Protein-protein Interaction Data Research Articles

Genome wide association studies are enriched for interacting genes.

Background : With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk. Results : We used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs. matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies. Conclusions : Our findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.

Homologous mapping yielded a comprehensive predicted protein–protein interaction network for peanut (Arachis hypogaea L.)

BackgroundProtein–protein interactions are the primary means through which proteins carry out their functions. These interactions thus have crucial roles in life activities. The wide availability of fully sequenced animal and plant genomes has facilitated establishment of relatively complete global protein interaction networks for some model species. The genomes of cultivated and wild peanut (Arachis hypogaea L.) have also been sequenced, but the functions of most of the encoded proteins remain unclear.ResultsWe here used homologous mapping of validated protein interaction data from model species to generate complete peanut protein interaction networks for A. hypogaea cv. ‘Tifrunner’ (282,619 pairs), A. hypogaea cv. ‘Shitouqi’ (256,441 pairs), A. monticola (440,470 pairs), A. duranensis (136,363 pairs), and A. ipaensis (172,813 pairs). A detailed analysis was conducted for a putative disease-resistance subnetwork in the Tifrunner network to identify candidate genes and validate functional interactions. The network suggested that DX2UEH and its interacting partners may participate in peanut resistance to bacterial wilt; this was preliminarily validated with overexpression experiments in peanut.ConclusionOur results provide valuable new information for future analyses of gene and protein functions and regulatory networks in peanut.

Multi-modal contrastive learning of subcellular organization using DICE.

The data deluge in biology calls for computational approaches that can integrate multiple datasets of different types to build a holistic view of biological processes or structures of interest. An emerging paradigm in this domain is the unsupervised learning of data embeddings that can be used for downstream clustering and classification tasks. While such approaches for integrating data of similar types are becoming common, there is scarcer work on consolidating different data modalities such as network and image information. Here, we introduce DICE (Data Integration through Contrastive Embedding), a contrastive learning model for multi-modal data integration. We apply this model to study the subcellular organization of proteins by integrating protein-protein interaction data and protein image data measured in HEK293 cells. We demonstrate the advantage of data integration over any single modality and show that our framework outperforms previous integration approaches. Availability: https://github.com/raminass/protein-contrastive Contact: raminass@gmail.com.

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

Abstract We135: Mechanistic Modeling Identifies Novel Regulators of Cardiac Hypertrophy

Introduction: Cardiac hypertrophy is a leading predictor of heart failure. The mechanisms that drive cardiac hypertrophy are poorly understood from a molecular and cellular perspective, due to the lack of knowledge of the governing signaling. Hypothesis and Aims: I hypothesize that there exist genes that regulate hypertrophy beyond the field’s current understanding that can be identified by combining the publicly available, high-throughput International Mouse Phenotyping Consortium (IMPC) database with network modeling. I aim to use a large-scale network model of cardiac hypertrophy to assess the IMPC genes’ effects on hypertrophic phenotypes and validate these findings in vitro to identify novel regulators. Materials & Methods: The IMPC was established by worldwide mouse clinics using in vivo models to perform single gene knockout experiments with primary phenotyping, forming a database with ~9000 genes, ~1000 of which can be associated with cardiac hypertrophy. 1 st neighbors (genes directly interacting with the hypertrophy network model as evidenced by protein-protein interaction databases) were identified and effects on hypertrophy were assessed through the IMPC and network model knockdown simulations. When both methods suggested hypertrophic regulation, a gene was considered a candidate regulator. siRNA experiments are being performed in neonatal rat cardiomyocytes for validation. High-content imaging and qPCR are used to quantify changes in phenotype and confirm gene knockdown. Results: Combining the IMPC with protein-protein interaction data, 37 1 st neighbor genes were identified. Knockdown simulation identified 2 genes (LRIG1, CBL) as negative regulators. 3 genes (VAV2, LEPR, RASA1) were identified as positive regulators. These genes became candidates for in vitro validation given the network-wide hypertrophic effects enacted by their knockdowns. siRNA experiments are being optimized to fully validate modeling findings. Conclusions: Mechanistic modeling identified 5 genes as novel regulators of cardiac hypertrophy. Sensitivity analysis highlighted known influencers of hypertrophy in the knockdown context. siRNA knockdown experiments are being performed to validate the model predictions.

