Human Protein Reference Database--2009 update

Honokiol in the treatment of triple-negative breast cancer: a network pharmacology approach and experimental validation

Triple-negative breast cancer (TNBC) is a rare and highly metastatic form of cancer. Honokiol (HNK), a biphenolic compound, has been utilized in TNBC treatment, though its specific targets remain unclear. This study aimed to elucidate the effects of HNK on TNBC by combining network pharmacology predictions and experimental validation to uncover its mechanisms. MDA-MB 231 and MDA-MB 468 cells were pre-treated with varying doses of HNK for 24 h. Cell viability, proliferation, and apoptosis were assessed using CCK8 and FACS assays, whereas a wound healing assay was used to evaluate cell migration. A tubule formation assay was used to assess blood vessel formation in HUVECs. Additionally, in vivo activity was confirmed using a zebrafish xenograft model. Network pharmacology and molecular docking predicted active ingredients, key targets, and potential mechanisms of HNK against TNBC. Results indicated that HNK induces apoptosis in MDA-MB 231 and MDA-MB 468 cells and inhibits their migration and proliferation. Furthermore, HNK suppressed blood vessel formation. Zebrafish xenograft experiments validated HNK's inhibitory effect on TNBC cells in vivo. Network pharmacology identified 36 potential HNK targets against TNBC, including HSP90AA1, AKT1, EGFR, ERBB2, HSP90AB1, PGR, MDM2, HDAC1, NR3C1, and MAPK14. Key signaling pathways such as PI3K-Akt, MAPK, Rap1, Ras, and FoxO were implicated in HNK's anti-TNBC mechanism. Molecular docking demonstrated spontaneous interactions between HNK and the targeted proteins. In conclusion, HNK may reduce angiogenesis by blocking the EGFR and HSP90AB1 pathways thereby decreasing proliferation and increasing apoptosis in TNBC cells.

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

Kinases regulate cellular processes and are essential for understanding cellular function and disease. To investigate the regulatory state of a kinase, numerous methods have been developed to infer kinase activities from phosphoproteomics data using kinase-substrate libraries. However, few phosphorylation sites can be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. Moreover, inferred activities vary across methods, necessitating evaluation for accurate interpretation. Here, we present benchmarKIN, an R package enabling comprehensive evaluation of kinase activity inference methods. Alongside classical perturbation experiments, benchmarKIN introduces a tumor-based benchmarking approach utilizing multi-omics data to identify highly active or inactive kinases. We used benchmarKIN to evaluate kinase-substrate libraries, inference algorithms and the potential of adding predicted kinase-substrate interactions to overcome the coverage limitations. Our evaluation shows most computational methods perform similarly, but the choice of library impacts the inferred activities with a combination of manually curated libraries demonstrating superior performance in recapitulating kinase activities. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance. We then demonstrate how kinase activity inference aids characterize kinase inhibitor responses in cell lines. Overall, benchmarKIN helps researchers to select reliable methods for identifying deregulated kinases.

Exploring the Influence of Gene Networks on Driver Gene Classification

Cancer is a complex disease caused by mutations in the genome of cells. Genetic mutations can be divided into driver mutations, which are significant for the initiation and progression of cancer, and passenger mutations, which have a neutral effect. In recent years, computational methods have been developed to identify driver genes. Some of these methods use data from gene networks to classify the genes. However, the impact of different gene networks on the performance of these methods remains unexplored. This article aims to analyze the influence of genetic networks in driver gene classification. We analyzed driver gene classification methods that use gene networks as input data, using different cancer mutation datasets and distinct gene networks. Computational methods show significant variation in their results when different gene networks are employed. The results highlight the need to carefully interpret driver gene classification and emphasize the importance of using different gene networks. These findings underline the necessity of developing more robust computational approaches that account for network variability, ensuring greater reliability in driver gene identification and its applications in cancer research.

Modeling Alzheimer's Disease: A Review of Gene-Modified and Induced Animal Models, Complex Cell Culture Models, and Computational Modeling

Alzheimer's disease, a complex neurodegenerative disease, is characterized by the pathological aggregation of insoluble amyloid β and hyperphosphorylated tau. Multiple models of this disease have been employed to investigate the etiology, pathogenesis, and multifactorial aspects of Alzheimer's disease and facilitate therapeutic development. Mammals, especially mice, are the most common models for studying the pathogenesis of this disease in vivo. To date, the scientific literature has documented more than 280 mouse models exhibiting diverse aspects of Alzheimer's disease pathogenesis. Other mammalian species, including rats, pigs, and primates, have also been utilized as models. Selected aspects of Alzheimer's disease have also been modeled in simpler model organisms, such as Drosophila melanogaster, Caenorhabditis elegans, and Danio rerio. It is possible to model Alzheimer's disease not only by creating genetically modified animal lines but also by inducing symptoms of this neurodegenerative disease. This review discusses the main methods of creating induced models, with a particular focus on modeling Alzheimer's disease on cell cultures. Induced pluripotent stem cell (iPSC) technology has facilitated novel investigations into the mechanistic underpinnings of diverse diseases, including Alzheimer's. Progress in culturing brain tissue allows for more personalized studies on how drugs affect the brain. Recent years have witnessed substantial advancements in intricate cellular system development, including spheroids, three-dimensional scaffolds, and microfluidic cultures. Microfluidic technologies have emerged as cutting-edge tools for studying intercellular interactions, the tissue microenvironment, and the role of the blood-brain barrier (BBB). Modern biology is experiencing a significant paradigm shift towards utilizing big data and omics technologies. Computational modeling represents a powerful methodology for researching a wide array of human diseases, including Alzheimer's. Bioinformatic methodologies facilitate the analysis of extensive datasets generated via high-throughput experimentation. It is imperative to underscore the significance of integrating diverse modeling techniques in elucidating pathogenic mechanisms in their entirety.

