3D Structure Research Articles

Enhanced photoelectrochemical water splitting by a 3D hierarchical sea urchin-like structure: ZnO nanorod arrays on TiO2hollow hemisphere.

A hierarchical sea urchin-like hybrid metal oxide nanostructure of ZnO nanorods deposited on TiO2 porous hollow hemisphere with a thin zinc titanate interface layer is specifically designed and synthesized forming a combined type I straddling and type II staggered junctions. The HHSs, synthesized by electrospinning, facilitate light trapping and scattering. The ZnO nanorods offer a large surface area for improved surface oxidation kinetics The interface layer of zinc titanate (ZnTiO3) between the TiO2 HHSs and ZnO nanorods regulates the charge separation in a closely coupled hierarchy structure of ZnO/ZnTiO3/TiO2. The synergistic effects of improved light trapping, charge separation, and fast surface reaction kinetics result in a superior photoconversion efficiency of 1.07% for the photoelectrochemical (PEC) water splitting with an outstanding photocurrent density of 2.8 mA cm-2 at 1.23 V vs. RHE.

Multimodal Protein Representation Learning and Target-aware Variational Auto-encoders for Protein-binding Ligand Generation

Abstract Without knowledge of specific pockets, generating ligands based on the global structure of a protein target plays a crucial role in drug discovery as it helps reduce the search space for potential drug-like candidates in the pipeline. However, contemporary methods require optimizing tailored networks for each protein, which is arduous and costly. To address this issue, we introduce TargetVAE, a target-aware variational auto-encoder that generates ligands with desirable properties including high binding affinity and high synthesizability to arbitrary target proteins, guided by a multimodal deep neural network built based on geometric and sequence models, named Protein Multimodal Network (PMN), as the prior for the generative model. PMN unifies different representations of proteins (e.g., primary structure - sequence of amino acids, 3D tertiary structure, and residue-level graph) into a single representation. Our multimodal architecture learns from the entire protein structure and is able to capture their sequential, topological, and geometrical information by utilizing language modeling, graph neural networks, and geometric deep learning. We showcase the superiority of our approach by conducting extensive experiments and evaluations, including predicting protein-ligand binding affinity in the PBDBind v2020 dataset as well as the assessment of generative model quality, ligand generation for unseen targets, and docking score computation. Empirical results demonstrate the promising and competitive performance of our proposed approach. Our software package is publicly available at https://github.com/HySonLab/Ligand_Generation

Genome mining of Mycobacterium tuberculosis: targeting SufD as a novel drug candidate through in silico characterization and inhibitor screening

Tuberculosis (TB) stands as the second most fatal infectious disease globally, causing 1.3 million deaths in 2022. The resurgence of TB and the alarming rise of antibiotic resistance demand urgent call to develop novel antituberculosis drugs. Despite concerted efforts to control TB, the disease persists and spreads rapidly on a global scale. Targeting stress response pathways in Mycobacterium tuberculosis (Mtb) has become imperative to achieve complete eradication. This study employs subtractive genomics to identify and prioritize potential drug targets among the hypothetical proteins of Mtb, focusing on indispensable pathways. Amongst 177 essential hypothetical proteins, 152 were nonhomologous to human. These proteins participated in 34 pathways, and a 20-fold enrichment of SUF pathway genes led to its selection as a target pathway. Fe–S clusters are fundamental, widely distributed protein cofactors involved in vital cellular processes. The survival of Mtb in a hypoxic environment relies on the iron–sulfur (Fe–S) cluster biogenesis pathway for the repair of damaged Fe–S clusters. It also protects pathogen against drugs, ensuring controlled iron utilization and contributing to drug resistance. In Mtb, six proteins of Fe–S cluster assembly pathway are encoded by the suf operon. The present study was focused on SufD because of its role in iron acquisition and prevention of Fenton reaction. The research further delves into the in silico characterization of SufD, utilizing bioinformatics tools for sequence and structure based analysis. The protein’s structural features, including the identification of conserved regions, motifs, and 3D structure prediction enhanced functional annotation. Target based virtual screening of compounds from the ChEMBL database resulted in 12 inhibitors with best binding affinities. Drug likeness and ADMET profiling of potential inhibitors identified promising compounds with favorable drug-like properties. The study also involved cloning in SUMO-pRSF-Duet1 expression vector, overexpression, and purification of recombinant SufD from E. coli BL21 (DE3) cells. Optimization of expression conditions resulted in soluble production, and subsequent purification highlighting the efficacy of the SUMO fusion system for challenging Mtb proteins in E. coli. These findings provide valuable insights into pharmacological targets for future experimental studies, holding promise for the development of targeted therapy against Mtb.