Integrated network pharmacology and molecular docking to investigate the potential mechanism of Tufuling on Alzheimer's disease

ObjectiveThis study aimed to investigate the mechanism of Tu Fu Ling in treating Alzheimer's disease (AD) using network pharmacology and molecular docking. MethodsThe TCMSP and Swiss target prediction databases were utilized to confirm the active components of Tu Fu Ling and their corresponding targets, with target gene names converted using the UniProt database. Genes related to AD were collected from DisGeNET, GeneCards, and the Open Target Platform databases. Common target genes between the disease and the drug were obtained using Venny 2.1 tools and visualized using Cytoscape software. Protein-protein interaction (PPI) data were further analyzed to determine correlations between common target genes, and GO and KEGG pathway enrichment analyses were performed for intersecting genes. Finally, PYmol, AutoDock Tool, Discovery Studio 2020, and PyRx software were used for preliminary computer virtual verification and visualization of active drug ingredients and target proteins. ResultsNine active ingredients meeting the screening criteria yielded a total of 168 genes after removing duplicates. A total of 3833 target genes were collected, with 129 overlapping target genes identified. GO enrichment analysis identified 643 biological processes, 82 cellular components, and 147 molecular functions. KEGG pathway enrichment analysis also revealed a pathway closely related to AD (hsa05010: Alzheimer's disease). In molecular docking analysis, the binding affinity between the 9 active ingredients and 10 core targets ranged from −3.5 to −12.3 kcal/mol, indicating strong binding. ConclusionThis study preliminarily verified the combination of Tu Fu Ling's screened active ingredient and the calculated core target, suggesting a potential mechanism of action to improve the symptoms of AD patients through multi-target and multi-pathway approaches. This provides a valuable reference for further exploration of the pharmacological mechanism of AD and the formulation of drug therapy.

Modular pharmacology: research progress and development of analysis platform

Based on the systematic deconstruction of multi-dimensional and multi-target biological networks, modular pharmacology explains the complex mechanism of diseases and the interactions of multi-target drugs. It has made progress in the fields of pathogenesis of disease, biological basis of disease and traditional Chinese medicine(TCM) syndrome, pharmacological mechanism of multi-target herbs, compatibility of formulas, and discovery of new drug of TCM compound. However, the complexity of multi-omics data and biological networks brings challenges to the modular deconstruction and analysis of the drug networks. Here, we constructed the "Computing Platform for Modular Pharmacology" online analysis system, which can implement the function of network construction, module identification, module discriminant analysis, hub-module analysis, intra-module and inter-module relationship analysis, and topological visualization of network based on quantitative expression profiles and protein-protein interaction(PPI) data. This tool provides a powerful tool for the research on complex diseases and multi-target drug mechanisms by means of modular pharmacology. The platform may have broad range of application in disease modular identification and correlation mechanism, interpretation of scientific principles of TCM, analysis of complex mechanisms of TCM and formulas, and discovery of multi-target drugs.

Mechanism of polygonum capitatum intervention in pulmonary nodule based on network pharmacology and molecular docking technology.

