Current Proteomics - Volume 21, Issue 5, 2024

The GCF, created by Professor Liu Shenlin, an esteemed expert in traditional Chinese medicine, is rooted in principles to improve blood flow, relieve blood congestion, clear heat, and detoxify the body. Developed as an empirical formula, it holds significance in CRC treatment.

This study aimed to predict and confirm the active elements, possible targets, and molecular mechanisms against colorectal cancer (CRC) in the GCF enema formula.

Using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), we screened active elements and drug targets. Colorectal cancer (CRC)-related targets were collected from the Online Mendelian Inheritance in Man (OMIM) database, Disease Gene Network (DisGeNET) database, and Therapeutic Target Database (TTD). The overlap between action targets of all active elements and CRC-related targets was determined to find common targets. A Protein-Protein Interaction (PPI) network of common targets was built using the String database, and Cytoscape software was used for visual analysis to identify core targets. Simultaneously, the common targets were analyzed using the Metascape database for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Additionally, core targets and active elements were tested using Discovery Studio 2019 for molecular docking. Furthermore, the expression differences and prognostic impacts of core targets were examined across various cancer databases.

Screening revealed 90 active elements and 251 drug targets in the GCF. There were 6113 disease targets, with 113 common targets between CRC and the GCF. Core targets identified through PPI analysis included AKT1, STAT3, MYC, SRC, EGFR, and IL6. KEGG enrichment analysis uncovered 101 relevant pathways related to these targets. Molecular docking experiments confirmed favorable interactions between core targets and multiple active elements in the GCF. Additionally, examination of the Human Protein Atlas (HPA) database highlighted differential expression of core targets MYC and EGFR in normal colorectal tissue compared to CRC tissue.

The GCF, comprising a combination of three drugs, appears to counteract CRC by influencing core targets, such as AKT1, STAT3, MYC, SRC, EGFR, and IL6. This process involves regulating multiple cancer-related signaling pathways, notably the PI3K-AKT pathway.

Osteoarthritis (OA) is one of the leading causes of pain and disability. Metabolomics is a recent approach to identifying moieties that can be used in diagnosis, prognosis, and as biomarkers. The articular fluid in OA witnesses its pathophysiology directly. Identifying differential metabolites and biological pathways associated with them can provide in-depth mechanisms and identify biomarkers.

In the present study, synovial fluid of confirmed OA patients (n=17) and matched healthy controls (n=21) was investigated for differential metabolites using untargeted metabolomics.

The distribution of the samples was observed using multidimensional PCA. The signaling pathways associated with metabolites were obtained by pathway enrichment analysis, and a correlation analysis of differential metabolites was performed to identify common and significant metabolites.

A total of 16 upregulated and 23 downregulated metabolites (log2foldchange >0.5, p<0.05) were identified by differential analysis. The essential amino acid, arginine, was recognized as the most significant metabolite present in most of the identified deregulated pathways. The pathways, arginine, and proline metabolisms, and mTOR, were found to be deregulated strongly in the present study.

These metabolites and their associated pathways can be beneficial for the diagnosis and treatment of osteoarthritis in clinical settings after further validation in large cohorts.

Biliary Atresia (BA) causes neonatal cholestasis jaundice. The primary therapeutic treatment for BA is the Kasai portoenterostomy. Current diagnostic approaches for BA are imprecise and time-consuming, making early diagnosis crucial for successful treatment outcomes.

This study aims to analyze proteins from Peripheral Blood Mononuclear Cells (PBMCs) obtained from children with BA compared with healthy children

We employed a large-scale, total shotgun quantitative serum proteomics approach to analyze the protein from PBMC samples from a discovery cohort. This approach allowed for the simultaneous identification and quantification of multiple proteins, enabling the detection of disease-specific protein expression patterns. The study is proteomic-based study.

We identified 24 proteins, by Liquid Chromatography-Mass Spectrometry (LC-MS) analysis that exhibited high discriminatory power for five subjects with BA post-Kasai operation compared to ten healthy controls. ATP2A3, LIN28B, SLC25A3, ITGB3, COX5A, and HLA-B identified proteins of upregulation were predicted to associate with BA post-Kasai operation.

