A proteome-scale map of the human interactome network

Qarles: a web server for the quick characterization of large sets of genes

The characterization of gene sets is a recurring task in computational biology. Identifying specific properties of a hit set compared to a reference set can reveal biological roles and mechanisms, and can lead to the prediction of new hits. However, collecting the features to evaluate can be time consuming, and implementing an informative but compact graphical representation of the multiple comparisons can be challenging, particularly for bench scientists. Here, I present Qarles (quick characterization of large sets of genes), a web server that annotates Saccharomyces cerevisiae gene sets by querying a database of 31 features widely used by the yeast community and that identifies their specific properties, providing publication-ready figures and reliable statistics. Qarles has a deliberately simple user interface with all the functionality in a single web page and a fast response time to facilitate adoption by the scientific community. Qarles provides a rich and compact graphical output, including up to five gene set comparisons across 31 features in a single dotplot, and interactive boxplots to enable the identification of outliers. Qarles can also predict new hit genes by using a random forest trained on the selected features. The web server is freely available at https://qarles.org.

Assessing the impact of sampling bias on node centralities in synthetic and biological networks

Centrality measures are crucial for network analysis, offering insights into node importance within complex networks. However, their accuracy is often affected by observational errors and incomplete data. This study investigates how sampling biases systematically impact centrality measures. We simulate six types of biased down-sampling, transitioning networks from dense to sparse states, using the initial network as the 'ground truth.' Changes in centrality values reveal the robustness of these measures under various sampling scenarios across synthetic and biological networks. Our results show that in synthetic networks, some sampling methods consistently exhibit higher robustness, particularly in scale-free networks. For biological networks, protein interaction networks are the most robust, followed by metabolite, gene regulatory, and reaction networks. Local centrality measures generally show greater robustness, while global measures are more heterogeneous and less reliable. This study highlights the limitations of centrality measures under sampling biases and informs the development of more robust methodologies.

Recent progress and future challenges in structure-based protein-protein interaction prediction

Protein-protein interactions (PPIs) play a fundamental role in cellular processes, and understanding these interactions is crucial for advances in both basic biological science and biomedical applications. This review presents an overview of recent progress in computational methods for modeling protein complexes and predicting PPIs based on 3D structures, focusing on the transformative role of artificial intelligence-based approaches. We further discuss the expanding biomedical applications of PPI research, including the elucidation of disease mechanisms, drug discovery, and therapeutic design. Despite these advances, significant challenges remain in predicting host-pathogen interactions, interactions between intrinsically disordered regions, and interactions related to immune responses. These challenges are worthwhile for future explorations and represent the frontier of research in this field.

Simple and effective embedding model for single-cell biology built from ChatGPT

Large-scale gene-expression data are being leveraged to pretrain models that implicitly learn gene and cellular functions. However, such models require extensive data curation and training. Here we explore a much simpler alternative: leveraging ChatGPT embeddings of genes based on the literature. We used GPT-3.5 to generate gene embeddings from text descriptions of individual genes and to then generate single-cell embeddings by averaging the gene embeddings weighted by each gene's expression level. We also created a sentence embedding for each cell by using only the gene names ordered by their expression level. On many downstream tasks used to evaluate pretrained single-cell embedding models-particularly, tasks of gene-property and cell-type classifications-our model, which we named GenePT, achieved comparable or better performance than models pretrained from gene-expression profiles of millions of cells. GenePT shows that large-language-model embeddings of the literature provide a simple and effective path to encoding single-cell biological knowledge.

Domain-specific embeddings uncover latent genetics knowledge

The inundating rate of scientific publishing means every researcher will miss new discoveries from overwhelming saturation. To address this limitation, we employ natural language processing to overcome human limitations in reading, curation, and knowledge synthesis, with domain-specific applications to genetics and genomics. We construct a corpus of 3.5 million normalized genetics and genomics abstracts and implement both semantic and network-based embedding models. Our methods not only capture broad biological concepts and relationships but also predict complex phenomena such as gene expression. Through a rigorous temporal validation framework, we demonstrate that our embeddings successfully predict gene-disease associations, cancer driver genes, and experimentally-verified protein interactions years before their formal documentation in literature. Additionally, our embeddings successfully predict experimentally verified gene-gene interactions absent from the literature. These findings demonstrate that substantial undiscovered knowledge exists within the collective scientific literature and that computational approaches can accelerate biological discovery by identifying hidden connections across the fragmented landscape of scientific publishing.

Allosteric modulation of protein-protein interactions in signal transduction with Nanobodies

Nanobodies (Nbs), the variable domains of heavy-chain only antibodies that naturally occur in camelids, are exquisite molecular tools to stabilize dynamic proteins in unique functional conformations. Recent developments in Nb discovery allow to select allosteric Nbs that perturb the distribution of conformational ensembles of protein complexes that mediate signaling, leading to the allosteric modulation of the signals they transmit. Evidence is also accumulating that such conformational specific Nbs do not stabilize new conformational states but rather change the distribution of existing states to allosterically induce transitions, imprinted by the natural ligands of the system.

Global Protein Interactome Mapping in Rice Using Barcode-Indexed PCR Coupled with HiFi Long-Read Sequencing

Establishing the protein-protein interaction network sheds light on functional genomics studies by providing insights from known counterparts. However, the rice interactome has barely been studied due to the lack of massive, reliable, and cost-effective methodologies. Here, the development of a barcode-indexed PCR coupled with HiFi long-read sequencing pipeline (BIP-seq) is reported for high throughput Protein Protein Interaction (PPI)identification. BIP-seq is essentially built on the integration of library versus library Y2H mating strategy to facilitate the efficient acquisition of random PPI colonies, semi-mechanized dual barcode-indexed yeast colony PCR for the large-scale indexed amplification of bait and prey cDNAs, and massive pac-bio sequencing of PCR amplicon pools. It is demonstrated that BIP-seq could map over 15 000 high-confidence (≈62.5% could be verified by Bimolecular fluorescence Complementation (BiFC)) rice PPIs within 2 months, outperforming the other reported methods. In addition, the obtained 23 032 rice PPIs, including 22,665 newly identified PPIs, greatly expanded the current rice PPI dataset, provided a comprehensive overview of the rice PPIs networks, and could be a valuable asset in facilitating functional genomics research in rice.

