NMR is one of the major techniques for investigating the structure, dynamics and interactions between biomolecules. However, non‐experts often experience NMR experimentation and data analysis as intimidating. We discuss a simple yet powerful NMR technique, the so‐called chemical shift perturbation (CSP) analysis, as a tool to elucidate macromolecular interactions in small‐ and medium‐sized complexes, including protein‐protein, protein‐drug, and protein‐DNA/RNA interactions. We discuss current software packages for NMR data analysis and present a new interactive graphical tool implemented in CcpNmr AnalysisAssign version‐3, which can drastically reduce the time required for the CSP analysis. Lastly, we illustrate the usefulness of a protein three‐dimensional structure for interpretation of the CSP data. As such, it is imperative to know at which time and place specific biomolecules are active to exert their function. At the root of our understanding, however, is the realisation that the interactions between individual molecules that together form active complexes of sometimes intricate complexity, constitute the underpinning basis of all the biological processes. Structural biology is the field of science which aims to describe such interactions between biologically relevant molecules at an atomic level. It is based on the notion that the interactions are facilitated by the specific molecular shapes and, as has nowadays become evident, also their dynamical changes. Together these are crucial in determining the affinities that drive the assembly of the macromolecular complexes NMR is one of the three major techniques that provides structural, dynamical and also interaction data 1H, 13C, 15N and 31P. NMR's exquisite (bio‐)chemical usefulness originates from its ability to discriminate the different nuclei in a biomolecule. The electronic environment of each nucleus slightly modifies its exact resonance frequency through a process called chemical shielding and consequently the positions of various peaks in the NMR spectrum are specific for each nucleus in the molecule. The position of a peak in an NMR spectrum is commonly referred to as the ‘chemical shift’ and denoted by the symbol δ. The chemical shift constitutes one of the most important parameters and in NMR provides a powerful tool for a biochemist; as it not only allows us to discriminate one nucleus from another, but also provides information about their conformation and nearby chemical environment. For example, an analysis using recorded chemical shifts from the Biological Magnetic Resonance Data Bank (BMRB) reveals distributions with median values of ~ 8.24, ~ 4.32 and ~ 1.39 parts per million (ppm) for the Alanine HN, H⍺ and Hβ, respectively  15N‐TROSY‐HSQC 15N‐HSQC spectrum affords greater resolution and valuable information as peaks can be used as a ‘fingerprint’ of a protein. In practice, nearly each peak represents a backbone amide group of an individual residue, with the exception of peaks originating from the HN containing side chains of amino acids Asn, Gln, His, Trp, Lys and Arg. However, except for His, the signals from the side‐chain moieties are easily recognisable. Furthermore, proline residues are absent in 2D heteronuclear 15N‐HSQC spectra due to the lack of an amide group. The chemical shift values of the various nuclei can be extremely useful when used as a proxy to monitor protein–ligand interactions, a process called chemical shift mapping or CSP.In practice, the analysis of one‐dimensional biomolecular spectra is prohibitive because of spectral overlap. To overcome this problem, it is possible to correlate one nucleus with another, generating two‐dimensional (2D) or even higher‐dimensional  NMR spectra. For proteins in particular, a simple and very informative example is the 2D heteronuclear D, 4D, nDWhen a protein is titrated with a ligand, e.g. with a drug or another biomolecule, the chemical shift of the nitrogen and proton nuclei of the residues that are in close proximity to the binding site will be most affected. Thus, the binding of the ligand results in changes in the chemical shifts of these nuclei, causing the resulting peaks to alter their position in the NMR spectrum. By recording a series of NMR experiments at varying stoichiometries of protein and ligand, the resulting series of spectra conveys information regarding the affinity of the ligand, as well as identifying the important residues involved in the interaction.i in a protein P at 7.0 δ ppm in a one‐dimensional (1D) NMR spectrum .In NMR, the above situation when the i values. In the case of multi‐dimensional spectra, such as the 15N‐HSQC spectrum ‐KH domain 15N‐HSQC spectra at the position of G78. The gradual change in peak position upon increasing ligand concentration is evident. Importantly, to ensure a valid analysis all spectra should be properly referenced as changes in peak positions could otherwise be misinterpreted. Fortunately, AnalysisAssign has routines to establish, and where needed report, on the spectral alignment that functions even in the case of non‐fully identical spectra i values are calculated for each Tstar‐KH residue i using Eqn 15N nuclei relative to the 1H nuclei, by convention set to 0.14 Hi and ∆δNi denote the observed changes of the proton and nitrogen chemical shifts for residue i, respectively. Crucially, the threshold below which the CSPi values are deemed not significant needs to be established. A value of 1σ derived from the distribution of all CSP values is set by default as the first estimate without a need of other filters vide infra) or on the basis of other data that confirms that they can be considered relevant for the binding event. Depending on koff and the residue‐specific ∆δi, peaks can disappear in any of the spectra with ligand concentrations > 0. Such situation still conveys useful information about the involvement of the specific residue, albeit that the exact magnitude of its CSPi value cannot be established. In general, the disappearance of a peak for a specific residue is assumed to imply a ∆δi (CSPi) value resulting in exchange broadening due to an intermediate exchange regime and hence signifies importance. Consequently, these residues are automatically flagged by the CSP analysis module, e.g. residue I97 in Tstar‐KH.Figure sing Eqn CSPi=ΔδHipymolet al. The true power of the CSP analysis is revealed when mapping the CSP results onto a molecular structure. The CSP module can automatically map the annotations and residue selections onto a molecular structure of the biomolecule under investigation using an external molecular visualisation programme, such as et al. In cases where no 3D structure is available, often the structures of homologous proteins can be used as a reference. Protein structure is more highly conserved than primary sequence; therefore, small changes in protein composition usually do not significantly alter the structure of the macromolecule. Alternatively, in many cases, protein structures can be fairly accurately obtained using homology modelling, where an existing structure of a homologous protein is used as a template to generate the structure of the protein of interest. Such models can be reliably used for mapping of protein–ligand interactions, assuming the binding interface has not been affected by the mutations. A description of contemporary available homology modelling software packages and servers are discussed in the review by Vyas 1H‐15N as it can accommodate any combination of nuclei, e.g. 1H–13C in case of methyl residues. AnalysisAssign is implemented in a flexible fashion that will facilitate easy adaptation to insert specific calculation modes for ∆δi values, automatic pre‐ and user‐defined Kd fittings as well as direct links to external auto‐docking software such as HADDOCK Currently freely available NMR software suites, such as Sparky, allow retrieving CSP values from their tables, but users are required to manually export to third party software to carry on the analysis or are limited to single and static plotting, like in the case of CcpNmr Analysis version‐2. The possibility to graphically and interactively inspect the NMR data that identify the residues involved in protein–ligand interactions, makes the new AnalysisAssign CSP module extremely useful for non‐experts and drastically reduces the time required for a CSP analysis. The CSP module is also not limited to A series of other programmes have also been developed to address specific tasks using peaks in NMR spectra and NMR assignments, such as the programme Farseer, which performs analyses on large and multivariable datasets, including CSP 15N or 13C, has now become routine in Escherichia coli and Pichia Pastoris, with new and promising developments for expression in higher eukaryotic systems, which guarantees the presence of a more complex folding machinery and post‐translational modifications 15N‐observation Chemical shift perturbation is very useful as a simple tool to elucidate macromolecular interactions in small‐ and medium‐sized complexes, including protein–protein, protein–drugs, and protein–DNA/RNA interactions In comparing the various programmes for CSP data analysis, we find that the CSP module of AnalysisAssign version‐3 provides for a simple and interactive graphical tool that allows users to significantly reduce the time required for a CSP analysis.http://www.ccpn.ac.uk/v3-software/downloads. The CSP module and tutorials will be included in its upcoming version 3.0 release.AnalysisAssign can be freely downloaded for non‐commercial academic usage from The authors declare no conflict of interest.LM designed and developed the CSP analysis module for CcpNmr AnalysisAssign Version 3.0. LM & GWV analysed the data. LM & GWV wrote the manuscript.


Fusarium oxysporum f.sp. lycopersici (FOL) is one of the most devastating diseases, that delimits the tomato production worldwide. Fungal short-chain dehydrogenases/reductases (SDRs) are NADP(H) dependent oxidoreductases, having shared motifs and common functional mechanism, have been demonstrated as biochemical targets for commercial fungicides. The 1,3,6,8 tetra hydroxynaphthalene reductase (T4HNR) protein, a member of SDRs family, catalyzes the naphthol reduction reaction in fungal melanin biosynthesis. We retrieved an orthologous member of T4HNR, (complexed with NADP(H) and pyroquilon from Magnaporthe grisea) in the FOL  based on homology search, percent identity and sequence similarity . The hypothetical protein FOXG_04696 (T4HNR like) had conserved T-G-X-X-X-G-X-G motif (cofactor binding site) at N-terminus, similar to M. grisea (1JA9) and Y-X-X-X-K motif, as a part of the active site, bearing homologies with two fungal keto reductases T4HNR (M. grisea) and 17-β-hydroxysteroid dehydrogenase from Curvularia lunata (teleomorph: Cochliobolus lunatus PDB ID: 3IS3). The catalytic tetrad of T4HNR was replaced with ASN115, SER141, TYR154, and LYS158 in the FOXG_04696. The structural alignment and superposition of FOXG_04696 over the template proteins (3IS3 and 1JA9) revealed minimum RMSD deviations of the C alpha atomic coordinates, and therefore, had structural conservation. The best protein model (FOXG_04696) was docked with 37 fungicides, to evaluate their binding affinities. The Glide XP and YASARA docked complexes showed discrepancies in results, for scoring and ranking the binding affinities of fungicides. The docked complexes were further refined and rescored from their docked poses through 50 ns long MD simulations, and binding free energies (ΔGbind) calculations, using MM/GBSA analysis, revealed Oxathiapiprolin and Famoxadone as better fungicides among the selected one. However, Famoxadone had better interaction of the docked residues, with best protein ligand contacts, minimum RMSD (high accuracy of the docking pose) and RMSF  at the specified docking site. The Famoxadone was found to