PrePPI: a structure-informed database of protein-protein interactions

Inhibition of GLUD1 mediated by LASP1 and SYVN1 contributes to hepatitis B virus X protein-induced hepatocarcinogenesis

Glutamate dehydrogenase 1 (GLUD1) is implicated in oncogenesis. However, little is known about the relationship between GLUD1 and hepatocellular carcinoma (HCC). In the present study, we demonstrated that the expression levels of GLUD1 significantly decreased in tumors, which was relevant to the poor prognosis of HCC. Functionally, GLUD1 silencing enhanced the growth and migration of HCC cells. Mechanistically, the upregulation of interleukin-32 through AKT activation contributes to GLUD1 silencing-facilitated hepatocarcinogenesis. The interaction between GLUD1 and AKT, as well as α-ketoglutarate regulated by GLUD1, can suppress AKT activation. In addition, LIM and SH3 protein 1 (LASP1) interacts with GLUD1 and induces GLUD1 degradation via the ubiquitin-proteasome pathway, which relies on the E3 ubiquitin ligase synoviolin (SYVN1), whose interaction with GLUD1 is enhanced by LASP1. In hepatitis B virus (HBV)-related HCC, the HBV X protein (HBX) can suppress GLUD1 with the participation of LASP1 and SYVN1. Collectively, our data suggest that GLUD1 silencing is significantly associated with HCC development, and LASP1 and SYVN1 mediate the inhibition of GLUD1 in HCC, especially in HBV-related tumors.

RNA 5-methylcytosine marks mitochondrial double-stranded RNAs for degradation and cytosolic release

Mitochondria are essential regulators of innate immunity. They generate long mitochondrial double-stranded RNAs (mt-dsRNAs) and release them into the cytosol to trigger an immune response under pathological stress conditions. Yet the regulation of these self-immunogenic RNAs remains largely unknown. Here, we employ CRISPR screening on mitochondrial RNA (mtRNA)-binding proteins and identify NOP2/Sun RNA methyltransferase 4 (NSUN4) as a key regulator of mt-dsRNA expression in human cells. We find that NSUN4 induces 5-methylcytosine (m5C) modification on mtRNAs, especially on the termini of light-strand long noncoding RNAs. These m5C-modified RNAs are recognized by complement C1q-binding protein (C1QBP), which recruits polyribonucleotide nucleotidyltransferase to facilitate RNA turnover. Suppression of NSUN4 or C1QBP results in increased mt-dsRNA expression, while C1QBP deficiency also leads to increased cytosolic mt-dsRNAs and subsequent immune activation. Collectively, our study unveils the mechanism underlying the selective degradation of light-strand mtRNAs and establishes a molecular mark for mtRNA decay and cytosolic release.

Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy

Tumors frequently harbor isogenic yet epigenetically distinct subpopulations of multi-potent cells with high tumor-initiating potential-often called Cancer Stem-Like Cells (CSLCs). These can display preferential resistance to standard-of-care chemotherapy. Single-cell analyses can help elucidate Master Regulator (MR) proteins responsible for governing the transcriptional state of these cells, thus revealing complementary dependencies that may be leveraged via combination therapy. Interrogation of single-cell RNA sequencing profiles from seven metastatic breast cancer patients, using perturbational profiles of clinically relevant drugs, identified drugs predicted to invert the activity of MR proteins governing the transcriptional state of chemoresistant CSLCs, which were then validated by CROP-seq assays. The top drug, the anthelmintic albendazole, depleted this subpopulation in vivo without noticeable cytotoxicity. Moreover, sequential cycles of albendazole and paclitaxel-a commonly used chemotherapeutic -displayed significant synergy in a patient-derived xenograft (PDX) from a TNBC patient, suggesting that network-based approaches can help develop mechanism-based combinatorial therapies targeting complementary subpopulations.

Hepatitis B virus core protein stabilizes RANGAP1 to upregulate KDM2A and facilitate hepatocarcinogenesis

PURPOSE

As a vital component of the hepatitis B virus (HBV) nucleocapsid, HBV core protein (HBC) contributes to hepatocarcinogenesis. Here, we aimed to assess the effects of RANGAP1 and KDM2A on tumorigenesis induced by HBC.

METHODS

Co-immunoprecipitation (Co-IP) combined with mass spectrometry were utilized to identify the proteins with the capacity to interact with HBC. The gene and protein levels of RANGAP1 and KDM2A in hepatocellular carcinoma (HCC) and HBV-positive HCC tissues were evaluated using different cohorts. The roles of RANGAP1 and KDM2A in HCC cells mediated by HBC were investigated in vitro and in vivo. Co-IP and western blot were used to estimate the interaction of HBC with RANGAP1 and KDM2A and assess RANGAP1 stabilization regulated by HBC.

