino acids and overall protein conformation with respect to phi (φ) and psi (ψ) angles by generating Ramachandran graph. The stability behavior of target protein is dependent upon the occurrence of residues in allowed and disallowed regions. Furthermore, the ProtParam tool was employed to predict their theoretical PIs, extinction coefficients, aliphatic and instability indexes, and GRAVY values16. The overall protein architecture and the statistical percentage values of α-helices, β-sheets, coils, and turns were retrieved from the online VADAR 1.8 server17. FRST serves to validate the energy of a protein structure18. The server computes both an overall and a per-residue energy profile of a protein structure. Furthermore, Quality Model Energy Analysis (QMEAN) was employed to further evaluate the model quality (https://swissmodel.expasy.org/qmean/). Another known model server SaliLab Model Evaluation Server  was employed to check the predicted model quality based on TSVMod and Modeller values. Finally, the modelled structure was further refine using GalaxyWEB  before going to utilize it for virtual screening, molecular docking, and dynamic simulation analysis. The graphical representation of model structure was done using Discovery Studio 2.1 Client19, and UCSF Chimera X20. Furthermore, after prediction of protein model Split-Statistical Potentials Server  was employed to identify the native conformation behavior and to evaluate the accuracy of protein folds21.The assemble model structure of MADD protein was analyzed using different computational approaches to check its stability behavior. To do this, initially Ramachandran Plot Server  having accession numbers (DB00843 & DB00674) and used as template molecule to screen FDA-approved drugs. All the screened drugs were ranked according to their predicted similarity scoring values. The best screened drugs were sketched in ACD/ChemSketch and utilized for molecular docking experiments.The Swiss Similarity, an online platform that allows you to identify the similar chemical hits from Food and Drug Administration (FDA) and other libraries with respect to the reference template structurehttp://prankweb.cz/), an online source that explores the probability of amino acids involved in the formation of active binding sites. Prankweb is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability centered on points placed on a solvent-accessible protein surface25. Points with a high ligandability score are then clustered to form the resulting ligand binding sites. The binding pocket information was not available in PDB; therefore, active binding sites residues of MADD protein were predicted by using Prankweb. The death domain is highly significant domain with functional association with AD12. Therefore, 3D modeled structure of death domain was utilized and predicts possible binding pockets with high probability values of amino acids25.The Prankweb  values and binding interaction pattern between ligands and target protein. The graphical depictions of all the docked complexes were accomplished by UCSF Chimera 1.10.1 and Discovery Studio (2.1.0), respectively.Before conducting our docking experiments, all the screened drugs were sketched in ACD/ChemSketch tool and accessed in mol format. Furthermore, UCSF Chimera 1.10.1 tool was employed for energy minimization of each ligand having default parameters such as steepest descent and conjugate gradient steps 100 with step size 0.02 (Å), and update interval was fixed at 10. In PyRx docking experiment, all screened drugs were docked with death domain of MADD using default procedure27. In brief, for receptor protein, the polar hydrogen atoms and Kollman charges were assigned. For ligand, Gasteiger partial charges were designated, and non-polar hydrogen atoms were merged. All the torsion angles for screened drugs were set free to rotate through the docking experiment. A grid map of 80 × 80 × 80 Å was adjusted on the binding pocket of MADD to generate the grid map and to get the best conformational state of docking. A total of 100 number of runs were adjusted using docking experiments. The Lamarckian genetic algorithm (LGA) and empirical free energy function were applied by taking docking parameters default28. All the docked complexes were further evaluated on lowest binding energy  values and hydrogen and hydrophobic interactions analysis using Discovery Studio (2.1.0) and UCSF Chimera 1.10.1.Furthermore, another docking experiment was employed on best screened drugs against MADD protein using AutoDock 4.2 toolhttps://www.dgidb.org/) and Drug Signatures Database (DSigDB) (http://dsigdb.tanlab.org/DSigDBv1.0/) were employed to obtain the possible list of different disease-associated genes. Furthermore, a detailed literature survey was performed against all predicted genes to identify its involvement in AD. Moreover, clumps of different diseases associated genes were sorted based on MADD and remaining disease-associated genes were eliminated from the dataset.To design the pharmacogenomics network model for best-selected drugs, Drug Gene Interaction Databases (DGIdb)  was generated on complex by editconf command. Moreover, box was filled with solvent molecules using gmx solvate command by employing spc216.gro water model. The overall system charge was neutralized by adding ions. The steepest descent approach (1000 ps) for protein structure was applied for energy minimization. For energy minimization the nsteps = 50,000 were adjusted with energy step size (emstep) 0.01 value. Particle Mesh Ewald (PME) method was employed for energy calculation and for electrostatic and van der waals interactions; cut-off distance for the short-range VdW (rvdw) was set to 14 Å, whereas neighbor list (rlist) and nstlist values were adjusted as 1.0 and 10, respectively, in em.mdp file31. This method permits the use of the Ewald summation at a computational cost comparable with that of a simple truncation method of 10 Å or less, and the linear constraint solver (LINCS)32 algorithm was used for covalent bond constraints and the time step was set to 0.002 ps. Finally, the molecular dynamics simulation was carried out at 100 ns with nsteps 50,000,000 in md.mdp file. Different structural evaluations such as root mean square deviations and fluctuations (RMSD/RMSF), solvent accessible surface areas (SASA) and radii of gyration (Rg) of back bone residues were analyzed through Xmgrace software (http://plasma-gate.weizmann.ac.il/Grace/).The best screened drugs-complexes having good energy values were selected to understand the residual backbone flexibility of protein structure; MD simulations were carried out by Groningen Machine for Chemicals Simulations  is an online tool which analyse the protein based on various physical and chemical parameters, respectively. The significant parameters include molecular weight (mg/mol), theoretical pI, amino acid composition, atomic composition, extinction coefficient, estimated half-life, instability/aliphatic indexes, and grand average of hydropathicity (GRAVY), respectively. The generated theoretical pI value of MADD protein is calculated by the accumulation of average isotopic masses and pK values of linear amino acids present in the proteins. The prior published research data depicted that proteins are distributed across a wide range of pI values (4.31–11.78)33. The MADD protein exhibited 5.49 pI value which is comparable with the standard values (4.31–11.78). Moreover, the aliphatic index value also showed the stability and relative volume occupied by aliphatic side chain residues. The predicted physiochemical properties showed the reliability, efficacy, and stability of the MADD protein. The predicted values of both instability and aliphatic indexes 58.81 and 74.96 are justifiable with standard values. The GRAVY value is the sum of hydropathy values of all residues in protein34. The prior research reports justified that the GRAVY negative and positive values show the hydrophilic and hydrophobic behavior of protein structure33. The negative GRAVY value (− 0.517) of MADD protein indicates the hydrophilic behavior , cDENN (289-429 AA), dDENN (431-565 AA) and death domain (1340-1415 AA), respectively with disordered regions (https://www.uniprot.org/uniprot/Q8WXG6)  is a human protein encoded by uDENN 14-68 AA, cDVADAR is an online server which predict the protein volume, area, dihedral angle reports and statistical evaluation. The generated MADD model structure showed different architectures related to α-helices, β-sheets, coils and turn, respectively. The overall protein structure analysis showed that MADD consists of 25% α-helices, 19% β-sheets, 55% coils and 20% turns, respectively. Moreover, Ramachandran plots and values depicted that 92.68% of amino acids were present in favored region with good accuracy of phi (φ) and psi (ψ) angles which showed the good accuracy of our predicted model.36. The initial predicted structure of MADD protein depicted MolProbity: 3.809–2.871, clash score: 88.4–47.8, poor rotamers: 5.7–1.4 and Rama favored: 