Our findings highlight the utility of proteomic techniques in BA research. The identified proteomic markers offer promise for improving BA diagnostic accuracy and timeliness, leading to enhanced treatment outcomes for affected children.

Proteomic analysis revealed a set of potential biomarkers for early and accurate diagnosis of biliary atresia. These biomarkers hold significant clinical value and have the potential to transform the management of biliary atresia by facilitating timely intervention and improving patient outcomes.

HIV-1 is the most virulent type, causing most AIDS cases worldwide. Therapeutics like NRTIs and NNRTIs terminate replication by terminating polymerization reactions. Natural-based therapeutics are increasingly being used to reduce side effects and combat disease.

The study focuses on identifying phytochemical compounds that effectively inhibit the HIV-1 reverse transcriptase process using molecular docking and molecular dynamic simulations.

Molecular docking results show anisomelolide has a significantly stronger binding affinity (-29.9992KJ/mol) compared to nevirapine (-13.34696 KJ/mol), forming more hydrogen bonds and hydrophobic interactions, indicating a more stable and specific binding. MD simulations further support these findings, with anisomelolide exhibiting lower RMSD and RMSF values, suggesting greater structural stability and lower flexibility. Interaction energy analysis reveals robust binding and stability for anisomelolide over time. Additionally, hydrogen bond analysis indicates more frequent and stronger interactions for anisomelolide.

The phytochemical compound anisomelolide exhibits superior binding affinity, structural stability, and interaction dynamics, making it a promising candidate for drug development against HIV-1 RT.

This study aims to analyze the wild type of arginine deiminase and three mutant forms of it (G320L, A303R, and E304L) in complex with arginine molecule using Molecular Dynamics (MD) simulation to find the best enzyme form with a lower level of energy and more stability.

Arginine Deiminase (ADI) is an arginine-degrading enzyme that has an anticancer effect on some cancers, such as Hepatocellular Carcinoma (HCC) and melanoma.

Homology models of native and mutant forms of ADI were generated and evaluated using the I-TASSER and other databases and prediction algorithms. All prepared enzyme structures via homology modeling were used as receptors in the molecular docking and MD simulation steps.

MD simulation results demonstrated that the G302L structure has the lowest value of Rg over MD simulation time, indicating its more appropriate intramolecular interactions than other structures. G302L has also established the most desirable interactions with arginine molecules during 80 ns MD simulation so that the G302L/ arginine complex has the maximum intermolecular hydrogen bonds. Arginine molecule in complex with G302L has the best position in the active site of ADI protein.

The G302L mutant ADI from Mycoplasma hominis can be a good candidate for better stability in arginine deprivation therapy in arginine auxotrophic cancers.

Multiple Myeloma (MM) is a hematologic malignancy that often progresses to a refractory relapse, posing significant treatment challenges due to prior treatments, drug response duration, clinical and molecular characteristics, comorbidities, and adverse reactions.

This single-center and single-arm study assessed the BPD regimen, which includes bendamustine, pomalidomide, and dexamethasone, for its efficacy and safety in 21 patients with Relapsed and Refractory Multiple Myeloma (RRMM), including those who were intolerant to prior bortezomib treatment.

The Overall Response Rate (ORR) after 1-8 cycles of BPD treatment was 58.8%. The 6- month Progression-Free Survival (PFS) was 70.5%, and the 12-month PFS was 52.9%. The 1-year Overall Survival (OS) rate was 82.35%. Hematologic toxicities were the main adverse reactions, with grade 3 or higher adverse events mainly linked to hematologic toxicity and infections.

The BPD regimen has shown to be highly effective, with a favorable ORR and survival rate in RRMM patients, indicating it a relatively safe and well-tolerated treatment option.

The aim of this study is to compare the molecular differences between the normal liver (NG) and attached liver (WG) of Glyptosternum maculatum (G. maculatum) based on label-free and TMT-labeled proteomics data. It provides a theoretical basis for the adaptation of G. maculatum in the plateau area with low temperature and low oxygen.