Novel Digital Anomalies, Hippocampal Atrophy, and Mutations Expand the Genotypic and Phenotypic Spectra of CNKSR2 in the Houge Type of X-Linked Syndromic Intellectual Development Disorder (MRXSHG)

The Houge type of X-linked syndromic intellectual developmental disorder (MRXSHG) encompasses a spectrum of neurodevelopmental disorders characterized by intellectual disability (ID), language/speech delay, attention issues, and epilepsy. These conditions arise from hemizygous or heterozygous deletions, along with point mutations, affecting CNKSR2, a gene located at Xp22.12. CNKSR2, also known as CNK2 or MAGUIN, functions as a synaptic scaffolding molecule within the neuronal postsynaptic density (PSD) of the central nervous system. It acts as a link connecting postsynaptic structural proteins, such as PSD95 and S-SCAM, by employing multiple functional domains crucial for synaptic signaling and protein-protein interactions. Predominantly expressed in dendrites, CNKSR2 is vital for dendritic spine morphogenesis in hippocampal neurons. Its loss-of-function variants result in reduced PSD size and impaired hippocampal development, affecting processes including neuronal proliferation, migration, and synaptogenesis. We present 15 patients including three from the MENA (Middle East and North Africa), a region with no documented mutations in CNKSR2. Each individual displays unique clinical presentations that encompass developmental delay, ID, language/speech delay, epilepsy, and autism. Genetic analyses revealed 14 distinct variants in CNKSR2, comprising five nonsense, three frameshift, two splice, and four missense variants, of which 13 are novel. The ACMG guidelines unanimously interpreted these 14 variants in 15 individuals as pathogenic, highlighting the detrimental impact of these CNKSR2 genetic alterations and confirming the molecular diagnosis of MRXSHG. Importantly, variants Ser767Phe and Ala827Pro may lead to proteasomal degradation or reduced PSD size, contributing to the neurodevelopmental phenotype. Furthermore, these two amino acids, along with another two affected by four missense variants, exhibit complete conservation in nine vertebrate species, illuminating their crucial role in the gene's functionality. Our study revealed unique new digital and brain phenotype, including pointed fingertips (fetal pads of fingertips), syndactyly, tapering fingers, and hippocampal atrophy. These novel clinical features in MRXSHG, combined with 13 novel variants, expand the phenotypic and genotypic spectra of MRXSHG associated with CNKSR2 mutations.

Drug-target interaction prediction based on metapaths and simplified neighbor aggregation

Drug-target interaction (DTI) prediction is critical in drug repositioning and discovery. In current metapath-based prediction methods, attention mechanisms are often used to differentiate the importance of various neighbors, enhancing the model's expressiveness. However, in biological networks with small-scale imbalanced data, attention mechanisms are prone to interference from noise and missing data, leading to instability in weight learning, reduced efficiency, and an increased risk of overfitting. To address these issues, we propose the use of average aggregation to mitigate noise, simplify model complexity, and improve stability. Specifically, we introduce a simplified mean aggregation method for DTI prediction. This approach uses average aggregation, effectively reducing noise interference, lowering model complexity, and preventing overfitting, making it especially suitable for current biological networks. Extensive testing on three heterogeneous biological datasets shows that SNADTI outperforms 12 leading methods across two evaluation metrics, significantly reducing training time and validating its effectiveness in DTI prediction. Complexity analysis reveals that our method offers a substantial computational speed advantage over other methods on the same dataset, highlighting its enhanced efficiency. Experimental results demonstrate that SNADTI excels in prediction accuracy, stability, and reproducibility, confirming its practicality and effectiveness in DTI prediction.

KRN-DTI: Towards accurate drug-target interaction prediction with Kolmogorov-Arnold and residual networks

Predicting drug-target interactions (DTIs) accurately is essential in the field of drug discovery. Recently, artificial intelligence (AI) technologies, especially graph convolutional networks (GCNs), have been developed to tackle this challenge. However, as the number of GCN layers increases, models may lose critical information due to excessive smoothing. Moreover, these methods often lack interpretability and are dependent on specific datasets, which limits their generalizability. Consequently, this study introduces a novel method, KRN-DTI, which employs interpretable GCN technology to predict DTIs based on a drug-target heterogeneous network. The method uses GCN technology to identify potential DTIs by leveraging known interactions and dynamically adjusting the weights, thereby enhancing the model's interpretability. Additionally, residual connection technology is employed to integrate GNN outputs, mitigating the over-smoothing issue. Furthermore, the model's interpretability is enhanced by adaptively adjusting weights using Kolmogorov-Arnold Networks (KAN) and attention mechanisms. Experimental results show that KRN-DTI outperforms several advanced computational methods on the benchmark dataset. Case studies further highlight the effectiveness of KRN-DTI in predicting potential DTIs, showcasing its potential for real-world applications in drug discovery. Our code and data are publicly accessible at: https://github.com/lizhen5000/KRN-DTI.git.

New Insights and Implications of Cell-Cell Interactions in Developmental Biology

The dynamic and meticulously regulated networks established the foundation for embryonic development, where the intercellular interactions and signal transduction assumed a pivotal role. In recent years, high-throughput technologies such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have advanced dramatically, empowering the systematic dissection of cell-to-cell regulatory networks. The emergence of comprehensive databases and analytical frameworks has further provided unprecedented insights into embryonic development and cell-cell interactions (CCIs). This paper reviewed the exponential increased CCIs works related to developmental biology from 2008 to 2023, comprehensively collected and categorized 93 analytical tools and 39 databases, and demonstrated its practical utility through illustrative case studies. In parallel, the article critically scrutinized the persistent challenges within this field, such as the intricacies of spatial localization and transmembrane state validation at single-cell resolution, and underscored the interpretative limitations inherent in current analytical frameworks. The development of CCIs' analysis tools with harmonizing multi-omics data and the construction of cross-species dynamically updated CCIs databases will be the main direction of future research. Future investigations into CCIs are poised to expeditiously drive the application and clinical translation within developmental biology, unlocking novel dimensions for exploration and progress.

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To address this problem, we propose a deep reinforcement learning-based computational approach named RL-GenRisk to identify ccRCC risk genes. Distinct from traditional supervised models, RL-GenRisk frames the identification of ccRCC risk genes as a Markov Decision Process, combining the graph convolutional network and Deep Q-Network for risk gene identification. Moreover, a well-designed data-driven reward is proposed for mitigating the limitation of scant known risk genes. The evaluation demonstrates that RL-GenRisk outperforms existing methods in ccRCC risk gene identification. Additionally, RL-GenRisk identifies eight potential ccRCC risk genes. We successfully validated epidermal growth factor receptor (EGFR) and piccolo presynaptic cytomatrix protein (PCLO), corroborated through independent datasets and biological experimentation. This approach may also be used for other diseases in the future.