72.4–85.8, respectively whereas, the values have been rectified to improve the modelled MADD structure (Supplementary Table https://www.umassmed.edu/zlab/) generate both Ramachandran graphs which have been mentioned in Fig. Galaxy-refine is an good source to resolve the ambiguities in the modelled structure based on different parameters such as GDT-HA, RMSD, MolProbity, clash score, poor rotamers and Rama favored, respectivelyhttp://protein.cribi.unipd.it/frst/). The rapdf potential37 is a distance-dependent residue-specific all-atom probability discriminatory function. Similarly, solvation potential to likelihood for each of 20 amino acids to adopt a given relative solvent accessibility is used to derive the pseudo-energy38. The torsion angle potential (TORS) is derived from the propensities of each amino acid to adopt different combinations of  torsion angles39. The total energy is again summed over all individual contributions in a protein. In our generated MADD results, rapdf, solvation, torsion and composite FRST energies  which showed the stability of model structure . The generated results showed that MADD protein exhibited − 6.64 value which is comparable with standard values . The prior data showed that different residues clumps showed lowest score value 0.1 which represents the quality assessment of predicted MADD model. However, most amino acids patches in the predicted model showed higher values > 0.6 depicts accurate and stable behavior of MADD protein. The blue peak showed highest prediction value having best quality results (> 0.8) of predicted model. Moreover, the purple color represents also showed good accuracy of MADD protein with scoring values range from 0.6 to 0.8. However, the orange values showed the de-stable behavior of MADD protein with values less than 0.6. The overall results showed that predicted MADD model exhibited good stable behavior and could be used for further analysis  is a composite scoring function which derive both global and local absolute quality estimates based on one single model , is a knowledge-based scoring functions used for correct folding of proteins, stability of mutant proteins, and assess outcomes of docking experiments. The generated results were evaluated based on five different parameters such as PAIR, ES3DC, ECOMB, ELOCAL, and E3DC respectively. The PAIR and ES3DC parameters are focused on amino acid frequencies along distances and their environments such as hydrophobicity of each amino acid, solvent accessibility, and secondary structure, respectively. Figure After prediction the protein models, native conformation behavior is significant parameter is to evaluate the accuracy of protein folds. SPS server  area represents the area having propensity to interacts with neighboring atoms. The pocket 1 showed good SAS value 152 as compared to other binding pockets values  with different amino acids of death domain of MADD protein. Moreover, the probability value of pocket 1 was also better as compared to other probabilities values , DB01466 (0.977), and DB11490 (0.974) showed scoring values and DB14703 depicted lowest structure similarity result 0.134. The top 100 screened drugs from each standard  were selected for molecular docking analysis to predict the most suitable candidate having good binding affinity values against MADD protein.In drug-repositioning approach, shape-based screening, pharmacogenomics and molecular docking simulation are significant parameters to predict the possible drugs from FDA approved medications by considering protein targetsPyRx is a virtual screening software for computational drug discovery that can be used to screen libraries of compounds against potential drug targets. In PyRx, donepezil-MADD docking results, among 100 screened drugs Table , 72 drug12. All the screened drug from both standard  exhibited good interaction scoring values with predicted genes which showed good correlation with AD. From donepezil screening, our results showed that 10 from 35 drugs exhibited good interaction scoring values and association with AD. The darifenacin showed good interaction scoring values with CHRM3 (0.85), CHRM2 (0.38), CHRM1 (0.29) and CYP2D6 (0.04) which have correlation with AD. The other drug astemizole showed interaction with multiple genes (14) which have association with multiple diseases. The 9 genes such as CYP2J2, HPSE, ABCB1, PPARD, CYP2D6, IDH1, CYP3A4, AR, and TP53 were directly associated with AD. The predicted results showed that astemizole showed highest interaction score 0.90 as compared to other genes. In tubocurarine gene network, 5 genes were interacted and among these four 2 (CHRNA2 and KCNN2) genes were showed interaction with AD. Both CHRNA2 and KCNN2 genes exhibited good interaction scoring values 6.37 and 4.24, respectively. Another important screened drug which exhibited good interaction prediction with AD is aripiprazole through interaction with different genes. The drug-gene association analysis showed that out of 14 genes, eleven  possessed good interaction scoring values and are directly involves in AD.Based on swiss similarity and virtual screening result, the selected drugs further undergo for pharmacogenomics analysis to check their possible interaction with genes encoded proteins and their association with AD. Multiple reports also justify this approach as worthwhile to predict the possible drug having repositioned functionalityFluspirilene-gene interactions analysis also showed their intimation with AD by targeting multiple genes. The predicted results showed that six gene such as DRD2, HTR1E, XBP1, HTR2A, HTR1A, and PPARD were showed their association with AD with different interaction scoring values. In elacridar-genetic complex, two genes ABCG2 1.54) and ABCB1 (0.27) were observed having showed their interaction with AD. Another important screened drug was sertindole which showed interactions multiple genes involves in AD and other diseases. A total of 17 genes were observed showed interaction with sertindole and from 17, eight genes (DRD2 (0.15), HTR2A (0.14), HTR2C (0.17), HTR1E (0.1), HTR1A (0.03), HTR1B (0.05), CYP2D6 (0.01), CYP3A4 (0.01)) showed association with AD with different interaction scoring values. Tariquidar interacted with ABCB1 and ABCG2 with scoring values 0.66 and 1.03, respectively. Sarizotan is another screened drug shows interactions with HTR1A (0.91) and DRD2 (0.36) having good interaction score values with AD. Similarly, fipexide interacted with CYP2D6 (0.03) and CYP3A4 (0.02) also depicted good correlation with AD , OPRK1 (0.23), PER1 (2.05), HMOX2 (2.05), ABCB1 (0.09), CYP2D6 (0.01), DRD2 (0.02) may predict the good therapeutic behavior of morphine as an anti-AD drug. Codeine is another screened drug extracted through galantamine that could be use as repositioned drug against AD. Our drug-gene predicted results showed that eight genes OPRD1 (1.22), OPRM1 (0.58), OPRK1 (0.63), UGT2B7 (0.76), ABCB1 (0.13), CYP3A4 (0.02), CYP2D6 (0.01), and AR (0.01) were interacted having different interaction values. The literature mining showed that four genes among eight were involved in AD. Quinine-gene interaction analysis showed that CYP3A7 (1.27), SLC29A4 (0.64), COP1 (1.27), IL2 (0.6), G6PD (0.18), ABCB1 (0.03), CYP3A4 (0.01), and CYP2D6 (0.01). Moreover, from gene literature mining it has been observed that 5 genes were involved in AD. Similarly, atropine, nalbuphine, quinidine, volinanserin and vernakalant are also screened drugs interacting with different genes which are linked with AD , astemizole (− 7.19), tubocurarine (− 8.26), elacridar (− 7.72), sertindole (− 7.58) and tariquidar (− 8.42) showed higher than − 7  docking energy values. The ligand efficiency is a useful metric for lead selection using computational resources207. Ligand efficiency is a way of normalizing the potency and MW of a compound to provide a useful comparison between compounds with a range of MWs and activities. In our docking results, the predicted ligand efficiency (LE) results, showed that all drugs exhibited good LE values, whereas lowest value is considered as most significant compared to other. The comparative results showed that Tubocurarine, Elacridar and Tariquidar exhibited − 0.18 value which was lowest compared to rest of all drugs.The selected drugs-death domain docked complexes were analyzed based on lowest binding energy values  and hydrogen/hydrophobic interaction analyses. Results showed that all drugs showed good binding energy values  and binds within the active site with appropriate conformational poses Table . Docking208. The intermolecular energy is the attractive intermolecular forces between particles that tend to draw the particles together and evaluates based on minimum energy values. According to AutoDock, the binding energy is the sum of the intermolecular forces acting upon the receptor-ligand complex209.Another significant parameter in drug analysis is inhibition constant (Ki). Autodock uses the binding energy  to calculate the inhibition constant. The binding energy is the free energy change for the protein-inhibitor interaction (ΔG). This is used to determine the inhibition constant (ki) which is, in turn, the dissociation constant (Kd) of the protein-inhibitor complex. In our predicted results, morphine, codeine, quinine, darifenacin, astemizole, nalbuphine, quinidine, aripiprazole, fluspirilene, elacridar, sertindole, vernakalant, sarizotan, fipexide and volinanserin exhibited good inhibition constant value on comparison with standard value 80.08 µM40. 