The differentially expressed proteins (DEPs) of label-free and TMT-labeled proteomics data were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein structural domain (PSD) enrichment, and protein network interactions were constructed to reveal protein characteristics.

In the label-free and TMT-labeled groups, we detected a total of 643 and 107 DEPs; 7 common DEPs (co-DEPs) were selected through PPI network information. Higher expression of evm.model.chr5.73, evm.model.chr18.610, evm.model.chr10.1201, and HIF-1 signaling pathway were associated with a hypoxic environment. evm.model.chr15.573, evm.model .chr5.320, evm.model.chr6.360, and evm.model.chr18.610 were associated with WG production. Significant expression of Ubiquitin-like protein 4A and Heat shock 70 kDa protein protects liver cells from injury in a hypothermic and hypoxic environment.

In terms of the number of DEPs, the labelling-free method is superior to the TMT-labeled method. The TMT-labeled method is better in terms of repeatability and accuracy. The myosin11 not only responds to low temperature and low oxygen but may also lead to WG production through Tight junctions. The NG may be more sensitive than WG in stress response to cancer and viral infections.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has had a significant global impact since its declaration as a public health emergency in January 2020. Symptoms of COVID-19 can range from mild to severe, including fever, cough, fatigue, and shortness of breath. This study aimed to investigate the clinical symptoms and proteomic differences between non-hospitalized COVID-19 patients and healthy individuals.

Clinical data of 6231 COVID-19 patients of different age groups and sexes were collected and analyzed. Proteins were separated by SDS-PAGE and identified by MALDI-TOF. 900 serum samples were collected, with 100 samples per patient group and one healthy control group.

In the control group of healthy individuals, five proteins (HAPTO, IGKC, FUT10, CO3, SESQ2) were expressed with a score of 1+, serving as a reference for the other groups. Group 9, consisting of individuals who had recovered (IgG positive), showed negative results for all five proteins due to anti-IgG antibody production in memory cells. The significant differences in protein expression compared to the control group indicated up-regulation and down-regulation of these proteins. Positive PCR or IgG and IgM results led to notable differences in protein expression across all studied groups.

The altered protein expression in infected individuals compared to healthy controls may suggest the potential for these proteins to serve as biomarkers for disease diagnosis and prognosis.

Proteins present in body fluids harbor the potential to act as markers for both diagnosing diseases and exploring their underlying mechanisms. As urine can be easily obtained non-invasively, analyzing its proteins is an ideal approach to identifying biomarkers for bladder cancer. This study aimed to identify proteins in urine that could serve as biomarkers for bladder cancer.

Urine samples were collected from patients with primary transitional cell carcinoma of the bladder and their age and sex-matched healthy individuals. The protein pellet underwent resolubilization and trypsin digestion to facilitate analysis using reverse phase nano-high performance liquid chromatography/electrospray ionization tandem mass spectrometry.

In samples obtained from 16 patients and 8 controls, 3192 peptides were identified, corresponding to 934 unique proteins, of which 60 were identified with higher confidence levels. Among them, Transferrin and Prostaglandin D2 Synthase (PTGDS) were found as potential markers of bladder cancer. In particular, the absence of PTGDS has a specificity of 100% and a sensitivity of 81%.

This study, which used proteomic approaches, identified PTGDS in urine as a potential biomarker of bladder cancer.

Saffron (Crocus sativus), also known as Red Gold has shown anticancer activity, but the molecular mechanisms underlying its potential therapeutic effects are not fully understood.

This study investigates the anticancer effects of safranal on cervical cancer, one of the most common cancer types in women worldwide, and elucidates the molecular mechanism underlying these effects through bioinformatics analysis RNA-sequencing.

The molecular mechanisms and key genes underlying the antiproliferative effect of safranal (400μM) on cervical cancer cells were determined with RNA sequencing bioinformatics analysis.