Positional distribution and conservation of major phosphorylated sites in the human kinome

The human protein kinome is a group of over 500 therapeutically relevant kinases. Exemplified by over 10,000 phosphorylated sites reported in global phosphoproteomes, kinases are also highly regulated by phosphorylation. Currently, 1008 phosphorylated sites in 273 kinases are associated with their regulation of activation/inhibition, and a few in 30 kinases are associated with altered activity. Phosphorylated sites in 196 kinases are related to other molecular functions such as localization and protein interactions. Over 8,000 phosphorylated sites, including all those in 517 kinases are unassigned to any functions. This imposes a significant bias and challenge for the effective analysis of global phosphoproteomics datasets. Hence, we derived a set of stably and frequently detected phosphorylated sites (representative phosphorylated sites) across diverse experimental conditions annotated in the PhosphoSitePlus database and presumed them to be relevant to the human kinase regulatory network. Analysis of these representative phosphorylated sites led to the classification of 449 kinases into four distinct categories (kinases with phosphorylated sites apportioned (PaKD) and enigmatic (PeKD), and those with predominantly within kinase domain (PiKD) and outside kinase domain (PoKD)). Knowledge-based functional analysis and sequence conservation across the family/subfamily identified phosphorylated sites unique to specific kinases that could contribute to their unique functions. This classification of representative kinase phosphorylated sites enhance our understanding of prioritized validation and provides a novel framework for targeted phosphorylated site enrichment approaches. Phosphorylated sites in kinases associated with dysregulation in diseases were frequently located outside the kinase domain, and suggesting their regulatory roles and opportunities for phosphorylated site-directed therapeutic approaches.

Spatial and functional profiles distinguish target sets of Parkinson's disease and antipsychotic drugs with different clinical effects

Several studies have examined the genetic factors shared between Parkinson's disease (PD) and schizophrenia (SZ), but the biological themes underlying their clinical relationships remain less explored. We employed systematic transcriptomic and network analyses to examine the genes targeted by two sets of antipsychotic drugs (APDs) - first-generation APDs inducing Parkinsonism and second-generation APDs typically effective against psychotic symptoms in PD - and two sets of PD drugs, one at risk of psychosis and the other with a lower risk of psychosis. Although global brain expression patterns did not effectively differentiate between the targets of the two sets of APDs, they did differentiate the targets of the two PD drug sets. However, both APD and PD target sets showed differences in mean expression levels in specific brain regions. Moreover, they showed significant enrichment for genes highly expressed in distinct adult and prenatal brain structures relative to the overall distribution of such genes among all brain-expressed genes. Specific neurotransmitter systems, either individually or in combinations, appeared to underlie the clinically informed drug categories, indicating their differential roles in inducing or not inducing PD and psychosis. Additionally, the target sets formed distinct network modules representing different biological mechanisms and exhibited differential proximity to putative PD and SZ risk genes in the human interactome. In summary, our study identified specific spatial and functional features that distinguish the target sets of PD and antipsychotic drugs with different clinical effects.

Towards a more accurate and reliable evaluation of machine learning protein-protein interaction prediction model performance in the presence of unavoidable dataset biases

The characterization of protein-protein interactions (PPIs) is fundamental to understand cellular functions. Although machine learning methods in this task have historically reported prediction accuracies up to 95 %, including those only using raw protein sequences, it has been highlighted that this could be overestimated due to the use of random splits and metrics that do not take into account potential biases in the datasets. Here, we propose a per-protein utility metric, pp_MCC, able to show a drop in the performance in both random and unseen-protein splits scenarios. We tested ML models based on sequence embeddings. The pp_MCC metric evidences a reduced performance even in a random split, reaching levels similar to those shown by the raw MCC metric computed over an unseen protein split, and drops even further when the pp_MCC is used in an unseen protein split scenario. Thus, the metric is able to give a more realistic performance estimation while allowing to use random splits, which could be interesting for more protein-centric studies. Given the low adjusted performance obtained, there seems to be room for improvement when using only primary sequence information, suggesting the need of inclusion of complementary protein data, accompanied with the use of the pp_MCC metric.

Theranostic Target NSUN2, a C(5)-Methyltransferase, Phospho-Regulatory Network Uncovered with Systematic Assembly of 805 Datasets

The RNA cytosine C(5)-methyltransferase NSUN2 is involved in RNA modification and regulates gene expression and genomic stability. Beyond multiple sequence/copy number variations, NSUN2 displays altered phosphoprotein expression in various cancers and developmental disorders, thereby making it a prime molecular target of relevance to both therapeutics and diagnostics, that is, theranostics. Despite its key role in kinase-regulated pathways and broader biological processes, the phospho-regulatory network of NSUN2 remains largely unexplored. We report here a systematic assembly of 805 phosphoproteomics datasets from the literature, among which 239 datasets showed differential regulation of NSUN2 phosphopeptides and 40 ensembled phosphosites in NSUN2. Significantly, the phosphorylation sites Ser456, Ser743, and Ser751 represented NSUN2 in ∼50% of these datasets. This is notable given that the functional roles of these phosphosites have been previously underappreciated and underrepresented in the scientific literature. Therefore, we implemented a codetection/coregulation approach based on the phosphosites in other proteins that are codifferentially regulated with phosphopeptides of NSUN2. This approach led to our identification of 55 interactors, 4 potential kinases, and 7 other methylases whose phosphopeptides were codifferentially regulated with NSUN2 phosphopeptides. To the best of our knowledge, this study provides the first phosphosite-centric regulatory network model of NSUN2 to employ theranostic strategies for targeting NSUN2 in cancers and other disorders.

Orchestrating Intracellular Calcium Signaling Cascades by Phosphosite-Centric Regulatory Network: A Comprehensive Analysis on Kinases CAMKK1 and CAMKK2

Intracellular calcium signaling is a cornerstone in cell biology and a key molecular target for human health and disease. Calcium/calmodulin dependent protein kinase kinases, CAMKK1 and CAMKK2 are serine/threonine kinases that contribute to the regulation of intracellular calcium signals in response to diverse stimuli. CAMKK1 generally has stable dynamics, whereas CAMKK2 dysregulation triggers oncogenicity and neurological disorders. To differentiate the phosphosignaling hierarchy associated with predominant phosphosites of CAMKK1 and CAMKK2, we assembled and analyzed the global cellular phosphoproteome datasets. We found that predominant phosphosites in CAMKK1 and CAMKK2 are located outside the kinase domain, and their phosphomotifs are highly homologous. Further, we employed a coregulation analysis approach to these predominant phosphosites, to infer the co-occurrence patterns of phosphorylations within CAMKKs and the coregulation patterns of other protein phosphosites with CAMKK sites. We report herein that independent phosphorylations at CAMKK2 S100 and S511 increase their enzymatic activity in the presence of calcium/calmodulin. In addition, the study unveils kinase-substrate associations such as RPS6KB1 as a novel high-confidence upstream kinase of both CAMKK1 S74 and CAMKK2 S100. Further, CAMKK2 was identified as a primary orchestrator in mediating intracellular calcium signaling cascades compared to CAMKK1 based on coregulation patterns of phosphosites from proteins involved in the calcium signaling pathway. These molecular details shed promising insights into the pathophysiology of several diseases such as cancers and psychiatric disorders associated with kinase activity dysregulations of CAMKK2 and further open the avenue for novel PTM-directed therapeutic strategies to regulate CAMKK2.