18 drugs were docked against death domain of MADD and superimposed all docking complexes to check the binding conformations inside the binding pocket. The predicted results showed that all 18 drugs were firmly bind within the active region of MADD however, the binding behavior was deviant with each other’s , astemizole (− 7.19), tubocurarine (− 8.26), elacridar (− 7.72), sertindole (− 7.58) and tariquidar (− 8.42) exhibited good docking energy values  and binding interaction profiles. In darifenacin docking, single hydrogen bond was observed with appropriate binding distance. The amino group (NH2) of darifenacin formed a hydrogen bond with Glu1020 having bond length 2.60 Å within the active region of death domain of MADD structure. Similarly, elacridar and tariquidar also formed hydrogen bonds with Glu1023 having appropriate bond lengths. The nitrogen atom of NH2 elacridar formed hydrogen bond with Glu1023 having bond length 3.00 Å, whereas tariquidar formed two hydrogen bonds with Glu1023 having bond distances 2.50 and 2.80 Å, respectively. Glutamic acid is an α-amino acid that is used by almost all living beings in the biosynthesis of proteins. Our docking results showed that screened drugs bind with Glu1020 and Glu1023 which may depict their significance in death domain formation and their involvement in the associated signaling pathways. The reported data showed that hydrogen bond between 2.5 and 3.0 Å may considered as standard bond length in ligand docking which strengthen the docked complexes212.Drug-protein binding interaction is significant parameter to better understand the molecular docking results and to deeply understand the conformational behaviorTherefore, intermolecular hydrogen bonds have good impact in the formation of drug-protein docking complex and enhance their accuracy. In all our docking analysis, darifenacin, elacridar and tariquidar exhibited comparative hydrogen bond length against the MADD protein. The comparative analysis showed that all drugs, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar were bind at same position inside the active region of death domain in MADD protein. The common interactions pattern among unique residues also showed the reliability of our docking results. Furthermore, in detail results, darifenacin is surrounded by Glu1020, Tyr1008, Ile1005, Met1004, Gln999, Met997 and Glu1023 residues, whereas elacridar and tariquidar also encompassed similar amino acids such as Leu1027, Leu1030, Met1004, Leu1009, Tyr1008, Glu1020, His1060, and Glu1023, respectively. The other three drugs also showed their binding potential at the same binding regions with different conformational symmetry. The occurrence of common amino acids in all docking complexes also ensure the significance of these amino acid particularly Glu1023 is essential for target binding , radius of gyration (Rg) and solvent accessible surface area (SASA) graphs.The protein backbone behavior in the simulation running time were evaluated through RMSD/F graphs. The RMSD graph results of six docking complexes  showed the steady and little fluctuated behavior through-out the simulation time. The RMSD graph lines displayed an increasing trend with RMSD values ranging from 0 to 0.1 nm from 0 to 100 ns.Initially, all the graph lines  of docked complexes showed an increasing trend with RMSD value 0–0.1 nm from 0 to 10 ns. However, in the same simulation time tariquidar graph line showed much stable behavior as compared to other complexes. From 10 to 20 ns, again fluctuated graphs lines were seen whereas, tariquidar (yellow) remained steady and stable at RMSD value around 0.6 nm. Tubocurarine (brown) displayed highest RMSD value (> 1 nm) at 20 ns as compared to all other graphs lines. Sertindole (blue) line showed upward movement from 0 to 10 ns whereas, from 10 to 20 ns the graph line showed downward movement. However, darifenacin and tubocurarine has been exposed in continuously increasing trend with increased RMSD values (nm). Elacridar (green) and astemizole also showed fluctuated behavior at this simulation time frame. From 20 to 40 ns much stable behavior has been observed in the tariquidar (yellow) and elacridar (green) graphs lines, respectively compared to other graph lines. Darifenacin (red), astemizole (indigo), tubocurarine (brown), elacridar (green), and sertindole (blue) showed high fluctuations in graph lines along with deviated RMSD values.213 and multiple research data exposed different RMSD values range from 0 to 0.3 nm in their MD simulation analysis203. The overall RMSD graphs lines showed that all docked complexes showed fluctuated behavior in the simulation time frame. The generated graphs results showed the stable behavior in the backbone of all protein complexes  remained displayed stable behavior and no fluctuations has been observed in the backbone of MADD protein in the docking complex. Similarly, elacridar (green) also represented similar results with tariquidar and still remined stable behavior in the docking complex. The rest of all other docking complexes showed little fluctuations with steady stable behavior in the simulation time. The RMSD is used to measure the difference between the backbones of a protein from its initial structural conformation to its final positionxes Fig. .Figure 9The RMSF results of all docked complexes dynamically fluctuated from residues N to C terminals. The protein structures are composed of with different structural architecture. There are different small fluctuations through-out the simulation time frame. However, most of protein complexes remain little stable in the RMSF graph. The tariquidar graph line displayed less fluctuations as compared to all other protein complexes which ensure his stable behavior in the docking complex. The comparative results showed that tubocurarine and sertindole were exhibited higher fluctuations peaks, however, the values remain at 0.1 nm in the simulation study. All the results have been displayed in Fig. 2 in the simulation time 0–100 ns  interaction energy and Lennard–Jones interaction energy, with their sum representing the total interaction energy. According to interaction energy analysis tariqudar drug showed the lowest interaction energy − 285.728 followed by elacridar and tobucorarine (− 248.6337 and − 160.0039 respectively). Moreover, the astemizole, sertindole, and darifenacin exhibit high interaction energy as compared to the top Table .Table 8MTo analyze the interaction of darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar during 100 ns MD simulation; MD binding Pose validation has been carried out against best selected drugs. Snapshots of all simulated drugs results demonstrated that all drugs remained in the active region of MADD during the 100 ns MD simulation and maintained strong interactions with binding pocket residues. The elacridar manifest the strongest interaction while tubocurarine and sertindole also exhibit single hydrogen bonds and in addition to hydrophobic interactions. Furthermore, astemizole, darifenacin, and tariquidar also exhibits strong hydrophobic interactions. The results demonstrate that the drugs remain bounded to the active pocket of the target protein till 100 ns and block the active site residues (Supplementary Fig. Novel drug development is time consuming process with relatively high debilitating cost. In present time, drug repositioning is an in-silico approach being employing for drug discovery. In the present research repositioning profiles of known drugs against AD has been explored using shape-based screening, molecular docking pharmacogenomics and MD simulation approaches. Swiss-Similarity results showed 282 drugs were retrieved with donepezil and 351 against galantamine and further evaluated based on similarity scoring values, docking energy values and pharmacogenomics analysis. The detailed pharmacogenomics and extensive data mining showed that three drugs have direct association with AD by targeting different genes. The detailed screening results, showed that darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar drugs exhibited good lead like behavior against AD. Moreover, in MD simulation result tariquidar displayed better stability behavior as compared to rest of other docking complexes drugs with respect to their RMSD, RMSF, SASA and Rg evaluations graphs. Taken together, it has been concluded that tariquidar predicted exhibited better repositioning profiles as compared to other screened FDA approved drugs and may be use in the treatment of AD after in-vitro and clinical assessment in future.Supplementary Information.


