Current Proteomics - Volume 21, Issue 6, 2024

The integration of multi-site functional Magnetic Resonance Imaging (fMRI) datasets with deep learning frameworks has yielded substantial advancements in the realm of Autism Spectrum Disorder (ASD) research. However, existing graph convolutional neural networks (GCNs) only aggregate neighbor information at a fixed scale and cannot adjust the scale parameters to aggregate the best information.

In this study, a population graph-based framework, homogeneous graph wavelet neural network (H-GWNN), is proposed to learn representations for graph classification in an end-to-end manner. Specifically, the multi-site heterogeneous data are handled with a balance homogenization algorithm (BHA), which is developed based on location and scale effects. Both image data and phenotypic data are fused to construct a population graph, and the most discriminative information on the graph is extracted by adjusting the scale in a graph wavelet neural network (GWNN).

The experiments on the autism dataset ABIDE show that the classification accuracy of H-GWNN on autism is 83.58%, which exceeds that of other related GCN frameworks.

The findings demonstrate that H-GWNN can not only tackle data heterogeneity but also analyze node features at the optimal scale. It captures discriminative features to improve classification performance for ASD identification.

Coronavirus disease 2019 (COVID-19), which emerged in 2019, has caused millions of deaths worldwide. Although effective vaccines have been developed to mitigate severe symptoms, certain populations, particularly the elderly and those with comorbidities, remain at high risk for severe outcomes and increased mortality. Consequently, early identification of the severity and clinical outcomes of the disease in these patients is vital to prevent adverse prognoses. Although traditional machine learning and deep learning models have been widely employed in this area, the potential of large language models (LLMs) remains largely unexplored.

Our research study focused primarily on constructing specialized prompts and adopting multi-objective learning strategies.

We started by selecting serological indicators that significantly correlate with clinical outcomes and disease severity to serve as input data for the model. Blood test samples often contain numerous missing values, and traditional models generally rely on imputation to handle these gaps in the data. In contrast, LLMs offer the advantage of robust language processing capability and certain semantic understanding. By setting prompts, we can explicitly inform the model when a feature’s value is missing, without the need for imputation. For the multi-objective learning strategy, the model was designed to first predict disease severity and then clinical outcomes. Given that LLMs utilize both the input text and the generated tokens as input for generating the next token, the predicted severity was used as a basis for generating the clinical outcome. During the fine-tuning of the LLM, the two objectives influenced and improved each other. Our experiments were implemented based on the ChatGLM model.

CovidLLM demonstrated superior performance compared to other traditional models in predicting disease severity and clinical outcomes.

The results demonstrated the effectiveness of LLMs in this task, suggesting promising potential for further development.

This study aims to gain insights into the EhSec1 binding mechanism and the corresponding amino acids responsible for interacting with the amoebic SNARE proteins, EhSyntaxin1A/1B, which would enable to create a platform for further exploration of the functions and applications of EhSec1 in managing amoebiasis.

Parasitic protozoa have long been responsible for increasing the burden on healthcare. However, the enteric protozoan Entamoeba histolytica, is dangerously neglected despite accounting for the greatest number of deaths from parasitic infection, closely after malaria and schistosomiasis. E. histolytica launches its attack via secretion of tissue degrading arsenal through vesicular transport. Sec1/Munc18-1 -like (SM) proteins are one of the key players of the vesicle transport system and, along with their interacting partners, play crucial roles in this transport machinery. This provides the basis for exploring the uncharacterized SM protein in E. histolytica, and its roles in vesicle transport.

This study aims to decode the novel SM protein, EhSec1, by performing detailed sequence and structure analysis and delving into the protein interaction studies with its partner SNARE proteins (Syntaxins) through molecular dynamic simulations and docking. The interactions will be compared with crystal structure exhibiting co-complexes of Sec1_Syntaxin to further highlight the role of EhSec1 amino acids in interacting with amoebic SNAREs, EhSyntaxin 1A/1B.