Supervised learning approaches for predicting Ebola-Human Protein-Protein interactions

The goal of this research work is to predict protein-protein interactions (PPIs) between the Ebola virus and the host who is at risk of infection. Since there are very limited databases available on the Ebola virus; we have prepared a comprehensive database of all the PPIs between the Ebola virus and human proteins (EbolaInt). Our work focuses on the finding of some new protein-protein interactions between humans and the Ebola virus using some state- of-the-arts machine learning techniques. However, it is basically a two-class problem with a positive interacting dataset and a negative non-interacting dataset. These datasets contain various sequence-based human protein features such as structure of amino acid and conjoint triad and domain-related features. In this research, we have briefly discussed and used some well-known supervised learning approaches to predict PPIs between human proteins and Ebola virus proteins, including K-nearest neighbours (KNN), random forest (RF), support vector machine (SVM), and deep feed-forward multi-layer perceptron (DMLP) etc. We have validated our prediction results using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Our goal with this prediction is to compare all other models' accuracy, precision, recall, and f1-score for predicting these PPIs. In the result section, DMLP is giving the highest accuracy along with the prediction of 2655 potential human target proteins.

Protein-protein interaction prediction using enhanced features with spaced conjoint triad and amino acid pairwise distance

Protein-protein interactions (PPIs) are pivotal in cellular processes, influencing a wide range of functions, from metabolism to immune responses. Despite the advancements in experimental techniques for PPI detection, their inherent limitations, such as high false-positive rates and significant resource demands, necessitate the development of computational approaches. This study presents a novel computational model named MFPIC (Multi-Feature Protein Interaction Classifier) for predicting PPIs, integrating enhanced sequence-based features, including a novel spaced conjoint triad (SCT) and amino acid pairwise distance (AAPD), with existing methods such as position-specific scoring matrices (PSSM) and AAindex-based features. The SCT captures complex sequence motifs by considering non-adjacent amino acid interactions, while AAPD provides critical spatial information about amino acid residues within protein sequences. The proposed model was evaluated across three benchmark datasets-Saccharomyces cerevisiae, Helicobacter pylori, and human proteins-demonstrating superior performance in comparison to state-of-the-art models. The results underscore the efficacy of integrating diverse and complementary features, achieving significant improvements in predictive accuracy, with the model achieving 95.90%, 99.33%, and 90.95% accuracy on the Saccharomyces cerevisiae, Helicobacter pylori, and human dataset, respectively. This approach not only enhances our understanding of PPI mechanisms but also offers valuable insights for the development of targeted therapeutic strategies.

Strategies to include prior knowledge in omics analysis with deep neural networks

High-throughput molecular profiling technologies have revolutionized molecular biology research in the past decades. One important use of molecular data is to make predictions of phenotypes and other features of the organisms using machine learning algorithms. Deep learning models have become increasingly popular for this task due to their ability to learn complex non-linear patterns. Applying deep learning to molecular profiles, however, is challenging due to the very high dimensionality of the data and relatively small sample sizes, causing models to overfit. A solution is to incorporate biological prior knowledge to guide the learning algorithm for processing the functionally related input together. This helps regularize the models and improve their generalizability and interpretability. Here, we describe three major strategies proposed to use prior knowledge in deep learning models to make predictions based on molecular profiles. We review the related deep learning architectures, including the major ideas in relatively new graph neural networks.

Proteogenomic characterization of molecular and cellular targets for treatment-resistant subtypes in locally advanced cervical cancers

We report proteogenomic analysis of locally advanced cervical cancer (LACC). Exome-seq data revealed predominant alterations of keratinization-TP53 regulation and O-glycosylation-TP53 regulation axes in squamous and adeno-LACC, respectively, compared to in early-stage cervical cancer. Integrated clustering of mRNA, protein, and phosphorylation data identified six subtypes (Sub1-6) of LACC among which Sub3, 5, and 6 showed the treatment-resistant nature with poor local recurrence-free survival. Elevated immune and extracellular matrix (ECM) activation mediated by activated stroma (PDGFD and CXCL1high fibroblasts) characterized the immune-hot Sub3 enriched with MUC5AChigh epithelial cells (ECs). Increased epithelial-mesenchymal-transition (EMT) and ECM remodeling characterized the immune-cold squamous Sub5 enriched with PGK1 and CXCL10high ECs. We further demonstrated that CIC mutations could trigger EMT activation by upregulating ETV4, and the elevation of the immune checkpoint PVR and neutrophil-like myeloid-derived suppressive cells (FCN1 and FCGR3Bhigh macrophages) could cause suppression of T-cell activation in Sub5. Increased O-linked glycosylation of mucin characterized adeno-LACC Sub6 enriched with MUC5AChigh ECs. These results provide a battery of somatic mutations, cellular pathways, and cellular players that can be used to predict treatment-resistant LACC subtypes and can serve as potential therapeutic targets for these LACC subtypes.

CAG Repeat Instability and Region-Specific Gene Expression Changes in the SCA12 Brain

Spinocerebellar ataxia type 12 (SCA12), an autosomal dominant cerebellar ataxia, caused by an expansion of (CAG)n in the 5' of the PPP2R2B gene on chr5q32, is common in India. The illness often manifests late in life, with diverse neurological and psychiatric symptoms, suggesting involvement of different brain regions. Prominent neuronal loss and atrophy of the cerebellum have been noted earlier. In Huntington's disease (HD), somatic instability associated with the size of the expanded CAG allele in HTT varies across regions of the brain, and influences the nature and severity of symptoms. We estimated CAG repeat size, methylation and gene expression in the PPP2R2B gene across regions in brain tissue from a person with SCA12. We also studied the regional expression of DNA repair pathway and cell cycle genes. Somatic mosaicism, manifested as CAG repeat instability, is detected across brain regions. The cerebellum showed the least somatic instability, and this was coupled with increased methylation, and lower expression, of the PPP2R2B gene. Interestingly, increased expression of DNA maintenance pathway related genes, which might partly explain the lowered DNA instability, was also observed. There was also decreased expression of cell cycle modulators, which could initiate apoptosis, and thus account for neuronal cell death seen in the brain sections. We suggest that drugs that improve DNA repeat stability, could thus be explored as a treatment option for SCA12.