There are continuous efforts to elucidate the structure and biological functions of short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms reside more than 0.3 Å closer than the sum of their van der Waals radii. In this work, we evaluate 1070 atomic-resolution protein structures and characterize the common chemical features of SHBs formed between the side chains of amino acids and small molecule ligands. We then develop a machine learning assisted prediction of protein-ligand SHBs (MAPSHB-Ligand) model and reveal that the types of amino acids and ligand functional groups as well as the sequence of neighboring residues are essential factors that determine the class of protein-ligand hydrogen bonds. The MAPSHB-Ligand model and its implementation on our web server enable the effective identification of protein-ligand SHBs in proteins, which will facilitate the design of biomolecules and ligands that exploit these close contacts for enhanced functions. In these cases, electronic and nuclear quantum effects combine to weaken the confinement of Donor-H bond and enable the proton to be shared between the donor and acceptor groups.Hydrogen bonding plays essential roles in mediating the structure, conformational transformation and biological functions of proteins. Canonical hydrogen bonds form from amino acid residues and ligands that contain O or N atoms and the distances between the heteroatoms, R, usually fall within the range of 2.8 – 3.2 Å.20 Low-barrier hydrogen bonds are often observed in the active site of proteins, and hence are associated with a variety of biological processes ranging from stabilizing the reaction intermediates in enzyme catalysis to regulating the binding of antibiotics in bacterial proteins and promoting biological signal transmission.29 Since their original proposal,17 low-barrier hydrogen bonds have been under extensive investigations although their geometry, strength and functional importance are still under debate.36 Conventionally, NMR spectroscopy is widely used for their exploration because the delocalized protons exhibit characteristically downfield chemical shifts and distinct isotope effects when replaced with deuterium.37 More recently, advancements in X-ray and neutron diffraction and optical spectroscopy have enabled the direct detection of the position and local environment of protons, providing crucial information on the structure and behavior of low-barrier hydrogen bonds in large proteins.36A notable type of SHBs is low-barrier hydrogen bonds where the proton transfer barrier is comparable to the zero-point energy of an O-H or N-H vibration, which is typically around 5 kcal/mol. It is proposed that the energy barrier becomes sufficiently low when R of a hydrogen bond lies between 2.45 and 2.65 Å and the proton affinities of the donor and acceptor groups are closely matched. In such a compact structure, nuclear quantum effects allow the proton to move freely between the heteroatoms and the hydrogen bond becomes exceptionally strong.39 However, unambiguous identification of these compact structures can only be achieved when proteins are resolved at atomic resolution (≤1.2 Å), which remain a demanding task for structure determination techniques such as X-ray and neutron scattering, NMR spectroscopy and cryo-electron microscopy single particle analysis. From a computational perspective, molecular mechanics based refinement and prediction methods typically rely on classical force fields that impose strong repulsion between close-by atoms and prevent the formation of SHBs.42 As a result of the coordinate errors in the crystal structures and the inaccuracies of conventional force fields, SHBs are often overlooked in the construction and prediction of protein structures. To tackle these challenges, we have recently designed the machine learning assisted prediction of SHBs (MAPSHB) model, which uses 21 structural, chemical and sequence features to detect SHBs that connect amino acid residues in proteins.38 In this work, we focus on hydrogen bonds between amino acids and small molecule ligands, such as enzyme cofactors and drugs, and create the machine learning assisted prediction of protein-ligand SHBs (MAPSHB-Ligand) model based on their common chemical and sequence characteristics.SHBs constitute approximately 24% of the hydrogen bonds that form between the side chains of amino acids and are frequently observed linking active-site residues and ligands.43 we collect 1070 crystal structures of protein-ligand complexes that are refined from X-ray or neutron diffraction experiments and have resolution greater than or equal to 1.1 Å. These atomic-resolution structures allow us to locate the O and N atoms with a coordinate error of 0.1 Å and properly distinguish SHBs from NHBs. After assessing each protein-protein and protein-ligand hydrogen bond, we categorize it as a SHB if its R is within 2.3 to 2.7 Å, or as a NHB if its R falls between 2.8 and 3.2 Å. In these analyses, we only consider the side chains of the amino acid residues given their frequent occurrence in SHBs (Tables S1 and S2), and ignore the polyol and inorganic anion ligands since they are primarily used in the preparation of protein crystals and are less likely to participate in biological functions (Table S4). This search yields 7070 SHBs and 22353 NHBs that link two amino acids, and 1272 SHBs and 2733 NHBs that involve both amino acids and small molecule ligands. Therefore, SHBs are present in approximately 1 out of every 3 protein-ligand hydrogen bonds and every 4 protein-protein hydrogen bonds, highlighting their prevalence in protein-ligand complexes. From 6 81% of these SHBs have O as both the donor and acceptor atoms, while the rest form mostly between O and N atoms in the amino acid side chains and ligands (Table S3).From the Protein Data Bank (PDB),α-L-fucose, β-D-glucose, α-D-mannose and their derivatives are commonly observed to engage in SHB interactions with active-site residues of carbohydrate-binding proteins. As an example, β-phosphoglucomutase, forming a transition state analog along the isomerization pathway of converting β-D-glucose 1-phosphate to β-D-glucose 6-phosphate.44 The active-site residue Asp10 is positioned such that its carboxylate side chain hydrogen bonds with the 1-OH group of G7P, forming a close contact of 2.56 Å and possibly facilitating the general base catalysis of the enzyme.44 Nucleotides account for 21.4% of protein-ligand SHBs and 72% of them are pyridine nucleotides, i.e., nicotinamide adenine dinucleotide (NAD), nicotinamide adenine dinucleotide phosphate (NADP) and flavin nucleotides such as flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). These nucleotide cofactors interact with dehydrogenases and flavoproteins and are essential electron carriers in cellular energy transfer and redox processes. For example, 45 The hydroxyl side chain of Ser163 forms a SHB with the phosphate group of NADP+ with an R of 2.64 Å, anchoring the cofactor for catalysis.45 From 48To evaluate the potential roles of protein-ligand SHBs in modulating the structures and functions of proteins, we group the ligands by their molecular geometries and chemical properties and identify a few key categories. As shown in PSHBs, and can be categorized into three types: (A1) Tyr, Asp and Glu; (A2) Ser, Thr and His; (A3) Arg, Lys, Asn, Gln and Trp. Type A1 amino acids are highly probable to engage in SHB interactions. In particular, Tyr has a phenol side chain that forms 142 SHBs and 50 NHBs with ligands, showing the highest PSHBs of 74% among all amino acids. This is followed by Asp and Glu with PSHBs values of 71% and 64%, respectively. Their carboxylate side chains are strongly favored as acceptors in the SHBs, especially when the interacting heteroatoms are within a distance of 2.6 Å. In type A2, Ser and Thr possess hydroxyl groups while His has a neutral or cationic imidazole group in their side chains, and they collectively act as proton donors or acceptors in 390 protein-ligand SHBs. However, they are equally susceptible to participate in NHB interactions and their PSHBs values are reduced to 45%, 50% and 41%, respectively. Unlike the other two cases, type A3 amino acids are more likely to form NHBs and their PSHB are below 16%.The protein alphabet contains 20 canonical amino acids, among which 11 have polar side chains and are capable of forming hydrogen bonds. As shown in PSHBs values: (L1) phenol; (L2) alkyl hydroxyl; (L3) sulfate, phosphate, carboxyl and carboxylate; (L4) ester, amide, alkyl amine and N-containing aromatic heterocycle. Despite only participating in 52 hydrogen bonds, ligands containing phenol groups are highly prone to forming SHB interactions with amino acids. Specifically, 38 of these hydrogen bonds are SHBs, leading to a notable PSHBs value of 73% for type L1 functional group. For type L2, alkyl hydroxyl groups form the largest number of protein-ligand hydrogen bonds and act primarily as the proton donors in 657 SHBs and 621 NHBs. Like the side chains of Ser and Thr, they are prone to form both classes of hydrogen bonds, resulting in a PSHBs value of 51%. In contrast, functional groups in the other two types show significantly lower tendencies to participate in SHBs, with PSHBs values ranging from 25% to 40% for type L3 and less than 14% for type L4. Compared to amino acids, ligands often have more complex molecular structures and comprise a variety of functional groups that can participate in hydrogen bonding interactions. For example, monosaccharides are aldehydes or ketones with multiple hydroxyl groups, heme contains a porphyrin ring that is often linked to carboxylate groups, and the pyridine nucleotides FAD and NADP are composed of nucleobases, riboses and phosphates. From pKa and pKb) and octanol-water partition coefficient (logP) to describe the ionization and lipophilicity characters of a ligand. In addition, we include the sequence of the neighboring 3 residues on both sides of the amino acid because the MAPSHB model has demonstrated that the adjacent protein sequence has a considerable influence on the propensity of an amino acid