The present study utilizes network pharmacology and molecular docking methodologies to investigate the mechanism of action behind the intervention of Polygonum capitatum Buch.-Ham.ex D. Don (THL) in treating pulmonary nodules (PN). This research aims to provide a theoretical foundation for broadening the clinical application of THL. Active components of THL were identified and screened through an extensive literature review and the PharmMapper database, followed by an analysis of their target interactions. Relevant targets associated with PN were selected using databases such as OMIM and GeneCards, with an intersection of the two sets being determined. STRING11.5 facilitated the acquisition of protein-protein interaction data, which was then imported into Cytoscape 3.7.2 to establish a protein interaction network topology. This enabled the identification of pivotal targets affected by THL intervention in PN. The study further employed the Metascape database to conduct GO and KEGG bioinformatics enrichment analyses, which illuminated core pathways involved in THL's therapeutic effects on PN. A comprehensive component-target-pathway diagram was constructed utilizing Cytoscape 3.7.2 software, with molecular docking validations carried out via Maestro software. A total of 49 active THL ingredients were discerned, implicating 67 PN-relevant targets. Subsequent software analysis pinpointed 10 key targets, including ALB, EGFR, and SRC. Molecular docking studies indicated strong binding affinities for most protein-compound pairs, with 44 out of 60 docking results exhibiting binding energies below -5 kcal/mol. Enrichment analysis highlights that key targets are mainly involved in pathways such as cancer, lipid metabolism and atherosclerosis, estrogen signaling, IL-17 signaling, complement and coagulation cascades, and chemical carcinogenesis through receptor activation. Through comprehensive network pharmacological approaches, this research delineates the synergy of THL's multiple components, targets, and pathways in mitigating PN. It posits that primary active ingredients of THL - quercetin, salidroside, and oleanolic acid - may exert effects on targets like ALB, EGFR, SRC, potentially modulating pathways associated with cancer, lipid and atherosclerosis, and IL-17 signaling in the context of PN intervention.

CrossPredGO: A Novel Light-weight Cross-modal Multi-attention Framework for Protein Function Prediction.

Proteins are represented in various ways, each contributing differently to protein-related tasks. Here, information from each representation (protein sequence, 3D structure, and interaction data) is combined for an efficient protein function prediction task. Recently, uni-modal has produced promising results with state-of-the-art attention mechanisms that learn the relative importance of features, whereas multi-modal approaches have produced promising results by simply concatenating obtained features using a computational approach from different representations which leads to an increase in the overall trainable parameters. In this paper, we propose a novel, light-weight cross-modal multi-attention (CrMoMulAtt) mechanism that captures the relative contribution of each modality with a lower number of trainable parameters. The proposed mechanism shows a higher contribution from PPI and a lower contribution from structure data. The results obtained from the proposed CrossPredGO mechanism demonstrate an increment in Fmax in the range of +(3.29 to 7.20)% with at most 31% lower trainable parameters compared with DeepGO and MultiPredGO.

Structure-guided Evolutionary Analysis of Interactome Network Rewiring at Single Residue Resolution in Yeasts

Protein-protein interactions (PPIs) are known to rewire extensively during evolution leading to lineage-specific and species-specific changes in molecular processes. However, the detailed molecular evolutionary mechanisms underlying interactome network rewiring are not well-understood. Here, we combine high-confidence PPI data, high-resolution three-dimensional structures of protein complexes, and homology-based structural annotation transfer to construct structurally-resolved interactome networks for the two yeasts S. cerevisiae and S. pombe. We then classify PPIs according to whether they are preserved or different between the two yeast species and compare site-specific evolutionary rates of interfacial versus non-interfacial residues for these different categories of PPIs. We find that residues in PPI interfaces evolve significantly more slowly than non-interfacial residues when using lineage-specific measures of evolutionary rate, but not when using non-lineage-specific measures. Furthermore, both lineage-specific and non-lineage-specific evolutionary rate measures can distinguish interfacial residues from non-interfacial residues for preserved PPIs between the two yeasts, but only the lineage-specific measure is appropriate for rewired PPIs. Finally, both lineage-specific and non-lineage-specific evolutionary rate measures are appropriate for elucidating structural determinants of protein evolution for residues outside of PPI interfaces. Overall, our results demonstrate that unlike tertiary structures of single proteins, PPIs and PPI interfaces can be highly volatile in their evolution, thus requiring the use of lineage-specific measures when studying their evolution. These results yield insight into the evolutionary design principles of PPIs and the mechanisms by which interactions are preserved or rewired between species, improving our understanding of the molecular evolution of PPIs and PPI interfaces at the residue level.

