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Foliar fertilization for biofortification is a targeted, economic and environment friendly approach rather than soil fertilization. In this study, we identified the appropriate growth stage(s) and right foliar iron (Fe) formulation in enhancing growth and Fe biofortification in soybean. In addition, we studied the physiological mechanism adopted by plants to endure foliar supplied Fe. For this purpose, a field experiment was conducted to evaluate various organic and inorganic Fe formulations such as Fe-citrate, FePO4, humic acid (HA) + Fe, HA alone and nano-Fe along with control (deionized water) in soybean (Glycine maxvar. DS-2614). Plants were sprayed at flowering (Set I), pod filling (Set II) and at both stages (Set III). Biomass and leaf area were significantly enhanced with application of Fe-citrate and FePO4followed by HA + Fe. The yield traits (pod number, seed yield, test weight) significantly increased with HA + Fe and nano-Fe application. Enhanced Fe accumulation in seed was observed with HA + Fe followed by Fe-citrate and nano-Fe treatment. Foliar application of Fe at pod filling stage improved growth whereas yield and Fe fortification improved in Set III. This response may be attributed to enhanced activities of antioxidant scavenging enzymes and reduced lipid peroxidation in leaves treated with HA + Fe followed by FePO4. Results suggest foliar application of HA + Fe (organic Fe) and nano-Fe to be promising for soybean in improving growth and seed Fe content.

Background: α-Synuclein has become the main therapeutic target in Parkinson's disease and related Synucleinopathies since the discovery of genetic associations between α-Synuclein and Parkinson's disease risk and the identification of aggregated α-Synuclein as the primary protein constituent of Lewy pathology two decades ago. The two new peptides K84s (FLVWGCLRGSAIGECVVHGGPPSRH) and K102s (FLKRWARSTRWGTASCGGS) have recently been found to significantly reduce the oligomerization and aggregation of α-Synuclein. However, it is still unclear where these peptides interact with α-Synuclein at the moment. Objective: To examine the locations where K84s and K102s interact with α-Synuclein. Methods: In this investigation, the PEPFOLD3 server was used to generate the 3-D structures of the K84s and K102s peptides. Using the PatchDock web server, the two peptides were docked to the α- Synuclein molecule. After that, 50 ns of Molecular Dynamics (MD) simulations using the Amberff99SBildn force field were performed on the two resulting docked complexes. The two complexes' structure, dynamics, energy profiles, and binding modes were identified through analysis of the respective MD simulation trajectories. By submitting the two complexes' lowest energy structure to the PDBsum website, the interface residues in the two complexes were identified. The per residue energy decomposition (PRED) analysis using the MM-GBSA technique was used to calculate the contributions of each residue in the α-Synuclein of (α-Synuclein-K84s/K102s) complexes to the total binding free energy. Results: The binding of the two peptides with the α-Synuclein was demonstrated to have high binding free energy. The binding free energies of the (α-Synuclein-K84s) and (α-Synuclein-K102s) complexes are -33.61 kcal/mol and -40.88 kcal/mol respectively. Using PDBsum server analysis, it was determined that in the (α-Synuclein-K84s) complex, the residues GLY 25, ALA 29, VAL 49, LEU 38, VAL 40, GLU 28, GLY 47, LYS 32, GLU 35, GLY 36, TYR 39, VAL 48 and VAL 26 (from α-Synuclein) and SER 23, LEU 7, ILE 12, HIS 25, PHE 1, HIS 18, CYS 6, ARG 24, PRO 21 and ARG 8 (from K84s peptide) were identified to be present at the interface. In the (α-Synuclein- K102s) complex, the residues VAL 40, GLY 36, GLU 35, TYR 39, LYS 45, LEU 38, LYS 43, VAL 37, THR 44, VAL 49, VAL 48, and GLU 46 (from α-Synuclein) and ARG 10, GLY 12, GLY 18, SER 15, THR 13, SER 19, TRP 11, ALA 14, CYS 16, ARG 7, ARG 4 and GLY 17 (from K102s peptide) were identified to be present at the interface. The PRED analysis revealed that the residues PHE 1, LEU 7, ILE 12, LEU 2, VAL 3, GLY 5, and PRO 21 of the K84s peptide and residues VAL 48, ALA 29, VAL 40, TYR 39, VAL 49, VAL 26 and GLY 36 of α-Synuclein in the (α- Synuclein-K84s) complex are responsible for the intermolecular interaction. The residues ARG 4, ARG 10, TRP 11, ALA 14, SER 15, CYS 16 and SER 19 of the K102s peptide and residues GLU 46, LYS 45, VAL 49, GLU 35, VAL 48, TYR 39, and VAL 40 of α-Synuclein are responsible for the intermolecular interaction in the instance of the (α-Synuclein-K102s) complex. Additionally, it has been found that a sizable portion of the helical structure is preserved when α-Synuclein is in a complex form with the K84s and K102s peptides. Conclusion: Taken together the data implies that the two new peptides investigated here could be suitable candidates for future therapeutic development against α-Synuclein aggregation.

Background: Aggregation of misfolded proteins under stress conditions in the cell might lead to several neurodegenerative disorders. Amyloid-beta (Aβ1-42) peptide, the causative agent of Alzheimer’s disease, has the propensity to fold into β-sheets under stress, forming aggregated amyloid plaques. This is influenced by factors such as pH, temperature, metal ions, mutation of residues, and ionic strength of the solution. There are several studies that have highlighted the importance of ionic strength in affecting the folding and aggregation propensity of Aβ1-42peptide. Objective: To understand the effect of ionic strength of the solution on the aggregation propensity of Aβ1-42peptide, using computational approaches. Materials and Methods: In this study, Molecular Dynamics (MD) simulations were performed on Aβ1-42peptide monomer placed in (i) 0 M, (ii) 0.15 M, and (iii) 0.30 M concentration of NaCl solution. To prepare the input files for the MD simulations, we have used the Amberff99SB force field. The conformational dynamics of Aβ1-42peptide monomer in different ionic strengths of the solutions were illustrated from the analysis of the corresponding MD trajectory using the CPPtraj tool. Results: From the MD trajectory analysis, we observe that with an increase in the ionic strength of the solution, Aβ1-42peptide monomer shows a lesser tendency to undergo aggregation. From RMSD and SASA analysis, we noticed that Aβ1-42peptide monomer undergoes a rapid change in conformation with an increase in the ionic strength of the solution. In addition, from the radius of gyration (Rg) analysis, we observed Aβ1-42peptide monomer to be more compact at moderate ionic strength of the solution. Aβ1-42peptide was also found to hold its helical secondary structure at moderate and higher ionic strengths of the solution. The diffusion coefficient of Aβ1-42peptide monomer was also found to vary with the ionic strength of the solution. We observed a relatively higher diffusion coefficient value for Aβ1-42peptide at moderate ionic strength of the solution. Conclusion: Our findings from this computational study highlight the marked effect of ionic strength of the solution on the conformational dynamics and aggregation propensity of Aβ1-42peptide monomer.