to form a SHB versus a NHB.38 The location of the amino acid in a protein-ligand hydrogen bond is fixed at the side chain of the protein.Based on these analyses, we choose 14 input features for the development of the MAPSHB-Ligand model. These include the charge, residue type and heteroatom of an amino acid, and the charge and functional group of a ligand. We further use the acid and base dissociation constants  curve and calculate the area under the curve (AUC) as a comprehensive metric to evaluate the model predictions across all possible classification thresholds.Asp-Gly-Thr-Asp and Asp-Gly-Thr-Asp-Asn-Asp-Tyr are often involved in SHB interactions with carbohydrates.57 In fact, these sequences are located in the calcium and monosaccharide binding loop of the lectin PA-IIL, and are conserved in various PA-IIL-like proteins found in bacteria.55 Apart from these three factors, the other input features combine to give an importance score of 4.7%  and provide researchers with convenient access to analyze these specialized interactions. The combination of the MAPSHB-Ligand and MAPSHB models presents an efficient approach for investigating protein-protein and protein-ligand interactions involving SHBs, especially in cases where the protein structures are of moderate or low resolution. The predictions obtained from these models can serve as additional restraints in the experimental and computational refinement of protein structures, and aid in the elucidation of the structural basis of protein-protein and protein-ligand interactions. The machine learning models can be further refined and optimized with the continuous advancements in the filed of structural biology and the increasing availability of high-quality protein structures. These and other models will enable new engineering strategies to enhance the stability and functions of proteins and facilitate rational drug design efforts to achieve improved efficacy by leveraging SHB interactions as a key molecular mechanism.As a summary, we have examined the top 1% highest quality structures in the PDB and developed the MAPSHB-Ligand model that effectively detects the presence of SHBs formed between amino acid side chains and small molecule ligands. We further integrate this model into a web server . We then modeled the proteins and ligands using the Amber14SB force field60 and the general Amber force field (GAFF),61 respectively, and optimized the complexes while holding the non-H atoms at their positions in the crystal structures. We used three geometric criteria to identify a hydrogen bond: the heteroatoms are O or N atoms; 2.3 Å ≤ R ≤ 3.2 Å; the Donor-H-Acceptor angle ≥ 135°. For each hydrogen bond, we obtained the charge, residue and heteroatom of the amino acid, the charge of the ligand, and the relevant sequence information from the Amber 2016 program.58 The pKa, pKb and logP of ligands were estimated using the Molecular Operating Environment (MOE) software v2020.0901.62 The functional groups of ligands were determined from their atomic connections.After collecting 1070 high-resolution structures from the PDB, we added H atoms to the amino acid residues and analyzed the protein-ligand complexes using the Amber 2016 software package.63 with an exponential loss function, using 5000 decision trees and a shrinkage of 0.01. We treated the interaction depth as a hyperparameter and determined it through 10-fold cross-validation. Specifically, we randomly divided each balanced dataset into 10 equal-sized subsets, from which 9 subsets were allocated for training the gradient boosting model and the remaining one was used for validation. This process was repeated 10 times with each subset serving as the validation set once. Each training-validation combination was referred to as a fold. In each fold, we trained the model using a candidate interaction depth between 1 and 12, evaluated its performance by applying it to the validation set, and recorded the loss. We then computed the final loss of the candidate interaction depth as the average of the losses obtained from all folds. We determined the optimal interaction depth as the one with the lowest final loss, and proceeded to retrain the gradient boosting model using the entire balanced dataset. The varImp function in the caret package64 was used to calculate the importance scores for each gradient boosting model, and the overall importance scores for the MAPSHB-Ligand model were obtained by averaging over those from the 10 boosting models. The data of the ROC curve were generated using the plotROC package,65 and the AUC score was computed using the auc function from the pROC package.66 More details of the hydrogen bond analysis and the development and evaluations of the machine learning models are provided in the Supplementary Information.We used the R programming language to develop the MAPSHB-Ligand model. A gradient boosting model was created for each balanced dataset by invoking the gbm function


Tunyasuvunakool et al.,Nature596, 590–596 (2021)]. This extraordinary achievement by Demis Hassabis and John Jumper and their coworkers at Google’s DeepMind and other collaborators was built on decades of experimental protein structure determination  as well as the gradual development of multiple strategies incorporating biologically inspired statistical approaches. But when Jumper and Hassabis added a brew of innovative neural network-based machine learning approaches to the mix, the results were explosive. Realizing the half-century-old dream of predicting protein structure has already accelerated the pace and creativity of many areas of Chemistry, Biology, and Medicine.This year’s Albert Lasker Basic Medical Research Award recognizes the invention of AlphaFold, a revolutionary advance in the history of protein research which for the first time offers the practical ability to accurately predict the three-dimensional arrangement of amino acids in the vast majority of proteins on a genomic scale on the basis of sequence alone [J. Jumper  Never has a scientific term been better chosen (or been more poorly understood at the time) than protein coined in 1838 by the Dutch chemist G. J. Mulder ] echoingUnderstanding how proteins fold up into a unique conformation(s) thus became a central problem in biology by the 1960s with the prediction of protein structure expected to be a major benefit. It was recognized early on that each polypeptide chain can in theory assume an astronomical number of potential conformations, the vast majority of which would result in aggregation and precipitation . Then in 1961, using the small secreted enzyme ribonuclease, Anfinsen  proved that the simplest possibility holds—that the sequence of amino acids, in itself, can determine the way the chain folds itself and that no additional genetic information is required in this process .This pivotal experiment redefined protein folding as a problem in physics, the simple idea being that the folded state is the lowest accessible energy state of the polypeptide chain. But the extreme complexity of the corresponding energy landscapes, with many local energy wells to trap folding intermediates  5) made made5) mBy 1990, it became clear that protein folding in living cells is even more complex, requiring genes encoding molecular chaperones. In fact, the multidomain and multisubunit proteins, prevalent in highly evolved organisms, typically do not efficiently or rapidly refold in an Anfinsen-style experiment. This in no way contradicts Anfinsen’s core idea, that the final conformation is dictated solely by the physics of the polypeptide chain. But the discovery of molecular chaperones as catalysts of protein folding was a fundamental advance in cell biology (recognized by the Lasker Award in 2011 to F.-Ulrich Hartl and Arthur Horwich). Chaperone proteins  are in eAs it gradually became evident that direct, physics-only approaches would ultimately fall short of atomic accuracy in the absence of revolutionary advances such as quantum computing, by around 2010 researchers had begun to turn to biologically inspired statistical work-arounds. One foundational idea  was thatThe final and most important ingredient proved to be Machine Learning, enabling the information in MSAs and Templates, as well as other data, to be far more efficiently utilized . While iTo assess progress objectively, the folding community established a biannual competition, the Critical Assessment of protein Structure Prediction (CASP) which measured the comparative accuracy of competing approaches for then-undisclosed experimentally solved structures. The DeepMind team  made itsBut Hassabis knew this was not good enough. According to Hassabis  to lead AlphaFold2. Hassabis recognized the importance of Jumper’s interdisciplinary background in protein physics and machine learning. Critically, they shared the bold conviction that the prediction problem could be solved  in the face of the seemingly daunting odds suggested by AlphaFold2’s worsening performance. “Jumper was critical to the problem being solved,” he told me.In the end, Jumper and Hassabis led their team to completely re-engineer the system including numerous innovations. The Nature paper describing the methods  is some The outstanding performance of AlphaFold at CASP14, at that time not yet documented by publication, left the scientific community eager to learn details beyond the overall framework. Intrigued by the DeepMind still-unpublished results, David Baker (University of Washington), an internationally recognized leader in the related field of protein design, then explored network architectures that incorporated different combinations of the five main methodological advances described by Jumper and Hassabis at CASP14 . They inEven though the AlphaFold neural network was designed by humans, like the human brain, we may never fully understand it. AlphaFold is a product of engineering, and as such , its internal anatomy is fully and explicitly known . UnderstA basic mechanism in the design is a machine-learning artificial neural network that captures both long-range and short-range interactions, differing from previous algorithms that commonly considered only local or pairwise interactions, achieving this in part by taking into account fundamental aspects of polypeptide chain geometry. The use of iterative refinement similar to how the brain is thought to work is another central concept. Other key features are a new architecture to jointly embed MSAs and pairwise features; a new output representation and associated loss that enable accurate end-to-end prediction; use of intermediate losses to achieve iterative refinement; and novel training procedures involving self-distillation.The network has two main stages . First, Second, the trunk is followed by the structure module that produces 3D structures in the form of coordinates for each residue. With each iteration, these rapidly develop and refine a highly accurate protein structure with atomic details. Key innovations include breaking up the chain structure to allow simultaneous refinement of all parts of the structure, perhaps providing a mechanism to avoid the multiple energy traps that carpet the landscape  5). Num. Num5). 