From petals to healing: consolidated network pharmacology and molecular docking investigations of the mechanisms underpinning Rhododendron arboreum flower's anti-NAFLD effects.

Rhododendron arboreum: Sm., also known as Burans is traditionally used as an anti-inflammatory, anti-diabetic, hepatoprotective, adaptogenic, and anti-oxidative agent. It has been used since ancient times in Indian traditional medicine for various liver disorders. However, the exact mechanism behind its activity against NAFLD is not known. The aim of the present study is to investigate the molecular mechanism of Rhododendron arboreum flower (RAF) in the treatment of NAFLD using network pharmacology and molecular docking methods. Bioactives were also predicted for their drug-likeness score, probable side effects and ADMET profile. Protein-protein interaction (PPI) data was obtained using the STRING platform. For the visualisation of GO analysis, a bioinformatics server was employed. Through molecular docking, the binding affinity between potential targets and active compounds were assessed. A total of five active compounds of RAF and 30 target proteins were selected. The targets with higher degrees were identified through the PPI network. GO analysis indicated that the NAFLD treatment with RAF primarily entails a response to the fatty acid biosynthetic process, lipid metabolic process, regulation of cell death, regulation of stress response, and cellular response to a chemical stimulus. Molecular docking and molecular dynamic simulation exhibited that rutin has best binding affinity among active compounds and selected targets as indicated by the binding energy, RMSD, and RMSF data. The findings comprehensively elucidated toxicity data, potential targets of bioactives and molecular mechanisms of RAF against NAFLD, providing a promising novel strategy for future research on NAFLD treatment.

Predicting RNA polymerase II transcriptional elongation pausing and associated histone code.

RNA Polymerase II (Pol II) transcriptional elongation pausing is an integral part of the dynamic regulation of gene transcription in the genome of metazoans. It plays a pivotal role in many vital biological processes and disease progression. However, experimentally measuring genome-wide Pol II pausing is technically challenging and the precise governing mechanism underlying this process is not fully understood. Here, we develop RP3 (RNA Polymerase II Pausing Prediction), a network regularized logistic regression machine learning method, to predict Pol II pausing events by integrating genome sequence, histone modification, gene expression, chromatin accessibility, and protein-protein interaction data. RP3 can accurately predict Pol II pausing in diverse cellular contexts and unveil the transcription factors that are associated with the Pol II pausing machinery. Furthermore, we utilize a forward feature selection framework to systematically identify the combination of histone modification signals associated with Pol II pausing. RP3 is freely available at https://github.com/AMSSwanglab/RP3.

Integration of protein sequence and protein–protein interaction data by hypergraph learning to identify novel protein complexes

Integration of protein sequence and protein–protein interaction data by hypergraph learning to identify novel protein complexes

Combinatorial microRNA activity is essential for the transition of pluripotent cells from proliferation into dormancy.

Dormancy is a key feature of stem cell function in adult tissues as well as in embryonic cells in the context of diapause. The establishment of dormancy is an active process that involves extensive transcriptional, epigenetic, and metabolic rewiring. How these processes are coordinated to successfully transition cells to the resting dormant state remains unclear. Here we show that microRNA activity, which is otherwise dispensable for preimplantation development, is essential for the adaptation of early mouse embryos to the dormant state of diapause. In particular, the pluripotent epiblast depends on miRNA activity, the absence of which results in the loss of pluripotent cells. Through the integration of high-sensitivity small RNA expression profiling of individual embryos and protein expression of miRNA targets with public data of protein-protein interactions, we constructed the miRNA-mediated regulatory network of mouse early embryos specific to diapause. We find that individual miRNAs contribute to the combinatorial regulation by the network, and the perturbation of the network compromises embryo survival in diapause. We further identified the nutrient-sensitive transcription factor TFE3 as an upstream regulator of diapause-specific miRNAs, linking cytoplasmic MTOR activity to nuclear miRNA biogenesis. Our results place miRNAs as a critical regulatory layer for the molecular rewiring of early embryos to establish dormancy.