RESULTS

We discovered that HBC could interact with RANGAP1 and KDM2A, the levels of which were markedly elevated in HCC tissues. Relying on RANGAP1 and KDM2A, HBC facilitated HCC cell growth and migration. The increased stabilization of RANGAP1 mediated by HBC was relevant to the disruption of the interaction between RANGAP1 and an E3 ligase SYVN1. RANGAP1 interacted with KDM2A, and it further promoted KDM2A stabilization by disturbing the interaction between KDM2A and SYVN1. HBC enhanced the interaction of KDM2A with RANGAP1 and upregulated the expression of KDM2A via RANGAP1 in HCC cells.

CONCLUSIONS

These findings demonstrate a novel mechanism by which HBC facilitates hepatocarcinogenesis. RANGAP1 and KDM2A could act as potential molecular targets for treating HBV-associated malignancy.

Fast and scalable querying of eukaryotic linear motifs with gget elm

MOTIVATION

Eukaryotic linear motifs (ELMs), or Short Linear Motifs, are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for investigating motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt Accessions on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited.

RESULTS

To allow swift, large-scale motif analyses on protein sequences using ELMs curated in the ELM database, we have extended the gget suite of Python and command line tools with a new module, gget elm, which does not rely on the ELM server for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt Accessions. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences.

AVAILABILITY AND IMPLEMENTATION

The manual and source code are available at https://github.com/pachterlab/gget.

Bifunctional Inhibitor Reveals NEK2 as a Therapeutic Target and Regulator of Oncogenic Pathways in Lymphoma

Expression of the serine/threonine kinase never in mitosis gene A (NIMA)-related kinase 2 (NEK2) is essential for entry into mitosis via its role in facilitating centrosome separation. Its overactivity can lead to tumorigenesis and drug resistance through the activation of several oncogenic pathways, including AKT. Although the cancer-enabling activities of NEK2 are documented in many malignancies, including correlations with poor survival in myeloma, breast, and non-small cell lung cancer, little is known about the role of NEK2 in lymphoma. Here, in tumors from patients with diffuse large B-cell lymphoma (DLBCL), the most common, aggressive non-Hodgkin lymphoma, we found a high abundance of NEK2 mRNA and protein associated with an inferior overall survival. Using our recently developed NEK2 inhibitor, NBI-961, we discovered that DLBCL cell lines and patient-derived cells exhibit a dependency on NEK2 for their viability. This compromised cell fitness was directly attributable to efficient NEK2 inhibition and proteasomal degradation by NBI-961. In a subset of particularly sensitive DLBCL cells, NBI-961 induced G2/mitosis arrest and apoptosis. In contrast, an existing indirect NEK2 inhibitor, INH154, did not prevent NEK2 autophosphorylation, induce NEK2 proteasomal degradation, or affect cell viability. Global proteomics and phospho-proteomics revealed that NEK2 orchestrates cell-cycle and apoptotic pathways through regulation of both known and new signaling molecules. We show the loss of NEK2-sensitized DLBCL to the chemotherapy agents, doxorubicin and vincristine, and effectively suppressed tumor growth in mice. These studies establish the oncogenic activity of NEK2 in DLBCL and set the foundation for development of anti-NEK2 therapeutic strategies in this frequently refractory and relapse-prone cancer.

Structural coverage of the human interactome

Complex biological processes in cells are embedded in the interactome, representing the complete set of protein-protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes' molecular details. Therefore, knowing the structural coverage of the interactome is important to show the current limitations. Structural modeling of protein-protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) were included. We then collected the interactions from the literature and databases to form the reference human interactome, resulting in 117 897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI and 73.62% and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome.

From observational to actionable: rethinking omics in biologics production

As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.

CACO: A Core-Attachment Method With Cross-Species Functional Ortholog Information to Detect Human Protein Complexes

Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise in PPI networks. Therefore, we propose a novel core-attachment method, named CACO, to detect human protein complexes, by integrating the functional information from other species via protein ortholog relations. First, CACO constructs a cross-species ortholog relation matrix and transfers GO terms from other species as a reference to evaluate the confidence of PPIs. Then, a PPI filter strategy is adopted to clean the PPI network and thus a weighted clean PPI network is constructed. Finally, a new effective core-attachment algorithm is proposed to detect protein complexes from the weighted PPI network. Compared to other thirteen state-of-the-art methods, CACO outperforms all of them in terms of F-measure and Composite Score, showing that integrating ortholog information and the proposed core-attachment algorithm are effective in detecting protein complexes.