Inferring kinase-phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets

Phosphorylation in eukaryotic cells plays a key role in regulating cell signaling and disease progression. Despite the ability to detect thousands of phosphosites in a single experiment using high-throughput technologies, the kinases responsible for regulating these sites are largely unidentified. To solve this, we collected the quantitative data at the transcriptional, protein, and phosphorylation levels of 10 159 samples from 23 tumor datasets and 15 adjacent normal tissue datasets. Our analysis aimed to uncover the potential impact and linkage of kinase-phosphosite (KPS) pairs through experimental evidence in publications and prediction tools commonly used. We discovered that both experimentally validated and tool-predicted KPS pairs were enriched in groups where there is a significant correlation between kinase expression/phosphorylation level and the phosphorylation level of phosphosite. This suggested that a quantitative correlation could infer the KPS interconnections. Furthermore, the Spearman's correlation coefficient for these pairs were notably higher in tumor samples, indicating that these regulatory interactions are particularly pronounced in tumors. Consequently, building on the KPS correlations of different datasets as predictive features, we have developed an innovative approach that employed an oversampling method combined with and XGBoost algorithm (SMOTE-XGBoost) to predict potential kinase-specific phosphorylation sites in proteins. Moreover, the computed correlations and predictions of kinase-phosphosite interconnections were integrated into the eKPI database (https://ekpi.omicsbio.info/). In summary, our study could provide helpful information and facilitate further research on the regulatory relationship between kinases and phosphosites.

TARSL: Triple-Attention Cross-Network Representation Learning to Predict Synthetic Lethality for Anti-Cancer Drug Discovery

Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exploiting the phenomenon of Synthetic Lethality (SL) by targeting the SL partner of cancer gene. Since traditional methods for SL prediction suffer from high-cost, time-consuming and off-targets effects, computational approaches have been efficient complementary to these methods. Most of existing approaches treat SL associations as independent of other biological interaction networks, and fail to consider other information from various biological networks. Despite some approaches have integrated different networks to capture multi-modal features of genes for SL prediction, these methods implicitly assume that all sources and levels of information contribute equally to the SL associations. As such, a comprehensive and flexible framework for learning gene cross-network representations for SL prediction is still lacking. In this work, we present a novel Triple-Attention cross-network Representation learning for SL prediction (TARSL) by capturing molecular features from heterogeneous sources. We employ three-level attention modules to consider the different contribution of multi-level information. In particular, feature-level attention can capture the correlations between molecular feature and network link, node-level attention can differentiate the importance of various neighbors, and network-level attention can concentrate on important network and reduce the effects of irrelated networks. We perform comprehensive experiments on human SL datasets and these results have proven that our model is consistently superior to baseline methods and predicted SL associations could aid in designing anti-cancer drugs.

Structure-Based Modeling of the Gut Bacteria-Host Interactome Through Statistical Analysis of Domain-Domain Associations Using Machine Learning

The gut microbiome, a complex ecosystem of microorganisms, plays a pivotal role in human health and disease. The gut microbiome's influence extends beyond the digestive system to various organs, and its imbalance is linked to a wide range of diseases, including cancer and neurodevelopmental, inflammatory, metabolic, cardiovascular, autoimmune, and psychiatric diseases. Despite its significance, the interactions between gut bacteria and human proteins remain understudied, with less than 20,000 experimentally validated protein interactions between the host and any bacteria species. This study addresses this knowledge gap by predicting a protein-protein interaction network between gut bacterial and human proteins. Using statistical associations between Pfam domains, a comprehensive dataset of over one million experimentally validated pan-bacterial-human protein interactions, as well as inter- and intra-species protein interactions from various organisms, were used for the development of a machine learning-based prediction method to uncover key regulatory molecules in this dynamic system. This study's findings contribute to the understanding of the intricate gut microbiome-host relationship and pave the way for future experimental validation and therapeutic strategies targeting the gut microbiome interplay.

Structure-Based Deep Learning Framework for Modeling Human-Gut Bacterial Protein Interactions

Background: The interaction network between the human host proteins and the proteins of the gut bacteria is essential for the establishment of human health, and its dysregulation directly contributes to disease development. Despite its great importance, experimental data on protein-protein interactions (PPIs) between these species are sparse due to experimental limitations. Methods: This study presents a deep learning-based framework for predicting PPIs between human and gut bacterial proteins using structural data. The framework leverages graph-based protein representations and variational autoencoders (VAEs) to extract structural embeddings from protein graphs, which are then fused through a Bi-directional Cross-Attention module to predict interactions. The model addresses common challenges in PPI datasets, such as class imbalance, using focal loss to emphasize harder-to-classify samples. Results: The results demonstrated that this framework exhibits robust performance, with high precision and recall across validation and test datasets, underscoring its generalizability. By incorporating proteoforms in the analysis, the model accounts for the structural complexity within proteomes, making predictions biologically relevant. Conclusions: These findings offer a scalable tool for investigating the interactions between the host and the gut microbiota, potentially yielding new treatment targets and diagnostics for disorders linked to the microbiome.

Emerging horizons of AI in pharmaceutical research

Artificial Intelligence (AI) has revolutionized drug discovery by enhancing data collection, integration, and predictive modeling across various critical stages. It aggregates vast biological and chemical data, including genomic information, protein structures, and chemical interactions with biological targets. Machine learning techniques and QSAR models are applied by AI to predict compound behaviors and predict potential drug candidates. Docking simulations predict drug-protein interactions, while virtual screening eliminates large chemical databases through efficient sifting. Similarly, AI supports de novo drug design by generating novel molecules, optimized against a particular biological target, using generative models such as generative adversarial network (GAN), always finding lead compounds with the most desirable pharmacological properties. AI used in clinical trials improves efficiency by pinpointing responsive patient cohorts leveraging genetic profiles and biomarkers and maintaining propriety such as dataset diversity and compliance with regulations. This chapter aimed to summarize and analyze the mechanism of AI to accelerate drug discovery by streamlining different processes that enable informed decisions and bring potential life-saving therapies to market faster, amounting to a breakthrough in pharmaceutical research and development.

