In silico Prediction, Characterization, Molecular Docking, and Dynamic Studies on Fungal SDRs as Novel Targets for Searching Potential Fungicides Against Fusarium Wilt in Tomato

Abstract

Vascular wilt of tomato caused by 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 (namely; FOXG_04696) based on homology search, percent identity and sequence similarity (93% query cover; 49% identity). 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 (ΔG_bind) 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 (structural integrity and conformational flexibility of docking) 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.

Keywords: MD simulations; MM/GBSA analysis; THN reductase; fungicide; homology modeling; melanin; protein–fungicide interaction.

FIGURE 1

General mechanism of DHN melanin biosynthesis pathway in fungi. The tetrahydroxynaphthalene reductase (T4HNR) catalyzes the NADP(H)-dependent reduction of 1,3,6,8-tetrahydroxynaphthalene (THN) into (+)-scytalone and 1,3,8-trihydroxynaphthalene into (–)-vermelone. 1,8-dihydroxynaphthalene (DHN) is the immediate precursor of the polymer.

FIGURE 2

Phylogenetic relationships between the different fungal taxa showing the evolution of short-chain dehydrogense/reductases (T4HNR like) protein. The tree was constructed based on distance-based neighbour-joining (NJ) method with 1000 bootstrap relications using MEGA6.0. The tree showed the existence of several clades for fungal short-chain dehydrogenases/reductases (SDRs) between the evolutionarily related taxa. The hypothetical protein (FOXG_04696) lacks common ancestor and therefore predicts the polyphyletic evolution of SDR in Fusarium oxysporum f.sp. lycopersici. The bootstrap values are mentioned below the tree.

FIGURE 3

The circos visualization map showing the similarities and differences for the SDRs (T4HNR like) among the five major phytopathogenic fungi, retrieved from genome comparision (based on sequential alignment). The circos map was generated at 50% cutoff score values and drawn using percentage identity matrices, calculated and obtained during phylogenetic clustering of the protein sequences using ClustalW, and represented the positional conservation and relationship between the genomic intervals.

FIGURE 4

(A) The comparison of the query protein (FOXG-04696) with the protein sequences of the T4HNR (M. grisea). The two protein sequences (1JA9 with the FOXG-04696) were aligned using the BL2seq (Blast-p). The hypothetical protein FOXG_04696 show more sequence similarity based on the percentage identities (49%) and query coverages (97%) with the 1JA9. The other structural homolog the 17-β-hydroxysteroid dehydrogenase (3IS3) had percentage identities (46%) and query coverages (96%). (B) Prediction of the FOXG_04696 protein encoding gene with coding sequences, transcription start sites and Poly A tail.

FIGURE 5

(A) Predicted structure of the FOXG_04696 modeled through homology modeling using Modeller v9.19 and visualized through the Discovery Studio 3.0 visualization tool. (B) The big red sphere represents the cavities surrounding the active sites and was visualized using the visualization module of the Discovery studio 3.0. The three binding sites were explored through the meta-pocket server. (C) The three putative binding sites as shown through three different colored red balls. (D) General view of protein-ligand interaction showing the residues from the active site (FOXG_04696) residues involved in making interaction with ligand (fungicide).

FIGURE 6

The qualitative assessment of the predicted model FOXG_04696 and its comparative evaluation with the X-Ray resoluted template proteins (1JA9) using the ProtSAV score. (A) The qualitative assessment of the modelled protein (FOXG_04696) based on ProTSAV score. (B) The ProTSAV score for the template protein 1,3,6,8-tetrahydroxynaphthalene reductase (T4HNR) complexed with NADPH and pyroquilon (1JA9). The ProTSAV evaluated the predicted model structures, based on some popular online servers and standalone tools, and furnishes with a single quality score in case of individual protein structure, along with a graphical representation and ranking in case of multiple protein structure assessment. In our results, the ProTSAV score was found close to 1JA9 which predicts the model has reasonable stability and accuracy in terms of qualitative and quantitative parameters.

FIGURE 7

(A) Functional annotation of the FOXG_04696 measured in the form of gene ontology enrichment analysis. The three ontological terms used were the molecular function, biological process involved, and cellular location. The sub-cellular localization of the protein was shown as a big pie chart and were retrieved through the Cello predictor. (B) The tag cloud diagram showing the keywords that are frequently associated with the FOXG_04696 protein and indicates its probable function in biosynthetic mechanisms and metabolism.

FIGURE 8

(A) The overall 3D surface view of the modeled protein FOXG_04696 represented to display all the possible H-bond donor and acceptor group when complexed with ligand (Famoxadone). (B) The interaction of the ligand (Famoxadone) with protein FOXG_04696 with the possible H-bond donor and acceptor groups, near the ligand interacting or binding sites (active sites). (C) The 3D representation of the ligand Oxathiapiprolin. (D) The 3D structure of the ligand Famoxadone.

FIGURE 9

(A) The ligand Famoxadone represented through the wire mesh diagram to show the probable H-bond donor or acceptor groups that could have crucial role in protein–fungicide interaction. (B) 3D representation of the ligand molecule when docked with the FOXG_04696 showing the crucial residues of protein that have important contribution in binding with the ligand donor or acceptor group.

FIGURE 10

(A) The 2D representation for the docked complex of the FOXG_04696. The figure showed the putative residues involved in interaction with Famoxadone. The different colors have been used for showing the different types of molecular interactions involved. (B) MD simulations trajectories for the FOXG_04696–Famoxadone complex showing the average potential energies of the docked complexes during the 50-ns MD simulations. (C) Plot of the root mean square deviation (RMSD) of Cα of T4HNR (protein) and the FOXG_04696–Famoxadone (complex). RMSDs were calculated using the initial structures as templates.

FIGURE 11

(A) The Oxathiapiprolin–FOXG_04696 interaction results during 50-ns MD simulations. (B) Interaction of the Famoxadone with FOXG_04696 showing the residues involved in protein–fungicide docking with different type of molecular interactions. (C) The correlation plot showing the values of correlation coefficient R² = 0.335 based on binding affinities (kcal/mol) and MM/GBSA (ΔG) binding free energy calculations showing the strong correlation between the binding free energies ΔG_bind and binding affinities for ranking/scoring of the fungicides in the docked complexes. The three later symbols with different colors have been used for representing the ligands. (D) The scatter plot displaying docking (XPG) and binding free energy MM/GBSA (ΔG_bind) scores represented in the quadrant view form for all the 19 protein–fungicide docked complexes.

FIGURE 12

The protein–protein interaction associative network for the FOXG_04696 through STRING server. The active interaction sources were set based on the seven parameters including experiments, co-expression, gene fusion, co-occurrence, databases, text mining, and neighborhood. (A) The interactions analyzed at the highest confidence level (0.90) with maximum five interacting partners from both shells of interactors. (B) Interaction at high confidence level (0.70). The color nodes describe query proteins and the first shell of interactors, whereas white nodes are the second shell of interactors. The large node size represents characterized proteins and smaller nodes for uncharacterized proteins.

FIGURE 13

In vitro assessment of the fungicide on growth response of FOL pathogen. Four different concentrations were selected 50, 100, 150, and 200 μL along with control at 4 days interval (A-I) and at 8 days interval (A-II). (B) The growth was recorded at even day’s interval (2, 4, 6, and 8) and the percentage inhibition was calculated using statistical tools.