A quantitative intracellular peptide-binding assay reveals recognition determinants and context dependence of short linear motifs

Transient protein-protein interactions play key roles in controlling dynamic cellular responses. Many examples involve globular protein domains that bind to peptide sequences known as short linear motifs (SLiMs), which are enriched in intrinsically disordered regions of proteins. Here we describe a novel functional assay for measuring SLiM binding, called systematic intracellular motif-binding analysis (SIMBA). In this method, binding of a foreign globular domain to its cognate SLiM peptide allows yeast cells to proliferate by blocking a growth arrest signal. A high-throughput application of the SIMBA method involving competitive growth and deep sequencing provides rapid quantification of the relative binding strength for thousands of SLiM sequence variants and a comprehensive interrogation of SLiM sequence features that control their recognition and potency. We show that multiple distinct classes of SLiM-binding domains can be analyzed by this method and that the relative binding strength of peptides in vivo correlates with their biochemical affinities measured in vitro. Deep mutational scanning provides high-resolution definitions of motif recognition determinants and reveals how sequence variations at noncore positions can modulate binding strength. Furthermore, mutational scanning of multiple parent peptides that bind human tankyrase ARC or YAP WW domains identifies distinct binding modes and uncovers context effects in which the preferred residues at one position depend on residues elsewhere. The findings establish SIMBA as a fast and incisive approach for interrogating SLiM recognition via massively parallel quantification of protein-peptide binding strength in vivo.

Cortex-Specific Tmem169 Deficiency Induces Defects in Cortical Neuron Development and Autism-Like Behaviors in Mice

The development of the nervous system is a complex process, with many challenging scientific questions yet to be resolved. Disruptions in brain development are strongly associated with neurodevelopmental disorders, such as intellectual disability and autism. While the genetic basis of autism is well established, the precise pathological mechanisms remain unclear. Variations on chromosome 2q have been linked to autism, yet the specific genes responsible for the disorder have not been identified. This study investigates the role of the transmembrane protein 169 (TMEM169) gene, located on human chromosome 2q35, which has not been previously characterized. Our findings indicate that Tmem169 is highly expressed in the nervous system, and its deletion in the male mouse dorsal forebrain results in neuronal morphological abnormalities and synaptic dysfunction. Notably, Tmem169-deficient mice, irrespective of sex, display behavioral traits resembling those observed in individuals with autism. These results suggest that Tmem169 interacts with several key neuronal proteins, many of which are implicated in neurodevelopmental diseases. Furthermore, we demonstrate that Tmem169 promotes neuronal process and synapse development through its interaction with Shank3.

Integrative Molecular Dynamics Simulations Untangle Cross-Linking Data to Unveil Mitochondrial Protein Distributions

Cross-linking mass spectrometry (XL-MS) enables the mapping of protein-protein interactions on the cellular level. When applied to all compartments of mitochondria, the sheer number of cross-links and connections can be overwhelming, rendering simple cluster analyses convoluted and uninformative. To address this limitation, we integrate the XL-MS data, 3D electron microscopy data, and localization annotations with a supra coarse-grained molecular dynamics simulation to sort all data, making clusters more accessible and interpretable. In the context of mitochondria, this method, through a total of 6.9 milliseconds of simulations, successfully identifies known, suggests unknown protein clusters, and reveals the distribution of inner mitochondrial membrane proteins allowing a more precise localization within compartments. Our integrative approach suggests, that two so-far ambigiously placed proteins FAM162A and TMEM126A are localized in the cristae, which is validated through super resolution microscopy. Together, this demonstrates the strong potential of the presented approach.

Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network

In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. However, the toxic side effects of drug combinations are probably increased with the increase of drugs numbers, so the accurate prediction of toxic side effects of drug combinations is equally important. In this paper, we built a Metapath-based Aggregated Embedding Model on Single Drug-Side Effect Heterogeneous Information Network (MAEM-SSHIN), which extracts feature from a heterogeneous information network of single drug side effects, and a Graph Convolutional Network on Combinatorial drugs and Side effect Heterogeneous Information Network (GCN-CSHIN), which transforms the complex task of predicting multiple side effects between drug pairs into the more manageable prediction of relationships between combinatorial drugs and individual side effects. MAEM-SSHIN and GCN-CSHIN provided a united novel framework for predicting potential side effects in combinatorial drug therapies. This integration enhances prediction accuracy, efficiency, and scalability. Our experimental results demonstrate that this combined framework outperforms existing methodologies in predicting side effects, and marks a significant advancement in pharmaceutical research.

Comprehensive analysis of the expression and prognostic value of ARMCs in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) has a very poor prognosis, and there are few treatments for PAAD. Therefore, it is important to find some biomarkers for the diagnosis and treatment of PAAD. Although some members of Armadillo repeat containing proteins (ARMCs) have been implicated in the development of certain cancers, their relationship with PAAD remains unknown. In this study, we aimed to explore the expression and prognostic value of ARMCs in PAAD.

METHODS

We used the The Cancer Genome Atlas (TCGA) database for survival analysis. Then, Gene Expression Profiling Interactive Analysis (GEPIA), the cBioPortal database, the Human Protein Atlas (HPA), Kaplan-Meier Plotter, LinkedOmics Database, Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG), Cytoscape and Timer were used to analyze the relationship between ARMCs and PAAD. In addition, we established a prognostic model of ARMCs for PAAD. Immunohistochemistry (IHC) was also performed. Then Image-J was used to analyze all images obtained from the experiment, and GraphPad-Prism (9.5.1) was used for statistical analysis to verify the expression of ARMCs in PAAD.

RESULTS

In the TCGA database, the expressions of ARMC1, 2, 3, 5, 6, 7, 8, 9 and 10 in PAAD tissues were significantly higher than those in normal tissues. And higher expressions of ARMC1 and 10 were associated with lower survival rate of PAAD patients. In addition, ARMC2, 5, 6, and 10 were positively associated with advanced stages of PAAD. ARMCs mutations occur in 11% of PAAD patients, and missense mutations and amplification of ARMCs account for most of them. In addition, ARMC5 and ARMC10 were independent prognostic factors in univariate and multivariate Cox regression analyses. Finally, through our confirmation experiment, it was found that the expression of ARMC1 and 10 in PAAD tissues was significantly increased compared with those in paracancer tissue.

CONCLUSION

This study suggests that ARMCs may be able to play important roles in PAAD, and they can act as biomarkers, providing valuable clues for the treatment and diagnosis of PAAD.