16The network was first trained from the open-access Protein Data Bank  (PDB), eAs pointed out earlier, real proteins generally require molecular chaperones to access their lowest energy/folded states efficiently and without aggregation. As catalysts, chaperones accelerate the physical moves and thus the actual folding trajectory across the energy landscape. This juxtaposition strikes me as interesting because it suggests that AlphaFold somehow achieves the same end computationally. Perhaps there is an algorithmic equivalent of chaperones not explicitly identified as such that helps accelerate the computational excursion. As mentioned above, chaperones inject free energy  to produce local disorder that fluidizes movement across the landscape and do so in a very general way—a mere handful of chaperone species functions for a genome-worth of proteins.Hassabis and Jumper’s success illustrates the creative energy that is unleashed when two fields fuse, in this case, machine learning with protein structure prediction. Demis Hassabis was born in London, England, of Greek Cypriot and Singaporean Chinese ancestry and was an official child prodigy, soon playing adults as a recognized chess master at 13. At 17, he joined Bullfrog productions where he designed games such as the bestseller, Theme Park. He then attended the University of Cambridge where he studied computer science and mathematics, and where he first heard about the protein folding problem from biology student friends. After graduating (1997), he joined Lionhead Studios and then founded his own successful company, Elixir Studios. Seemingly a nonlinear trajectory, it was anything but in Hassabis’ mind  followed by a year Marshall Scholarship to the University of Cambridge. Unsatisfied, he decided to take a break as a “quant” in finance and in 2008 applied to the storied firm DE Shaw in New York, founded by David Shaw, a computer scientist. As it happened, but unbeknownst to Jumper, until he applied, Shaw had returned from investing to basic research and was specially designing computers to tackle protein folding and drug discovery at D.E. Shaw Research, and he asked Jumper to join his research team. After 3 y of working on the forefront of computation, Jumper was both committed to the problem but also well aware that he would likely never again have available such awesome computational capacity. When he returned to graduate school, now at the University of Chicago (PhD in Chemistry 2017), he continued his research on protein structure prediction, but now focused on developing computation-sparing machine learning methods. Hearing rumors that DeepMind was getting into folding after Go, he managed to get his CV to Hassabis, and the duo recognized by this year’s Albert Lasker Basic Medical Research Award started working together in 2017.Even as we laud the invention of the first technology that can accurately predict protein structures from gene sequences on a genomic scale, it is somehow also a bit disquieting to many scientists that we haven’t extracted generalizable principles as expected for traditional scientific explanations . With KeNo doubt the current version of AlphaFold is just the beginning , 26. Alr


Kaposi’s sarcoma-associated herpesvirus (KSHV) encodes six highly conserved core replication proteins essential for the viral lytic DNA synthesis, including ORF6 (single-stranded DNA-binding protein), ORF9 (DNA polymerase), ORF40/41 (primase-associated factor), ORF44 (helicase), ORF56 (primase), and ORF59 (polymerase processivity factor). Since the protein-protein interactions among KSHV core replication proteins are largely unknown, this study aimed to decipher their interrelationships. Herein, we propose a protein-protein interaction network of these six core replication proteins according to the results obtained from confocal fluorescence microscopy, coimmunoprecipitation, and mammalian two-hybrid (GAL4/VP16) reporter assays. In this interaction network, ORF40/41 plays a central role in the connection of different replication subcomplexes. In addition to the well-conserved helicase-primase subcomplex  and the replisome subcomplex (consisting of ORF9 and ORF59), our data also suggest that several discrete, stable subcomplexes exist in the cell nucleus. Among these replication subcomplexes in the nucleus, the tetrameric subcomplex composed of ORF44, ORF56, ORF40/41, and ORF6 exhibited the ability to trigger a DNA damage response. By using an established GAL4/VP16-based reporter system and confocal fluorescence microscopy, we additionally found that the heat shock protein 90 (Hsp90) inhibitor, radicicol, significantly inhibited the formation of both the helicase-primase subcomplex and the replisome subcomplex in a dose-dependent manner. Collectively, these data not only provide further insights into the interaction and assembly of KSHV-encoded core replication proteins but also suggest a critical role of Hsp90 in assisting the construction of the viral core replication machinery.Eukaryotic DNA replication is a highly regulated process that requires multiple replication enzymes assembled onto DNA replication origins. Due to the complexity of the cell’s DNA replication machinery, most of what we know about cellular DNA replication has come from the study of viral systems. Herein, we focus our study on the assembly of the Kaposi’s sarcoma-associated herpesvirus core replication complex and propose a pairwise protein-protein interaction network of six highly conserved viral core replication proteins. A detailed understanding of the interaction and assembly of the viral core replication proteins may provide opportunities to develop new strategies against viral propagation. The lytic DNA replication of herpesviruses is an important model system to study eukaryotic DNA replication , 2. To eBased on current knowledge, all herpesviruses may use very similar DNA replication machineries to amplify their genomes , 7, 8. HKaposi’s sarcoma-associated herpesvirus (KSHV), also known as human herpesvirus 8), is the most recently identified human herpesvirus belonging to the gamma-2 herpesvirus subfamily , 20. ThiIn this study, we aimed to investigate the protein-protein interactions of KSHV-encoded core replication proteins and the assembly of their associated replication intermediates. Different methods including confocal fluorescence microscopy, coimmunoprecipitation, and mammalian two-hybrid (GAL4/VP16) reporter assays were utilized in the study. Based on the results obtained, a protein-protein interaction network of the six viral core replication proteins was proposed. Importantly, we found that radicicol, a heat shock protein 90 (Hsp90) inhibitor, could disrupt the formation of both the conserved helicase-primase subcomplex  and the replisome subcomplex (consisting of ORF9 and ORF59). Our results, therefore, suggest that Hsp90 may play a critical role in the assembly of KSHV core replication complex.To characterize the expression and subcellular localization of KSHV core replication proteins, the viral DNA fragments corresponding to the coding regions of ORF6 (SSB), ORF9 (POL), ORF40/41 (PAF), ORF44 (HEL), ORF56 (PRI), and ORF59 (PPF)  were indORF56 is a primase protein essential for initiating the viral DNA synthesis. As shown in Although coexpression of both F-ORF40/41 and F-ORF44 was essential for the nuclear transport of GFP-ORF56, there were no punctate structures detected under the condition . To furtSince GFP-ORF56 could form nuclear puncta in the presence of F-ORF44, F-ORF40/41, and F-ORF6, we examined whether GFP-ORF56 puncta were associated with cellular DNA damage. Accordingly, several sensitive DNA damage markers such as phosphorylated histone H2AX (γH2AX) , 29, p53r = 0.80 ± 0.15, r = 0.78 ± 0.11, and r = 0.68 ± 0.11, respectively). However, no significant colocalization was detected between GFP-ORF56 and F-ORF6 or F-ORF59 . In these experiments, we additionally noticed that coexpression of GFP-ORF56 and F-ORF40/41 in cells caused retention of F-ORF40/41 in the cytoplasm.Although the viral proteins ORF56, ORF40/41, and ORF44 were believed to form a conserved helicase-primase subcomplex in the cell, the protein-protein interactions among these three proteins actually remain unclear. Moreover, little is known about the interaction of the helicase-primase subcomplex components with the other core replication proteins. To determine the pairwise protein-protein interactions among these viral replication proteins, we first chose GFP-ORF56 as a target protein to analyze its association with the other FLAG-tagged core replication proteins in 293T cells by confocal fluorescence microscopy . Confocar = 0.58 ± 0.14, r = 0.82 ± 0.13, and r = 0.81 ± 0.15, respectively) but not with F-ORF6 and F-ORF59   . Our resctively) .r = 0.74 ± 0.11 for F-ORF6; r = 0.64 ± 0.15 for F-ORF9; r = 0.68 ± 0.10 for F-ORF56; r = 0.62 ± 0.11 for F-ORF44; r = 0.26 ± 0.12 for F-ORF59) (r < 0.5). Since the fluorescence signals of GFP-ORF40/41 were diffusely distributed in both the nucleus