be acceptable based on in silico toxicity and in vitro growth inhibition assessment. We conclude that the FOXG_04696, could be employed as a novel candidate protein, for structure-based design, and screening of target fungicides against the FOL pathogen.Vascular wilt of tomato caused by  Lycopersicon esculentum Mill.) is one of the most widespread vegetable crops grown across the globe. However, the growth and economic productivity of tomato crop are well constrained by various biotic and abiotic stress conditions (Fusarium oxysporum f.sp. lycopersici (FOL) (Sacc.) W. C. Snyder and H. N. Hans (FOL) is one of the most destructive diseases prodione , flutolaprodione , mancozeprodione , mancozeprodione , metiramprodione , thiophaprodione , propicoprodione , Nativo prodione .M. grisea T4HNR (SDR) showed high sequence similarity with other fungal keto reductases, involved in the biosynthesis of fungal melanin and mycotoxins, that includes versicolorin reductase from Magnaporthe oryzae (99%), Verticillium alfalfae (77%), Verticillium dahliae (76%), Colletotrichum graminicola (79%), versicolorin reductase (VerA) from Emericella nidulans (52%), and 17β-hydroxysteroid dehydrogenase (17β-HSDcl) of Cochliobolus lunatus (52%). The crucial role of the fungal SDR gene in M. oryzae is required for infection related development and pathogenicity (adh1) encoding for alcohol dehydrogenase has been reported to play a crucial role in virulence of Fusarium wilt pathogen in tomato (Short-chain dehydrogenases/reductases (SDRs) are NADP(H)-dependent oxidoreductases characterized by conserved catalytic tetrad (N-S-Y-K) and cofactor binding site (TGxxxGxG)  with havgenicity . The funn tomato .Cochliobolus heterostrophus , polyketide synthase (VDAG_00190), and scytalone dehydratase (VDAG_03393)  gene clusters have crucial role in biosynthesis of DHN type of pigment   have been well evaluated  (F. oxysporum copper nitrite reductase (NirK), involved in the fungal denitrification process were searched using hierarchical in silico screening approach that consists of pharmacophore modeling and molecular docking  . Recentl pigment . The phythis PKS . Some chthis PKS . In the this PKS . The forase PGL1 . Furtherdy (MAb) . The fundy (MAb) . Dicarboxysporum . The senvaluated . Furthervaluated . The 17- radita) . In a re docking . The ran docking .in silico characterization of an unknown hypothetical proteins/essential genes from pathogenic microbes, that might have a possible role in regulation of metabolic process, or play an indispensable role in microbial pathogenicity  have a better fungicidal action   as a putative receptor protein, and a novel target, for structure-based protein–fungicide complex interactions. The predicted protein was found to be good enough based on qualitative and quantitative parameters and was further docked with 37 known commercial fungicides, frequently used against different phytopathogens, to find the best fungicide/agrochemicals (among the selected) that could target the FOXG_04696 and therefore, useful for controlling vascular wilt fungi. The environmental toxicity assessment could be used to decide the dosage formulations that could be used safely without having any loss to the non-target organism. With this view, the selected fungicides were further evaluated based on  grisea] . The objMagnaporthe grisea was selected for searching all the sequential homolog and orthologs using NCBI Blast server1  and pyroquilon fungicide, and solved through X-ray diffraction in  server1  keeping  server1 . Multipl server1 . The phyrver1  complexed with NADP(H) and pyroquilon (1JA9) was searched to find its sequential orthologs in the FOL pathogen, using Blast-p against the non-redundant database. Furthermore, the two protein sequences (1JA9 and the FOXG_04696) were aligned using BL2 seq (Blast-p). The FOXG_04696 protein sequence was also aligned with the protein sequence of 17-β-hydroxysteroid dehydrogenase [other closely related structural homolog (3IS3)]. Furthermore, the FOXG_04696 protein was searched using the tBlastn against the Refseq (reference protein) genome database, searching for 7 13  server f14 . The PDB file of the template (3IS3) was retrieved from the Protein Data Bank (PDB). The alignment file was generated using CLUSTALX. The target sequence file, alignment file, and template’s PDB file, PDB file (3IS3) was initialized in the Modeller script file (script.py). The script file (script.py) was executed using Modeller command prompt. Twenty-five models were generated for the FOXG_04696, each with having a DOPE score. Furthermore, the protein model with least DOPE score was selected for final validation.The homology modeling of the protein FOXG_04696 (T4HNR like) was performed using Modeller v9.19. The protein sequence was queried against the PDB database14  with havψ) and backbone conformation using PROCHECK module of the PDBSum server15 (16 (17 (18 (statistics of non-bonded atomic interactions and distribution of atoms) (19 (20 (M. grisea (1JA9) to compare their structural alignment and similarities using the Automated Structural Alignment Server (AuStrAlis)21. The final model was submitted to an online repository protein modeling databases (PMDB)22  and psi  . The oveoms) (19 . The qua (19 (20 . The mod(PMDB)22 .The ligands were retrieved from the PubChem database. The FOXG_04696 protein was selected as a target receptor protein and was imported to the Maestro v11. The structure was prepared using protein preparation wizard of the Schrödinger. Optimization of protein was done at neutral pH and then the structure was minimized by applying optimized potentials for liquid simulations (OPLS-3) force field for all atoms . A recepThe Glide XP ligand docking protocol was employed to predict the scoring and binding interactions between the FOXG_04696 and the ligand Famoxadone. The prepared ligand was docked into the binding site of the FOXG_04696. The van der Waals radii of non-polar regions of the T4HNR were limited to 1.0Å with the partial atomic charge of 0.25 in the receptor grid generation. Rigid receptor docking was utilized to dock each ligand into every refined low-energy conformation of the T4HNR produced from the earlier phases . XP docked complexes were evaluated using Xtra precision Glide score (XPG Score). The XPG score optimized the ligand binding energy on the behalf of the force field parameters, and penalties that had significant influences over the receptor-ligand binding. The following equation denotes the formulae for XPG calculations.a = 0.065 and b = 0.130 are coefficient constants of van der Waals energy and Coulomb energy, respectively.where vdW, Coul, Lipo, H bond, metal, BuryP, Rot B, and Site denote van der Waals energy, Coulomb energy, lipophilic contacts, hydrogen-bonding, metal-binding, penalty for buried polar groups, penalty for freezing the rotatable bonds, and polar interactions with the residues in the active site, respectively; Kd) of the docked molecular complexes  . The YASomplexes .Score  analysis to predict the free binding energies of the protein–fungicide docked complexes. The binding energy calculated through MM/GBSA was more accurate than the XPG Score . The binquations .Gsolv contains both polar and non-polar contributions. The binding free energy (ΔGbind) could be dissociated into various energy terms. Since the same trajectory was selected for extraction of receptor protein, ligand, and protein–ligand complex, we neglected the internal energy change (ΔEint). Therefore, the gas–phase interaction energy (ΔEgas) between the receptor and the ligand was the sum of electrostatic (ΔEELE) and van der Waals (ΔEV DW) interaction energies. The solvation free energy (ΔGsol) could be distributed into non-polar and polar energy terms, and the polar solvation energy (ΔGGB) is calculated by using the VSGB2.1 GB model, and was default parameter for Prime calculations using the OPLS2.1/3/3e force field. The Post-docking MM/GBSA is implemented in Schrödinger software using the program Prime, with options to include receptor and ligand flexibility; the entropy term is neglected by default. Simulations were performed using GBSA continuum model. The Gaussian surface area model instead of vdW was employed for denoting the solvent accessible surface area.These energy contributions are computed from the atomic coordinates of the protein, ligand and complex using the (gas phase) molecular mechanics energy function (or force field). The solvation free energy term The receptor–ligand interactions for fungicides having minimum binding energy (stronger binding) were further evaluated using molecular dynamics simulations analysis. MD simulations studies were performed up to 50 ns through Desmond v 4.2 to analyze the conformational stability of the FOXG_04696–Famoxadone, and the complexes in the solvated model system, embedded with ordered water molecules (ordered water molecules may involve in protein binding sites and influence protein ligand binding by bridging protein–ligand interactions and can make large contributions to the binding affinity). The Desmond supports algorithms typically used to perform fast and accurate MD simulations. Long-range electrostatic energy and forces were calculated using particle-mesh-based Ewald techniques. The FOXG_04696–ligand docked complexes were solvated, using orthorhombic simple point charge (SPC) water model. The solvated system was neutralized with counter ions and physiological salt concentration was limited to 0.15 M. The receptor–ligand complex system was assigned with optimized potentials for liquid simulations-AA (OPLS-AA) 2005 force field . The sysin vitro assessment against the FOL pathogen. The pathogenic culture was obtained from Laboratory of Mycopathology and Microbial Technology, Department of Botany, Institute of Science, Banaras Hindu University, Varanasi, India and the fungicide Famoxadone  was used for evaluating its in vitro efficacy. Four separate concentrations 50, 100, 150, and 200 μL were employed along with the control solution (having only PDA) and amended in 20-mL PDA medium. A 5-mm culture disc was extracted from the freshly inoculated pathogen culture in each of the four plates. The plates were further incubated at 27 ± 2°C under observation and the radial growth of the hyphae was recorded at even  days interval.The selected fungicide (Famoxadone) was used for C − T)/C × 100] where I = inhibition percentage; C = radial growth of the pathogen in control, and T = radial growth of the pathogen fungicide treatment (P ≤ 0.05 of significance level.The percent growth inhibition (PI) was calculated using the following formula , which further confirms the existence of similar T4HNR and 17-β-hydroxysteroid dehydrogenase-like protein (FOXG_04696) in the FOL pathogen. The phylogenetic tree was constructed based on the neighbour-joining end (NJ) method revealed the polyphyletic origin of the FOXG_04696 protein  database with organism n Figure . The PROn Figure . The cirM. grisea (XP_003715430.1). By contrast, the BL2seq query with 3IS3 resulted into . This confirms that T4HNR (1JA9) is closely related with 1JA9 based on percentage identity and query cover values. The Fgenesh results located the position of the FOXG_04696 encoding gene along with transcriptional start sites (TSS) and poly A tail across the full-length genome  results revealed that (XP_018239507.1) protein was found to have  with the T4HNR protein of e Figure .in silico characterization and docking studies. The predicted model was visualized through the visualization module of the Discovery Studio 3.0  score values. The model with least values for DOPE score  was selected as a final model for 0 Figure . The thr0 Figure . It was 0 Figure . The res0 Figure .Z score value −8.11  was found to be −9.67. The ProSA evaluates the qualitative values of the modeled structures based on atomic coordinates. The energy