Elucidation of genome-wide understudied proteins targeted by PROTAC-induced degradation using interpretable machine learning

Proteolysis-targeting chimeras (PROTACs) are hetero-bifunctional molecules that induce the degradation of target proteins by recruiting an E3 ligase. PROTACs have the potential to inactivate disease-related genes that are considered undruggable by small molecules, making them a promising therapy for the treatment of incurable diseases. However, only a few hundred proteins have been experimentally tested for their amenability to PROTACs, and it remains unclear which other proteins in the entire human genome can be targeted by PROTACs. In this study, we have developed PrePROTAC, an interpretable machine learning model based on a transformer-based protein sequence descriptor and random forest classification. PrePROTAC predicts genome-wide targets that can be degraded by CRBN, one of the E3 ligases. In the benchmark studies, PrePROTAC achieved a ROC-AUC of 0.81, an average precision of 0.84, and over 40% sensitivity at a false positive rate of 0.05. When evaluated by an external test set which comprised proteins from different structural folds than those in the training set, the performance of PrePROTAC did not drop significantly, indicating its generalizability. Furthermore, we developed an embedding SHapley Additive exPlanations (eSHAP) method, which extends conventional SHAP analysis for original features to an embedding space through in silico mutagenesis. This method allowed us to identify key residues in the protein structure that play critical roles in PROTAC activity. The identified key residues were consistent with existing knowledge. Using PrePROTAC, we identified over 600 novel understudied proteins that are potentially degradable by CRBN and proposed PROTAC compounds for three novel drug targets associated with Alzheimer's disease.

Identification of glycogen phosphorylase L as a potential target for lung cancer

Traditional Chinese medicine (TCM) has been widely used for cancer treatment. Identification of anti-cancer targets of TCM is the first and principal step in discovering molecular mechanisms of TCM as well as obtaining novel targets for cancer therapy. In this study, glycogen phosphorylase L (PYGL) was identified as one of the targeted proteins for several TCMs and was upregulated in various cancer types. The expression level of PYGL was positively correlated with the stage of lung cancer and the poor prognosis of patients. Meanwhile, knockdown of PYGL significantly inhibited proliferation and migration in lung cancer cells. In addition, PYGL was associated with spindle, kinetochore, and microtubule, the cellular components that are closely related to mitosis, in lung cancer. Moreover, PYGL was more susceptible to be upregulated by 144 mutated genes. Taken together, PYGL is a potential target for lung cancer treatment and its molecular mechanism probably influences the mitotic function of cells by regulating energy metabolism.

Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partner

Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.

SVSBI: sequence-based virtual screening of biomolecular interactions

Virtual screening (VS) is a critical technique in understanding biomolecular interactions, particularly in drug design and discovery. However, the accuracy of current VS models heavily relies on three-dimensional (3D) structures obtained through molecular docking, which is often unreliable due to the low accuracy. To address this issue, we introduce a sequence-based virtual screening (SVS) as another generation of VS models that utilize advanced natural language processing (NLP) algorithms and optimized deep K-embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking. We demonstrate that SVS outperforms state-of-the-art performance for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for protein-protein interactions in five biological species. SVS has the potential to transform current practices in drug discovery and protein engineering.

Network-centric analysis of co-fractionated protein complex profiles using SECAT

The Size-Exclusion Chromatography Analysis Toolkit (SECAT) elucidates protein complex dynamics using co-fractionated bottom-up mass spectrometry (CF-MS) data. Here, we present a protocol for the network-centric analysis and interpretation of CF-MS profiles using SECAT. We describe the technical steps for preprocessing, scoring, semi-supervised machine learning, and quantification, including common pitfalls and their solutions. We further provide guidance for data export, visualization, and the interpretation of SECAT results to discover dysregulated proteins and interactions, supporting new hypotheses and biological insights. For complete details on the use and execution of this protocol, please refer to Rosenberger et al. (2020).1.

Hepatitis B virus X protein promotes MAN1B1 expression by enhancing stability of GRP78 via TRIM25 to facilitate hepatocarcinogenesis

BACKGROUND

GRP78 has been implicated in hepatocarcinogenesis. However, the clinical relevance, biological functions and related regulatory mechanisms of GRP78 in hepatitis B virus (HBV)-associated hepatoma carcinoma (HCC) remain elusive.

METHODS

The association between GRP78 expression and HBV-related HCC was investigated. The effects of HBV X protein (HBX) on GRP78 and MAN1B1 expression, biological functions of GRP78 and MAN1B1 in HBX-mediated HCC cells and mechanisms related to TRIM25 on GRP78 upregulation to induce MAN1B1 expression in HBX-related HCC cells were examined.

RESULTS

GRP78 expression was correlated with poor prognosis in HBV-positive HCC. HBX increased MAN1B1 protein expression depending on GRP78, and HBX enhanced the levels of MAN1B1 to promote proliferation, migration and PI3-K/mTOR signalling pathway activation in HCC cells. GRP78 activates Smad4 via its interaction with Smad4 to increase MAN1B1 expression in HBX-expressing HCC cells. TRIM25 enhanced the stability of GRP78 by inhibiting its ubiquitination. HBX binds to GRP78 and TRIM25 and accelerates their interaction of GRP78 and TRIM25, leading to an increase in GRP78 expression.