and the cytoplasm  . Howeverytoplasm , the assr = 0.66 ± 0.14, r = 0.68 ± 0.17, r = 0.78 ± 0.16, and r = 0.73 ± 0.13, respectively) but not with F-ORF6 (r = –0.18 ± 0.08)  . Based o ± 0.08) .r = 0.16 ± 0.11, r = –0.12 ± 0.08, and r = –0.25 ± 0.16, respectively). This was consistent with the observational results shown in r = 0.46 ± 0.17 and r = 0.62 ± 0.11, respectively). Since GFP-ORF59 enabled the nuclear transport of F-ORF9 from the cytoplasm (r < 0.5), the interaction between these two proteins needed to be further determined. To clarify this ambiguous protein-protein interactions, a GFP-ORF59 mutant defective in the nuclear transport was generated (r = 0.54 ± 0.17) although both proteins were retained in the cytoplasm. However, GFP-ORF59(NLS-mt) displayed little colocalization with F-ORF6 and F-ORF40/41 . These results suggested that ORF9 might be the only core replication protein that interacts with ORF59 . However, the core replication proteins including F-ORF56, F-ORF9, and F-ORF44 did not show any significant colocalization with GFP-ORF6 . Additionally, a moderate degree of colocalization between GFP-ORF6 and F-ORF59 was detected in the experiment (r = 0.32 ± 0.13). To further clarify the association between GFP-ORF6 and F-ORF40/41 or F-ORF59, a NLS-mutated GFP-ORF6 was generated . These results suggested that ORF40/41 could be the only core replication protein that interacts with ORF6  and in confocal microscopy , we attempted to use mammalian two-hybrid (GAL4/VP16)-based reporter system to characterize the assembly of the multi-subunit protein subcomplex. In this assay system, the DNA-binding domain of a yeast transcription factor GAL4 and the activation domain of HSV-1 VP16 were separately fused to different core replication proteins. A physical contact between the resultant GAL4- and VP16-fused proteins in the nucleus might lead to the reconstitution of a functional transactivator able to activate the reporter construct containing five GAL4-binding sites [pGal4(5×)-Luc]. For studying the assembly of the heterotrimeric helicase-primase subcomplex, the GAL4 DNA-binding domain was first fused to the N-terminus of ORF56 (designated as GAL4-ORF56), and the VP16 activation domain was fused to the N-terminus of ORF44 (designated as VP16-ORF44) ; Fig. S3To verify the consistency of this assay system, another fusion construct VP16-ORF40/41 was combined with GAL4-ORF56 in the experiments -ii. As sTo apply the GAL4/VP16-based reporter system to analyze the interaction between ORF59 and ORF9, the fusion constructs GAL4-ORF59 and VP16-ORF9 were generated . Confocal microscopic analysis showed that VP16-ORF9 alone was localized in the cytoplasm, whereas coexpression of GAL4-ORF59 allowed the nuclear transport of VP16-ORF9 . For theAccording to the results from confocal microscopic experiments and Co-IP experiments, ORF9 or ORF59 alone could not interact with ORF6 . To inveAs proposed in As described above, all the subunits of the helicase-primase subcomplex were able to individually interact with ORF9 . Due to To further investigate the association between the ORF56–ORF44–ORF40/41 subcomplex and the ORF9–ORF59 subcomplex, we used “GAL4-ORF56” as a representative of the ORF56–ORF44–ORF40/41 subcomplex and “VP16-ORF9” as a representative of the ORF9–ORF59 subcomplex in the GAL4/VP16-mediated reporter assay . UnfortuTo study whether small molecules could specifically disrupt the assembly or nuclear transport of the viral helicase-primase subcomplex and the replisome subcomplex, a compound library was screened by using the GAL4/VP16-based reporter system. We here found that radicicol, an inhibitor of heat shock protein 90 (Hsp90), could significantly inhibit the reporter activation induced by coexpression of GAL4-ORF56, VP16-ORF44, and F-ORF40/41  and by cAlthough the lytic DNA replication of herpesviruses has been studied for several decades, the protein-protein interactions of herpesviral DNA replication proteins are not yet fully understood. In this report, we characterize the protein-protein interactions of six KSHV-encoded core replication proteins by using different methods, and a complete protein-protein interaction network of these conserved core replication proteins is proposed. This study may be important not only to deepen our knowledge about the assembly of the viral DNA replication complex but also to provide opportunities to develop new strategies against viral propagation.During the KSHV lytic cycle, the expression and the interaction of the viral core replication components must be tightly controlled to allow the correct assembly of a functional replication complex in the cell nucleus. Although the lytic DNA replication of KSHV takes place within the cell nucleus, we here showed that three viral replication components with enzymatic activity including ORF56, ORF44, and ORF9 when expressed individually, were restricted to the cytoplasm . It is pLike HSV-1 and EBV, the KSHV core replication complex could be generally divided into three modules including the ORF56–ORF44–ORF40/41 trimeric subcomplex, the ORF9–ORF59 dimeric subcomplex, and the ORF6 subunit . To moniDetailed characterization of the pairwise protein-protein interactions revealed that all three components within the helicase-primase subcomplex could interact with each other . Furthercis-acting lytic origin (oriLyt). The interrelationship of these replication components remains to be further determined. Moreover, since dimerization or multimerization of specific core replication proteins may be necessary for the assembly of the replication complex, future experiments may need to determine whether certain replication proteins are able to form dimers or multimers.Although a protein-protein interaction network of six KSHV-encoded core replication proteins is proposed here, the interaction network still needs to be further confirmed in the context of KSHV infection. To achieve the goal, both the preparation of reliable antibodies specific to endogenous core replication proteins and the creation of a set of PEL cell lines with specific gene knockout in KSHV genome may be helpful for experimental investigations. In addition to the six KSHV replication core proteins, the assembly of a complete functional replication complex is believed to require other viral proteins (such as ORF50 and K8) or cellular proteins as well as the In the study, different methods including confocal fluorescence microscopy, Co-IP, and the GAL4/VP16-mediated reporter system were used to determine the protein-protein interactions of KSHV core replication proteins. However, these methods individually have certain limitations. For instance, low-resolution images taken by confocal fluorescence microscopy might lead to false data interpretation. Although Co-IP assay is considered a gold standard method for studying the protein-protein interaction, the low-affinity protein-protein interactions might not be easily detected. Herein, we attempted to establish the GAL4/VP16-based reporter system for probing interactions among subunits of different subcomplexes. This approach is a fast and highly sensitive assay, which may serve as a screening tool for identifying small compounds that inhibit the protein-protein interactions of the viral core replication proteins. Although the GAL4/VP16-mediated reporter system is useful to determine physiologically relevant protein-protein interactions, there are two major limitations for this approach. First, this established method is not suitable for detecting the assembly intermediates that are localized in the cytoplasm . Second, due to the fact that the formation of multi-subunit subcomplexes might cause steric hindrance of GAL4- and/or VP-16-containing proteins, this method might have a problem to detect the assembly of certain subcomplexes  and the full replication complex .Hsp90 is an evolutionally conserved molecular chaperone , 40. PreIn summary, we characterize the protein-protein interactions of six KSHV-encoded core replication proteins by using different methods and a complete protein-protein interaction network of these viral core replication proteins is proposed. In addition to the well-conserved helicase-primase and replicase subcomplexes, several stable assembly intermediates are also identified in the cell nucleus. Furthermore, we show that Hsp90 plays significant roles in the assembly of the KSHV core replication complex.The human embryonic kidney cell line 293T  was cultTo generate the expression plasmids encoding ORF6, ORF9, ORF40/41, ORF44, ORF56, and ORF59, the corresponding DNA fragments amplified by PCR were cloned into pFLAG-CMV-2  or into pEGFP-C2 . The resultant plasmids were designated pCMV-F-ORF6, pCMV-F-ORF9, pCMV-F-ORF40/41, pCMV-F-ORF44, pCMV-F-ORF56, and pCMV-F-ORF59, as well as pCMV-GFP-ORF6, pCMV-GFP-ORF9, pCMV-GFP-ORF40/41, pCMV-GFP-ORF44, pCMV-GFP-ORF56, and pCMV-GFP-ORF59. The NLS-mutated constructs of GFP-ORF6 and GFP-ORF59 were created in pCMV-GFP-ORF6 or pCMV-GFP-ORF59 using QuickChang site-directed mutagenesis kit . The expression plasmids encoding GAL4-ORF56, GAL4-ORF40/41, GAL4-ORF6, GAL4-ORF9, and GAL4-ORF59 were generated by inserting the the coding region of the GAL4 DNA-binding domain into pCMV-F-ORF56, pCMV-F-ORF40/41, pCMV-F-ORF6, pCMV-F-ORF9, and pCMV-ORF59, respectively. The expression plasmids encoding VP16-ORF44, VP16-ORF40/41, VP16-ORF6, VP16-ORF9, VP16-ORF59 were generated by inserting the coding region of the VP16 activation domain into pCMV-F-ORF44, pCMV-F-ORF40/41, pCMV-F-ORF6, pCMV-F-ORF9, and pCMV-F-ORF59, respectively. To construct the reporter plasmid pGal4(5×)-Luc, the DNA fragment containing five GAL4 binding sites and an adenovirus E1b minimal promoter from pG5CAT  were cloned into pGL3-Basic .Detailed procedures for Western blotting analysis were mentioned previously . BrieflyThe procedures for immunofluorescence staining of cell cultures have been described previously , 47. BriCoimmunoprecipitation assays were carried out as mentioned previously . Briefly5 cells/well) one day before transfection. Each transfection used a ﬁxed amount (0.8 µg) of plasmid DNA including the reporter plasmid pGal4(5×)-Luc and effector plasmids. At 24 h post-transfection, luciferase reporter activities of transfected cell samples were measured using the luciferase reporter assay system . Fold activation was calculated as the luciferase activity of the reporter construct in the presence of effectors divided by that in the presence of the empty control vector. All experiments were performed at least three times with duplicate samples.293T cells were seeded on 24-well plates  software , and the P-values less than 0.05 were considered statistically significant.All data were presented as means ± standard deviation (SD). The Mann-Whitney