Exploring drug-target interaction prediction on cold-start scenarios via meta-learning-based graph transformer

Predicting drug-target interaction (DTI) is of great importance for drug discovery and development. With the rapid development of biological and chemical technologies, computational methods for DTI prediction are becoming a promising approach. However, there are few solutions to the cold-start problem in DTI prediction scenarios, as these methods rely on existing interaction information to support their modeling. Consequently, they are unable to effectively predict DTIs for new drugs or targets with limited interaction data in the existing work. To this end, we propose a graph transformer method based on meta-learning named MGDTI (short for Meta-learning-based Graph Transformer for Drug-Target Interaction prediction) to fill this gap. Technically, we employ drug-drug similarity and target-target similarity as additional information to mitigate the scarcity of interactions. Besides, we trained MGDTI via meta-learning to be adaptive to cold-start tasks. Moreover, we employed graph transformer to prevent over-smoothing by capturing long-range dependencies. Extensive results on the benchmark dataset demonstrate that MGDTI is effective on DTI prediction under cold-start scenarios.

ISLRWR: A network diffusion algorithm for drug-target interactions prediction

Machine learning techniques and computer-aided methods are now widely used in the pre-discovery tasks of drug discovery, effectively improving the efficiency of drug development and reducing the workload and cost. In this study, we used multi-source heterogeneous network information to build a network model, learn the network topology through multiple network diffusion algorithms, and obtain compressed low-dimensional feature vectors for predicting drug-target interactions (DTIs). We applied the metropolis-hasting random walk (MHRW) algorithm to improve the performance of the random walk with restart (RWR) algorithm, forming the basis by which the self-loop probability of the current node is removed. Additionally, the propagation efficiency of the MHRW was improved using the improved metropolis-hasting random walk (IMRWR) algorithm, facilitating network deep sampling. Finally, we proposed a correction of the transfer probability of the entire network after increasing the self-loop rate of isolated nodes to form the ISLRWR algorithm. Notably, the ISLRWR algorithm improved the area under the receiver operating characteristic curve (AUROC) by 7.53 and 5.72%, and the area under the precision-recall curve (AUPRC) by 5.95 and 4.19% compared to the RWR and MHRW algorithms, respectively, in predicting DTIs performance. Moreover, after excluding the interference of homologous proteins (popular drugs or targets may lead to inflated prediction results), the ISLRWR algorithm still showed a significant performance improvement.

MVCL-DTI: Predicting Drug-Target Interactions Using a Multiview Contrastive Learning Model on a Heterogeneous Graph

Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views. Tested under various conditions on benchmark data sets, including varying positive-to-negative sample ratios, conducting hard negative sampling experiments, and masking known DTIs with different ratios, as well as redundant DTIs with various similarity metrics, MVCL-DTI exhibits strong robust generalization. The model is then employed to predict novel DTIs, with a particular focus on COVID-19-related drugs, highlighting its practical applicability. Ultimately, through features visualization and computational properties analysis, we've pinpointed critical elements, including Gene Ontology and substituent nodes, along with a proper initialization strategy, underscoring their vital role in DTI prediction tasks.

Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning

Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-based methods, offering greater biological accuracy by integrating three-dimensional spatial and biochemical features. This work summarizes the recent advances in computational approaches leveraging protein structure information for PPI prediction, focusing on machine learning (ML) and deep learning (DL) techniques. These methods not only improve predictive accuracy but also provide insights into functional sites, such as binding and catalytic residues. However, challenges such as limited high-resolution structural data and the need for effective negative sampling persist. Through the integration of experimental and computational tools, structure-based prediction paves the way for comprehensive proteomic network analysis, holding promise for advancements in drug discovery, biomarker identification, and personalized medicine. Future directions include enhancing scalability and dataset reliability to expand these approaches across diverse proteomes.

DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks

Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the issue of feature redundancy. We aim to refine DTI prediction accuracy by eliminating redundant features and capitalizing on the node topological structure to enhance feature extraction. To achieve this, we introduce a PCA-augmented multi-layer heterogeneous graph-based network that concentrates on key features throughout the encoding-decoding phase. Our approach initiates with the construction of a heterogeneous graph from various similarity metrics, which is then encoded via a graph neural network. We concatenate and integrate the resultant representation vectors to merge multi-level information. Subsequently, principal component analysis is applied to distill the most informative features, with the random forest algorithm employed for the final decoding of the integrated data. Our method outperforms six baseline models in terms of accuracy, as demonstrated by extensive experimentation. Comprehensive ablation studies, visualization of results, and in-depth case analyses further validate our framework's efficacy and interpretability, providing a novel tool for drug discovery that integrates multimodal features.

PhosNetVis: A web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate, and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers by rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at https://gumuslab.github.io/PhosNetVis/.

ScDrugAct: a comprehensive database to dissect tumor microenvironment cell heterogeneity contributing to drug action and resistance across human cancers

The transcriptional heterogeneity of tumor microenvironment (TME) cells is a crucial factor driving the diversity of cellular response to drug treatment and resistance. Therefore, characterizing the cells associated with drug treatment and resistance will help us understand therapeutic mechanisms, discover new therapeutic targets and facilitate precision medicine. Here, we describe a database, scDrugAct (http://bio-bigdata.hrbmu.edu.cn/scDrugAct/), which aims to establish connections among drugs, genes and cells and dissect the impact of TME cellular heterogeneity on drug action and resistance at single-cell resolution. ScDrugAct is curated with drug-cell connections between 3838 223 cells across 34 cancer types and 13 857 drugs and identifies 17 274 drug perturbation/resistance-related genes and 276 559 associations between >10 000 drugs and 53 cell types. ScDrugAct also provides multiple flexible tools to retrieve and analyze connections among drugs, genes and cells; the distribution and developmental trajectories of drug-associated cells within the TME; functional features affecting the heterogeneity of cellular responses to drug perturbation and drug resistance; the cell-specific drug-related gene network; and drug-drug similarities. ScDrugAct serves as an important resource for investigating the impact of the cellular heterogeneity of the TME on drug therapies and can help researchers understand the mechanisms of action and resistance of drugs, as well as discover therapeutic targets.