CONCLUSIONS

HBX enhances the stability of GRP78 through TRIM25 to increase the expression of MAN1B1 to facilitate tumorigenesis, and we provide new insights into the molecular mechanisms underlying HBV-induced malignancy.

CM2D3: Furnishing the human interactome with structural models of protein complexes derived by comparative modeling and docking

The human interactome is composed of around half a million interactions according to recent estimations and it is only for a small fraction of those that three-dimensional structural information is available. Indeed, the structural coverage of the human interactome is very low and given the complexity and time-consuming requirements of solving protein structures this problem will remain for the foreseeable future. Structural models, or predictions, of protein complexes can provide valuable information when the experimentally determined 3D structures are not available. Here we present CM2D3, a relational database containing structural models of the whole human interactome derived both from comparative modeling and data-driven docking. Starting from a consensus interactome derived from integrating several interactomics databases, a strategy was devised to derive structural models by computational means. Currently, CM2D3 includes 33338 structural models of which 5121 derived from comparative modeling and the remaining from docking. Of the latter, the structures of 14554 complexes were derived from monomers modeled by M4T while the rest were modeled with structures as predicted by AlphaFold2. Lastly, CM2D3 complements existing resources by focusing on models derived from both free-docking, as opposed to template-based docking, and hence expanding the available structural information on protein complexes to the scientific community. Database URL:http://www.bioinsilico.org/CM2D3.

SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network

While deep learning (DL)-based models have emerged as powerful approaches to predict protein-protein interactions (PPIs), the reliance on explicit similarity measures (e.g. sequence similarity and network neighborhood) to known interacting proteins makes these methods ineffective in dealing with novel proteins. The advent of AlphaFold2 presents a significant opportunity and also a challenge to predict PPIs in a straightforward way based on monomer structures while controlling bias from protein sequences. In this work, we established Structure and Graph-based Predictions of Protein Interactions (SGPPI), a structure-based DL framework for predicting PPIs, using the graph convolutional network. In particular, SGPPI focused on protein patches on the protein-protein binding interfaces and extracted the structural, geometric and evolutionary features from the residue contact map to predict PPIs. We demonstrated that our model outperforms traditional machine learning methods and state-of-the-art DL-based methods using non-representation-bias benchmark datasets. Moreover, our model trained on human dataset can be reliably transferred to predict yeast PPIs, indicating that SGPPI can capture converging structural features of protein interactions across various species. The implementation of SGPPI is available at https://github.com/emerson106/SGPPI.

The structural flexibility of MAD1 facilitates the assembly of the Mitotic Checkpoint Complex

The spindle assembly checkpoint (SAC) safeguards the genome during cell division by generating an effector molecule known as the Mitotic Checkpoint Complex (MCC). The MCC comprises two subcomplexes: BUBR1:BUB3 and CDC20:MAD2, and the formation of CDC20:MAD2 is the rate-limiting step during MCC assembly. Recent studies show that the rate of CDC20:MAD2 formation is significantly accelerated by the cooperative binding of CDC20 to the SAC proteins MAD1 and BUB1. However, the molecular basis for this acceleration is not fully understood. Here, we demonstrate that the structural flexibility of MAD1 at a conserved hinge near the C-terminus is essential for catalytic MCC assembly. This MAD1 hinge enables the MAD1:MAD2 complex to assume a folded conformation in vivo. Importantly, truncating the hinge reduces the rate of MCC assembly in vitro and SAC signaling in vivo. Conversely, mutations that preserve hinge flexibility retain SAC signaling, indicating that the structural flexibility of the hinge, rather than a specific amino acid sequence, is important for SAC signaling. We summarize these observations as the 'knitting model' that explains how the folded conformation of MAD1:MAD2 promotes CDC20:MAD2 assembly.

PPI-Miner: A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method

Protein-protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/services/crbn-subslib.

Gene transferability and sociality do not correlate with gene connectivity

The connectivity of a gene, defined as the number of interactions a gene's product has with other genes' products, is a key characteristic of a gene. In prokaryotes, the complexity hypothesis predicts that genes which undergo more frequent horizontal transfer will be less connected than genes which are only very rarely transferred. We tested the role of horizontal gene transfer, and other potentially important factors, by examining the connectivity of chromosomal and plasmid genes, across 134 diverse prokaryotic species. We found that (i) genes on plasmids were less connected than genes on chromosomes; (ii) connectivity of plasmid genes was not correlated with plasmid mobility; and (iii) the sociality of genes (cooperative or private) was not correlated with gene connectivity.