The objectives were fulfilled by performing rigorous studies on EhSec1, falling under the heads of comparative sequence and structure analysis, physicochemical studies, modeling, and molecular docking, and protein-protein interaction studies supported by molecular dynamic simulations.

EhSec1 is a thermally stable, 70kDa globular protein composed of three domains where domains 1 and 2 adopt an α-β-α fold. Domain 2 is split into 2a and 2b, separated by domain 3. This domain has two parts, 3a and 3b, at an angle of 56.7° to each other. EhSec1 shows stable interaction with Syntaxin 1 isoforms (EhSyntaxin1A/1B) and Rab GTPase (EhRabX10). Molecular simulation investigating the dynamics of EhSec1 with Syntaxin1A, showed that the interaction is stable due to the formation of 14 strong hydrogen bonds (bond length <2.4 Å). The pivotal residues of the interaction interface belong to domain 1 (53D, 60K, and 62E) and domain 3a (259K and 314N) of EhSec1; Hc region (110R and 114N) and SNARE motif (234E, 237E, 242E) of Syntaxin 1A/1B. EhRabX10 binds to EhSec1 via its G3 region, and the key interacting residues of EhSec1 (224R-225H, 490L-495F, and 518K) fall in domain 2.

Our study reveals that the Syntaxin 1 isoforms and EhRabX10 form stable complexes with EhSec1, assembling the minimal template for the SNARE-based vesicle transport of Eh. Our investigation aims to enhance comprehension of vesicle transport in Eh and establish the potential of EhSec1 as a viable drug target in future applications.

Hantavirus illness is characterized by increased vascular permeability and hemorrhagic fever with renal syndrome or cardiopulmonary syndrome.

In this study, the domain search approach, a bioinformatics method, was utilized to understand more about hantavirus E protein structures.

Activities of Ca2+ binding domain, C-type lectin, Dockerin, glycosyl hydrolase (cellulase), PI3K, threonine kinase, PTEN, GTPase, PPM, flippase, and other domains were identified in Hantavirus membrane glycoprotein E.

According to the results in this current study, the activation of EF-hand promotes the lectin activity of E protein, which then binds to fibrocystin in the form of cohesin-dockerin. The glycosyl hydrolase activity of E protein hydrolyzes glycosidic linkages, destroying the protective capsule of cells (fibrocystin) so that it may bind to receptors such as integrins. Additionally, the enzyme activities of PTEN and PI3K permit the E protein to insert and anchor on the cell membrane. Moreover, the GTPase, SecA, and flipase activities of E proteins mediate the creation of fusion pores and the release of genetic materials. The aggressive invasion of Hantavirus causes tissue damage and bleeding, resulting in severe blood vessel leakage.

This pilot study aimed to analyze serum differential proteins in Vestibular Migraine (VM) group (n=21) and the healthy controls (HC) group (n=21).

Serum samples collected from subjects were analyzed using the relative quantitative proteomics Tandem Mass Tag (TMT) quantification technique for protein identification.

Based on TMT proteomics technology and bioinformatics analysis, we identified a total of 35 differentially expressed proteins, including 24 up-regulated proteins and 11 down-regulated proteins.

Proteomic analysis was able to reveal differences in protein expression between VM sufferers and healthy controls. Similar to other neurological diseases characterized by neuroinflammation, the serum proteome of VM patients shows an abundance of proteins that indicate cellular damage and inflammation. If this relevant inflammatory status is confirmed in a larger series, it could serve as a target for VM treatment.

Post-Translational Modifications (PTMs) are covalent modifications of amino acids added to proteins that can significantly affect proteins’ structures and functions. PTMS are, therefore, important biomarkers due to their regulation of various bioactivities. Protein array is a robust tool for detecting and quantifying proteins with high throughput, small sample requirement, and high sensitivity.

On the basis of a high-density array, we developed a new platform to detect the PTM level, such as phosphorylation and acetylation, and changes in larger scales using an anti-PTM antibody.