Nano-kirigami/origami fabrications and optical applications

Emerging nano-kirigami/origami technology enables the flexible transformations of 2D planar patterns into exquisite 3D structures in situ and has aroused great interest in the areas of nanophotonics and optoelectronics. This paper briefly reviews some milestone research and breakthrough progresses in nano-kirigami/origami from the aspects of stimuli approaches and application directions. Versatile stimuli for kirigami/origami, including capillary force, residual stress, mechanical force, and irradiation-induced stress, are introduced in the micro/nanoscale region. Appealing optical applications and reconfigurable schemes of nano-kirigami/origami structures are summarized, offering effective routes to realize tunable nanophotonic and optoelectronic devices. Future challenges and promising pathways are also envisioned, including design methods, innovative materials, multi-physics field driving, and reprogrammable devices.

Glucose-Binding Dioclea bicolor Lectin (DBL): Purification, Characterization, Structural Analysis, and Antibacterial Properties.

In this study, we purified a lectin isolated from the seeds of Dioclea bicolor (DBL) via affinity purification. Electrophoresis analysis revealed that DBL had three bands, α, β, and γ chains, with molecular masses of approximately 29, 14, and 12kDa, respectively. Gel filtration chromatography revealed that the native form of DBL had a molecular mass of approximately 100kDa, indicating that it is a tetramer. Interestingly, DBL-induced hemagglutination was inhibited by several glucosides, mannosides, ampicillin, and tetracycline with minimum inhibitory concentration (MIC) values of 1.56-50mM. Analysis of the complete amino acid sequence of DBL revealed the presence of 237 amino acids with high similarity to other Diocleinae lectins. Circular dichroism showed the prominent β-sheet secondary structure of DBL. Furthermore, DBL structure prediction revealed a Discrete Optimized Protein Energy (DOPE) score of -26,642.69141/Normalized DOPE score of -1.84041. The DBL monomer was found to consist a β-sandwich based on its 3D structure. Molecular docking showed the interactions between DBL and α-D-glucose, N-acetyl-D-glucosamine, α-D-mannose, α-methyl-D-mannoside, ampicillin, and tetracycline. In addition, DBL showed antimicrobial activity with an MIC of 125μg/mL and exerted synergistic effects in combination with ampicillin and tetracycline (fractional inhibitory concentration index ≤ 0.5). Additionally, DBL significantly inhibited biofilm formation and showed no toxicity in murine fibroblasts (p < 0.05). These results suggest that DBL exhibits antimicrobial activity and works synergistically with antibiotics.

Investigating Microstructure–Property Relationships of Nonwovens by Model-Based Virtual Material Testing

AbstractQuantifying the relationship between geometric descriptors of microstructure and effective properties like permeability is essential for understanding and improving the behavior of porous materials. In this paper, we employ a previously developed stochastic model to investigate microstructure–property relationships of nonwovens. First, we show the capability of the model to generate a wide variety of realistic nonwovens by varying the model parameters. By computing various geometric descriptors, we investigate the relationship between model parameters and microstructure morphology and, in this way, assess the range of structures which may be described by our model. In a second step, we perform virtual materials testing based on the simulation of a wide range of nonwovens. For these 3D structures, we compute geometric descriptors and perform numerical simulations to obtain values for permeability as an effective material property. We then examine and quantify the relationship between microstructure morphology and permeability by fitting parametric regression formulas to the obtained data set, including but not limited to formulas from the literature. We show that for structures which are captured by our model, predictive power may be improved by allowing for slightly more complex formulas.