TSG101 associates with PARP1 and is essential for PARylation and DNA damage-induced NF-κB activation

In a genome-wide screening for components of the dsDNA-break-induced IKK-NF-κB pathway, we identified scores of regulators, including tumor susceptibility gene TSG101. TSG101 is essential for DNA damage-induced formation of cellular poly(ADP-ribose) (PAR). TSG101 binds to PARP1 and is required for PARP1 activation. This function of TSG101 is independent of its role in the ESCRT-I endosomal sorting complex. In the absence of TSG101, the PAR-dependent formation of a nuclear PARP1-IKKγ signalosome, which triggers IKK activation, is impaired. According to its requirement for PARP1 and NF-κB activation, TSG101-deficient cells are defective in DNA repair and apoptosis protection. Loss of TSG101 results in PARP1 trapping at damage sites and mimics the effect of pharmacological PARP inhibition. We also show that the loss of TSG101 in connection with inactivated tumor suppressors BRCA1/2 in breast cancer cells is lethal. Our results imply TSG101 as a therapeutic target to achieve synthetic lethality in cancer treatment.

ProteinPrompt: a webserver for predicting protein-protein interactions

Motivation

Protein-protein interactions (PPIs) play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine learning algorithms to calculate specific, currently unknown PPIs. Our tool is designed to quickly and reliably predict contact propensities based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification.

Results

We collected and thoroughly filtered a comprehensive database of known binders from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest (RF) algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network (GNN) implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for binders. To evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the RF method achieved an accuracy rate of 0.88 and an area under the curve of 0.95. The GNN achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of RF and GNNs reduces the likelihood of errors. The consensus reached an accuracy of 0.89.

Availability and implementation

ProteinPrompt is available online at: http://proteinformatics.org/ProteinPrompt, where training and test data used to optimize the methods are also available. The server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.

SLiMAn: An Integrative Web Server for Exploring Short Linear Motif-Mediated Interactions in Interactomes

Cells express thousands of macromolecules, and their functioning relies on multiple networks of intermolecular interactions. These interactions can be experimentally determined at different spatial and temporal resolutions. But, physical interfaces are not often delineated directly, especially in high-throughput experiments. A large fraction of protein-protein interactions involves domain and so-called SLiMs (for Short Linear Motifs). Often, SLiMs lie in disordered regions or loops. Their small size, limited sequence conservation, and loosely folded nature prevent straightforward detection. SLiMAn (Short Linear Motif Analysis), a new web server, is provided to help thorough analysis of interactomics data. From a list of putative interactants (e.g., output from an interactomics study), SLiMs (from ELM) and SLiM-recognition domains (from Pfam) are extracted, and putative pairings are displayed. Predicted results can be filtered using motif E-values, IUPred2 scores, or BioGRID interaction matches. When structural templates are available, a given SLiM and its recognition domain can be modeled using SCWRL. We illustrate here the use of SLiMAn on distinct examples, including one real-case study. We oversee wide-range applications for SLiMAn in the context of the massive analysis of protein-protein interactions. This new web server is made freely available at https://sliman.cbs.cnrs.fr.

Complementary omics strategies to dissect p53 signaling networks under nutrient stress

Signaling trough p53is a major cellular stress response mechanism and increases upon nutrient stresses such as starvation. Here, we show in a human hepatoma cell line that starvation leads to robust nuclear p53 stabilization. Using BioID, we determine the cytoplasmic p53 interaction network within the immediate-early starvation response and show that p53 is dissociated from several metabolic enzymes and the kinase PAK2 for which direct binding with the p53 DNA-binding domain was confirmed with NMR studies. Furthermore, proteomics after p53 immunoprecipitation (RIME) uncovered the nuclear interactome under prolonged starvation, where we confirmed the novel p53 interactors SORBS1 (insulin receptor signaling) and UGP2 (glycogen synthesis). Finally, transcriptomics after p53 re-expression revealed a distinct starvation-specific transcriptome response and suggested previously unknown nutrient-dependent p53 target genes. Together, our complementary approaches delineate several nodes of the p53 signaling cascade upon starvation, shedding new light on the mechanisms of p53 as nutrient stress sensor. Given the central role of p53 in cancer biology and the beneficial effects of fasting in cancer treatment, the identified interaction partners and networks could pinpoint novel pharmacologic targets to fine-tune p53 activity.