plots represent the potential problems spotted in protein structures. The Z score revealed the protein structures could be correlated well with crystal structures of similar lengths, where the positive value corresponds to problematic or erroneous parts of the input structure. ProQ is a neural network-based predictor based on a number of structural features predicts the quality of protein model. The ProQ result showed LG score of 5.677 which represents that the structure is of very good quality. The ERRAT score for the modeled structure was found to be 90.40% against the template (99.20%)  and were provided with having accession number PM0081606.The selected model was verified for their stereochemical quality assessment. Furthermore, in each case of qualitative assessment, a comparative study was done with experimentally solved crystal structures, to check the quality, reliability, accuracy, stability and compatibility of the computationally predicted protein. The Ramachandran plot obtained through RAMPAGE server revealed that the predicted model FOXG_04696 had 99.3% residues [97.7% (favored) + 1.6% ] lying in favored region [compared to the experimentally solved and X-ray resolved template protein structure (3IS3) where we found 98.0% (favored) and 1.6%  residues against the expected values 98.0% (favored) and 2.0%  regions]. The other sequential homolog 1JA9 had similar results like 3IS3 98.0% (favored) and 1.6% . The PROCHECK module of the PDBSum server, further justified the stereochemical goodness of the predicted model, with 94.2% residues accommodating in the most favored regions  and only 4.9% residues occupied in the additionally allowed regions  with the G factor value 0.12  compared to the active site residues for template (1JA9)   which might play an indispensable role and imparts specificity to FOXG_04696.The putative ligand binding sites (both major and minor) for the predicted protein were identified through Meta-pocket 2.0 server. The conserved domain databases (CDD) server prediction revealed the conservation of the catalytic tetrad (NSYK) ASN+ binding (GO: 0070403), -butanediol dehydrogenase activity (GO: 0047512), and alcohol dehydrogenase (NAD) activity (GO: 0004022). The scattered plot diagram was generated through the ReviGO web server was based on non-redundant GO terms with scoring values higher is better. The first five significant terms structured around three ontologies, which discussed biological processes, molecular function and a subcellular component of predicted protein has been shown  type (3), (A) three layer (aba) sandwich type architecture (3.40), having Rossmann fold (3.40.50) and bearing to NADP binding Rossmann fold  like domain family protein (30.40.50.720). The functional annotation using Funfam  revealed the possible biological role of the characterized protein based on three ontological terms that include biological process, molecular function, and cellular component. The first five significant GO terms in biological processes included secondary metabolite biosynthetic process (GO: 0044550), secondary metabolite process (GO: 0019748), pigment biosynthetic process (GO: 0046148), and sterigmatocystin biosynthetic process (GO: 0045461). The significant terms in molecular processes found were versicolorin reductase activity (GO: 0042469), tropinone reductase activity (GO: 0050358), NADGbind was computed by using MM/GBSA calculations. In many studies, it has been demonstrated that binding free energies predicted by MM/GBSA-based rescoring of the docked complexes are in good agreement with experimental binding affinities (Gbind of –75.50 (±0.54) kcal/mol and XPG docking score of −1.86 kcal/mol with 17 binding site residues  of the T4HNR were found to involve in van der Waals interactions with Oxathiapiprolin. The Famoxadone had the ΔGbind of −66.90 (±0.47) kcal/mol and lower XPG score (than Oxathiapiprolin) of −3.30 kcal/mol, it displayed two hydrogen bonds with key binding site residues TYR154 and THR186 and 27 residues were found to be involved in making van der Waals interactions GLY13, SER15, ARG16, GLY17, ILE18, GLY19, TYR37, VAL38, ASN91, SER92, GLY93, ILE94, GLU95, ILE139, SER140, SER141, ILE142, SER143, TYR154, LYS158, PRO184, LYS185, THR186, ASP187, MET188, TYR189, ALA192, ALA193, and TYR196) within 4Å binding site region of Famoxadone with T4HNR. The 3D surface view of the docked Famoxadone–FOXG_04696 complex has been shown to represent the putative H bond acceptor and donor group  μM. The maximum YASARA score was found to be associated with the Oxathiapiprolin  with least dissociation constant Kd value 1.86 (μM) followed by the Famoxadone . The protein–ligand docking through the YASARA showed the efficient, stronger, and stable binding with positive YASARA score∗   and dissociation constant (binding)  with Oxabinding) . The 3D binding) .M. grisea PDB ID: 1JA9), and targets the residues, forming active sites of the T4HNR (SDR) protein. We have investigated the X-Ray determined crystal structure of the T4HNR (1JA9) complexed with fungicide pyroquilon to find out the residues that were involved in binding with T4HNR in an accurate and flexible docking poses .The YASARA based soring, dissociation constant, and contact receptor residues involved in binding with the FOXG_04696 have been shown in Supplementary Table 13, SER15, ARG16, VAL38, SER39, SER40, ASP64, VAL65, SER66, SER92, GLY93, ILE94, LYS110, and VAL114. Since, we did not find any stable docking conformation for stable binding of the Oxathiapiprolin at that particular specified docking site (like Famoxadone). The lower ranking of the Oxathiapiprolin (higher XPG score) binding over the Famoxadone (lower XPG score) could be interpreted from the fact that the Oxathiapiprolin was found to docked in an alternative conformation docked with the residues that were either absent from any major or minor binding site, or were present beyond the limit required for an accurate docking pose prediction. The visualization of the Glide XP docked complexes revealed that Oxathiapiprolin bounded with LEU100, VAL103, ILE108, LEU112, VAL116, TRP146, GLY147, VAL148, PRO149, ARG150, HIS151, ALA152, LEU153, SER155, ALA156, SER157, and ALA160 rather than the specified docking sites. The molecular complexes formed after protein–fungicides interaction for different fungicides has been visualized through the visualization tool of Discovery studio 3.0 and have been represented  for each-protein—fungicide complex in order to separate those ligands that don’t bind, in a ranked list. Furthermore, analysis of the YASARA results for the two top scored docked protein–fungicide complexes (Oxathiapiprolin and Famoxadone) revealed that Famoxadone docked with FOXG_04696 in an accurate and flexible docking pose with residues that constituted the major binding site  GLY143, ALA144, VAL145, GLY147, and SER155), three water mediated hydrogen bonds , four hydrophobic and water-mediated hydrogen bonds  with seven hydrophobic interactions  with the key binding site residues to form a stable complex  and 15 hydrophobic interactions  with the key binding site residues in forming a stable complex  and MM/GBSA binding free energy (ΔGbind) calculations showing the strong correlation between the predicted binding free energies and ranking affinities/scoring of the fungicides for the docked complexes  or located at other binding cavities rather than the residues that were involved in the main binding sites . The ultimate goal for MM/GBSA calculations was to estimate the enthalpy change on ligand binding, through comparison of an average enthalpy change for the bound and unbound states. The MM/GBSA results re-ranked the docked complexes in terms of their accurate pose prediction and efficacy for binding affinities Table . We did The functional interactive network formed by the FOXG_04696 protein at the highest confidence level (0.90) has been shown in Figure in vitro assessment against the FOL pathogen showed growth inhibition at each and every increasing concentration of fungicides. With increasing concentrations of fungicides, the growth rates were correspondingly retarded and sporulation was reduced. The maximum growth inhibition was recorded on eighth day post inoculation. The percentage inhibitions measured in the form of radial growth were subjected to statistical analysis. The percent inhibitions recorded on 4th day were 90.53, 74.42, 63.04, and 44.36 at 50, 100, 150, and 200 μL concentrations, respectively. By contrast, the percent inhibitions recorded on 8th day post inoculation were 25.73, 19.99, 11.22, and 7.04 at 50, 100, 150, and 200 μL concentrations, respectively. The growth inhibition recorded on the 4th and 8th days at different concentrations of fungicides has been shown in Figures The Famoxadone solution used for P score values 4.699, and molar refractivity 103.70. The admetSAR results predicted that the selected ligand (Famoxadone) was found to be non-carcinogen (0.7751), non-AMES toxic (0.5395), and non-inhibitor (0.8941), with weak hERG (the human Ether-à-go-go-Related Gene) inhibitor (0.9732) and with non-required carcinogenetic (0.4799).The Famoxadone was checked and evaluated for toxicity assessment for its safe environmental disposition. The FAF-drugs 4.0 tool performed the computational prediction of some ADME-Tox properties  for Famoxadone and it was found that the drug is non-carcinogenic and acceptable . FurtherF. oxysporum f.sp. lycopersici (FOL) is very destructive and widespread plant disease that causes enormous economic losses. The wilt pathogen directly penetrates roots and colonizes the vascular tissue (M. grisea (teh gene). Sordaria macrospora, an Ascomycetous fungal model system, which accumulates the melanin during its sexual development. It was found that the T4HNR protein showed sequence similarity and homology with Aspergillus fumigatus (taxid: 746128) (51.8% identity), Cochliobolus heterostrophus (taxid: 5016) (79.1% identity), M. grisea (taxid: 148305) (51.6% identity), and Neurospora crassa (taxid: 5141) (96.3% identity). By contrast, the polyketide synthase (encoded by pks gene) (DHN melanin enzyme) had sequence similarity with A. fumigatus (42.2% identity), C. heterostrophus (46.0% identity), Colletotrichum lagenarium (66.5% identity), M. grisea (69.6% identity), and N. crassa (85.6% identity) (pks), a tetrahydroxynaphthalene reductase (teh), a scytalone dehydratase (sdh), and a trihydroxynaphthalene reductase (tir) were used from other Ascomycetous fungi (mentioned above) to retrieve the sequences of the above genes and further for their experimental demonstration in S. macrospora (S. macrospora. (S. macrospora (TER) and M. grisea (TER), the protein Blast-p results revealed the 99% sequence query coverages and 51% sequence identity with S. macrospora (XP_003345723.1), 93% query coverages and 49% sequence identity with M. grisea (TER) (PDB ID 1JA9), and only 91% query coverages and 46% sequence identities with M. grisea (TIR). Based on such in silico-based comparative studies, one could predict the existence of DHN melanin pathways in other filamentous fungi (M. grisea and S. macrospora) utilize two reductases the 1,3,8-THN reductase (3HNR) and the 1,3,6,8-THN reductase (T4HNR). In other cases, scytalone dehydratase was considered to activate both dehydration steps, of the scytalone and vermelone  with 49% identity and 93% (query coverages) (FOXG 04696: XP_018239507.1). By contrast, the Blast-p results with the M. grisea 1,3,8-trihydroxynaphthalene reductases (TIR) (PDB ID: 1G0O) revealed the lesser sequence similarity with the FOXG_04696 (46% identity and 91% query coverages) which reflected, the identity of our target protein as the T4HNR and were found to be more closer to the M. grisea T4HNR (1JA9). Moreover, the scytalone dehydratase (EC: 4.2.1.94) protein  and the mRNA (XM_018393275.1) have been well characterized in the FOL