The total number of expressed genes in HeLa and C- 4 I was determined as 1101 and 1190, respectively after 24 hours of incubation of safranal. KDA showed the key driver genes EIF2AK2, USP18, PARP14, GBP1, SAMD9L, SP110, DDX60, IFI44L and MX2 of the HeLa group, while in the C- 4 I group, key driver genes are DNAJC3, DNAJB9, DNAJB8, SRPX2, PRSS8, MRC2, IER2, RPS19BP1, CRELD2 and SDF2L1. KEGG and GO analysis revealed that safranal induced cell arrest in the G1 phase, cell death apoptosis, and necroptosis in cervical cancer cells. These antiproliferative effects were shown based on regulated signaling pathways: MAPK, TGF-beta, p-53, TNF and FoxO signaling pathways.

Based on the results obtained by RNA sequencing, it can be concluded that safranal has a potential antiproliferative effect and could be used in the clinic as an anticancer agent.

Carbonic anhydrase (CA) has been an enzyme of great interest for its application in carbon dioxide sequestration. A total of 66 protein sequences of plant CAs were computationally analyzed and submitted to bioinformatics for motif and domain identification, multiple sequence alignment, and phylogenetic analysis.

The current work aims to extend knowledge among researchers in better understanding the structure of AZDI CA by analyzing its physicochemical properties, secondary structure prediction, 3D modeling of protein sequence, and its validation using a variety of conventional computational methods.

The accuracy of the predicted 3D structure checked by the Ramachandran plot generated by PROCHECK showed that for the CA protein sequence 93.1%, was observed in the most favored regions, VERIFY 3D 69.44% and ERRAT 98.09%. AZDI CA was docked with various anions and sulphonamide derivatives to study their inhibitory effect and calculate binding energy by using Autodock 4.2. Ethoxzolamide is found to be a better inhibitor of AZDI CA with binding energy -8.71 kCal/mol and KI value 0.0004 mM. Further MD simulation of the docked complex of ethoxzolamide with AZDI CA was done using SCHRODINGER DESMOND software.

The outcomes of this research will have a significant influence on biochemistry, biotechnology, and potential applications of AZDI CA and similar plant CAs in determining potent inhibitors for drug targets in treating various diseases.

The identification and classification of natural products are vital in drug discovery and bioactive compound exploration. Traditional methods are laborious and time-consuming, necessitating innovative tools for accurate predictions using advanced AI techniques.

This paper presents NaturePred, a user-friendly tool designed to predict the class of natural products and calculate eight physicochemical properties of protein sequences. It aims to accurately predict five distinct classes of natural product biosynthetic gene clusters (BGCs): Polyketide Synthases (PKS), Non-ribosomal Peptide Synthetases (NRPS), Ribosomally Synthesized and Post-Translationally Modified Peptides (RiPPs), Terpenes, and PKS-NRPS Hybrids. It also addresses reliability in multi-class classification with a 90% confidence score threshold.

NaturePred offers three input options: single protein sequence, CSV file, or GenBank (.gbk) file. It uses a pipeline with a Natural Language Processing model based on TF-IDF (Term Frequency- Inverse Document Frequency) and a Logistic Regression classifier. Predictions are made if the confidence score exceeds 90%; otherwise, “None of the above class” is predicted. Evaluation with unseen data from the MiBIG database shows high accuracy (~96%) in assigning BGCs.

NaturePred provides accurate predictions with high confidence scores, demonstrating reliability across different datasets. It calculates eight physicochemical properties of protein sequences, offering valuable insights for further analysis.

NaturePred's integrated features, including versatile input options, accurate predictions, and physicochemical property calculations, make it an indispensable tool in natural product research. By addressing classification challenges, NaturePred facilitates drug discovery and bioactive compound exploration, advancing the field. Tool available: (http://login1.cabgrid.res.in:5101/).

Acute myeloid leukemia (LAML) is among the most prevalent subtypes of acute leukemia. Consequently, it is essential to understand the molecular causes of LAML and find its predictive and diagnostic biomarkers. The aim of this study is to determine the molecular functions of fibroblast growth factor receptor 1(FGFR1) involved in LAML pathogenesis and its potential therapeutic effect for LAML treatment.