THP-1 cells treated with phorbol 12-myristate 13-acetate (PMA) and lipopolysaccharide (LPS) were used for testing the new system and quantifying the phosphorylation and acetylation level change. The proteins whose phosphorylation and acetylation levels changed significantly were screened and compared with reported phenotypic change.

By using antibodies against phosphorylation and acetylation, the PTM change for the same protein can be detected. Based on the proteins whose PTM is significantly different before and after treatment, it was found that the enriched pathways and biological progress agreed with the stimulation of PMA and LPS.

Our results supported the idea that this platform can be used to effectively compare the phosphorylation and acetylation level changes among samples and screen for biomarkers on the proteomic scale.

The stability of Rho depends on its amino acids, protein structures, oligomerization, strong interactions, salt bridges, and bonding patterns. A single amino acid change can alter the protein's tertiary structure. Rhomboids cut misfolded membranes. They regulate protein synthesis, mitochondrial integrity, parasite invasion, and growth factor secretion. Research into these proteins and their inhibitors can improve therapeutic targeting of the Rho protein, which reduces type II diabetes and Parkinson's disease.

The primary aim is to examine Antarctic Rho sequences, amino acid contents, and active domains. Additionally, modeling tools will be used to build three-dimensional Rho structures from extremophiles. Docking simulations determine the predicted proteases' proteolytic and aminopeptidase activity.

PATRIC discovered Antarctic Rho—MAFFT-aligned structures. Meanwhile, InterProScan and ProtParam determined the amino acid content and active domain. To foretell the 3D structure of proteins, I-TASSER, CB-Dock, and Discovery Studio were used.

Rhomboid gene alignments amongst Antarctica isolates show that protein evolution was limited at cold temperatures. All isolated proteins had similar active domains and amino acid sequences. Rhombic structure and proteolytic activity have not changed appreciably across evolution. A spatial arrangement of Rho reduces resilience. Antarctic Rho contained critical amino acid residues LEU (292, 251, 252, 289) and ALA 304.

Antarctic isolates' Rhomboid alignment demonstrates restricted evolution, possibly via bacterial horizontal gene transfer. Despite multiple actions, active domain presence is maintained physically and functionally. Bacterial Rhomboids are therapeutic targets due to their rising role in various illnesses.

Sepsis is defined as the extreme response of a body to an infection, leading to untimely death if left untreated. The human gut microbiome is characterized by the presence of several microorganisms in the gastrointestinal tract. This study provides insight into the potential therapeutic effects of a peptide present in the human gut microbiome that helps control sepsis.

This study aimed to explore the therapeutic application of a peptide from Lactobacillus sp. in the human gut microbiome as an alternative to MRSA, which causes severe, fatal diseases like sepsis. It also elucidates the peptide-protein interactions that enhance the efficacy of infection control and treatment.

We aimed to investigatethe interactions between protein-peptide and protein-drug complexes through in silico analyses.

Molecular docking was performed using PyRx and HADDOCK tools. Next, we performed molecular simulation studies using GROMACS v2020.6 at different physiological pH values of 4, 6, and 7.4. Stability, compactness, and binding energies were analyzed usingparameters such as RMSD, Rg, and MMPBSA, among other parameters.

We observed stability on docking between Plantaricin KL-1Y, an effective bacteriocin from Lactobacillus plantarum (organism from gut microbiome), and PBP2a from Staphylococcus aureus (causative organism of sepsis). This was indicated by a binding affinity of -13.4 kcal/mol, higher than that of PBP2a-FDA-approved drug (-8 kcal/mol). The MMPBSA results of the PBP2a-Plantaricin KL-1Y complex showed a significantly higher binding affinity at pH 7.4 of -228.451 kcal/mol in comparison to -69.5747 kcal/mol for the PBP2a-Ceftaroline fosamil complex.

These results indicate the possible use of a peptide from the human gut as a potential therapeutic agent against S. aureus infection.