pathogen.Vascular wilt caused by r tissue . One of r tissue . The 1,3. grisea . The meldentity) . The ortcrospora . Interesrospora. . By contus fungi . Howeverermelone . Based oOphiostoma floccosum. It was demonstrated that the hydroxynaphthalene reductases (HNR) of the fungus O. floccosum, shared the functional homology with other fungal HNR  and polyketide synthases (PKSs) of 12 different species belonging to Fusarium genera revealed the 52 NRPSs and 52 PKSs orthology group  virulence in tomato plants. In this context, the functional relevance of the FOXG_04696 could be predicted from conserved functional motif and domains, measured in terms of gene ontology, and/or shared domain–domain interaction. Since, the protein structure is 3–10 times more conserved than its sequence  interacted with fatty acid synthase alpha subunit reductase (XP_003349949.1), fatty acid synthase beta subunit dehydratase (XP_003349948.1), and 3-oxoacyl (acyl carrier protein) synthase (XP_003351602.1). Moreover, the STRING based results for our characterized and predicted model the FOXG_04696, revealed the same interacting partner at highest confidence level values such as fatty acid synthase subunit alpha-reductase (FOXG_06391), fatty acid synthase subunit beta hydratase (FOXG_06392) (enoyl-[acyl carrier protein] reductase (NADH) activity) fatty acid synthase subunit beta hydratase (FOXG_15138), and fatty acid synthase subunit beta-dehydratase (FOXG_14342) indicating the similar functional association with the FOXG_04696 protein.The functional conservation of protein homology was also evaluated based on the protein interaction networks. Since, protein–protein interaction studies are mediated by a limited set of domain–domain interactions , and pro115 SER141 TYR154 and LYS158. The three conserved residues including SER141, TYR154, and LYS158 form the structural motif with ASN115 through H bonding with other residues. The superimposition of the template protein over the predicted model resulted into the structural resemblances with the minimum RMSD (0.47Å) and the relative RMSD values (0.025Å). The superposition results also aligned the identical residues found in between the T4HNR and the FOXG_04696 protein. The optimized model was found to be suitable based on several qualitative backgrounds including the RAMPAGE, ProSA, ERRAT, PROCHECK (PDB Sum), and Verify-3D. The Ramachandran plot which evaluated that the predicted models were closer to the template (98% residues lying in the favored regions). The ERRAT score values (92%) and Verify-3D results were good enough signifying the consistency of the model prediction and explained that the predicted model was reliable and satisfactory, as it was reported that, for a model having good resolution (approximately 2.5–3.0Å), the ideal score values for Verify-3D should be 80%, and that for the ERRAT around 95% , which is within the range observed for the native set of proteins of the same size. It was reported that the Z score values for any modeled structure lying outside the range of native proteins that were resolved through X-ray and NMR predict the erroneous structure  through the ScanPROSITE program revealed that the input protein sequences have signature sequences, belonging to SDR family. A broad range of different activities is catalyzed by the enzyme (SDRs) that includes metabolism of organic biomolecules such as carbohydrates, lipids, amino acids, steroids, cofactors, and aromatic compounds and act in redox sensing . Sequencound 95% . Furthertructure . This wa178 with TYR154, LYS182 with LYS158, PRO208 with PRO184, THR213 with THR186, ASP214 with ASP187, MET215 with MET188, and TYR223 with TYR196 in the FOXG_04696. It was found that all the replaced residues in the FOXG_04696 were present in either major or another major (first two) cavities predicted by the metapocket server. In our previous studies, the structure of the functional domain of the proteins that belong to the WRKY gene superfamily members has been modeled for its qualitative and quantitative evaluation, to unravel the DNA–protein interaction studies, in a stimulus-specific manner in tomato  and pyroquilon (1JA9) revealed that fungicide pyroquilon binds with the crucial residues forming active site of the T4HNR protein and therefore, interrupt its functional mechanism. The fungicides that interact with the residues forming active sites or interact with the major residues that form the catalytic center of protein might have good results, for disrupting the functional aspect of proteins and therefore, would affect its possible biological roles. The metapocket server analyzed all the possible binding sites that might be occupied with the ligands, during the protein–fungicide interactions. The structural alignment unravelled the conserved T4HNR and replaced the key residues such as TYRn tomato , 2018. IThe MD simulations of the protein–fungicide interaction reflected the time-dependent behavior of the biological complexes. The molecular docking and virtual screening revealed the two better fungicides including the Oxathiapiprolin and Famoxadone. The stability of the protein–fungicide docked complexes was measured at 50-ns MD simulations. It was found that both Oxathiapiprolin and Famoxadone disclosed the steadiness of the docked complexes, with an average potential energy of −113166.16 and −112628.96 kcal/mol, respectively. However, Famoxadone had comparatively better docking with XPG score of −3.30 kcal/mol  along with lower values of RMSD and RMSF for protein–ligand contact, and better interaction within the particular specified docking site (within 4 Å binding site region of FOXG_04696). The lower XPG score −3.30 kcal/mol (more negative), and lesser RMSD and RMSF values for Famoxadone-FOXG_04696 (compared to Oxathiapiprolin_FOXG_04696) predicted the stability and reproducibility of the docking results, to find the crystallographic relevant and accurate binding pose.Kd values, and accurate docking poses were further refined and rescored through the MD simulations, and MM/GBSA methods, to validate the top scored docking results. In each case, we found that the Famoxadone docked complex with FOXG_04696 had good docking score, with accurate docking pose, and was reliable and reproducible. The YASARA results evaluated the docking calculations based on YASARA score and dissociation constant (Kd). Based on YASARA results, one could predict that fungicides having high YASARA scores and low Kd must bind with the receptor protein FOXG_04696 in a good docking pose. However, MD simulations analysis for these complexes (top docking score) were either failed to bind with the target receptor protein or were reported to be docked in an alternative conformation (other binding sites). Moreover, the fungicides having low dissociation constant (Kd) values for the docked complexes such as Thiophanate methyl , Trifloxystrobin , Boscalid , Pyraclostrobin , and Isopyrazam , despite of having good docking score and lesser Kd values, did not bind in a good docking pose in the MD simulations, or bound with sites in altered conformation . By contrast, fungicides such as Carbendazim , Cymoxanil , Dithane , Famoxadone , Fluberidazole , Metiram (Zineb) , Pterostilbin , Tebuconazole , and Oxathiapiprolin  bounded with some of the core residues that constituted, the major or minor binding sites of receptor protein in a good docking pose as revealed through YASARA. The YASARA score, Kd values, and MM/GBSA free energy binding values for Metiram (Zineb) and Dithane were found to be similar −55.63 (±0.38) as both share similar structure, and Dithane is the dimer unit of Metiram. The docking conformation of the Famoxadone and Oxathiapiprolin with the FOXG_04696 analyzed, and reported to be good from all the docking servers. Moreover, the MD simulations of Famoxadone and Oxathiapiprolin protein complexes showed better results with minimum interaction energies (Oxathiapiprolin followed by famoxadone), and also had lesser Kd (Oxathiapiprolin followed by Famoxadone). The MD simulations of Oxathiapiprolin complex had minimum interaction energy and Kd values, but the residues involved in interaction were non-significant, and were present beyond the binding sites (both major and minor) of target receptor protein. The residues that were found to be involved in Oxathiapiprolin binding during MD simulations were LEU100, VAL103, ILE108, LEU112, VAL116, TRP146, GLY147, VAL148, PRO149, ARG150, HIS151, ALA152, LEU153, SER155, ALA156, SER157, and ALA160. Moreover, Famoxadone had good binding affinity from all the platforms with having maximum residues from first binding site (major) including catalytic tetrad, of the FOXG_04696. The residues involved with the Famoxadone binding during MD simulations were GLY13, SER15, ARG16, GLY17, ILE18, GLY19, TYR37, VAL38, ASN91, SER92, GLY93, ILE94, GLU95, ILE139, SER140, SER141, ILE142, SER143, TYR154, LYS158, PRO184, LYS185, THR186, ASP187, MET188, TYR189, ALA192, ALA193, and TYR196 (exclusively forming major binding site of the receptor protein)  could be involved in binding a ligand with proteins, and small conformational changes might have big effects on binding affinities . FurtherIn our results, we found the discrepancies in the ranking of ligand binding affinities from two different popular molecular docking programs (Glide XP and YASARA scores). In the YASARA binding energy function, the energy was calculated as the difference between the sum of potential and solvation energies of the separated compounds, and the sum of potential and solvation energies of the complex in the YAMBER3 force field. Thus, more positive YASARA score (difference) means higher affinity , 2015. IIt has been reported that docking calculations performed through different servers and tools has several limitations such as a wrong binding site of target receptor protein, the choice of docking poses, high docking scores, but failed in MD simulations . FurtherGbinding = ΔGcomplex - (ΔGfreereceptor + ΔGfree ligand). The enthalpic contributions for docked complexes are assessed through molecular mechanics. MM/GBSA ΔGbind negative value indicates stronger binding of the ligands with receptor protein. MM/GBSA (ΔGbind) can be expected to agree reasonably well with ranking based on experimental binding affinity. The results obtained for binding energies calculations of the protein–ligand interactions through MM/GBSA calculation were reported to be highly reproducible and stable  the efficacy/affinities of a ligand binding to the receptor proteins in the protein–ligand docked complexes  solvation models. With this view, the computational calculation for estimating the free binding energies are predicted from the difference between the free energy of each ligand bound to the protein and the free energies of the components of the complex, i.e., Δd stable , and indd stable . The cald stable . Furtherd stable . This coomplexes . MD simuomplexes . Recentlomplexes .r2 = 0.335 for the selected protein–fungicide docked complexes, and reported Famoxadone and Oxathiapiprolin having better binding efficiency with FOXG_04696 than other fungicides.Overall, one important conclusion from our study revealed that, docking studies must be harmonized with MD simulations, as MD simulations provide core information to complement the docking prediction, and unravelled the docking poses that were actually unstable. The MD simulations equilibrate the system to achieve a stable conformation. If the initial structure was energetically unstable, the system appropriately changes the conformation in subsequent MD simulations . Moreovein vitro inhibitory test against the FOL pathogen. It was found that the selected fungicide was good enough as the mycelial growth was found to be inhibited at every increased concentration at an increased time interval. The in silco toxicity assessment tools further predicted the