Network Medicine-Based Strategy Identifies Maprotiline as a Repurposable Drug by Inhibiting PD-L1 Expression via Targeting SPOP in Cancer

Immune checkpoint inhibitors (ICIs) are drugs that inhibit immune checkpoint (ICP) molecules to restore the antitumor activity of immune cells and eliminate tumor cells. Due to the limitations and certain side effects of current ICIs, such as programmed death protein-1, programmed cell death-ligand 1, and cytotoxic T lymphocyte-associated antigen 4 (CTLA4) antibodies, there is an urgent need to find new drugs with ICP inhibitory effects. In this study, a network-based computational framework called multi-network algorithm-driven drug repositioning targeting ICP (Mnet-DRI) is developed to accurately repurpose novel ICIs from ≈3000 Food and Drug Administration-approved or investigational drugs. By applying Mnet-DRI to PD-L1, maprotiline (MAP), an antidepressant drug is repurposed, as a potential PD-L1 modifier for colorectal and lung cancers. Experimental validation revealed that MAP reduced PD-L1 expression by targeting E3 ubiquitin ligase speckle-type zinc finger structural protein (SPOP), and the combination of MAP and anti-CTLA4 in vivo significantly enhanced the antitumor effect, providing a new alternative for the clinical treatment of colorectal and lung 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.

Fatty acid metabolism-related signature suggests an oncogenic role of TEKT1 in endometrial cancer

OBJECTIVE

The study aims to construct a prognostic signature to detect the molecular interaction between the fatty acid metabolism and the progression of endometrial cancer.

MATERIALS AND METHODS

A total of 309 fatty acid metabolism relative genes were analyzed in the endometrial cancer cohort from The Cancer Genome Atlas. Dataset GSE216872 was applied for external validation. The R program was chosen to detect the expression of FAMRGs and execute GO/KEGG analysis. Considering the clinical information and pathological features, a prognostic gene signature was constructed using LASSO Cox regression analysis. According to FAM risk score, endometrial cancer patients were divided into high and low FAM-risk score groups. By differential expression analysis and cytoscape, TEKT1 was identified as the hub gene. Functional enrichments explored the biological process TEKT1 participated. Cellular proliferation, clone formation, migration, invasion, cycle, and apoptosis were assessed to detect the effect of TEKT1 in endometrial cancer. Co-immunoprecipitation and Western blot were performed to determine the mechanism of TEKT1.

RESULTS

A prognostic signature based on 10 FAMRGs (ACACB, PTGIS, BMPR1B, DHCR24, FAAH, GPX1, GPX4, INMT, PON3, PPT2) was generated using Lasso Cox hazards regression analysis in TCGA. TEKT1 was identified as the gene related to fatty acid metabolism. Next, we demonstrated that TEKT1 was highly expressed in endometrial cancer tissues in CPTAC and was mainly involved in the cell cycle and biosynthesis of unsaturated fatty acids. Ex-vivo experiments revealed that TEKT1 promoted proliferation, migration, and invasiveness while inhibiting apoptosis. Further experiments demonstrated that TEKT1 might promote fatty acid synthesis by binding to AMPK-γ for ACC and FASN downregulation.

CONCLUSION

A reliable predictive signature based on genes related to fatty acid metabolism in endometrial cancer was conducted, wherein TEKT1 was mainly implicated as the primary gene of interest. TEKT1 showed affinity to AMPK-γ and probably promoted FA synthesis in endometrial cancer.

Competitive SPR chaser assay to study biomolecular interactions with very slow binding dissociation rate constant

Binding kinetics of drug and its target protein is crucial for the efficacy and safety of the drug. Using surface plasmon resonance (SPR) technology, we performed a competitive SPR chaser assay, a method to study biomolecular interactions with very slow dissociation rate constants (kd < 1E-4 s-1). This report described the principle and the experimental setup of the chaser assay, which involves using a competitive probe (chaser) to detect changes in target occupancy by a test molecule over time. We demonstrated the applicability of the chaser assay for both small and large molecules and compared the results with conventional SPR kinetic analysis and other methods. We suggest that the chaser assay is a useful and robust technique to characterize very tight biomolecular interactions, and that it can also be used to study cooperativity in ternary complex formation.

TPepPro: a deep learning model for predicting peptide-protein interactions

MOTIVATION

Peptides and their derivatives hold potential as therapeutic agents. The rising interest in developing peptide drugs is evidenced by increasing approval rates by the FDA of USA. To identify the most potential peptides, study on peptide-protein interactions (PepPIs) presents a very important approach but poses considerable technical challenges. In experimental aspects, the transient nature of PepPIs and the high flexibility of peptides contribute to elevated costs and inefficiency. Traditional docking and molecular dynamics simulation methods require substantial computational resources, and the predictive accuracy of their results remain unsatisfactory.

RESULTS

To address this gap, we proposed TPepPro, a Transformer-based model for PepPI prediction. We trained TPepPro on a dataset of 19,187 pairs of peptide-protein complexes with both sequential and structural features. TPepPro utilizes a strategy that combines local protein sequence feature extraction with global protein structure feature extraction. Moreover, TPepPro optimizes the architecture of structural featuring neural network in BN-ReLU arrangement, which notably reduced the amount of computing resources required for PepPIs prediction. According to comparison analysis, the accuracy reached 0.855 in TPepPro, achieving an 8.1% improvement compared to the second-best model TAGPPI. TPepPro achieved an AUC of 0.922, surpassing the second-best model TAGPPI with 0.844. Moreover, the newly developed TPepPro identify certain PepPIs that can be validated according to previous experimental evidence, thus indicating the efficiency of TPepPro to detect high potential PepPIs that would be helpful for amino acid drug applications.

AVAILABILITY AND IMPLEMENTATION

The source code of TPepPro is available at https://github.com/wanglabhku/TPepPro.

Beyond Misfolding: A New Paradigm for the Relationship Between Protein Folding and Aggregation

Aggregation is intricately linked to protein folding, necessitating a precise understanding of their relationship. Traditionally, aggregation has been viewed primarily as a sequential consequence of protein folding and misfolding. However, this conventional paradigm is inherently incomplete and can be deeply misleading. Remarkably, it fails to adequately explain how intrinsic and extrinsic factors, such as charges and cellular macromolecules, prevent intermolecular aggregation independently of intramolecular protein folding and structure. The pervasive inconsistencies between protein folding and aggregation call for a new framework. In all combined reactions of molecules, both intramolecular and intermolecular rate (or equilibrium) constants are mutually independent; accordingly, intrinsic and extrinsic factors independently affect both rate constants. This universal principle, when applied to protein folding and aggregation, indicates that they should be treated as two independent yet interconnected processes. Based on this principle, a new framework provides groundbreaking insights into misfolding, Anfinsen's thermodynamic hypothesis, molecular chaperones, intrinsic chaperone-like activities of cellular macromolecules, intermolecular repulsive force-driven aggregation inhibition, proteome solubility maintenance, and proteinopathies. Consequently, this paradigm shift not only refines our current understanding but also offers a more comprehensive view of how aggregation is coupled to protein folding in the complex cellular milieu.