Bovine Seminal Ribonuclease (BS-RNase) is a unique member of the RNase A family found in the bovine seminal fluid. It is recognized for its antiviral and antitumor properties, which make it a potential therapeutic agent.

This study aimed to compare the stability of monomeric and dimeric forms of BS-RNase using in silico methods.

The tertiary structures of BS-RNase as monomers and dimers were obtained from the Protein Data Bank, and missing amino acids were modeled using the Modeller server. The predicted structures were validated using SAVES 6 and ProSA web tools. Molecular dynamics simulations were performed using GROMACS, and the resulting RMSD, RMSF, and Rg plots were analyzed.

The results indicated that the monomer's ERRAT score, Ramachandran plot, and Z-score were better than the dimer's. RMSD, RMSF, and Rg plots were favorable for both structures, with the monomer showing better stability than the dimer.

Consequently, the monomeric form of BS-RNase is more stable than its dimeric form, and the monomer can be more reliably used in pharmaceutical studies.

Identifying disease-protein associations is a key step in treating disease, understanding pathomechanisms, and developing drugs. Although experimental methods can be used to identify disease-protein associations, they are often time-consuming, laborious, and expensive. Therefore, there is a strong need to develop theoretical computational methods to identify potential disease-protein associations.

This work aimed to study the effect of the graph embedding algorithm and reliable negative sample screening methods on predicting disease-protein association.

In our study, information on disease similarity, disease-protein association, and protein-protein interaction was used to construct a heterogeneous network, including protein-protein interaction subnetwork, disease similarity subnetwork, and disease-protein association subnetwork. Then, a graph embedding algorithm was utilized to obtain network node features to characterize the disease-protein relationships. The support vector data description algorithm was applied to screen the reliable negative samples. Finally, random forest algorithm was employed to construct a model for identifying potential disease-protein associations.

The present method achieved an accuracy of 94.55%, a specificity of 98.49%, a precision of 98.36%, a Matthew's correlation coefficient of 0.8938, an area under the receiver operating characteristic curve of 0.9815, and an area under the precision-recall curve of 0.9591, based on a constructed benchmark dataset and a 10-fold cross-validation test. Results from a series of non-redundant datasets and an independent test dataset showed our method to be robust for data redundancy and that it can accurately identify disease-related proteins, protein-related diseases, and potential disease-protein associations. Based on the constructed model, the large-scale prediction study identified more than 1.7 million potential disease-protein association pairs with a probability greater than 99%. The top five predicted disease-protein association pairs were further confirmed by literature and molecular docking simulations.

Extensive experimental results showed that the proposed method can effectively identify potential disease-protein associations. It is expected that the current method can help not only in understanding disease mechanisms at the protein level, but also in discovering new protein targets and potential small molecule drugs.

Venomous scorpions play a crucial role in medicine and public health. Buthotus saulcyi scorpion is known as one of the most populous species in East Asia and Iran, while its venom proteome has still not been fully determined.

In the current research, the proteomic profile of Buthotus saulcyi scorpion venom to determine the structural and functional characteristics of its compounds used for treatment will be examined for the first time.

2D-PAGE, HPLC, SDS-PAGE, sequencing, and MALDI-TOF MS techniques were used to investigate the properties of these peptides.

The 2D-PAGE analysis of crude toxin from B. saulcyi revealed a minimum of 96 protein spots, with isoelectric points ranging from 4 to 9 and molecular weights spanning from 3.6 to 205 kDa. Following this, HPLC was used to isolate 14 fractions of crude toxins, and the protein content of these fractions was measured. SDS-PAGE analysis identified 7 protein bands within the B. saulcyi crude toxin fractions, with molecular weights ranging from 13 to 217 kDa. Further examination of fraction 7 through amino acid sequencing resulted in the identification of two protein bands labeled peptide 3 and peptide 4. Ultimately, these protein bands were extracted, and their molecular mass and amino acid sequences were analyzed using MALDI-TOF MS.