Rare Variants in Inborn Errors of Immunity Genes Associated With Covid-19 Severity

Host genetic factors have been shown to play an important role in SARS-CoV-2 infection and the course of Covid-19 disease. The genetic contributions of common variants influencing Covid-19 susceptibility and severity have been extensively studied in diverse populations. However, the studies of rare genetic defects arising from inborn errors of immunity (IEI) are relatively few, especially in the Chinese population. To fill this gap, we used a deeply sequenced dataset of nearly 500 patients, all of Chinese descent, to investigate putative functional rare variants. Specifically, we annotated rare variants in our call set and selected likely deleterious missense (LDM) and high-confidence predicted loss-of-function (HC-pLoF) variants. Further, we analyzed LDM and HC-pLoF variants between non-severe and severe Covid-19 patients by (a) performing gene- and pathway-level association analyses, (b) testing the number of mutations in previously reported genes mapped from LDM and HC-pLoF variants, and (c) uncovering candidate genes via protein-protein interaction (PPI) network analysis of Covid-19-related genes and genes defined from LDM and HC-pLoF variants. From our analyses, we found that (a) pathways Tuberculosis (hsa:05152), Primary Immunodeficiency (hsa:05340), and Influenza A (hsa:05164) showed significant enrichment in severe patients compared to the non-severe ones, (b) HC-pLoF mutations were enriched in Covid-19-related genes in severe patients, and (c) several candidate genes, such as IL12RB1, TBK1, TLR3, and IFNGR2, are uncovered by PPI network analysis and worth further investigation. These regions generally play an essential role in regulating antiviral innate immunity responses to foreign pathogens and in responding to many inflammatory diseases. We believe that our identified candidate genes/pathways can be potentially used as Covid-19 diagnostic markers and help distinguish patients at higher risk.

Systems Biology of Virus-Host Protein Interactions: From Hypothesis Generation to Mechanisms of Replication and Pathogenesis

As obligate intracellular parasites, all viruses must co-opt cellular machinery to facilitate their own replication. Viruses often co-opt these cellular pathways and processes through physical interactions between viral and host proteins. In addition to facilitating fundamental aspects of virus replication cycles, these virus-host protein interactions can also disrupt physiological functions of host proteins, causing disease that can be advantageous to the virus or simply a coincidence. Consequently, unraveling virus-host protein interactions can serve as a window into molecular mechanisms of virus replication and pathogenesis. Identifying virus-host protein interactions using unbiased systems biology approaches provides an avenue for hypothesis generation. This review highlights common systems biology approaches for identification of virus-host protein interactions and the mechanistic insights revealed by these methods. We also review conceptual innovations using comparative and integrative systems biology that can leverage global virus-host protein interaction data sets to more rapidly move from hypothesis generation to mechanism. Expected final online publication date for the Annual Review of Virology, Volume 9 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

RNF8 up-regulates AR/ARV7 action to contribute to advanced prostate cancer progression

Androgen receptor (AR) signaling drives prostate cancer (PC) progression. Androgen deprivation therapy (ADT) is temporally effective, whereas drug resistance inevitably develops. Abnormal expression of AR/ARV7 (the most common AR splicing variant) is critical for endocrine resistance, while the detailed mechanism is still elusive. In this study, bioinformatics and immunohistochemical analyses demonstrate that RNF8 is high expressed in PC and castration-resistant PC (CRPC) samples and the expression of RNF8 is positively correlated with the Gleason score. The high expression of RNF8 in PCs predicts a poor prognosis. These results provide a potential function of RNF8 in PC progression. Furthermore, the mRNA expression of RNF8 is positively correlated with that of AR in PC. Mechanistically, we find that RNF8 upregulates c-Myc-induced AR transcription via altering histone modifications at the c-Myc binding site within the AR gene. RNF8 also acts as a co-activator of AR, promoting the recruitment of AR/ARV7 to the KLK3 (PSA) promoter, where RNF8 modulates histone modifications. These functions of RNF8 are dependent on its E3 ligase activity. RNF8 knockdown further reduces AR transactivation and PSA expression in CRPC cells with enzalutamide treatment. RNF8 depletion restrains cell proliferation and alleviates enzalutamide resistance in CRPC cells. Our findings indicate that RNF8 may be a potential therapeutic target for endocrine resistance in PC.

Functional analysis of the stable phosphoproteome reveals cancer vulnerabilities

MOTIVATION

The advance of mass spectrometry-based technologies enabled the profiling of the phosphoproteomes of a multitude of cell and tissue types. However, current research primarily focused on investigating the phosphorylation dynamics in specific cell types and experimental conditions, whereas the phosphorylation events that are common across cell/tissue types and stable regardless of experimental conditions are, so far, mostly ignored.

RESULTS

Here, we developed a statistical framework to identify the stable phosphoproteome across 53 human phosphoproteomics datasets, covering 40 cell/tissue types and 194 conditions/treatments. We demonstrate that the stably phosphorylated sites (SPSs) identified from our statistical framework are evolutionarily conserved, functionally important and enriched in a range of core signaling and gene pathways. Particularly, we show that SPSs are highly enriched in the RNA splicing pathway, an essential cellular process in mammalian cells, and frequently disrupted by cancer mutations, suggesting a link between the dysregulation of RNA splicing and cancer development through mutations on SPSs.