A comprehensive two-hybrid analysis to explore the Legionella pneumophila effector-effector interactome

Legionella pneumophila uses over 300 translocated effector proteins to rewire host cells during infection and create a replicative niche for intracellular growth. To date, several studies have identified L. pneumophila effectors that indirectly and directly regulate the activity of other effectors, providing an additional layer of regulatory complexity. Among these are "metaeffectors," a special class of effectors that regulate the activity of other effectors once inside the host. A defining feature of metaeffectors is direct, physical interaction with a target effector. Metaeffector identification, to date, has depended on phenotypes in heterologous systems and experimental serendipity. Using a multiplexed, recombinant barcode-based yeast two-hybrid technology we screened for protein-protein interactions among all L. pneumophila effectors and 28 components of the Dot/Icm type IV secretion system (>167,000 protein combinations). Of the 52 protein interactions identified by this approach, 44 are novel protein interactions, including 10 novel effector-effector interactions (doubling the number of known effector-effector interactions).

IMPORTANCE

Secreted bacterial effector proteins are typically viewed as modulators of host activity, entering the host cytosol to physically interact with and modify the activity of one or more host proteins in support of infection. A growing body of evidence suggests that a subset of effectors primarily function to modify the activities of other effectors inside the host. These "effectors of effectors" or metaeffectors are often identified through experimental serendipity during the study of canonical effector function against the host. We previously performed the first global effector-wide genetic interaction screen for metaeffectors within the arsenal of Legionella pneumophila, an intracellular bacterial pathogen with over 300 effectors. Here, using a high-throughput, scalable methodology, we present the first global interaction network of physical interactions between L. pneumophila effectors. This data set serves as a complementary resource to identify and understand both the scope and nature of non-canonical effector activity within this important human pathogen.

Integration of biological data via NMF for identification of human disease-associated gene modules through multi-label classification

Proteins associated with multiple diseases often interact, forming disease modules that are critical for understanding disease mechanisms. This study integrates protein-protein interactions (PPIs) and Gene Ontology data using non-negative matrix factorization (NMF) to identify gene modules associated with human diseases. We leverage two biological sources of information, protein-protein interactions (PPIs) and Gene Ontology data, to find connections between novel genes and diseases. The data sources are first converted into networks, which are then clustered to obtain modules. Two types of modules are then integrated through an NMF-based technique to obtain a set of meta-modules that preserve the essential characteristics of interaction patterns and functional similarity information among the proteins/genes. Each meta-module is labeled based on its statistical and biological properties, and a multi-label classification technique is employed to assign new disease labels to genes. We identified 3,131 gene-disease associations, validated through a literature review, Gene Ontology, and pathway analysis.

MNSFβ promotes LPS-induced TNFα expression by increasing the localization of RC3H1 to stress granules, and the interfering peptide HEPN2 reduces TNFα production by disrupting the MNSFβ-RC3H1 interaction in macrophages

Abnormally elevated tumor necrosis factor-α (TNFα) levels at the maternal-fetal interface can lead to adverse pregnancy outcomes, including recurrent miscarriage (RM), but the mechanism underlying upregulated TNFα expression is not fully understood. We previously reported that the interaction between monoclonal nonspecific suppressor factor-β (MNSFβ) and RC3H1 upregulates TNFα expression, but the precise mechanisms are unknown. In this study, we found that MNSFβ stimulated the LPS-induced TNFα expression by inactivating the promoting effect of RC3H1 on TNFα mRNA degradation rather than directly inhibiting the expression of RC3H1 in THP1-Mϕs. Mechanistically, the 81-326 aa region of the RC3H1 protein binds to the 101-133 aa region of the MNSFβ protein, and MNSFβ facilitated stress granules (SGs) formation and the translocation of RC3H1 to SGs by interacting with RC3H1 and fragile X mental retardation 1 (FMR1) in response to LPS-induced stress. The SGs-localization of RC3H1 reduced its inhibitory effect on TNFα expression in LPS-treated THP1-Mϕs. The designed HEPN2 peptide effectively reduced the LPS-induced expression of TNFα in THP1-Mϕs by interfering with the MNSFβ-RC3H1 interaction. Treatment with the HEPN2 peptide significantly improved adverse pregnancy outcomes, including early pregnancy loss (EPL) and lower fetal weight (LFW), which are induced by LPS in mice. These data indicated that MNSFβ promoted TNFα expression at least partially by increasing the localization of RC3H1 to SGs under inflammatory stimulation and that the HEPN2 peptide improved the adverse pregnancy outcomes induced by LPS in mice, suggesting that MNSFβ is a potential pharmacological target for adverse pregnancy outcomes caused by abnormally increased inflammation at early pregnancy.

Insights into the human cDNA: A descriptive study using library screening in yeast

The utilization of human cDNA libraries in yeast genetic screens is an approach that has been used to identify novel gene functions and/or genetic and physical interaction partners through forward genetics using yeast two-hybrid (Y2H) and classical cDNA library screens. Here, we summarize several challenges that have been observed during the implementation of human cDNA library screens in Saccharomyces cerevisiae (budding yeast). Upon the utilization of DNA repair deficient-yeast strains to identify novel genes that rescue the toxic effect of DNA-damage inducing drugs, we have observed a wide range of transcripts that could rescue the strains. However, after several rounds of screening, most of these hits turned out to be false positives, most likely due to spontaneous mutations in the yeast strains that arise as a rescue mechanism due to exposure to toxic DNA damage inducing-drugs. The observed transcripts included mitochondrial hits, non-coding RNAs, truncated cDNAs, and transcription products that resulted from the internal priming of genomic regions. We have also noticed that most cDNA transcripts are not fused with the GAL4 activation domain (GAL4AD), rendering them unsuitable for Y2H screening. Consequently, we utilized Sanger sequencing to screen 282 transcripts obtained from either four different yeast screens or through direct fishing from a human kidney cDNA library. The aim was to gain insights into the different transcription products and to highlight the challenges of cDNA screening approaches in the presence of a significant number of undesired transcription products. In summary, this study describes the challenges encountering human cDNA library screening in yeast as a valuable technique that led to the identification of important molecular mechanisms. The results open research venues to further optimize the process and increase its efficiency.

A comprehensive benchmark study of methods for identifying significantly perturbed subnetworks in cancer

Network-based methods utilize protein-protein interaction information to identify significantly perturbed subnetworks in cancer and to propose key molecular pathways. Numerous methods have been developed, but to date, a rigorous benchmark analysis to compare the performance of existing approaches is lacking. In this paper, we proposed a novel benchmarking framework using synthetic data and conducted a comprehensive analysis to investigate the ability of existing methods to detect target genes and subnetworks and to control false positives, and how they perform in the presence of topological biases at both gene and subnetwork levels. Our analysis revealed insights into algorithmic performance that were previously unattainable. Based on the results of the benchmark study, we presented a practical guide for users on how to select appropriate detection methods and protein-protein interaction networks for cancer pathway identification, and provided suggestions for future algorithm development.