Magnitude mb: Reducing Processing-Related Variability

Abstract It is well known that magnitude mb shows a large variation between different stations and agencies for the same event. In this study we investigate the various causes of this variation and we have found that apart from the regional variation caused by the Earth’s 3D structure, a major contribution to the variation is the way the data are processed: automatic or manual determination of amplitude and period is different for the same signal at different agencies or processing systems and/or there are errors in the amplitude measurements when correcting the signal from the simulated World Wide Standard Seismic Network Short Period (WWSSN-SP) response. Part of the problem to objectively investigate the causes for the variation is that few agencies use the International Association of Seismology and Physics of the Earth’s Interior standard for reporting the observations. To get less variability in mb, a standardized way of reading amplitude and period must be developed and used.

Near-field coupling of interlayer excitons in MoSe2/WSe2 heterobilayers to surface plasmon polaritons.

Two-dimensional (2D) transition metal dichalcogenides have emerged as promising quantum functional blocks benefitting from their unique combination of spin, valley, and layer degrees of freedom, particularly for the tremendous flexibility of moiré superlattices formed by van der Waals stacking. These degrees of freedom coupled with the enhanced Coulomb interaction in 2D structures allow excitons to serve as on-chip information carriers. However, excitons are spatially circumscribed due to their low mobility and limited lifetime. One way to overcome these limitations is through the coupling of excitons with surface plasmon polaritons (SPPs), which facilitates an interaction between remote quantum states. Here, we showcase the successful coupling of SPPs with interlayer excitons in molybdenum diselenide/tungsten diselenide heterobilayers. Our results indicate that the valley polarization can be efficiently transferred to SPPs, enabling preservation of polarization information even after propagating tens of micrometers.

Self-assembly of a AuNPs/Ti3C2 MXene hydrogel for cascade amplification of microRNA-122 biosensing.

A three-dimensional (3D) self-assembled AuNPs/Ti3C2 MXene hydrogel (AuNPs/Ti3C2 MXH) nanocomposite was prepared for the fabrication of a novel microRNA-122 electrochemical biosensor. The 3D hydrogel structure was gelated from two-dimensional MXene nanosheets with the assistance of graphite oxide and ethylenediamine. MXene hydrogels supported the in situ formation of Au nanoparticles (AuNPs) that predominantly exploring the (111) facet, and these AuNPs are utilized as carriers for hairpin DNA (hpDNA) probes, facilitating DNA hybridization. MXene acted as both a reductant and stabilizer, significantly improving the electrochemical signal. In addition, the conjugation of PAMAM dendrimer-encapsulated AuNPs and H-DNA worked as an ideal bridge to connect targets and efficient electrochemical tags, providing a high amplification efficiency for the sensing of microRNA-122. A linear relationship between the peak currents and the logarithm of the concentrations of microRNA-122 from 1.0 × 10-2 to 1.0 × 102 fM(I = 1.642 + 0.312 lgc, R2 = 0.9891), is obtained. The detection limit is 0.8 × 10-2 fM (S/N = 3). The average recovery for human serum detection ranged from 97.32 to 101.4% (RSD < 5%).

General and facile synthesis of Co/CoO nanoparticals supported by nitrogen-doped graphenic networks as efficient oxygen electrocatalyst forZn-air batteries.

Reasonable design of low-cost, high-efficiency and stable bifunctional oxygen electrocatalysts is of great significance to improve the reaction efficiency of Zn-air batteries, which is still a huge challenge. Here, we report a highly efficient bifunctional oxygen electrocatalyst with three-dimensional (3D) N-doped graphene network-supported cobalt and cobalt oxide nanoparticles (Co/CoO-NG), which can be in situ synthesized by inducing metal ions on metal plates via graphene oxide as an inducer. This 3D network structure and open active center show excellent bifunctional oxygen electrocatalytic activity under alkaline conditions, and can be used as an air electrode in rechargeable Zn-air batteries, with significantly better power density (244.28 mW cm-2) and stability (over 340 h) than commercial Pt/C + RuO2mixtures. This work is conducive to advancing the practical application of graphene-based materials as air electrodes for rechargeable zinc-air batteries.

Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma

Solid tumors are complex ecosystems with heterogeneous 3D structures, but the spatial intra-tumor heterogeneity (sITH) at the macroscopic (i.e., whole tumor) level is under-explored. Using a phylogeographic approach, we sequence genomes and transcriptomes from 235 spatially informed sectors across 13 hepatocellular carcinomas (HCC), generating one of the largest datasets for studying sITH. We find that tumor heterogeneity in HCC segregates into spatially variegated blocks with large genotypic and phenotypic differences. By dissecting the transcriptomic heterogeneity, we discover that 30% of patients had a “spatially competing distribution” (SCD), where different spatial blocks have distinct transcriptomic subtypes co-existing within a tumor, capturing the critical transition period in disease progression. Interestingly, the tumor regions with more advanced transcriptomic subtypes (e.g., higher cell cycle) often take clonal dominance with a wider geographic range, rejecting neutral evolution for SCD patients. Extending the statistical tests for detecting natural selection to many non-SCD patients reveal varying levels of selective signal across different tumors, implying that many evolutionary forces including natural selection and geographic isolation can influence the overall pattern of sITH. Taken together, tumor phylogeography unravels a dynamic landscape of sITH, pinpointing important evolutionary and clinical consequences of spatial heterogeneity in cancer.

Symmetry classification of 2D materials: layer groups versus space groups

Abstract The symmetry of a crystal structure with a three-dimensional (3D) lattice can be classified by one of the 230 space group types. For some types of crystals, e.g. crystalline films, surfaces, or planar interfaces, it is more appropriate to assume a 2D lattice. With this assumption the structure can be classified by one of the 80 layer group types. We have implemented an algorithm to determine the layer group type of a 3D structure with a 2D lattice, and applied it to more than 15 000 monolayer structures in the Computational 2D Materials (C2DB) database. We compare the classification of monolayers by layer groups and space groups, respectively. The latter is defined as the space group of the 3D bulk structure obtained by repeating the monolayer periodically in the direction perpendicular to the 2D lattice (AA-stacking). By this correspondence, nine pairs of layer group types are mapped to the same space group type due to the inability of the space group to distinguish the in-plane and out-of-plane axes. In total 18% of the monolayers in the C2DB belong to one of these layer group pairs and are thus not properly classified by the space group type. Our results show that symmetry classification of 2D materials should be based on layer groups rather than the commonly used space groups.

Linewidth characterization of a self-traceable grating by SEM

To achieve high-precision nanometrology, a self-traceable grating reference material has been reported and prepared using atom lithography and soft x-ray interference techniques (Liu et al 2021 Nanotechnology 32 175 301). In this work, we employ a Monte Carlo simulation method to investigate the scanning electron microscopy (SEM) image contrast and linewidth characterization of the grating linewidth. The 3D structure of mushroom-shaped grating lines made of multilayers (Pt, SiO2 and Si) is modeled according to transmission electron microscopy (TEM) images, enabling the SEM linescan profiles of secondary electron signals to be obtained for different values of structural linewidth parameters from Monte Carlo simulations. Using the principle of the model-based library method, a model database of Monte Carlo-simulated SEM linescan profiles is thus constructed by varying the incident electron beam conditions and the grating linewidths; then, the grating linewidth is successfully characterized using experimental SEM images. The comparison with the TEM measurement reveals that the measurement accuracy is verified to within 0.3% for the linewidth of ∼25 nm.

Single-Step Generation of 1D FeCo Nanostructures

Abstract Magnetic one-dimensional structures are attractive nanomaterials due to the variety of potential applications they can provide. The fabrication of bimetallic 1D structures further expands the capabilities of such structures by tailoring the magnetic properties. Here, a single-step template-free method is presented for the fabrication of 1D FeCo alloy nanochains. In this approach, charged single-crystalline FeCo nanoparticles are first generated by the co-ablation of pure Fe and Co electrodes under a carrier gas at ambient pressures and attracted to a substrate using an electric field. When reaching the surface, the particles are self-assembled into parallel nanochains along the direction of an applied magnetic field. The approach allows for monitoring the self-assembly particle by particle as they are arranged into linear 1D chains with an average length controlled by the deposited particle concentration. Facilitated by the self-assembly, the magnetic properties of the structures can be studied in detail. Magnetometry measurements revealed that arranging nanoparticles into nanochains results in a 100% increase in the remanent magnetization, indicating significant shape anisotropy. Furthermore, by combining X-ray microscopy and micromagnetic simulations, we have studied the local magnetization configuration along the nanochains. Our findings show that variations in magnetocrystalline anisotropy along the structure play a crucial role in the formation of magnetic domains.