According to our results, the alignment of P3 and P4 protein sequences revealed the highest similarity to chrysophsin 2 and pheromone-bound protein 2, respectively.

Membrane proteins participate in many physiological and biochemical functions essential for cellular function. Identifying membrane protein types is a critical task in biology for studying the tertiary structure of membrane proteins.

In this paper, a novel classification method is proposed to predict membrane protein types based on the ensemble learning model, fusing protein sequence features and secondary structure information.

The performance for predicting the type of membrane proteins was improved compared with other machine learning models.

Utilizing multimodal features and machine learning methods can effectively predict and classify membrane protein types.

Acinetobacter baumannii is a well-known, multidrug-resistant bacteria that poses a serious risk to public health everywhere. The discovery of novel antibacterial drugs has become an urgent need due to the emergence of multi-drug resistance strains and the lack of appropriate antibiotics. To develop effective treatments for A. baumannii infections, this work explores the evolutionary analysis of Outer Membrane Proteins (OMPs), specifically OMP33-36.

The structural data and sequence information of OMP33-36 were retrieved from Protein Data Bank and UniProt, respectively. A range of bio-computational techniques including ConSurf web server, MEGA XI, and BioEdit were exploited to carry out hydrophobicity analysis, entropy, sequence alignment, and functional conserved site identification. By revealing close relatives of A. baumannii, phylogenetic research clarified the evolutionary relationship of OMPs among 70 bacterial species. Six remarkably conserved areas in OMPs from various bacterial species were found through a conserved domain search using BioEdit.

This study has explored the evolutionary dynamics and intricacies in functional regions of OMPs.

The outcomes of this study highlight the significant understanding of the structural and evolutionary aspects of OMP, which will help in the development of effective and precise therapy for A. baumannii infections. The construction of OMPs targeted inhibitors lowers the possibility of off-target effects on human cells.

Recent investigation revealed that arbuscular mycorrhizal fungi (AMF) brought major changes in the transcriptome of non-host plant- Arabidopsis thaliana (A. thaliana) within the AM network constructed by the hyphae of AMF connecting multiple plant roots. Although there is enormous omics data available for A. thaliana, most AM-related information has been restricted to transcriptome studies.

We aimed to provide a comprehensive toolset for analyzing AM signaling-driven molecular interactions in A. thaliana.

We developed ten modules: 1) Epigenetic regulation in protein–nucleic acid interactions (PNI), 2) DNA structure and metal binding profiles, 3) Transcription factor (TF) binding profiles, 4) Protein domain–domain interactions (DDI), 5) Profiling of protein-metal and protein-ligand interactions with complex structures (PLP) based on alignment of similar protein structures, 6) Carbohydrate-lipid-protein interactions (CLP) – analysis of lipidome-proteome interactions, N-glycosylation/glycan structure data, and carbohydrate-active enzyme/substrate predictions, 7) Metabolic pathway analysis, 8) Multiple omics association studies, 9) Gene Ontology (GO) and Plant Ontology (PO) analysis, and 10) Medicago transcriptome and epigenetic information.

For the program demonstration, we generated various comparative datasets based on differentially expressed genes (DEGs) from Arabidopsis thaliana (A. thaliana) of non-arbuscular mycorrhizal (non-AM) and arbuscular mycorrhizal (AM) phenotypes, as well as DEGs from Medicago truncatula (M. truncatula). These datasets were analyzed using statistical methods and artificial neural networks. The program demonstrated a range of advantages in studying molecular interactions related to AM symbiosis.

To aid in the inference of AM-driven changes and the identification of AM-derived molecules during AM symbiosis, the program offers a user-friendly platform for generating datasets with key features, which can then be integrated with various downstream statistical methods. The program code is freely available for download at www.artfoundation.kr.