ICoN: integration using co-attention across biological networks

Motivation

Molecular interaction networks are powerful tools for studying cellular functions. Integrating diverse types of networks enhances performance in downstream tasks such as gene module detection and protein function prediction. The challenge lies in extracting meaningful protein feature representations due to varying levels of sparsity and noise across these heterogeneous networks.

Results

We propose ICoN, a novel unsupervised graph neural network model that takes multiple protein-protein association networks as inputs and generates a feature representation for each protein that integrates the topological information from all the networks. A key contribution of ICoN is exploiting a mechanism called "co-attention" that enables cross-network communication during training. The model also incorporates a denoising training technique, introducing perturbations to each input network and training the model to reconstruct the original network from its corrupted version. Our experimental results demonstrate that ICoN surpasses individual networks across three downstream tasks: gene module detection, gene coannotation prediction, and protein function prediction. Compared to existing unsupervised network integration models, ICoN exhibits superior performance across the majority of downstream tasks and shows enhanced robustness against noise. This work introduces a promising approach for effectively integrating diverse protein-protein association networks, aiming to achieve a biologically meaningful representation of proteins.

Availability and implementation

The ICoN software is available under the GNU Public License v3 at https://github.com/Murali-group/ICoN.

Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data

As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such datasets include diversity of content, size and privacy. Effective transformation of data into information requires harmonization of stakeholder goals, implementation, enforcement of standards regarding quality and completeness, and availability of resources for maintenance and updates. Systems such as the Biomedical Data Translator leverage knowledge graphs (KGs), structured and machine learning readable knowledge representation, to encode knowledge extracted through inference. We focus here on the transformation of data from multiomics datasets and EHRs into compact knowledge, represented in a KG data structure. We demonstrate this data transformation in the context of the Translator ecosystem, including clinical trials, drug approvals, cancer, wellness, and EHR data. These transformations preserve individual privacy. We provide access to the five resulting KGs through the Translator framework. We show examples of biomedical research questions supported by our KGs, and discuss issues arising from extracting biomedical knowledge from multiomics data.

Graph-based machine learning model for weight prediction in protein-protein networks

Proteins interact with each other in complex ways to perform significant biological functions. These interactions, known as protein-protein interactions (PPIs), can be depicted as a graph where proteins are nodes and their interactions are edges. The development of high-throughput experimental technologies allows for the generation of numerous data which permits increasing the sophistication of PPI models. However, despite significant progress, current PPI networks remain incomplete. Discovering missing interactions through experimental techniques can be costly, time-consuming, and challenging. Therefore, computational approaches have emerged as valuable tools for predicting missing interactions. In PPI networks, a graph is usually used to model the interactions between proteins. An edge between two proteins indicates a known interaction, while the absence of an edge means the interaction is not known or missed. However, this binary representation overlooks the reliability of known interactions when predicting new ones. To address this challenge, we propose a novel approach for link prediction in weighted protein-protein networks, where interaction weights denote confidence scores. By leveraging data from the yeast Saccharomyces cerevisiae obtained from the STRING database, we introduce a new model that combines similarity-based algorithms and aggregated confidence score weights for accurate link prediction purposes. Our model significantly improves prediction accuracy, surpassing traditional approaches in terms of Mean Absolute Error, Mean Relative Absolute Error, and Root Mean Square Error. Our proposed approach holds the potential for improved accuracy in predicting PPIs, which is crucial for better understanding the underlying biological processes.

LMTK2 switches on canonical TGF-β1 signaling in human bronchial epithelial cells

Transforming growth factor (TGF-β1) is a critical profibrotic mediator in chronic lung disease, and there are no specific strategies to mitigate its adverse effects. Activation of TGF-β1 signaling is a multipart process involving ligands, transmembrane receptors, and transcription factors. In addition, an intricate network of adaptor proteins fine-tunes the signaling strength, duration, and activity. Namely, Smad7 recruits growth arrest and DNA damage (GADD34) protein that then interacts with the catalytic subunit of phosphoprotein phosphatase 1 (PP1c) to inactivate TGF-β receptor (TβR)-I and downregulate TGF-β1 signaling. Little is known about how TGF-β1 releases TβR-I from the GADD34-PP1c inhibition to activate its signaling. Transmembrane lemur tyrosine kinase 2 (LMTK2) is a PP1c inhibitor, and our published data showed that TGF-β1 recruits LMTK2 to the cell surface. Here, we tested the hypothesis that TGF-β1 recruits LMTK2 to inhibit PP1c, allowing activation of TβR-I. First, LMTK2 interacted with the TGF-β1 pathway in the human bronchial epithelium at multiple checkpoints. Second, TGF-β1 inhibited PP1c by an LMTK2-dependent mechanism. Third, TGF-β1 used LMTK2 to activate canonical Smad3-mediated signaling. We propose a model whereby the LMTK2-PP1c and Smad7-GADD34-PP1c complexes serve as on-and-off switches in the TGF-β1 signaling in human bronchial epithelium.NEW & NOTEWORTHY Activation of the transforming growth factor (TGF)-β1 signaling pathway is complex, involving many ligands, transmembrane receptors, transcription factors, and modulating proteins. The mechanisms of TGF-β1 signaling activation/inactivation are not fully understood. We propose for the first time a model by which transmembrane lemur tyrosine kinase 2 (LMTK2) forms a complex with phosphoprotein phosphatase 1 (PP1c) to activate TGF-β1 signaling and Smad7, growth arrest and DNA damage (GADD34), and PP1C form a complex to inactivate TGF-β1 signaling in human bronchial epithelium.

A quantitative intracellular peptide binding assay reveals recognition determinants and context dependence of short linear motifs

Transient protein-protein interactions play key roles in controlling dynamic cellular responses. Many examples involve globular protein domains that bind to peptide sequences known as Short Linear Motifs (SLiMs), which are enriched in intrinsically disordered regions of proteins. Here we describe a novel functional assay for measuring SLiM binding, called Systematic Intracellular Motif Binding Analysis (SIMBA). In this method, binding of a foreign globular domain to its cognate SLiM peptide allows yeast cells to proliferate by blocking a growth arrest signal. A high-throughput application of the SIMBA method involving competitive growth and deep sequencing provides rapid quantification of the relative binding strength for thousands of SLiM sequence variants, and a comprehensive interrogation of SLiM sequence features that control their recognition and potency. We show that multiple distinct classes of SLiM-binding domains can be analyzed by this method, and that the relative binding strength of peptides in vivo correlates with their biochemical affinities measured in vitro. Deep mutational scanning provides high-resolution definitions of motif recognition determinants and reveals how sequence variations at non-core positions can modulate binding strength. Furthermore, mutational scanning of multiple parent peptides that bind human tankyrase ARC or YAP WW domains identifies distinct binding modes and uncovers context effects in which the preferred residues at one position depend on residues elsewhere. The findings establish SIMBA as a fast and incisive approach for interrogating SLiM recognition via massively parallel quantification of protein-peptide binding strength in vivo.