Pan-cancer analysis reveals age-associated genetic alterations in protein domains

Cancer incidence and mortality differ among individuals of different ages, but the functional consequences of genetic alterations remain largely unknown. We systematically characterized genetic alterations within protein domains stratified by affected individual's age and showed that the mutational effects on domains varied with age. We further identified potential age-associated driver genes with hotspots across 33 cancers. The candidate drivers involved numerous cancer-related genes that participate in various oncogenic pathways and play central roles in human protein-protein interaction (PPI) networks. We found widespread age biases in protein domains and identified the associations between hotspots and age. Age-stratified PPI networks perturbed by hotspots were constructed to illustrate the function of mutations enriched in domains. We found that hotspots in young adults were associated with premature senescence. In summary, we provided a catalog of age-associated hotspots and their perturbed networks, which may facilitate precision diagnostics and treatments for cancer.

State of the interactomes: an evaluation of molecular networks for generating biological insights

Advancements in genomic and proteomic technologies have powered the creation of large gene and protein networks ("interactomes") for understanding biological systems. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 45 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP, Reactome, and SIGNOR demonstrate stronger performance in interaction prediction. Our study provides a benchmark for interactomes across diverse biological applications and clarifies factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

Clinical functional proteomics of intercellular signalling in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) has an atypical, highly stromal tumour microenvironment (TME) that profoundly contributes to its poor prognosis1. Here, to better understand the intercellular signalling between cancer and stromal cells directly in PDAC tumours, we developed a multidimensional proteomic strategy called TMEPro. We applied TMEPro to profile the glycosylated secreted and plasma membrane proteome of 100 human pancreatic tissue samples to a great depth, define cell type origins and identify potential paracrine cross-talk, especially that mediated through tyrosine phosphorylation. Temporal dynamics during pancreatic tumour progression were investigated in a genetically engineered PDAC mouse model. Functionally, we revealed reciprocal signalling between stromal cells and cancer cells mediated by the stromal PDGFR-PTPN11-FOS signalling axis. Furthermore, we examined the generic shedding mechanism of plasma membrane proteins in PDAC tumours and revealed that matrix-metalloprotease-mediated shedding of the AXL receptor tyrosine kinase ectodomain provides an additional dimension of intercellular signalling regulation in the PDAC TME. Importantly, the level of shed AXL has a potential correlation with lymph node metastasis, and inhibition of AXL shedding and its kinase activity showed a substantial synergistic effect in inhibiting cancer cell growth. In summary, we provide TMEPro, a generically applicable clinical functional proteomic strategy, and a comprehensive resource for better understanding the PDAC TME and facilitating the discovery of new diagnostic and therapeutic targets.

Deciphering Cancer Complexity: Integrative Proteogenomics and Proteomics Approaches for Biomarker Discovery

Proteomics has revolutionized the field of cancer biology because the use of a large number of in vivo (SILAC), in vitro (iTRAQ, ICAT, TMT, stable-isotope Dimethyl, and 18O) labeling techniques or label-free methods (spectral counting or peak intensities) coupled with mass spectrometry enables us to profile and identify dysregulated proteins in diseases such as cancer. These proteome and genome studies have led to many challenges, such as the lack of consistency or correlation between copy numbers, RNA, and protein-level data. This review covers solely mass spectrometry-based approaches used for cancer biomarker discovery. It also touches on the emerging role of oncoproteogenomics or proteogenomics in cancer biomarker discovery and how this new area is attracting the integration of genomics and proteomics areas to address some of the important questions to help impinge on the biology and pathophysiology of different malignancies to make these mass spectrometry-based studies more realistic and relevant to clinical settings.

PhosNetVis: A web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/.

Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives

BACKGROUND

Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.

AIM OF REVIEW

This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.

Novel mTORC2/HSPB4 Interaction: Role and Regulation of HSPB4 T148 Phosphorylation

HSPB4 and HSPB5 (α-crystallins) have shown increasing promise as neuroprotective agents, demonstrating several anti-apoptotic and protective roles in disorders such as multiple sclerosis and diabetic retinopathy. HSPs are highly regulated by post-translational modification, including deamidation, glycosylation, and phosphorylation. Among them, T148 phosphorylation has been shown to regulate the structural and functional characteristics of HSPB4 and underlie, in part, its neuroprotective capacity. We recently demonstrated that this phosphorylation is reduced in retinal tissues from patients with diabetic retinopathy, raising the question of its regulation during diseases. The kinase(s) responsible for regulating this phosphorylation, however, have yet to be identified. To this end, we employed a multi-tier strategy utilizing in vitro kinome profiling, bioinformatics, and chemoproteomics to predict and discover the kinases capable of phosphorylating T148. Several kinases were identified as being capable of specifically phosphorylating T148 in vitro, and further analysis highlighted mTORC2 as a particularly strong candidate. Altogether, our data demonstrate that the HSPB4-mTORC2 interaction is multi-faceted. Our data support the role of mTORC2 as a specific kinase phosphorylating HSPB4 at T148, but also provide evidence that the HSPB4 chaperone function further strengthens the interaction. This study addresses a critical gap in our understanding of the regulatory underpinnings of T148 phosphorylation-mediated neuroprotection.

Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

The molecular subtypes of autoimmune diseases

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

PbsNRs: predict the potential binders and scaffolds for nuclear receptors

Nuclear receptors (NRs) are a class of essential proteins that regulate the expression of specific genes and are associated with multiple diseases. In silico methods for prescreening potential NR binders with predictive binding ability are highly desired for NR-related drug development but are rarely reported. Here, we present the PbsNRs (Predicting binders and scaffolds for Nuclear Receptors), a user-friendly web server designed to predict the potential NR binders and scaffolds through proteochemometric modeling. The utility of PbsNRs was systemically evaluated using both chemical compounds and natural products. Results indicated that PbsNRs achieved a good prediction performance for chemical compounds on internal (ROC-AUC = 0.906, where ROC is Receiver-Operating Characteristic curve and AUC is the Area Under the Curve) and external (ROC-AUC = 0.783) datasets, outperforming both compound-ligand interaction tools and NR-specific predictors. PbsNRs also successfully identified bioactive chemical scaffolds for NRs by screening massive natural products. Moreover, the predicted bioactive and inactive natural products for NR2B1 were experimentally validated using biosensors. PbsNRs not only aids in screening potential therapeutic NR binders but also helps discover the essential molecular scaffold and guide the drug discovery for multiple NR-related diseases. The PbsNRs web server is available at http://pbsnrs.badd-cao.net.