Glycogen synthase kinase 3-beta (GSK3-β) is a protein that is linked to the formation of amyloid-beta (Aβ) plaques and neurofibrillary tangles, both of which are characteristic of Alzheimer's disease. The enzyme's hyperactivity phosphorylates tau proteins, forming neurofibrillary tangles that impair material transport between axons and dendrites and disrupt neuronal function. GSK3-β impacts amyloid precursor protein production, causing Aβ accumulation and neurodegeneration.

This study aimed to investigate the inhibitory mechanism of natural compounds from Bryophyllum pinnatum against GSK3-β.

Computational approach, extra precision glide docking, and the Maestro molecular interface of Schrodinger suites were utilized to determine the binding free energy of the compounds against the prepared GSK3-β. The compounds' ADME parameters and Lipinski's rule of five were also evaluated. Using AutoQSAR, predictive models were constructed for both protein targets.

Three hit compounds (kaempferol, quercetin, and 5,7,4'-trihydroxy-3,8-dimethoxyflavone) were found in this study. These compounds met the recommended range for the specified ADME parameters and passed the rule of five. Additionally, the hit compounds' predicted pIC50 values were promising.

Our study suggests that the investigated compounds can be used to design GSK3-β inhibitors.

The application of molecular docking and Machine Learning (ML) calculations in evaluating peptide-based inhibitors allows for the systematic investigation of sequence-activity relationships, guiding the design of potent peptides with optimal binding characteristics.

This study aimed to screen short peptides using computational simulation to identify promising inhibitors against SARS-CoV-2 Mpro.

Short peptides were screened using molecular docking to identify promising candidates. The ML model was applied to confirm the docking outcome. The PreADME server was then used to analyze the HIA and toxicity of the peptides.

168,420 short peptides were docked to identify 5 tetrapeptides with promising docking scores against SARS-CoV-2 Mpro including, PYPW, WWPF, WWPY, HYPW, and WYPF. The obtained results were also confirmed via ML calculations. The analyses highlighted the importance of residues Thr190 and Asn142 that are crucial in the binding process. All of top-lead peptides adopt low toxicity and can be absorbed via the human intestine. They can also cross the blood brain barier.

This work enhances our understanding of Mpro interactions and informs future ligand design, contributing to the development of therapeutic strategies against COVID-19.

Centella asiatica is a tropical medicinal herb traditionally used for the treatment of different diseases, such as arthritis, kidney disease, diabetes mellitus, etc. Diabetes mellitus is emerging as a global health concern, demanding research to provide insights into it.

The current research study aimed at employing the Network Pharmacology and Molecular Docking approach to unearth and validate the possible molecular mechanism involved in the treatment of diabetic mellitus with herbal constituents from Centella asiatica.

The phytocompounds and targets of Centella asiatica were screened from different databases. An herb-core-target-ingredient-diabetes mellitus network was established via Cystoscope 3.7.2. Next, Go and KEGG enrichment analysis was performed. Lastly, the interaction between ligands and targets was investigated via molecular docking.

According to the results obtained, we identified 49 core targets of diabetes mellitus and 37 active ingredients of Centella asiatica. Next, Go and KEGG resulted in a total of 455 biological processes for the treatment of diabetes mellitus. The KEGG enrichment analysis reported that the targets were related to metabolic pathways, insulin signaling pathways, glycolysis/gluconeogenesis, oxidative stress, insulin resistance, etc. On the basis of KEGG enrichment and protein-protein interaction, we selected Fructose-1-6 bisphosphate1 (FBP1), Glucokinase (GCK), Cytochromes P450 (CYP19A1), fatty acid binding protein 1 (FABP1), Interleukin 2 (IL2) and angiotensin-converting enzyme (ACE), and phytocompounds from Centella asiatica for docking. From the docking study, it was concluded that several targets had a stable binding affinity with Centella asiatica phytocompounds.

We explored the biological mechanism of phytocompounds involved in the treatment of diabetes mellitus through different biological processes and signaling pathways, and lastly, docking provides us commending results that direct for experiments ahead.