The molecular docking interaction of the Cytarabine with its target FGFR1 was examined. The Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2), and UALCAN tools database were used to obtain the LAML gene expression datasets. Gene functional annotation was performed to investigate the DEGs' possible role. Using the interactive gene database retrieval tool STRING and a few chosen hub modules from the GeneMANIA database, the gene-gene and protein-protein interaction (PPI) network were constructed. A survival analysis was performed on the effects of hub genes on the overall survival of LAML patients.

As a result of docking, a strong interaction was observed between cytarabine and FGFR1. It has been discovered that cytarabine can reverse FGFR1 expression. The survival study results showed an association between the prognosis of LAML patients and one of the central genes, FGFR1.

The expression profile and functions of FGFR1 were determined in LAML patients. It has been shown that FGFR1 can be a viable therapeutic target for LAML and a possible biomarker for diagnosis.

Entrapment is supposed to be the most effective and simple method among various strategies of enzyme immobilization as it preserves the original conformation and biological activity of the enzyme with greater immobilization yield. A suitable and cost-effective protocol for the entrapment of 1,4-α-D-glucan glucanohydrolase obtained from halotolerant Micrococcus spp., K11 has been developed.

The major objective of the present study was to explore halotolerant bacteria as potential producer of 1, 4-α-D-glucan glucanohydrolase from salt mines.

A total of 11 bacterial strains were isolated and purified using the halophilic medium. Strain K11 was selected on the basis of a large zone of starch hydrolysis. The crude enzyme extract was utilized to entrap in agar-agar scaffolds. Kinetic studies of agar-agar entrapped 1,4-α-D-glucan glucanohydrolase were assessed and compared with the properties of soluble enzyme.

It was observed that optimum immobilization of 1,4-α-D-glucan glucanohydrolase was attained at 4% concentration of agar-agar. Maximum entrapped enzyme activity was noticed after 15 minutes, highlighting the 5-minute increase as compared to the free enzyme. Moreover, temperature maxima for optimal enzyme substrate reaction were recorded to be 30°C for both immobilized and soluble 1,4-α-D-glucan glucanohydrolase, whereas pH maxima of 1,4-α-D-glucan glucanohydrolase were shifted from 6.5 to 7.0 after entrapment. The need for optimum substrate concentration for entrapped amylase activity was recorded to be 3% (gm), and for soluble 1,4-α-D-glucan glucanohydrolase, 2% (gm) starch was required for improved enzymatic efficacy. The reusability studies showed that agar-agar immobilized 1,4-α-D-glucan glucanohydrolase could be consumed up to 6 repeated cycles.

It is concluded that exploited features of immobilized 1,4-α-D-glucan glucanohydrolase enhance its applicability in several industrial processes.

Necrosis, a form of uncontrolled cell death, can be triggered by a variety of stressors, including infection, injury, toxins, and ischemia. Such necrotic events, particularly when induced by pathogenic infections, can lead to severe health complications. The mixed lineage kinase domain-like pseudokinase (MLKL) has been identified as a crucial drug target for mitigating necrosis.

The objective of this study is to identify potential MLKL inhibitors that act against necroptosis via a pharmacophore model and virtual screening.

In this study, we developed a ligand-based pharmacophore model to facilitate the identification of inhibitors that target MLKL. Comprehensive ADMET analysis, virtual screening, and molecular docking were employed to identify potential therapeutic candidates. Subsequently, molecular dynamics (MD) simulations and free energy calculation of a leading candidate were conducted using GROMACS and gmxMMPBSA tool to assess the stability of the MLKL-inhibitor complex.

Our investigations identified 26 potential MLKL binders, with three compounds emerging as frontrunners on the basis of their favorable pharmacokinetic profiles, including high/low gastrointestinal absorption, optimal bioavailability, solubility, and non-hepatotoxicity. The MD simulations further corroborated the structural stability of the MLKL-drug complex.

The integrated computational approach adopted here could serve as a model for accelerating the discovery of drug candidates in other therapeutic areas as well. These findings necessitate further experimental validation before progressing to clinical trials.