toxicity assessment of the fungicide and was found to be acceptable for environmental disposition, and could be used safely against the FOL pathogen for controlling the vascular wilt disease.The selected fungicide (Famoxadone) was further evaluated for in silco predicted protein could be deduced and determined experimentally using mutant analysis and genetic complementation studies. The data from our study will drive future experimentation for determining the predictive function of this protein in the FOL pathogen.It is well known that fungal SDRs are large family enzymes and play a crucial role in various metabolic processes, their functional characterization in the FOL pathogen, is an interesting approach. The predictive function of the desired protein could be useful in understanding the virulence mechanism and resistance of the FOL pathogen to target fungicides. Moreover, this protein could be better deployed in structure-based drug design and catalysis. The functional relevance of the FOXG_04696 (T4HNR like) is not quite understood. In this context, we could predict that the hypothetical protein FOXG_04696 might have possible functional role in secondary metabolic process (3-oxoacyl-[acyl-carrier protein] reductase), versicolorin reductase (melanin pigment biosynthesis), or play crucial role in the FOL virulence  (based on the results of significant hits of Blast-p annotation). The possible functional relevance of the in silico screening approach (pharmacophore modeling and molecular docking) could be employed for comparative binding studies of an experimentally derived molecular complex, with unknown hypothetical protein and novel ligands. The experimental data available for protein–ligand interaction at good resolutions could help in analyzing the other relevant proteins and complexes for the better modulation of their functional activity in a more efficacious and reliable manner. The computational screening for getting a novel inhibitor (fungicide) followed by in vitro assessment, could be useful to develop commercial formulations either alone or in combination with other better fungicides, or used with other integrated approaches, for the better management of the Fusarium wilt disease.The present research work provides an insight into the structural, functional, and dynamical aspects of fungal SDR (T4HNR like) in the FOL pathogen. The computational modeling of protein 3D structures, with high accuracy and functional characterization, revealed the core information regarding the homology and conservation of SDRs among the closely related fungal taxonomic groups. The fungal SDRs play a crucial role in various metabolic processes including biosynthesis of melanin and other pigments, mycotoxin biosynthesis, secondary metabolism, fungal defense response, and fungal pathogenicity; these enzymes could be deployed as novel targets, for the discovery of novel agrochemicals against the phytopathogenic fungi. We reported the interaction of Famoxadone with FOXG_04696 (T4HNR like) with best protein ligand contacts through the core residues from major binding site of receptor protein. The protein–ligand interaction also targeted the functional residues that constituted the (active sites) and in a good docking pose with least binding energy. Interestingly, the X-ray diffracted crystal structures or NMR-derived solution structures, of protein–ligand complexes, could be used for interaction studies with unknown hypothetical proteins. Moreover, the inhibitors discovered through hierarchical in vitro experimental work, and also helped in computational analysis of the results. MD assisted MM in in vitro experimental work, and also helped MA in the computational analysis of the results. SKK, SPK, and AU analyzed the MD simulations analysis of the protein–fungicide interactions. MA, SKK, and MD prepared the final version of manuscript. SS, RU, and AU assisted in manuscript writing, data validation, and supervised the work throughout the study. All authors revised and approved it for publication.MA and VS conceived the idea and planned the experiments. MA performed all the experiments, did the computational analysis of results, and finally prepared and wrote the manuscript. VS assisted in the computational analysis of results. MM performed the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The non-covalent interactions between small drug molecules and disease-related proteins (ligand-target interactions) mediate various pharmacological processes in the treatment of different diseases. The development of the analytical methods to assess those interactions, including binding sites, binding energies, stoichiometry and association-dissociation constants, could assist in clarifying the mechanisms of action, precise treatment of targeted diseases as well as the targeted drug discovery. For the last decades, mass spectrometry (MS) has been recognized as a powerful tool to study the non-covalent interactions of the ligand-target complexes with the characteristics of high sensitivity, high-resolution, and high-throughput. Soft ionization mass spectrometry, especially the electrospray mass spectrometry (ESI-MS) and matrix assisted laser desorption ionization mass spectrometry (MALDI-MS), could achieve the complete transformation of the target analytes into the gas phase, and subsequent detection of the small drug molecules and disease-related protein complexes, and has exerted great advantages for studying the drug ligands-protein targets interactions, even in case of identifying active components as drug ligands from crude extracts of medicinal plants. Despite of other analytical techniques for this purpose, such as the NMR and X-ray crystallography, this review highlights the principles, research hotspots and recent applications of the soft ionization mass spectrometry and its hyphenated techniques, including hydrogen-deuterium exchange mass spectrometry (HDX-MS), chemical cross-linking mass spectrometry (CX-MS), and ion mobility spectrometry mass spectrometry (IMS-MS), in the study of the non-covalent interactions between small drug molecules and disease-related proteins. For example, due to its soft ionization conditions, soft ionization MS will not be limited by the solubility and Mw in the study of the interactions between small drug molecules and biological macromolecules  has been characterized by the high sensitivity, rapidity, and specificity  and then form their own different motion tracks in a high vacuum mass analyzer with applied electric field or magnetic field, and the final mass spectrogram is generated through data recording and conversion. The corresponding technology and instrument are called as the mass spectrometer , which gin vitro drug analysis and new drug research and development  MS to investigate the non-covalent interactions of the complexes formed by the immune protein FKBP and immune drug FK506, and found that the determination conditions strongly affected the ionic strength of the complexes, while the compositions of the complexes were independent of the drug concentration in solution, and the stoichiometric ratio was 1:1  is a novel ionization method based on an improved ESI ion source  exists extensively in the milk of most mammals and is the main component of whey protein. It has good biodegradability and adaptability and can be used as an excellent carrier  is a high-throughput (HTS) technique for the fast screening and identification of the active components from the complex mixtures, such as the medicinal plant extracts and compounds libraries. Targeted to the key proteins in the process of disease occurrence and development, this technique could effectively fish out the active compounds from the unbound inactive components in the complex mixtures based on the biological affinities between the small molecule compounds and the targeted proteins in the body, and thus has the characteristics of high sensitivity and rapidity  with UF-LC/MS  inhibitors, in which structure-activity relationship indicated that the flavonoids UF-LC/MS  , and the following detection of the mass shift of the enzymatic peptide by MS to determine the position of the protein sequence where the H/D atoms exchange occurs  refers to the exchange of hydrogen atoms of the sample protein molecules with deuterated solvents  is a trace chemical analysis technology developed in the late 1960s and early 1970s by Cohen and Karasek  can also be used to study the interactions of complex systems with proteins  RuCl (en)] PF6, [(η6-biphenyl) RuCl (en)] PF6 (en = ethylenediamine) using HPLC-ESI-Q-TOF MS. In order to improve the coverage rate of peptide identification, the protein and protein-ruthenium complexes were denatured, reduced disulfide bonds, and blocked cysteine residues were treated prior to enzymatic hydrolysis of the protein sample to allow HSA trypsin to digest peptides. On this basis, it was found that [(η6-cymene) Ru (en)]2+ could form a coordination bond with the sulfhydryl group of the amino acid residue Cys34, and then induce the sulfhydryl oxidation to produce the sulfinic acid, which has an irreversible effect on the antioxidant function of HAS, while the [(η6-biphenyl) Ru (en)]2+ could not bind to the Cys34 due to the steric hindrance of the organic ligand biphenyl. In addition, the [(η6-cymene) RuCl (en)] PF6 and [(η6-biphenyl) RuCl (en)] PF6 could covalently bind to the residues of the His128, His247, His510 histidine, Met298 methionine on the HSA surface in the form of coordination bonds, and the former exhibited higher binding affinities than the latter. Therefore, this finding was of great significance for further optimizing the structure of ruthenium as an organometallic anticancer compound and improving the transport efficiency and bioavailability of drugs in the body.The HSA is the most abundant protein in human plasma, which binds to and transports a variety of endogenous metabolites and drugs. Currently, most of the platinum and ruthenium metal anticancer drugs are administered intravenously, and these drugs are widely bound to the HSA after entering the human body  method. Analysis of platinum-containing fragments showed that the cis-platin and oxaliplatin were both bound to the ubiquitin Met1 residues, while the anti-platinum was bound to the peptide fragment of 19pro-ser-asp-thr-ile-glu24, but the bound amino acid residues could not be further determined. In addition, it was found that the platinum-containing protein fragments could be obtained by the CID and IRMPD (infrared multiphoton dissociation) pyrolysis methods, while could not be detected by the ECD (electron capture dissociation) pyrolysis methods. The reason may be that the platinum-containing fragments produced were neutralized due to the capture of electrons by platinum ions and could not be detected by mass spectrometry.Ubiquitin is a protein containing 76 amino acid residues and is widely found in the cytoplasm and nucleus. This protein plays a key role in the ubiquitination signaling pathway of cells, which is closely related to the proliferation of tumors and is a potential target of metal anticancer drugs in cancer cells , and found that four PGG molecules could be bound to a single BSA molecule. Moreover, Yanes et al.  is a new type of molecular imaging technique that has received great attention in recent years. By scanning samples with ionized probes with different principles and structures, the cis-platin was significantly improved in the case of inhibiting the expression of cytochrome c oxidase. At present, the highest spatial resolution of MALDI-MSI could reach 1.4 μm  is a rapidly evolving mass spectrometry technique for studying protein structures and interactions in recent years Sinz, . The croBased on the crystal structures of 1252 kinase-ligands, van Linden et al.  obtainedThe main reason for MALDI's breakthrough in macromolecular ionization is the