A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding

The rapid development of high-throughput technology has generated a large amount of protein–protein interaction (PPI) data, which provide a large amount of data support for constructing dynamic protein–protein interaction networks (PPINs). Constructing dynamic PPINs and applying them to recognize protein complexes has become a hot research topic. Most existing methods for complex recognition cannot fully mine the information of PPINs. To address this problem, we propose a construction method of dynamic weighted protein network by multi-level embedding (DWPNMLE). It can reflect the protein network’s dynamics and the protein network’s higher-order proximity. Firstly, the protein active period is calculated to divide the protein subnetworks at different time points. Then, the connection probability is used for the proteins possessing the same time points to judge whether there is an interaction relationship between them. Then, the corresponding protein subnetworks (multiple adjacency matrices) are constructed. Secondly, the multiple feature matrices are constructed using one-hot coding with the gene ontology (GO) information. Next, the first embedding is performed using variational graph auto-encoders (VGAEs) to aggregate features efficiently, followed by the second embedding using deep attributed network embedding (DANE) to strengthen the node representations learned in the first embedding and to maintain the first-order and higher-order proximity of the original network; finally, we compute the cosine similarity to obtain the final dynamic weighted PPIN. To evaluate the effectiveness of DWPNMLE, we apply four classical protein-complex-recognition algorithms on the DWPNMLE and compare them with two other dynamic protein network construction methods. The experimental results demonstrate that DWPNMLE significantly enhances the accuracy of complex recognition with high robustness, and the algorithms’ efficiency is also within a reasonable range.

I'm Walking into Spiderwebs: Making Sense of Protein-Protein Interaction Data.

Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.

Molecular mechanisms underlying the therapeutic effects of Linggui Zhugan decoction in stroke: Insights from network pharmacology and single-cell transcriptomics analysis.

Linggui Zhugan decoction (LZD), a traditional Chinese medicine formula, has demonstrated significant therapeutic effects in managing poststroke cognitive impairment and hemiplegia. However, the precise molecular mechanisms underlying its efficacy remain incompletely elucidated. The active ingredients and target proteins of LZD were retrieved from the traditional Chinese medicine systems pharmacology database and analysis platform database, which is specifically designed for traditional Chinese medicine research. The stroke-related genes were obtained from publicly available databases. Protein-protein interaction, enrichment analysis, and single-cell data analysis were conducted to identify key cells, targets, and pathways. Molecular docking was employed to assess the binding affinity between key components and targets. Network pharmacology analysis identified 190 active ingredients and 248 targets in LZD. These targets were significantly enriched in processes and pathways such as cellular response to lipid, orexin receptor pathway, and were significantly associated with Cerebral infarction and Middle Cerebral Artery Occlusion. Intersection analysis with 2035 stroke-related genes revealed 144 potential targets, which exhibited 2870 interactions and were significantly enriched in signaling pathways such as PI3K-AKT single pathway, MAPK single pathway, and tumor necrosis factor single pathway. Gene set variation analysis showed that the targets of LZD exhibited higher enrichment scores in microglia, M2 macrophages, endothelial cells, and neutrophils, while lower enrichment scores were observed in oligodendrocytes. Furthermore, molecular docking demonstrated a strong binding affinity between key active ingredients and targets. Network pharmacology and single-cell sequencing analysis elucidated the key cells, pathways, targets, and components involved in the therapeutic mechanism of LZD for the treatment of stroke.