Sleep apnea is a significant health impediment, and it presently lacks efficacious therapeutic interventions. Thus, it is imperative to discover novel therapeutic targets that could guide clinical treatment strategies.

This study aims to utilize an integrative analytic approach to unearth previously unappreciated protein-encoding genes implicated in sleep apnea susceptibility.

Through the Multi-Marker Analysis of Genomic Annotation (MAGMA), we aligned Single-Nucleotide Polymorphism (SNP) summary statistics from Genome-Wide Association Studies (GWAS) of gene bodies to discern potential risk genes. Following the MAGMA results, we conducted a round of Transcriptome-Wide Association Studies (TWAS) and Proteome-Wide Association Studies (PWAS) to expedite the conversion of genetic associations into probable protein targets. Mendelian Randomization (MR) and co-localization analysis were employed to ascertain the causal linkage between the candidate target genes and sleep apnea. Finally, a mediation analysis was undertaken to explore the possible intermediary role of 150 inflammatory metabolites and 1,124 proteins.

The MAGNA analysis revealed 2,819 genes in association with sleep apnea. TWAS and PWAS analyses indicated that cis-regulation of nine particular genes could play a role in sleep apnea onset via blood protein level alterations. MR and co-localization analyses also suggested a causal relationship with sleep apnea for three genes (ACADVL, CCDC134, UPP1). Consistent associations were found between genetically predicted biomarkers and Albumin, HCC-1, N-acetyl carnosine, and RELT, pointing towards their potential mediating roles in sleep apnea's etiological pathway.

Our findings indicate that ACADVL, CCDC134, and UPP1 genes are potentially significant targets for further functional investigation and therapeutic interventions for sleep apnea.

Insulin is a key hormone in our systems. Upon binding of insulin to its receptors in fat and muscle tissues, tens of proteins in the insulin signaling pathway are involved in the process of GLUT4 vesicle recruitment to the Plasma Membrane (PM) and the absorption of serum glucose. Deficits in the aforementioned pathway lead to insulin resistance and eventually to Type II Diabetes Mellitus.

We appreciate the contribution of phytochemicals in the treatment of diabetes. Yet, in vitro and in silico studies are needed to validate the safety and efficacy of the phytochemicals, plus their action mechanisms.

Herein, we tested two phytochemicals, caffeic acid and ferulic acid in vitro and in silico. We shed light on the insulin signaling proteins as plausible therapeutic targets using in silico studies, via AutoDock and SwissADME.

Results obtained in vitro indicate that Caffeic Acid (CA) increased GLUT4 translocation at 125µM by 31% in the absence of insulin, and 24.5% in presence of insulin, when compared to the control. Ferulic Acid (FA) was less potent as an enhancer of GLUT4 translocation. Best docking results were found for the binding of the phytochemicals CA and FA to PDK1, AKT, IRS1 and PTEN proteins of the insulin signaling, with comparable results.

These findings indicate that CA and FA possess a limited anti-diabetic potency by increasing GLUT4 trafficking to the PM in skeletal muscles. These results suggest that these compounds are candidates for further investigation in pre-clinical and clinical stages of drug discovery.

Curcumin possesses numerous properties, including anti-influenza effects. Various natural analogs and metabolites of curcumin have been identified.

To better understand the mechanism of curcumin's anti-influenza effect, the inhibitory effect of natural analogs and secondary metabolites of curcumin on 11 influenza virus proteins was investigated in a simulated environment.

Molecular data and structural files of curcumin, its natural analogs, and secondary metabolites were retrieved from the PubChem database for conversion to PDB files. Viral protein sequences were obtained from uniprot.org, and PDB structures of proteins were predicted using an online protein structure and I-TASSER server. Using AutoDock 4.2 software, the molecular docking studies were performed.

Three natural analogs of curcumin had a greater affinity to the PB1-F2 protein of the virus. The Binding Energies (BEs) of curcumin, bisdemethoxycurcumin, and demethoxycurcumin to the PB1-F2 protein were -8.28, -8.44, and -8.46 kcal/mol, respectively. Although bisdemethoxycurcumin had the lowest BE, it interacted with fewer amino acids in the active site of the protein compared to the other analogs. Curcumin metabolites were less likely to bind to influenza virus protein than curcumin.