use of matrixes. Black et al.  successf2 gas (linear velocity up to 350 m/s) ejected from the outer casing of the atomizer quickly atomizes and accelerates the solvent, causing the charged droplets to hit the sample surface. The sample is sputtered into the gas phase after being hit by high-speed droplets. At the same time, due to the scavenging and drying of N2 gas, the charged droplets containing sample are dissolvated and migrated along the ion transport tube at atmospheric pressure to the capillary at the front end of the mass spectrum, which is then detected by the detector of the mass spectrometer  techniques. Its ionization is achieved by spraying charged droplets generated by ESI onto the samples . Generalin situ property of the sample are also ensured , which will lead to the destruction of three-dimensional structure of proteins. In addition, for some commonly used separation methods, such as reversed-phase HPLC, the pH conditions of the mobile phase and the high concentration of organic compounds that are usually required also cause protein denaturation. When studying the non-covalent protein-ligand complexes, in order to prevent the non-covalent bonds from being destroyed by the above conditions, the native-denatured experimental conditions must be employed, thus producing the native MS technique.The native MS combines certain soft ionization technique with MS, with the purpose to introduce the protein-ligand complexes into the gas phase environment of the mass spectrometer without destroying the non-covalent interactions, and to be recorded and analyzed completely. So there is no need to use complex protein engineering methods to analyze the protein-target system expressed at the physiological level, thereby completing the interactions of the non-covalent protein-ligand complexes and their structural and dynamic change process. In addition, for a more complex protein sample, the protein charge number will be reduced due to the decreased protons in the system when analyzed under the native-denatured conditions, thus the number of mass spectral peaks is reduced accordingly, and thereby causing the decreases of the overlap between different charge state interference, making it easier to obtain the molecular mass information for each component in the complex protein sample  was affected by more than 50%. This work highlights the regulation of ions on non-covalent interactions and the importance of the selection of AmAc concentrations to quantify the receptor-target binding strengths. Besides, considering the inability of the native MS to discriminate the specific ligand-protein interactions from the non-specific interactions, Zheng et al.  and the action intensity, so as to provide better help for drug design and disease treatment. With the development of soft ionization technologies, MS-based methods will continue to develop and improve, which will definitely promote the analysis and research of protein/enzyme-ligand interactions, so as to better serve for human health.MG, JW, and BS conceived, designed, and supervised the manuscript. GC, MF, YL, ML, and NL collected the literatures, analyzed the data, and wrote the manuscript. All authors reviewed and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Plants use surface receptors to perceive information about many aspects of their local environment. These receptors physically interact to form both steady state and signalling competent complexes. The signalling events downstream of receptor activation impact both plant developmental and immune responses. Here, we present a comprehensive study of the physical interactions between the extracellular domains of leucine-rich repeat receptor kinases (LRR-RKs) in Arabidopsis. Using a sensitized assay, we tested reciprocal interactions among 200 of the 225 Arabidopsis LRR-RKs for a total search space of 40,000 interactions. Applying a stringent statistical cut-off and requiring that interactions performed well in both bait-prey and prey-bait orientations resulted in a high-confidence set of 567 bidirectional interactions. Additionally, we identified a total of 2,586 unidirectional interactions, which passed our stringent statistical cut-off in only one orientation. These datasets will guide further investigation into the regulatory roles of LRR-RKs in plant developmental and immune signalling decisions. Arabidopsis thaliana (hereafter Arabidopsis)2. This family of receptors controls key developmental and immune responses4. In spite of great efforts, the vast majority of these proteins still have no defined biological functions, and therefore little progress has been made in understanding how the signals downstream of these receptors are integrated to guide decisions that regulate both plant development and pathogen defense processes5.Due to their sessile nature, plants must be able to accurately sense ever-changing signals from their local growth environment. The detection of environmental signals at the cell surface is mediated partly through the molecular activities of an expanded protein family of leucine-rich repeat receptor kinases (LRR-RKs), with 225 members in 4. The identification of shared components between immune and developmental signalling complexes provides at least one possible mechanism for the observed cross-talk between these pathways6. Systematic information about how members of the LRR-RK family physically interact to affect signalling has not been previously available, as these proteins are biochemically poorly tractable. To systematically identify the physical interactions between the ECDs of LRR-RKs in the model plant Arabidopsis, we undertook a large-scale screening effort. The interactions between LRR-RKs are known to be transient and of low affinity, especially in the absence of an activating ligand, and we therefore implemented the sensitized extracellular interaction assay (ECIA) method to test for interactions7. The method is based on the avidity-based extracellular interaction screen (AVEXIS) technique, which has been optimized to observe weak interactions. In AVEXIS the prey protein construct includes a pentamerization domain to increase assay sensitivity8. In a recent publication, we cloned and expressed the ECDs of 200 of the 225 Arabidopsis LRR-RKs in both bait and prey constructs to conduct an all-by-all protein interaction screen, and thus assayed the total possible LRR-RK interaction space to a 79% completeness9.The best-studied examples of LRR-RK activation initially involve a ligand binding to the extracellular domain (ECD) of a cognate receptor. This induces the physical interaction between the ECDs of the receptor LRR-RK and a co-receptor LRR-RK to form a signalling competent complex9. However, the use of such stringent statistical cut-offs to produce the bidirectional dataset has likely resulted in the omission of biologically relevant data. For instance, interactions occurring only in one of the bait-prey or prey-bait orientations  have the potential to yield further biological insights.Here, we present the data from that study in its most expanded form, including the data used for the published analyses, an additional analysis that identifies a set of interactions that are found in only one orientation, and the raw data needed for the implementation of other normalization and hit-calling protocols. These data provide unique opportunities to formulate experimentally testable hypotheses aimed at understanding further how physical interactions in LRR-RK complexes control plant developmental and immune responses. We chose an extremely stringent cut-off to build a high-confidence interaction network including the most reliable bidirectional interactions. Next, we used these data to: i- assign biological function to previously uncharacterized receptors, and ii- demonstrate that the interconnectivity of physical interactions between LRR-RKs is a requisite to appropriately transduce a complex range of environmental signals to the plant9.The methods described here are expanded from those found in our related work on this topic10, and the transmembrane domain predicted using Phobius11, TMHMM12, and other prediction programs for secondary structure prediction such as InterPro13. We further improved ECD boundary prediction by visual inspection of primary amino acid sequences to identify the location of the N- and C-terminal cysteine-capping consensus motifs (CXXXXC and variations thereof). The LRR domains form a hydrophobic core and these motifs are thought to cap this region and produce disulphide bonds to maintain proper tertiary structure. We have found that removal of these cysteine caps results in reduced expression and solubility in vitro. Once the proper sequence was determined, we designed primers that added the additional sequences required for RecA-mediated Sequence and Ligation Independent Cloning  and the pECIA-14 (for expression as a prey) vectors for expression in Drosophila melanogaster Schneider 2 (S2) cells (vectors were a gift from C. K. Garcia)7. These expression vectors are modified versions of pMT/BiP/V5 , which are driven by a copper-inducible Drosophila metallothionein promoter and contain the Drosophila BiP protein signal sequence. Sequences were cloned between the existing BiP signal sequence and the C-terminal epitope tags specific to each vector, and the presence of the correct ECD insert was confirmed with Sanger sequencing prior to expression.The ECDs of LRR-RKs present several challenges for effective expression, which has led to a dearth of studies involving the use of recombinant proteins on a large scale. To express these domains, we first identified the location of signal peptides and transmembrane domains to determine the boundaries of the extracellular domains. The signal peptide was identified using SignalP4.0 Cloning . AmplifiDrosophila S2 cells cultured at 27 °C in ESF 921 Insect Cell Culture Medium, Protein Free (Expression Systems). S2 cells were transiently transfected with expression vectors using Effectene (Qiagen) per manufacturer’s instructions followed by incubation at 21 °C. Twenty-four hours later, protein expression was induced with 1 mM CuSO4 and the supernatant was collected after three days of induction. After harvest the media containing the expressed ECDs was supplemented with protease inhibitor cocktail (Sigma) and 0.02% NaN3 and then stored at 4 °C until use. Protein expression was assessed by western blotting using anti-V5 antibodies (Invitrogen) for bait proteins or by alkaline phosphatase activity quantification for prey proteins.All proteins were expressed using 7. The media containing the recombinant ECDs was diluted four-fold with PBS buffer containing 1 mM CaCl2, 1 mM MgCl2 (equilibration buffer), and 0.1% bovine serum albumin . First, assay plates were prepared by adsorbing 100 μl of media containing bait proteins fused with an Fc domain, directly to 96-well protein-A-coated plates (Thermo Fisher Scientific) by overnight incubation at 4 °C. The coated plates were then washed with a PBS solution containing 0.1% Tween-20 before use to remove any loose protein that could interfere with subsequent protein interaction. The washed plates were then blocked with 100 μl of equilibration buffer containing 1% BSA for 3 h at 4 °C and then washed once more. 100 μl of the diluted media containing the prey proteins fused to alkaline phosphatase was then added to the wells and incubated for 2 h at 4 °C and then washed away prior to adding the alkaline phosphatase (AP) substrate (KPL). Protein interaction between bait and prey was quantified by measuring the absorbance at 650 nm using a Synergy H4 Multi-Mode plate reader (BioTek) after 2 h of incubation at room temperature. In addition to quantitative data, an image was captured of each 96-well plate for visual inspection. Visual inspection ensured that the included positive (containing the known