Boron-Nitrogen-Embedded Polycyclic Aromatic Hydrocarbon-Based Controllable Hierarchical Self-Assemblies through Synergistic Cation-π and C-H···π Interactions for Bifunctional Photo- and Electro-Catalysis.

Boron-Nitrogen-embedded polycyclic aromatic hydrocarbons (BN-PAHs) as novel π-conjugated systems have attracted immense attention owing to their superior optoelectronic properties. However, constructing long-range ordered supramolecular assemblies based on BN-PAHs remains conspicuously scarce, primarily attributed to the constraints arising from coordinating multiple noncovalent interactions and the intrinsic characteristics of BN-PAHs, which hinder precise control over delicate self-assembly processes. Herein, we achieve the successful formation of BN-PAH-based controllable hierarchical assemblies through synergistically leveraged cation-π and C-H···π interactions. By carefully adjusting the solvent conditions in two progressive assembly hierarchies, the one-dimensional (1D) supramolecular assemblies with "rigid yet flexible" assembled units are first formed by cation-π interactions, and then they can be gradually fused into two-dimensional (2D) structures under specific C-H···π interactions, thus realizing the precise control of the transformation process from BN-PAH-based 1D primary structures to 2D higher-order assemblies. The resulting 2D-BNSA, characterized by enhanced electrical conductivity and ordered 2D layered structure, provides anchoring and dispersion sites for loading two appropriate nanocatalysts, thus facilitating the efficient photocatalytic CO2 reduction (with a remarkable CH4 evolution rate of 938.7 μmol g-1 h-1) and electrocatalytic acetylene semihydrogenation (reaching a Faradaic efficiency for ethylene up to 98.5%).

Kirigami-inspired, three-dimensional piezoelectric pressure sensors assembled by compressive buckling

Piezoelectric sensors whose sensing performances can be flexibly regulated hold significant promise for efficient signal-acquisition applications in the healthcare field. The existing methods for regulating the properties of polyvinylidene fluoride (PVDF) films mainly include material modification and structural design. Compared to material modification, which has a long test period and an unstable preparation process, structural design is a more efficient method. The irigami structure combined with compressive buckling can endow the flexible film with rich macrostructural features. Here, a method is fabricated to modulate the sensing performance by employing distinct 3D structures and encapsulation materials with varying Young’s moduli. The relationship among the aspect ratio (α), pattern factor (η), elastic modulus of encapsulation materials, and equivalent stiffness is obtained by finite element simulation, which provides theoretical guidance for the design of the 2D precursor and the selection of encapsulation materials. In the demonstration applications, the sensor accurately captures pulse waveforms in multiple parts of the human body and is employed for the pressure monitoring of different parts of the sole under various posture states. This method of structure design is efficient, and the preparation process is convenient, providing a strategy for the performance control of piezoelectric pressure sensors.

Use of in silico approaches, synthesis and profiling of Pan-filovirus GP-1,2 preprotein specific antibodies.

Intermolecular interactions of protein-protein complexes play a principal role in the process of discovering new substances used in the diagnosis and treatment of many diseases. Among such complexes of proteins, we have to mention antibodies; they interact with specific antigens of two genera of single-stranded RNA viruses belonging to the family Filoviridae-Ebolavirus and Marburgvirus; both cause rare but fatal viral hemorrhagic fever in Africa, with pandemic potential. In this research, we conduct studies aimed at the design and evaluation of antibodies targeting the filovirus glycoprotein precursor GP-1,2 to develop potential targets for the pan-filovirus easy-to-use rapid diagnostic tests. The in silico research using the available 3D structure of the natural antibody-antigen complex was carried out to determine the stability of individual protein segments in the process of its formation and maintenance. The computed free binding energy of the complex and its decomposition for all amino acids allowed us to define the residues that play an essential role in the structure and indicated the spots where potential antibodies can be improved. Following that, the study involved targeting six epitopes of the filovirus GP1,2 with two polyclonal antibodies (pABs) and 14 monoclonal antibodies (mABs). The evaluation conducted using Enzyme Immunoassays tested 62 different sandwich combinations of monoclonal antibodies (mAbs), identifying 10 combinations that successfully captured the recombinant GP1,2 (rGP). Among these combinations, the sandwich option (3G2G12* - (rGP) - 2D8F11) exhibited the highest propensity for capturing the rGP antigen.