Our study indicated curcumin and its analogs to have the greatest affinity to the PB1-F2 protein compared to other viral proteins. Given the role of this protein in increasing inflammation caused by influenza, curcumin may reduce inflammation in patients by affecting the function of this protein.

Bermudagrass (Cynodon dactylon L.) simultaneously has three types of stems: shoots, stolons, and rhizomes, which lays the basis for the fast clonal growth of this important warm-season turfgrass species. However, the mechanisms underlying the differentiation, growth, and development of the three types of stems remain unclear.

In this study, the annotation information of the assembled bermudagrass genome was used to reanalyze the mass spectrometry raw data generated in the comparative proteomics analysis of bermudagrass shoots and stolons as well as stolons and rhizomes. One-way analysis of variance and the Student-Newman-Keuls test was performed to identify the Differentially Expressed Proteins (DEPs) in paired comparison of shoots versus stolons, shoots versus rhizomes, and stolons versus rhizomes.

A total of 3190 proteins were simultaneously expressed in the three types of stems, whereas 135, 1012, and 876 DEPs were identified between shoots and stolons, shoots and rhizomes, and stolons and rhizomes, respectively. Venn diagram analysis indicated that 23 DEPs were simultaneously identified in the three paired comparisons. Functional enrichment analysis indicated endocytosis and terpenoid backbone biosynthesis to be the most significantly DEP-enriched biochemical pathways among the three types of stems.

The results of this study not only provided new insights into the specialization of shoots, stolons, and rhizomes in bermudagrass, but also pointed out the importance of high-quality genome assembly and annotation in proteomics research.

Polyamines are vital in regulating stress signaling pathways, inhibiting reactive oxygen species, and stabilizing the photosynthetic apparatus in plants, with their levels fluctuating under stress conditions. Among the key enzymes in polyamine biosynthesis is spermidine synthase, which synthesizes spermidine in the presence of putrescine and S-adenosyl 3-(methylsulfanyl) propylamine.

This study focused on the structural analysis of the maize spermidine synthase enzyme (SPDS-Maiz), highlighting its significance in plant stress tolerance. The enzyme's three-dimensional structure was modeled using the amino acid sequence and SWISS-MODEL, followed by refinement with the GalaxyRefine2 web server. Molecular docking studies were performed for its cofactor and substrate. Molecular dynamics simulations performed for 100 ns using Gromacs software confirmed the conformational stability of the apo and holoenzyme’s structure.

The structural model showed 90.3% of its amino acids in favored areas of the Ramachandran plot and obtained a notable score of 95.528% ERRAT. Molecular dynamics simulation confirmed the stability of the SPDS-Maiz structure by analyzing the physical movements of atoms and molecules. The docking studies showed that Asp226 is crucial in the interaction of putrescine and dc-SAM with the active site of the enzyme. Additional amino acids, including Gln122, Asp207, Ser227, Ser228, Glu236, Gln122, Asp226, Ser227, and Gln259, played a role in supporting the enzyme-dc-SAM-putrescine complex. Putrescine (PUT) exhibited stronger van der Waals interactions (-48.23 kcal/mol) and nonpolar solvation energy (-7.46 kcal/mol) compared to S-adenosylmethionine (dc-SAM). However, PUT incurred higher polar solvation penalties (+134.68 kcal/mol) due to its +2 charge, whereas dc-SAM benefited from slightly lower desolvation costs.

The study successfully modeled and refined the three-dimensional structure of the SPDS-Maiz enzyme, highlighting the critical role of Asp226 and other amino acids in substrate binding. Although dc-SAM had better electrostatic complementarity (-60.27 kcal/mol), the overall binding free energy (-20.58 kcal/mol for PUT vs. -11.22 kcal/mol for dc-SAM) indicated that PUT achieved stronger binding affinity, driven by its hydrophobic interactions. These findings provide insights into how the enzyme and substrate interact and the underlying molecular mechanisms in spermidine synthesis in plants.