Abstract 1525: Integration of high-plex tumor-Immune phenotyping and checkpoint interactions for deeper spatial characterization of human cancer tissues

Abstract Background: Immune evasion is a key cancer hallmark, and one of the primary mechanisms is the PD-1/PD-L1 checkpoint axis. While immunotherapies that target these interactions have reshaped cancer treatment, the variable efficacy in patients is not fully understood. Due to the weak correlation between PD-1/PD-L1 expression measured by single-plex assays and observed clinical outcomes, there is a need to understand protein interactions at the cellular and subcellular level. Proximity Ligation Assays, detecting spatial interactions between ligand-receptor targets, provide insights into the activation of signaling pathways. Coupled with high-plex spatial phenotyping with tumor and immune profiling biomarkers, the integration of cellular and functional information will be beneficial for a deeper characterization of the tumor microenvironment (TME), understanding immune responses, and identifying spatial signatures for patient stratification and targeted treatment strategies. Methods: In this study, we phenotyped biopsies from head and neck cancer patients enrolled in immune checkpoint inhibitor therapies. We integrated Naveni® PD-1/PD-L1 proximity ligation assays and a customizable high-plex PhenoCode™ Signature Panel (CD3ε/CD8/CD20/CD68/PanCK) for high-throughput profiling of the TME utilizing the PhenoImager® HT 2.0 platform. Comprehensive bioinformatic analyses were conducted for whole-slide segmentation, identifying cellular phenotypes, spatial neighborhoods, functional interactions, and distinct spatial signatures via the open-source image analysis software QuPath. Results: Our analyses revealed key differences in the localization of the PD-1/PD-L1 interactions within the TME of head and neck cancers. Areas of PD-1/PD-L1 interactions are associated with immune cell types on the periphery of the tumor and some within the tumor-infiltrating lymphocytes. The PhenoCode Signature panels helped resolve the localization of PD-1/PD-L1 interactions to immune cell types within the TME and provided a dual workflow for spatial co-localization of interacting receptor/ligand interactions and cell phenotypes within the TME. Conclusions: By combining spatial immune profiling with Akoya’s PhenoCode Signature panels and protein-protein interaction data from Navinci’s Naveni® PD-1/PD-L1 assay, deep correlative insights of the TME can be applied to improve predictions of clinical outcomes. This innovative approach enriches our comprehension of underlying mechanisms and stands as a promising tool for refining patient selection and optimizing treatment outcomes in immunotherapy. Citation Format: Sara Bodbin, Ning Ma, Aditya Pratapa, Nadya Nikulina, James Monkman, Niyati Jhaveri, Hampus Elofsson, Subham Basu, Agata Zieba-Wicher, Arutha Kulasinghe. Integration of high-plex tumor-Immune phenotyping and checkpoint interactions for deeper spatial characterization of human cancer tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1525.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits.

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Dynamic Model Inference of Gene Regulatory Network based on Hybrid Parallel Genetic Algorithm and Threshold Qualification Method

Gene regulation is the process by which various substances in cells regulate the behaviour of gene expression, thereby controlling almost all cellular activities. Therefore, studying gene regulation not only helps to uncover the internal laws governing life processes but also plays a crucial role in predicting, diagnosing, treating, and designing drugs for genetic diseases. By utilizing multi-source biological information such as gene expression profiles, transcription factor information, and protein interaction data, a network model can be developed to depict the regulatory relationships between genes, facilitating further research. To address the limitations of traditional gene regulatory network construction methods, a novel dynamic model has been created by combining hybrid genetics and threshold restriction. This model comprises two parts: solution space reduction and parameter fitting. During solution space reduction, singular value decomposition is employed to define a mathematically feasible gene regulatory network, reducing unnecessary calculations. Subsequently, the control genes of each gene are constrained within a certain range using threshold limitation, enhancing computational efficiency while adhering to bioinformatics principles. In the parameter fitting phase, parallel genetic algorithms are utilized to expediently optimize the entire solution space. The mountain climbing method is then applied to solve problems meticulously within a limited scope, improving calculation accuracy. In this study, this approach was applied to establish genetic regulatory systems for complex skin melanoma and type 2 diabetes. Through comparison with actual networks, the validity of the method was confirmed. Compared to traditional genetic and particle swarm optimization methods, the effectiveness of the proposed method was verified. This paper models the intricate mechanism of gene regulation and elucidates the regulatory process involving genes, proteins, and small biological molecules in greater detail than other models, aligning more closely with intracellular dynamics laws.