A natural inhibitor of diapophytoene desaturase attenuates methicillin-resistant Staphylococcus aureus (MRSA) pathogenicity and overcomes drug-resistance.

At present, the inhibition of staphyloxanthin biosynthesis has emerged as a prominent strategy in combating methicillin-resistant Staphylococcus aureus (MRSA) infection. Nonetheless, there remains a limited understanding regarding the bio-structural characteristics of staphyloxanthin biosynthetic enzymes, as well as the molecular mechanisms underlying the interaction between inhibitors and proteins. Furthermore, the functional scope of these inhibitors is relatively narrow. In this study, we address these limitations by harnessing the power of deep learning techniques to construct the 3D structure of diapophytoene desaturase (CrtN). We perform efficient virtual screening and unveil alnustone as a potent inhibitor of CrtN. Further investigations employing molecular modelling, site-directed mutagenesis and biolayer interferometry (BLI) confirmed that alnustone binds to the catalytic active site of CrtN. Transcriptomic analysis reveals that alnustone significantly down-regulates genes associated with staphyloxanthin, histidine and peptidoglycan biosynthesis. Under the effects of alnustone, MRSA strains exhibit enhanced sensitivity to various antibiotics and the host immune system, accompanied by increased cell membrane permeability. In a mouse model of systemic MRSA infection, the combination of alnustone and antibiotics exhibited a significant therapeutic effect, leading to reduced bacterial colony counts and attenuated pathological damage. Alnustone, as a natural inhibitor targeting CrtN, exhibits outstanding antibacterial properties that are single-targeted yet multifunctional. This finding provides a novel strategy and theoretical basis for the development of drugs targeting staphyloxanthin producing bacteria.

Pathogenic mutations of human phosphorylation sites affect protein–protein interactions

Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated mutations affecting phosphorylation sites. Our results unveil significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We find that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments reveal the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering insights into potential molecular mechanisms underlying pathogenesis.

Statistical optimized production of Phytase from Hanseniaspora guilliermondii S1 and studies on purification, Homology modelling and growth promotion effect

This investigation was performed to obtain a promising phytase enzyme producing yeast. In this regard, the PSM was used to isolate the phytase-producing Hanseniaspora guilliermondii S1 (MG663578) from sugarcane juice. The SSF optimum conditions for phytase generation were optimized using (OVAT) one-variable-at-a-time strategy using both Box-Behnken design and shake flask method (g/100ml: 0.05 yeast extract, 0.15 Peptone, 0.05 malt extract 0.50 dextrose, pH 5.8 and 28ᵒC). The protein model developed was shown to be adequate for phytase production (91% accuracy), with the greatest phytase productivity in shake flask with substrate jack fruit seed powder being 395± 0.43 U/ml compared to 365U/ml for the BBD projected value. Crude Phytase was partially purified with a protein recovery of 43%, revealing a molecular weight of 120 kDa. It had an enzyme kinetic value of Km 3.3 mM and a Vmax of 19.1 mol/min. The 3D structure of PhyS1 amino acid sequences (PhyS1.B99990002) was simulated using Modeler 9.23, and the validated result revealed that 86.7% were in the favored region by Ramachandran plot. The SAVES server verified the 3D PDB file as satisfactory, and the model (in.pdb format) was uploaded in the PMDB database with the accession number ID: PM0082974. At the lab level, Hanseniaspora guilliermondii S1 (MG663578) producing phytase exhibited successful plant growth promotion activity in Ragi - CO 19 (Eleusine coracana L.) and Rice -Navarai - IR 64 (Oryza sativa L.). As a result, a phytase-based formulation for sustainable agriculture must be developed and tested on a large scale in diverse geographical areas of agricultural lands to determine its effect and potential on plant development.