HGLA: Biomolecular Interaction Prediction Based on Mixed High-Order Graph Convolution With Filter Network via LSTM and Channel Attention

Predicting biomolecular interactions is significant for understanding biological systems. Most existing methods for link prediction are based on graph convolution. Although graph convolution methods are advantageous in extracting structure information of biomolecular interactions, two key challenges still remain. One is how to consider both the immediate and high-order neighbors. Another is how to reduce noise when aggregating high-order neighbors. To address these challenges, we propose a novel method, called mixed high-order graph convolution with filter network via LSTM and channel attention (HGLA), to predict biomolecular interactions. Firstly, the basic and high-order features are extracted respectively through the traditional graph convolutional network (GCN) and the two-layer Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing (MixHop). Secondly, these features are mixed and input into the filter network composed of LayerNorm, SENet and LSTM to generate filtered features, which are concatenated and used for link prediction. The advantages of HGLA are: 1) HGLA processes high-order features separately, rather than simply concatenating them; 2) HGLA better balances the basic features and high-order features; 3) HGLA effectively filters the noise from high-order neighbors. It outperforms state-of-the-art networks on four benchmark datasets.

Neuroinflammation inhibition and neuroprotective effects of purpurin, a potential anti-AD compound, screened via network proximity and gene enrichment analyses

Alzheimer's disease (AD) is a complex neurodegenerative disease without any effective preventive or therapeutic drugs. Natural products with stable structures and pharmacological characteristics are valuable sources for the development of novel drugs for many complex diseases. This study aimed to discover potential natural compounds for the treatment of AD using new technologies and methods and explore the efficacy and mechanism of candidate compounds. AD-related large-scale genetic datasets were collated to construct disease-PPIs and natural products were collected from six databases to construct compound-protein interactions (CPIs). Potential relationships between natural compounds and AD were predicted via network proximity and gene enrichment analyses. Then, five AD-related cell models and d-galactose-induced aging rat model were established to evaluate the neuroprotective effects of candidate compounds in vitro and in vivo. We identified that 267 natural compounds were predicted to have close connections with AD and 19 compounds could exert protective effect in at least one cell model. Notably, purpurin exerted protective effect in three cell models and significantly improved the cognitive learning and memory functions, reduced the oxidative stress injuries and neuroinflammation, and enhanced the synaptic plasticity and neurotrophic effect in the brain of d-galactose-treated rats. In this study, AD-related natural compounds were identified via network proximity and gene enrichment analyses. In vivo and in vitro experiments revealed the therapeutic potential of purpurin for AD treatment, laying the foundation for further in-depth research and providing valuable information for the development of novel anti-AD drugs.

Accurate and sensitive interactome profiling using a quantitative protein-fragment complementation assay

An accurate description of protein-protein interaction (PPI) networks is key to understanding the molecular mechanisms underlying cellular systems. Here, we constructed genome-wide libraries of yeast strains to systematically probe protein-protein interactions using NanoLuc Binary Technology (NanoBiT), a quantitative protein-fragment complementation assay (PCA) based on the NanoLuc luciferase. By investigating an array of well-documented PPIs as well as the interactome of four proteins with varying levels of characterization-including the well-studied nonsense-mediated mRNA decay (NMD) regulator Upf1 and the SCF complex subunits Cdc53 and Met30-we demonstrate that ratiometric NanoBiT measurements enable highly precise and sensitive mapping of PPIs. This work provides a foundation for employing NanoBiT in the assembly of more comprehensive and accurate protein interaction maps as well as in their functional investigation.

The MTR4/hnRNPK complex surveils aberrant polyadenylated RNAs with multiple exons

RNA surveillance systems degrade aberrant RNAs that result from defective transcriptional termination, splicing, and polyadenylation. Defective RNAs in the nucleus are recognized by RNA-binding proteins and MTR4, and are degraded by the RNA exosome complex. Here, we detect aberrant RNAs in MTR4-depleted cells using long-read direct RNA sequencing and 3' sequencing. MTR4 destabilizes intronic polyadenylated transcripts generated by transcriptional read-through over one or more exons, termed 3' eXtended Transcripts (3XTs). MTR4 also associates with hnRNPK, which recognizes 3XTs with multiple exons. Moreover, the aberrant protein translated from KCTD13 3XT is a target of the hnRNPK-MTR4-RNA exosome pathway and forms aberrant condensates, which we name KCTD13 3eXtended Transcript-derived protein (KeXT) bodies. Our results suggest that RNA surveillance in human cells inhibits the formation of condensates of a defective polyadenylated transcript-derived protein.

Design and Synthesis of Small Molecule Probes of MDA-9/Syntenin

MDA-9/Syntenin, a key scaffolding protein and a molecular hub involved in a diverse range of cell signaling responses, has proved to be a challenging target for the design and discovery of small molecule probes. In this paper, we report on the design and synthesis of small molecule ligands of this key protein. Genetic algorithm-based computational design and the five-eight step synthesis of three molecules led to ligands with affinities in the range of 1-3 µM, a 20-60-fold improvement over literature reports. The design and synthesis strategies, coupled with the structure-dependent gain or loss in affinity, afford the deduction of principles that should guide the design of advanced probes of MDA-9/Syntenin.

Large-scale map of RNA-binding protein interactomes across the mRNA life cycle

mRNAs interact with RNA-binding proteins (RBPs) throughout their processing and maturation. While efforts have assigned RBPs to RNA substrates, less exploration has leveraged protein-protein interactions (PPIs) to study proteins in mRNA life-cycle stages. We generated an RNA-aware, RBP-centric PPI map across the mRNA life cycle in human cells by immunopurification-mass spectrometry (IP-MS) of ∼100 endogenous RBPs with and without RNase, augmented by size exclusion chromatography-mass spectrometry (SEC-MS). We identify 8,742 known and 20,802 unreported interactions between 1,125 proteins and determine that 73% of the IP-MS-identified interactions are RNA regulated. Our interactome links many proteins, some with unknown functions, to specific mRNA life-cycle stages, with nearly half associated with multiple stages. We demonstrate the value of this resource by characterizing the splicing and export functions of enhancer of rudimentary homolog (ERH), and by showing that small nuclear ribonucleoprotein U5 subunit 200 (SNRNP200) interacts with stress granule proteins and binds cytoplasmic RNA differently during stress.

Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation

Transcription factor (TF)-cofactor (COF) interactions define dynamic, cell-specific networks that govern gene expression; however, these networks are understudied due to a lack of methods for high-throughput profiling of DNA-bound TF-COF complexes. Here, we describe the Cofactor Recruitment (CoRec) method for rapid profiling of cell-specific TF-COF complexes. We define a lysine acetyltransferase (KAT)-TF network in resting and stimulated T cells. We find promiscuous recruitment of KATs for many TFs and that 35% of KAT-TF interactions are condition specific. KAT-TF interactions identify NF-κB as a primary regulator of acutely induced histone 3 lysine 27 acetylation (H3K27ac). Finally, we find that heterotypic clustering of CBP/P300-recruiting TFs is a strong predictor of total promoter H3K27ac. Our data support clustering of TF sites that broadly recruit KATs as a mechanism for widespread co-occurring histone acetylation marks. CoRec can be readily applied to different cell systems and provides a powerful approach to define TF-COF networks impacting chromatin state and gene regulation.

Network medicine and systems pharmacology approaches to predicting adverse drug effects

Identifying and understanding the relationships between drug intake and adverse effects that can occur due to inadvertent molecular interactions between drugs and targets is a difficult task, especially considering the numerous variables that can influence the onset of such events. The ability to predict these side effects in advance would help physicians develop strategies to avoid or counteract them. In this article, we review the main computational methods for predicting side effects caused by drug molecules, highlighting their performance, limitations and application cases. Furthermore, we provide an overall view of resources, such as databases and tools, useful for building side effect prediction analyses.

MediatorWeb: a protein-protein interaction network database for the RNA polymerase II Mediator complex

The protein-protein interaction (PPI) network of the Mediator complex is very tightly regulated and depends on different developmental and environmental cues. Here, we present an interactive platform for comparative analysis of the Mediator subunits from humans, baker's yeast Saccharomyces cerevisiae, and model plant Arabidopsis thaliana in a user-friendly web-interface database called MediatorWeb. MediatorWeb provides an interface to visualize and analyze the PPI network of Mediator subunits. The database facilitates downloading the untargeted and unweighted network of Mediator complex, its submodules, and individual Mediator subunits to better visualize the importance of individual Mediator subunits or their submodules. Further, MediatorWeb offers network visualization of the Mediator complex and interacting proteins that are functionally annotated. This feature provides clues to understand functions of Mediator subunits in different processes. In an additional tab, MediatorWeb provides quick access to secondary and tertiary structures, as well as residue-level contact information for Mediator subunits in each of the three model organisms. Another useful feature of MediatorWeb is detection of interologs based on orthologous analyses, which can provide clues to understand the functions of Mediator complex in less explored kingdoms. Thus, MediatorWeb and its features can help the user to understand the role of Mediator complex and its subunits in the transcription regulation of gene expression.

Chemical Synthesis of Human Proteoforms and Application in Biomedicine

Limited understanding of human proteoforms with complex posttranslational modifications and the underlying mechanisms poses a major obstacle to research on human health and disease. This Outlook discusses opportunities and challenges of de novo chemical protein synthesis in human proteoform studies. Our analysis suggests that to develop a comprehensive, robust, and cost-effective methodology for chemical synthesis of various human proteoforms, new chemistries of the following types need to be developed: (1) easy-to-use peptide ligation chemistries allowing more efficient de novo synthesis of protein structural domains, (2) robust temporary structural support strategies for ligation and folding of challenging targets, and (3) efficient transpeptidative protein domain-domain ligation methods for multidomain proteins. Our analysis also indicates that accurate chemical synthesis of human proteoforms can be applied to the following aspects of biomedical research: (1) dissection and reconstitution of the proteoform interaction networks, (2) structural mechanism elucidation and functional analysis of human proteoform complexes, and (3) development and evaluation of drugs targeting human proteoforms. Overall, we suggest that through integrating chemical protein synthesis with in vivo functional analysis, mechanistic biochemistry, and drug development, synthetic chemistry would play a pivotal role in human proteoform research and facilitate the development of precision diagnostics and therapeutics.

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.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations

Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform "N-of-1" analyses on individual patients with ultra-rare diseases. The increasing sizes of ultra-rare disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development.1,2 The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale N-of-1 analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We further release a software package, RaMeDiES, enabling automated cross-analysis of deidentified sequenced cohorts for new diagnostic and research discoveries. Gene-level findings and variant-level information across the cohort are available in a public-facing browser (https://dbmi-bgm.github.io/udn-browser/). These results show that N-of-1 efforts should be supplemented by a joint genomic analysis across cohorts.

Understanding the molecular mechanisms of human diseases: the benefits of fission yeasts

The role of model organisms such as yeasts in life science research is crucial. Although the baker's yeast (Saccharomyces cerevisiae) is the most popular model among yeasts, the contribution of the fission yeasts (Schizosaccharomyces) to life science is also indisputable. Since both types of yeasts share several thousands of common orthologous genes with humans, they provide a simple research platform to investigate many fundamental molecular mechanisms and functions, thereby contributing to the understanding of the background of human diseases. In this review, we would like to highlight the many advantages of fission yeasts over budding yeasts. The usefulness of fission yeasts in virus research is shown as an example, presenting the most important research results related to the Human Immunodeficiency Virus Type 1 (HIV-1) Vpr protein. Besides, the potential role of fission yeasts in the study of prion biology is also discussed. Furthermore, we are keen to promote the uprising model yeast Schizosaccharomyces japonicus, which is a dimorphic species in the fission yeast genus. We propose the hyphal growth of S. japonicus as an unusual opportunity as a model to study the invadopodia of human cancer cells since the two seemingly different cell types can be compared along fundamental features. Here we also collect the latest laboratory protocols and bioinformatics tools for the fission yeasts to highlight the many possibilities available to the research community. In addition, we present several limiting factors that everyone should be aware of when working with yeast models.

Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication

Proteins on the surfaces of cells serve as physical connection points to bridge one cell with another, enabling direct communication between cells and cohesive structure. As biomedical research makes the leap from characterizing individual cells toward understanding the multicellular organization of the human body, the binding interactions between molecules on the surfaces of cells are foundational both for computational models and for clinical efforts to exploit these influential receptor pathways. To achieve this grander vision, we must assemble the full interactome of ways surface proteins can link together. This review investigates how close we are to knowing the human cell surface protein interactome. We summarize the current state of databases and systematic technologies to assemble surface protein interactomes, while highlighting substantial gaps that remain. We aim for this to serve as a road map for eventually building a more robust picture of the human cell surface protein interactome.