interaction pair BAK1-BIR416) and negative (prey only) control wells performed as expected, allowing the associated plate to be included in downstream analysis. The complete set of raw absorbance values was combined into a binary dataset using an in-house designed script (Platero v0.1.4), and then subjected to post-experimental statistical analysis to remove both false positive and false negative interactions.Pairwise reciprocal interaction assays were performed largely as previously described for the extracellular interactome assay with the modifications noted below18. In the data matrix, each row represented an individual bait preparation, and each column represented an individual prey protein preparation. The 2-way median polish has the effect of removing first any assay wide effect (the background level of absorbance in the assay and any effect of the 96-well plates used), followed by removing any row or column specific effects (e.g. the intrinsic level of absorbance associated with a given protein preparation). The result is a data matrix containing only the residual values, which in effect are the amount of the observed absorbance that can be attributed to the physical interaction between the bait and prey having considered the overall level of absorbance associated with those proteins screen-wide. Thus, those protein pairs that physically interact can be viewed as outliers in the dataset, regardless of whether the individual proteins involved show an overall high or low level of background absorbance in the screen.The complete set of raw absorbance values (Data Citation 1) for each protein pair in both directions was compiled into a data matrix containing 200 columns and 200 rows. To accurately compare absorbance values, we first needed to eliminate any bias in the data arising from the differential background binding capacities of the individual bait and prey protein preparations. This may arise due to variation in protein expression, protein stability, quality of Protein A coated plates, or intrinsic ‘stickiness’ of certain proteins. We implemented a two-way median polish approach to remove any effects that arose as a result of specific protein preparationsThe calculated residuals were then used to identify true interactions, which appear as outliers in the data distribution. We hypothesized that the data should contain mainly protein pairs that show no physical interaction, with a small subset of pairs that show high levels of interaction. This would result in a unimodal distribution centred on 0 with a tail to the right containing the interactions. Therefore, any statistical methods we use must be robust to outliers. We chose to use the median and median absolute deviation (MAD), rather than mean and standard deviation, to score the screen for this reason. The MAD is the median of the absolute deviation of each data point from the overall data median. After calculating the MAD value for the data, we can then relate each individual residual measurement in terms of the number of MAD units that it is away from the median value.et al.9, doi:10.1038/nature25184): http://bar.utoronto.ca/interactions.We chose an arbitrary, yet widely used, score cut-off of 2.5 to identify high confidence interactions, which results in a set of 2586 protein pairs that interact in one orientation . In addition, we determined that 567 interactions were found in both the bait-prey and the prey-bait orientations . To isolate these bidirectional interactions, we chose to use the geometric mean of the scores of the interaction as measured in the bait–prey and prey–bait orientations to avoid any undue influence by extreme values. Any value for which the geometric mean product was greater than 2.5 was considered significant for the highest-quality, bidirectional dataset (Data Citation 3). These two datasets are publicly available online at the Bio-Analytic Resource (BAR) under accession  normalization step. This results in scaled data that can be directly compared. For both datasets the geometric mean absorbance for each bidirectional interaction was calculated using the bait-prey and prey-bait values. To identify an appropriate cut-off for identification of positive physical interactions, the threshold geometric mean value for inclusion in the bidirectional dataset in the primary screen was calculated and found to be 0.090989. Therefore, any interaction in the retest screen with a geometric mean value > 0.090989 was considered positive, while all others were considered negative.https://github.com/AdamMott/platero-code.The custom PLATERO script used for concatenating the interaction absorbance values is available from et al.9. The column headings are as follows:The raw interaction absorbance data from the CSI has been deposited to FigShare (Data Citation 1), and has previously been available in Supplementary Table 11 of Smakowska-Luzan This is the unique locus identifier for the Arabidopsis gene from which the nucleic acid sequence corresponding to the ECD was extracted and cloned into the bait expression plasmid.This is the unique locus identifier for the Arabidopsis gene from which the nucleic acid sequence corresponding to the ECD was extracted and cloned into the prey expression plasmid.The raw absorbance value measured in the primary screen for the bait-prey pair listed.http://www.imexconsortium.org) consortium through IntAct19 and assigned the identifier IM-26261 (Data Citation 2), while those described in Smakowska-Luzan et al.9 can be found in IM-26260 (Data Citation 3).The protein interactions from this publication have been submitted to the IMEx . As expected, the non-interacting protein pair data distributions from both the primary and retest screens were statistically indistinguishable from the pooled negative controls, while the bidirectional interaction sets were statistically significantly different from all negative sets. We also directly compared the values from the primary screen and retest, which showed a Spearman correlation coefficient of 0.769, further demonstrating the reproducibility and reliability of the interactions measured in the screen.There are numerous potential uses for the presented datasets. First, direct physical interaction with a receptor of known biological function is a valuable predictor of function for uncharacterized receptors. Thus, the data can be used as a powerful hypothesis generation tool for subsequent experiments to identify novel actors in well-studied biological processes.9. Our network analysis is only one such representation, and similar networks could be built using different input data arising from the unidirectional interaction set, or from novel analyses of the raw data. A novel analysis of the network structure in fact has recently been used to derive powerful insights into how pathogens target the network to subvert plant immunity20. Such network structures are excellent tools for further inspection of other publicly available datasets, such as mapping gene expression data onto the network to identify co-expressed or co-regulated sub-networks.Second, the dataset as a whole can be used to construct a network analysis of the interactions, as we demonstrated in our recent publicationHow to cite this article: Mott, G. A. et al. Map of physical interactions between extracellular domains of Arabidopsis leucine-rich repeat receptor kinases. Sci. Data. 6:190025 https://doi.org/10.1038/sdata.2019.25 (2019).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Begomovirus is a major constraint to cotton (Gossypium hirsutum) production in many cotton-growing regions of the world. Symptoms of the disease are caused by Cotton leaf curl Multan betasatellite (CLCuMB) that encodes a pathogenicity determinant protein, βC1. Here, we report the identification of interacting regions in βC1 protein by using computational approaches including sequence recognition, and binding site and interface prediction methods. We show the domain-level interactions based on the structural analysis of G. hirsutum SnRK1 protein and its domains with CLCuMB-βC1. To verify and validate the in silico predictions, three different experimental approaches, yeast two hybrid, bimolecular fluorescence complementation and pull down assay were used. Our results showed that ubiquitin-associated domain (UBA) and autoinhibitory sequence (AIS) domains of G. hirsutum-encoded SnRK1 are involved in CLCuMB-βC1 interaction. This is the first comprehensive investigation that combined in silico interaction prediction followed by experimental validation of interaction between CLCuMB-βC1 and a host protein. We demonstrated that data from computational biology could provide binding site information between CLCuD-associated viruses/satellites and new hosts that lack known binding site information for protein–protein interaction studies. Implications of these findings are discussed.Cotton leaf curl disease (CLCuD) caused by viruses of genus  Geminivirdae is classified into nine genera, having single-stranded (ss) circular genome encapsidated in a twin icosahedral particle that range in size from 18 to 30 nm  in the Indian subcontinent and Africa  are circular ssDNA molecules mostly associated with monopartite begomoviruses of OW (Ageratum yellow vein virus (AYVV)-infected Ageratum conyzoides plant (Tomato yellow leaf curl China virus (TYLCCNV) infection, interacts with Asymmetric leaves1 (AS1) to prevent normal leaf development and usurp cellular resources by interfering with jasmonic acid (JA) responsive genes to induce infestation by insect vector Bemisia tabaci -βC1, thus acts as an antiviral protein (Tomato golden mosaic virus (TGMV) and mutagenesis study determined the function of interacting domains involved in binding with the virus (Interaction study at domain level was performed for Sucrose-non-fermenting 1 (SNF1)-related kinase (SnRK1) protein present in  protein . Therefohe virus . All of he virus .in silico study is a great source to identify putative binding site between host and begomovirus to control CLCuD in future (Leading to protein–protein interaction (PPI), high-throughput technologies and bioinformatics data possess information for number of proteins at host side that are monitored during CLCuD development. Geminivirus proteins interact with a large number of host proteins during infection and n future . So far n future . Interacn future .G. hirsutum-encoded SnRK1α (GhSnRK1) protein at domain level. By using the sequence and structure information about the CLCuMB-βC1 and GhSnRK1 complex, it was determined that the α-helix in CLCuMB-βC1 where GhSnRK1 possessing ubiquitin-associated (UBA) and autoinhibitory sequence (AIS) domains are responsible for interaction during CLCuD. This in silico interaction data was verified by three independent experimental methods, yeast two hybrid (Y2H), bimolecular fluorescence complementation (BiFC) and pull-down assays. Findings provided a deeper understanding and insights into interactions underlying the begomovirus-host protein interactions.Here, we investigated cotton leaf curl Multan betasatellite (CLCuMB)-encoded βC1 protein’s binding with Multiple approaches were employed to identify interaction between virus CLCuMB and host GhSnRK1 protein. Host domain information was deduced from NCBI conserved domain database , InterPrin planta protein interaction study, wild type Nicotiana benthamiana seeds were grown in Sunshine Mix LC1 (Sun Gro Horticulture) in growth chambers with 120 μmol photons m-2 s-2, 16 h light/8 h dark cycle, 20°C. A distinct isolate of cotton leaf curl Multan betasatellite (acc AM774307) was used as inoculum source for this study. SNF1-related kinase protein GhSnRK1 from cotton (G. hirsutum) cultivar UA222 resistant variety has been used for isolation of host proteins. All the GhSnRK1 domains based data were also generated from the same cultivar UA222.For Total RNA was extracted using