AVAILABILITY AND IMPLEMENTATION

The source code for data analysis in this study is available from Github repository https://github.com/PYangLab/SPSs under the open-source license of GPL-3. The data used in this study are publicly available (see Section 2.8).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comprehensive Profiling of Paper Mulberry (Broussonetia papyrifera) Crotonylome Reveals the Significance of Lysine Crotonylation in Young Leaves

Lysine crotonylation is a newly discovered and reversible posttranslational modification involved in various biological processes, especially metabolism regulation. A total of 5159 lysine crotonylation sites in 2272 protein groups were identified. Twenty-seven motifs were found to be the preferred amino acid sequences for crotonylation sites. Functional annotation analyses revealed that most crotonylated proteins play important roles in metabolic processes and photosynthesis. Bioinformatics analysis suggested that lysine crotonylation preferentially targets a variety of important biological processes, including ribosome, glyoxylate and dicarboxylate metabolism, carbon fixation in photosynthetic organisms, proteasome and the TCA cycle, indicating lysine crotonylation is involved in the common mechanism of metabolic regulation. A protein interaction network analysis revealed that diverse interactions are modulated by protein crotonylation. These results suggest that lysine crotonylation is involved in a variety of biological processes. HSP70 is a crucial protein involved in protecting plant cells and tissues from thermal or abiotic stress responses, and HSP70 protein was found to be crotonylated in paper mulberry. This systematic analysis provides the first comprehensive analysis of lysine crotonylation in paper mulberry and provides important resources for further study on the regulatory mechanism and function of the lysine crotonylated proteome.

Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures

Proteins are the workhorses of cells to carry out sophisticated and complex cellular processes. Such processes require a coordinated and regulated interactions between proteins that are both time and location specific. The strength, or binding affinity, of protein-protein interactions ranges between the micro- and the nanomolar association constant, often dictating the molecular mechanisms underlying the interaction and the longevity of the complex, i.e., transient or permanent. In consequence, there is a need to quantify the strength of protein-protein interactions for biological, biomedical, and biotechnological applications. While experimental methods are labor intensive and costly, computational ones are useful tools to predict the affinity of protein-protein interactions. In this chapter, we review the methods developed by us to address this question. We briefly present two methods to comprehend the structure of the protein complex derived by either comparative modeling or docking. Then we introduce BADOCK, a method to predict the binding energy without requiring the structure of the protein complex, thus overcoming one of the major limitations of structure-based methods for the prediction of binding affinity. BADOCK utilizes the structure of unbound proteins and the protein docking sampling space to predict protein-protein binding affinities. We present step-by-step protocols to utilize these methods, describing the inputs and potential pitfalls as well as their respective strengths and limitations.

Discovery of Novel Dual-Target Inhibitor of Bromodomain-Containing Protein 4/Casein Kinase 2 Inducing Apoptosis and Autophagy-Associated Cell Death for Triple-Negative Breast Cancer Therapy

Bromodomain-containing protein 4 (BRD4) is an attractive epigenetic target in human cancers. Inhibiting the phosphorylation of BRD4 by casein kinase 2 (CK2) is a potential strategy to overcome drug resistance in cancer therapy. The present study describes the synthesis of multiple BRD4-CK2 dual inhibitors based on rational drug design, structure-activity relationship, and in vitro and in vivo evaluations, and 44e was identified to possess potent and balanced activities against BRD4 (IC₅₀ = 180 nM) and CK2 (IC₅₀ = 230 nM). In vitro experiments show that 44e could inhibit the proliferation and induce apoptosis and autophagy-associated cell death of MDA-MB-231 and MDA-MB-468 cells. In two in vivo xenograft mouse models, 44e displays potent anticancer activity without obvious toxicities. Taken together, we successfully synthesized the first highly effective BRD4-CK2 dual inhibitor, which is expected to be an attractive therapeutic strategy for triple-negative breast cancer (TNBC).

The impact of whole genome duplications on the human gene regulatory networks

This work studies the effects of the two rounds of Whole Genome Duplication (WGD) at the origin of the vertebrate lineage on the architecture of the human gene regulatory networks. We integrate information on transcriptional regulation, miRNA regulation, and protein-protein interactions to comparatively analyse the role of WGD and Small Scale Duplications (SSD) in the structural properties of the resulting multilayer network. We show that complex network motifs, such as combinations of feed-forward loops and bifan arrays, deriving from WGD events are specifically enriched in the network. Pairs of WGD-derived proteins display a strong tendency to interact both with each other and with common partners and WGD-derived transcription factors play a prominent role in the retention of a strong regulatory redundancy. Combinatorial regulation and synergy between different regulatory layers are in general enhanced by duplication events, but the two types of duplications contribute in different ways. Overall, our findings suggest that the two WGD events played a substantial role in increasing the multi-layer complexity of the vertebrate regulatory network by enhancing its combinatorial organization, with potential consequences on its overall robustness and ability to perform high-level functions like signal integration and noise control. Lastly, we discuss in detail the RAR/RXR pathway as an illustrative example of the evolutionary impact of WGD duplications in human.