Detection of minimal extended driver nodes in energetic costs reduction

Structures of complex networks are fundamental to system dynamics, where node state and connectivity patterns determine the cost of a control system, a key aspect in unraveling complexity. However, minimizing the energy required to control a system with the fewest input nodes remains an open problem. This study investigates the relationship between the structure of closed-connected function modules and control energy. We discovered that small structural adjustments, such as adding a few extended driver nodes, can significantly reduce control energy. Thus, we propose MInimal extended driver nodes in Energetic costs Reduction (MIER). Next, we transform the detection of MIER into a multi-objective optimization problem and choose an NSGA-II algorithm to solve it. Compared with the baseline methods, NSGA-II can approximate the optimal solution to the greatest extent. Through experiments using synthetic and real data, we validate that MIER can exponentially decrease control energy. Furthermore, random perturbation tests confirm the stability of MIER. Subsequently, we applied MIER to three representative scenarios: regulation of differential expression genes affected by cancer mutations in the human protein-protein interaction network, trade relations among developed countries in the world trade network, and regulation of body-wall muscle cells by motor neurons in Caenorhabditis elegans nervous network. The results reveal that the involvement of MIER significantly reduces control energy required for these original modules from a topological perspective. Additionally, MIER nodes enhance functionality, supplement key nodes, and uncover potential mechanisms. Overall, our work provides practical computational tools for understanding and presenting control strategies in biological, social, and neural systems.

INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction

An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constraints in time and cost of the associated 'wet lab' experiments. In silico PPI inference methods are ideal tools to overcome these limitations, but often struggle with cross-species predictions. We present INTREPPPID, a method that incorporates orthology data using a new 'quintuplet' neural network, which is constructed with five parallel encoders with shared parameters. INTREPPPID incorporates both a PPI classification task and an orthologous locality task. The latter learns embeddings of orthologues that have small Euclidean distances between them and large distances between embeddings of all other proteins. INTREPPPID outperforms all other leading PPI inference methods tested on both the intraspecies and cross-species tasks using strict evaluation datasets. We show that INTREPPPID's orthologous locality loss increases performance because of the biological relevance of the orthologue data and not due to some other specious aspect of the architecture. Finally, we introduce PPI.bio and PPI Origami, a web server interface for INTREPPPID and a software tool for creating strict evaluation datasets, respectively. Together, these two initiatives aim to make both the use and development of PPI inference tools more accessible to the community.

SENSE-PPI reconstructs interactomes within, across, and between species at the genome scale

Ab initio computational reconstructions of protein-protein interaction (PPI) networks will provide invaluable insights into cellular systems, enabling the discovery of novel molecular interactions and elucidating biological mechanisms within and between organisms. Leveraging the latest generation protein language models and recurrent neural networks, we present SENSE-PPI, a sequence-based deep learning model that efficiently reconstructs ab initio PPIs, distinguishing partners among tens of thousands of proteins and identifying specific interactions within functionally similar proteins. SENSE-PPI demonstrates high accuracy, limited training requirements, and versatility in cross-species predictions, even with non-model organisms and human-virus interactions. Its performance decreases for phylogenetically more distant model and non-model organisms, but signal alteration is very slow. In this regard, it demonstrates the important role of parameters in protein language models. SENSE-PPI is very fast and can test 10,000 proteins against themselves in a matter of hours, enabling the reconstruction of genome-wide proteomes.

Intracellular galectin interactions in health and disease

In the galectin family, a group of lectins is united by their evolutionarily conserved carbohydrate recognition domains. These polypeptides play a role in various cellular processes and are implicated in disease mechanisms such as cancer, fibrosis, infection, and inflammation. Following synthesis in the cytosol, manifold interactions of galectins have been described both extracellularly and intracellularly. Extracellular galectins frequently engage with glycoproteins or glycolipids in a carbohydrate-dependent manner. Intracellularly, galectins bind to non-glycosylated proteins situated in distinct cellular compartments, each with multiple cellular functions. This diversity complicates attempts to form a comprehensive understanding of the role of galectin molecules within the cell. This review enumerates intracellular galectin interaction partners and outlines their involvement in cellular processes. The intricate connections between galectin functions and pathomechanisms are illustrated through discussions of intracellular galectin assemblies in immune and cancer cells. This underscores the imperative need to fully comprehend the interplay of galectins with the cellular machinery and to devise therapeutic strategies aimed at counteracting the establishment of galectin-based disease mechanisms.

Unveiling the peptidases of parasites from the office chair - The endothelin-converting enzyme case study

The emergence of high-throughput methodologies such as next-generation sequencing and proteomics has necessitated significant advancements in biological databases and bioinformatic tools, therefore reshaping the landscape of research into parasitic peptidases. In this review we outline the development of these resources along the -omics technologies and their transformative impact on the field. Apart from extensive summary of general and specific databases and tools, we provide a general pipeline on how to use these resources effectively to identify candidate peptidases from these large datasets and how to gain as much information about them as possible without leaving the office chair. This pipeline is then applied in an illustrative case study on the endothelin-converting enzyme 1 homologue from Schistosoma mansoni and attempts to highlight the contemporary capabilities of bioinformatics. The case study demonstrate how such approach can aid to hypothesize enzyme functions and interactions through computational analysis alone effectively and emphasizes how such virtual investigations can guide and optimize subsequent wet lab experiments therefore potentially saving precious time and resources. Finally, by showing what can be achieved without traditional wet laboratory methods, this review provides a compelling narrative on the use of bioinformatics to bridge the gap between big data and practical research applications, highlighting the key role of these technologies in furthering our understanding of parasitic diseases.

Mapping adipocyte interactome networks by HaloTag-enrichment-mass spectrometry

Mapping protein interaction complexes in their natural state in vivo is arguably the Holy Grail of protein network analysis. Detection of protein interaction stoichiometry has been an important technical challenge, as few studies have focused on this. This may, however, be solved by artificial intelligence (AI) and proteomics. Here, we describe the development of HaloTag-based affinity purification mass spectrometry (HaloMS), a high-throughput HaloMS assay for protein interaction discovery. The approach enables the rapid capture of newly expressed proteins, eliminating tedious conventional one-by-one assays. As a proof-of-principle, we used HaloMS to evaluate the protein complex interactions of 17 regulatory proteins in human adipocytes. The adipocyte interactome network was validated using an in vitro pull-down assay and AI-based prediction tools. Applying HaloMS to probe adipocyte differentiation facilitated the identification of previously unknown transcription factor (TF)-protein complexes, revealing proteome-wide human adipocyte TF networks and shedding light on how different pathways are integrated.