RNeasy Plant Mini Kit (Qiagen) from virus infected sample and cotton plant following the manufacturer’s instructions. Purified RNA was then reverse transcribed to generate cDNA using a RevertAid first strand cDNA synthesis kit (Thermo Fisher Scientific). RT-PCR based amplified products were then inserted into pENTR-D-TOPO vector (Invitrogen). Virus and host sequences are available at GenBank . Further, Gateway cloning based amplicons were used for all destination vectors using specific primers .Saccharomyces cerevisiae) strain EGY48 possessing pSH18-34 was used in lithium acetate yeast transformation procedure described as  protein (TSWV-N) was used as a positive control to ensure the performance of the transformation protocol and screening steps during this assay . For destination vectors, pEZY202 and pEZY45 (Addgene plasmid # 18704 and 18705)  plasmidsribed as . GhSnRK1is assay . To furtin vivo virus host interaction study, binary vectors based on yellow fluorescent marker (pSITE-nEYFP-C1 and pSITE-cEYFP-C1) were obtained from Arabidopsis Biological Research center . Host proteins were cloned at “N” terminal and CLCuMB-βC1 at “C” terminal. All clones were confirmed using gene specific primers  pull down assay was performed as described in detail previously (E. coli BL21 (DE3) strain using sonication method and mixed together to produce three tubes as “Load,” “Flow-Through,” and “Elution” after consecutive washes and final elution step. All purified proteins with their controls  were separated on sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) followed by Western blotting using monoclonal anti-GST antibody (primary) which was further probed with secondary antibody goat HRP-conjugated anti-rabbit IgG (Bio-Rad). Positive signals were acquired on short exposure x-ray films using the ECL method based on Versa Doc imaging system (Bio-Rad) following the manufacturer’s details to determine the interacting domains of host with CLCuMB-βC1.For eviously . MBP-tagC-score is a confidence score used to estimate the accuracy of the predicted models in the range of -5 to +2. G. hirsutum-coded SnRK1 protein structure and CLCuMB-βC1 were used for domain-based interaction prediction, whereas Solanum lycopersicum-coded SnRK1 and TYLCCNB-βC1 complex were used as a control.GhSnRK1 and its domain-based information was retrieved using online data servers shown in the Section “Materials and Methods.” This sequence analysis showed GhSnRK1 encodes a 506 aa protein, consisting of four domains, kinase domain KD (259 aa), Ubiquitin-associated domain UBA (59 aa) and autoinhibitory sequence AIS (104 aa), and C-terminal domain CTD (65 aa) . StructuSequence and structure alignment of TYLCCNB with CLCuMB and host protein SiSnRK1 with GhSnRK1 using Water  and PyMOTo determine the possible interaction between CLCuMB-βC1 and GhSnRK1, we used several binding affinity prediction methods. For binding energy, change in Gibbs free energy ΔΔ(G) score between CLCuMB-βC1 and GhSnRK1 protein was determined. Negative value for ΔΔG  indicateIn case of domain-based interaction, ΔΔG values for KD and CTD were less negative while UBA and AIS (bold) values were more negative shown in In addition to the binding affinity prediction, we used multiple binding site prediction methods based on protein sequence and structure. Binding score from sequence-based methods and predicted relative accessible surface area (RASA) values from structure-based methods identified residues mostly from UBA and AIS domains and very few (13 out of 269) residues were predicted from KD domain. This interaction was further studied in detail with docking and machine learning based methods. Docking methods such as ZDOCK 3.0.2  and DockPredictions obtained with all computational interaction methods were combined to identify potential interacting site(s) in GhSnRK1 protein using majority consensus. This bioinformatics approach was applied to CLCuMB-βC1 with GhSnRK1 and the control, TYLCCNB-βC1 with SiSnRK1 . For hosin silico mutagenesis study, chain “A” shows virus protein and “C” shows host protein structure in the complex, and yellow colored residues in pdb# are the amino acids mainly from UBA and AIS domain  for subcellular localization of the nucleus/nucleolus in the leaves  overexpressed SnRK1 protein through UBA and AIS domains resulting in a delay in symptom induction and reduced DNA level by phosphorylating βC1 . This prG. hirsutum coded SnRK1 protein also consist of four domains with a size of 506 aa, and in order to overcome some of the logistical constraints in analyzing the data, we first adopted a computational approach based on multi-variant approach which helped us to identify domains that are involved in interaction with CLCuMB-βC1. PPI study investigates the interaction among interfacial residues of two proteins using sequence and structure information  that acts as an important key regulator against abiotic stresses especially in abscisic acid signaling . Againstormation . Sequencormation . Sequencng sites . From biSequence-based methods rely on knowledge-based data that requires prediction for each residue present in a protein. Binding site prediction methods alone may not generate reliable information especially where the structure has not been determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy . Here wein vivo tool to investigate possible interacting partners, identifying protein role at a cellular level (G. hirsutum interacts with CLCuMB-βC1. Further, positive results for UBA and AIS domains on 3-AT-SD/-His/-Leu/-Trp/-Ura media also confirmed the in silico predictions for PPI. Same results were obtained in planta BiFC assay. Gateway vectors compatible for in planta detection of multiple protein interactions using BiFC system is a robust and rapid method to identify subcellular localization of a protein in organelle (To validate the predictions obtained by computational analyses, three independent molecular techniques were used to know the interaction between GhSnRK1 and CLCuMB-βC1 at the domain level. Y2H is an ar level . Y2H datrganelle . We perfBasically, SnRK1 are protein kinases that are involved in various physiological processes which regulates energy metabolism in plants to provide nutrients against biotic and abiotic stress . The SnRIn conclusion, our findings provide new insights into begomovirus–cotton interactions at the molecular level and lays foundation for further studies on structure-function relationships. Our findings suggested that bioinformatics approach could predict potential protein binding sites in viral- and host-coded proteins. And the evidence that CLCuMB-βC1 binds with GhSnRK1 through UBA and AIS domains. Our study also demonstrated that substitution mutants in a host protein could be a better choice to produce resistance against viruses, while at the same time keeping the host protein structure and function stable. Our results have shown that computational methods followed in present study could be useful in predicting and validating PPI. We also postulated that GhSnRK1 function becomes irreversible in the presence of any third protein related to the defense mechanism that may unphosphorylate CLCuMB-βC1, that enhances virus replication and movement caused during CLCuD.The comprehensive bioinformatics analyses that was carried out could facilitate further in depth study on the effects of substitution mutations of predicted binding residues on protein structure and its conformation, to gain further insights into the complex biological mechanisms of the cell. The computational approach combined with experimental verification presented here could be used to further understand the interactions between geminivirus-host and their biological significance. Geminiviruses mainly interfere with plant’s signaling pathways and its defense mechanism. The tools and materials developed in this study could facilitate further studies on fine-structure mapping of various motifs in both viral and host proteins and their role in modulating geminivirus replication and spread in cotton and the interacting partners in cotton. Knowledge gained from such studies could be useful in developing novel virus suppression strategies.HK, RM, MH, and MK did the experimental work. F-u-AM and MF did the bioinformatic analyses. DT provide the TSWV nucleoprotein gene construct. HK, IA, HP, and SM wrote the first draft. IA, SM, and HP conceived the idea. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Here we review the results and the evolution of the techniques used to study the interaction between metallo-proteins and DNA operators, all involved in the nickel and iron metabolism of pathogenic bacteria, focusing in particular on Helicobacter pylori (Hp). In the first part of the article we discuss the methods used to calculate the structure of complexes of proteins involved in the activation of the nickel-dependent enzyme urease. In the second part of the article, we concentrate on two applications of protein-DNA docking conducted on the transcription factors HpFur (ferric uptake regulator) and HpNikR (nickel regulator). In both cases we discuss the technical expedients used to take into account the conformational variability of the multi-domain proteins involved in the calculations.The interaction between macromolecules is a fundamental aspect of most biological processes. The computational techniques used to study protein-protein and protein-nucleic acid interactions have evolved in the last few years because of the development of new algorithms that allow the  S. SN-terme of DNA .HpFur  . . ca. 30 trations . The waytrations ,108, andtrations ,110. TheHpNikR interaction with the urease operator OPureA, based primarily on NMR spectroscopy in solution, have recently been reported [HpNikR-OPureA interaction using only NMR, the two protein domains were investigated separately. An NMR-based analysis of the perturbations, induced on the DBD by the presence of OPureA, provided information useful to guide the subsequent docking calculation aimed to build a computational model of the HpNikR-OPureA complex [HpNikR in the cis conformation was calculated in a previous study [E. coli in the cis conformation  [H. pylori, E. coli, and P. horikoshii) was produced using ClustalW [cis conformation of tetrameric HpNikR using the program Modeller [ureA operator was generated using the DNA analysis and rebuilding software 3DNA implemented in the 3D-DART server [ureA comprises nucleotides from −56 to −87 with respect to the urease operon transcriptional start site in H. pylori strain G27 [H. pylori strain G27 genome. The model was generated in the canonical B-DNA conformation. The holo-HpNikR model structure in the cis conformation was docked onto OPureA using the data-driven docking program HADDOCK 2.1 and the same protocol used previously in the case of Fur (see ref. [H. pylori strain G27).Some molecular details of the reported . Conside complex . The homus study . The mod 3.00 Å) . The aliClustalW  and manuModeller . Four sqModeller ,111. A sT server . OPureA see ref.  and abovHpNikR and OPureA , flat, and relatively featureless [Current methods and libraries for drug discovery work well with a limited number of targets, such as enzymes, ion channels or receptors, which feature a well-defined and structurally-stable concave binding sites. The G protein-coupled receptors (GPCRs), protein kinases and proteases super-families are good examples of this kind of targets with “druggable” characteristics ,117,118.tureless , making tureless . Indeed,tureless . Moreovetureless ,123. Fintureless .The development of protocols to mimic these difficult interactions is a key answer to these pressing needs, with the potential to offer the molecular details of new druggable targets. First it has been recognized that energetic hotspots contribute much of interface interaction free energy in many PPIs , but als