Accurate modeling of protein–protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations.Here, we present PIQLE, a deep graph learning method for protein–protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE.https://github.com/Bhattacharya-Lab/PIQLE.An open-source software implementation of PIQLE is freely available at Bioinformatics Advances online. Protein–protein interactions are the actuators of numerous biological processes , modelinEncouraging progress has been made in protein complex scoring and quality estimation. Physics-based approaches, such as ZRANK , demonstDespite the progress, these methods do not consider two key factors that can significantly improve protein–protein interface quality estimation performance. First, the geometry of the interaction interface often carries key information about the spatial organization of the interacting partners and therefore provides a rich representation of a complex structure  are within 10 Å  of the interacting interface residue pairs into 17 bins ranging from 2 to 10 Å having a bin width of 0.5 Å. The discretized interaction distance is represented by one-hot encoding, resulting in 17 edge features.Multimeric interaction orientation: In addition to Cβ–Cβ distances, we also include the orientations of the interacting interface residue pairs by extending the work of trRosetta (12 and 21) and two planar angles , as shown in β atoms of the interacting interface residue pairs, and 12, 12  angles specify the direction of the Cβ atom of interface residue of the first (second) interacting monomer in a reference frame centered on the interface residue of the second (first) interacting monomer. Unlike the symmetric torsion angle where problems . We thenβ–Cβ distance between the interacting interface residue pairs in the predicted complex structural model , comprising 61 dimeric protein complex targets having lengths ranging from 107 and 892 residues. We discard all targets from the Dockground v1 test dataset overlapping with our training set Dockground v2 using an average pairwise sequence identity cutoff of 20%, resulting in 23 dimer targets with an average of 109 decoys per target. Additionally, we use the Heterodimer-AF2   for the ligand (LRMS) and interface (iRMS) between the model and the target are calculated as follows:where F1 scores  between the estimated quality of the protein complexes and their corresponding DockQ scores. Consequently, a higher correlation indicates better reproducibility. For the second criterion, we use the top-N success rate  analysis using a DockQ score cutoff of 0.23 to separate acceptable and incorrect models. Meanwhile, the area under the ROC curve (AUC) quantifies the ability of a method to distinguish between acceptable and incorrect models with a higher AUC indicating better distinguishing ability.DockQ scores range between 0 and 1 with a higher score indicating better model quality. Using the DockQ scores as the ground truth, we employ three evaluation criteria to measure the protein–protein interface quality estimation performance: (i) ability to reproduce the ground truth DockQ scores, (ii) ability to rank complex structural models and (iii) ability to distinguish acceptable from incorrect models. For the first criterion, we use the Spearman correlation coefficient  between the estimated qualities of the protein–protein interfaces and their corresponding ground truth DockQ scores for PIQLE and the other competing methods. PIQLE consistently outperforms all other competing methods in both Dockground v1 and HAF2 datasets. On Dockground v1, PIQLE attains the highest Spearman correlation of 0.519, which is much better than the second-best 3DCNN-based method DOVE-Atom20 (0.305), the recent graph transformer network (GTN)-based method DProQA (0.111) and physics-based scoring function ZRANK2 (0.159). The same trend continues for the HAF2 dataset, in which PIQLE attains the highest Spearman correlation of 0.429, which is significantly better than the other competing methods. The 3DCNN-based method DOVE remains the second-best method with its variant DOVE-Atom+GOAP attaining a Spearman correlation of 0.382. The recent GNN-based methods DProQA and GNN-DOVE, however, fail to generalize on the HAF2 dataset attaining negative Spearman correlations of −0.015 and −0.331, respectively. In summary, our method PIQLE exhibits an improved ability to reproduce ground truth DockQ scores with high fidelity.N ranked models. As shown in N rankings in the Dockground v1 dataset. The noticeably higher N, such as top-1 (∼44%) and top-5 (∼74%), is particularly noteworthy. In the HAF2 dataset  between the estimated qualities of the protein–protein interfaces and their corresponding ground truth DockQ scores when various features are isolated from the full-fledged version of PIQLE. The results demonstrate that all features contribute to the overall performance achieved by PIQLE. For example, we notice an accuracy decline when we isolate the sequence-based features one by one including amino acid residue encoding (no residue encoding) and relative residue positioning (no relative residue positioning). Importantly, discarding evolutionarily information noticeably declines the overall performance (no evolutionarily information), indicating the effectiveness of MSA-derived evolutionarily information. Not surprisingly, we notice a dramatic performance drop when both the residue-based features and the evolutionary information are isolated (no residue+evolutionarily information). Similarly, we also notice a performance drop when the feature based on local backbone geometry is discarded . Additionally, we notice a consistent accuracy decline when we discard the newly introduced edge features based on multimeric interaction distance (no multimeric interaction distance), multimeric orientation (no multimeric interaction orientation) and their combination (no multimeric interaction geometry). That is, the improved performance of our method is connected to the effective integration of multimeric interaction geometries.To examine the relative importance of the features adopted in PIQLE, we conduct feature ablation experiments by gradually isolating the contribution of individual feature or groups of features during model training and evaluating the accuracy on the independent ZDOCK validation dataset. ρ) between the estimated qualities of the protein–protein interfaces and their corresponding ground truth DockQ scores. The multi-head GAT architecture of PIQLE significantly outperforms the other baseline networks, demonstrating its effectiveness for protein–protein interface quality estimation task.To further investigate the contribution of the multi-head GAT model used in PIQLE, we train two baseline GNN-based models for protein–protein interface quality estimation: graph convolutional network (GCN)  and GTN https://colab.research.google.com/github/sokrypton/ColabDesign/blob/main/af/examples/AF2Rank.ipynb as of February 26, 2023, for generating the ipTM scores. ρ) on the ZDOCK set for the estimated interface quality scores by PIQLE and the ipTM scores predicted by the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring. PIQLE convincingly outperforms (ρ = 0.367) the repurposed self-assessment complex scoring of AlphaFold-Multimer (ρ = 0.349), even though the feature ablated variants of PIQLE (We further compare the performance of PIQLE with the interface predicted TM scores (ipTM) predicted by the self-assessment module of AlphaFold-Multimer . It is iof PIQLE  as well of PIQLE  fall shoThis work introduces PIQLE, a new method for protein–protein interface quality estimation by deep graph learning of multimeric interaction geometries. PIQLE exploits multi-head GAT architecture leveraging multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of the individual interactions between the interfacial residues and then probabilistically combines the estimated quality of the interfacial residues for scoring the overall interface. We demonstrate that PIQLE attains state-of-the-art protein–protein interface quality estimation performance by conducting large-scale benchmarking on multiple widely used protein docking decoy sets. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring on an independent validation set confirm the contribution of various features adopted in PIQLE and the effectiveness of the multi-head GAT architecture.Our study leads to a number of future directions to consider: of particular interest is the possibility of broadening the applicability of our method for higher order oligomers and large protein assemblies. Further, a promising direction for future work is to consider the diversity of predictive modeling ensemble and conformational states of the interacting monomers for interface quality estimation for interacting proteins having multi-state conformational dynamics. Finally, integrating complementary features, such as residue-level self-assessment confidence estimates for the interacting protein chains and sequence-based disorder prediction coupled with a richer deep graph representation learning framework may further boost protein–protein interface quality estimation performance. We expect our method to be extended to other biomolecular interface characterization, including estimating the quality of predicted protein interaction with other molecules, such as DNA, RNA and small ligands.vbad070_Supplementary_DataClick here for additional data file.