Distinguishable topology of the task-evoked functional genome networks in HIV-1 reservoirs

HIV-1 reservoirs display a heterogeneous nature, lodging both intact and defective proviruses. To deepen our understanding of such heterogeneous HIV-1 reservoirs and their functional implications, we integrated basic concepts of graph theory to characterize the composition of HIV-1 reservoirs. Our analysis revealed noticeable topological properties in networks, featuring immunologic signatures enriched by genes harboring intact and defective proviruses, when comparing antiretroviral therapy (ART)-treated HIV-1-infected individuals and elite controllers. The key variable, the rich factor, played a pivotal role in classifying distinct topological properties in networks. The host gene expression strengthened the accuracy of classification between elite controllers and ART-treated patients. Markov chain modeling for the simulation of different graph networks demonstrated the presence of an intrinsic barrier between elite controllers and non-elite controllers. Overall, our work provides a prime example of leveraging genomic approaches alongside mathematical tools to unravel the complexities of HIV-1 reservoirs.

Automatic construction of Petri net models for computational simulations of molecular interaction network

Petri nets are commonly applied in modeling biological systems. However, construction of a Petri net model for complex biological systems is often time consuming, and requires expertise in the research area, limiting their application. To address this challenge, we developed GINtoSPN, an R package that automates the conversion of multi-omics molecular interaction network extracted from the Global Integrative Network (GIN) into Petri nets in GraphML format. These GraphML files can be directly used for Signaling Petri Net (SPN) simulation. To demonstrate the utility of this tool, we built a Petri net model for neurofibromatosis type I. Simulation of NF1 gene knockout, compared to normal skin fibroblast cells, revealed persistent accumulation of Ras-GTPs as expected. Additionally, we identified several other genes substantially affected by the loss of NF1's function, exhibiting individual-specific variability. These results highlight the effectiveness of GINtoSPN in streamlining the modeling and simulation of complex biological systems.

A comprehensive survey of drug-target interaction analysis in allopathy and siddha medicine

Effective drug delivery is the cornerstone of modern healthcare, ensuring therapeutic compounds reach their intended targets efficiently. This paper explores the potential of personalized and holistic healthcare, driven by the synergy between traditional and allopathic medicine systems, with a specific focus on the vast reservoir of medicinal compounds found in plants rooted in the historical legacy of traditional medicine. Motivated by the desire to unlock the therapeutic potential of medicinal plants and bridge the gap between traditional and allopathic medicine, this survey delves into in-silico computational approaches for studying Drug-Target Interactions (DTI) within the contexts of allopathy and siddha medicine. The contributions of this survey are multifaceted: it offers a comprehensive overview of in-silico methods for DTI analysis in both systems, identifies common challenges in DTI studies, provides insights into future directions to advance DTI analysis, and includes a comparative analysis of DTI in allopathy and siddha medicine. The findings of this survey highlight the pivotal role of in-silico computational approaches in advancing drug research and development in both allopathy and siddha medicine, emphasizing the importance of integrating these methods to drive the future of personalized healthcare.

Integrating Similarities via Local Interaction Consistency and Optimizing Area Under the Curve Measures via Matrix Factorization for Drug-Target Interaction Prediction

In drug discovery, identifying drug-target interactions (DTIs) via experimental approaches is a tedious and expensive procedure. Computational methods efficiently predict DTIs and recommend a small part of potential interacting pairs for further experimental confirmation, accelerating the drug discovery process. Although fusing heterogeneous drug and target similarities can improve the prediction ability, the existing similarity combination methods ignore the interaction consistency for neighbour entities. Furthermore, area under the precision-recall curve (AUPR) and area under the receiver operating characteristic curve (AUC) are two widely used evaluation metrics in DTI prediction. However, the two metrics are seldom considered as losses within existing DTI prediction methods. We propose a local interaction consistency (LIC) aware similarity integration method to fuse vital information from diverse views for DTI prediction models. Furthermore, we propose two matrix factorization (MF) methods that optimize AUPR and AUC using convex surrogate losses respectively, and then develop an ensemble MF approach that takes advantage of the two area under the curve metrics by combining the two single metric based MF models. Experimental results under different prediction settings show that the proposed methods outperform various competitors in terms of the metric(s) they optimize and are reliable in discovering potential new DTIs.

Reinforced Metapath Optimization in Heterogeneous Information Networks for Drug-Target Interaction Prediction

Graph neural networks offer an effective avenue for predicting drug-target interactions. In this domain, researchers have found that constructing heterogeneous information networks based on metapaths using diverse biological datasets enhances prediction performance. However, the performance of such methods is closely tied to the selection of metapaths and the compatibility between metapath subgraphs and graph neural networks. Most existing approaches still rely on fixed strategies for selecting metapaths and often fail to fully exploit node information along the metapaths, limiting the improvement in model performance. This paper introduces a novel method for predicting drug-target interactions by optimizing metapaths in heterogeneous information networks. On one hand, the method formulates the metapath optimization problem as a Markov decision process, using the enhancement of downstream network performance as a reward signal. Through iterative training of a reinforcement learning agent, a high-quality set of metapaths is learned. On the other hand, to fully leverage node information along the metapaths, the paper constructs subgraphs based on nodes along the metapaths. Different depths of subgraphs are processed using different graph convolutional neural network. The proposed method is validated using standard heterogeneous biological benchmark datasets. Experimental results on standard datasets show significant advantages over traditional methods.

A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis

BACKGROUND

DNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However, constructing a prognostic model for distinguishing LTBI from active tuberculosis (ATB) is very challenging due to the noisy nature of data and lack of a generally stable analysis approach.

METHODS

In the present study, we proposed an accurate predictive model with the help of data fusion at the decision level. In this regard, results of filter feature selection and wrapper feature selection techniques were combined with multiple-criteria decision-making (MCDM) methods to select 10 genes from six microarray datasets that can be the most discriminative genes for diagnosing tuberculosis cases. As the main contribution of this study, the final ranking function was constructed by combining protein-protein interaction (PPI) network with an MCDM method (called Decision-making Trial and Evaluation Laboratory or DEMATEL) to improve the feature ranking approach.

RESULTS

By applying data fusion at the decision level on the 10 introduced genes in terms of fusion of classifiers of random forests (RF) and k-nearest neighbors (KNN) regarding Yager's theory, the proposed algorithm reached a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. Finally, with the help of cumulative clustering, the genes involved in the diagnosis of latent and activated tuberculosis have been introduced.

CONCLUSIONS

The combination of MCDM methods and PPI networks can significantly improve the diagnosis different states of tuberculosis.

CLINICAL TRIAL NUMBER

Not applicable.

A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.