Gene duplication is one of the main mechanisms driving genome evolution. The duplication of a genomic segment can be the result of a local event, involving only a small portion of the genome, or of a dramatic duplication of the whole genome, which is however only rarely retained. All vertebrates descend from two rounds of Whole-Genome Duplication (WGD) that occurred approximately 500 Mya. We show that these events influenced in unique ways the evolution of different human gene regulatory networks, with sizeable effects on their current structure. We find that WGDs statistically increased the presence of specific classes of simple genetic circuits, considered to be fundamental building blocks of more sophisticated circuitry and commonly associated to complex functions. Our findings support the hypothesis that these rare, large-scale events have played a substantial role in the emergence of complex traits in vertebrates.

Human-Soybean Allergies: Elucidation of the Seed Proteome and Comprehensive Protein-Protein Interaction Prediction

The soybean crop, Glycine max (L.) Merr., is consumed by humans, Homo sapiens, worldwide. While the respective bodies of literature and -omics data for each of these organisms are extensive, comparatively few studies investigate the molecular biological processes occurring between the two. We are interested in elucidating the network of protein-protein interactions (PPIs) involved in human-soybean allergies. To this end, we leverage state-of-the-art sequence-based PPI predictors amenable to predicting the enormous comprehensive interactome between human and soybean. A network-based analytical approach is proposed, leveraging similar interaction profiles to identify candidate allergens and proteins involved in the allergy response. Interestingly, the predicted interactome can be explored from two complementary perspectives: which soybean proteins are predicted to interact with specific human proteins and which human proteins are predicted to interact with specific soybean proteins. A total of eight proteins (six specific to the human proteome and two to the soy proteome) have been identified and supported by the literature to be involved in human health, specifically related to immunological and neurological pathways. This study, beyond generating the most comprehensive human-soybean interactome to date, elucidated a soybean seed interactome and identified several proteins putatively consequential to human health.

Revealing protein-protein interactions at the transcriptome scale by sequencing

We describe PROPER-seq (protein-protein interaction sequencing) to map protein-protein interactions (PPIs) en masse. PROPER-seq first converts transcriptomes of input cells into RNA-barcoded protein libraries, in which all interacting protein pairs are captured through nucleotide barcode ligation, recorded as chimeric DNA sequences, and decoded at once by sequencing and mapping. We applied PROPER-seq to human embryonic kidney cells, T lymphocytes, and endothelial cells and identified 210,518 human PPIs (collected in the PROPER v.1.0 database). Among these, 1,365 and 2,480 PPIs are supported by published co-immunoprecipitation (coIP) and affinity purification-mass spectrometry (AP-MS) data, 17,638 PPIs are predicted by the prePPI algorithm without previous experimental validation, and 100 PPIs overlap human synthetic lethal gene pairs. In addition, four previously uncharacterized interaction partners with poly(ADP-ribose) polymerase 1 (PARP1) (a critical protein in DNA repair) known as XPO1, MATR3, IPO5, and LEO1 are validated in vivo. PROPER-seq presents a time-effective technology to map PPIs at the transcriptome scale, and PROPER v.1.0 provides a rich resource for studying PPIs.

Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes

Because of their considerable number and diversity, membrane proteins and their macromolecular complexes represent the functional units of cells. Their quaternary structure may be stabilized by interactions between the α-helices of different proteins in the hydrophobic region of the cell membrane. Membrane proteins equally represent potential pharmacological targets par excellence for various diseases. Unfortunately, their experimental 3D structure and that of their complexes with other intramembrane protein partners are scarce due to technical difficulties. To overcome this key problem, we devised PPIMem, a computational approach for the specific prediction of higher-order structures of α-helical transmembrane proteins. The novel approach involves proper identification of the amino acid residues at the interface of molecular complexes with a 3D structure. The identified residues compose then nonlinear interaction motifs that are conveniently expressed as mathematical regular expressions. These are efficiently implemented for motif search in amino acid sequence databases, and for the accurate prediction of intramembrane protein-protein complexes. Our template interface-based approach predicted 21,544 binary complexes between 1,504 eukaryotic plasma membrane proteins across 39 species. We compare our predictions to experimental datasets of protein-protein interactions as a first validation method. The online database that results from the PPIMem algorithm with the annotated predicted interactions are implemented as a web server and can be accessed directly at https://transint.univ-evry.fr.

Detecting Rewiring Events in Protein-Protein Interaction Networks Based on Transcriptomic Data

Proteins rarely carry out their cellular functions in isolation. Instead, eukaryotic proteins engage in about six interactions with other proteins on average. The aggregated protein interactome of an organism forms a “hairy ball”-type protein-protein interaction (PPI) network. Yet, in a typical human cell, only about half of all proteins are expressed at a particular time. Hence, it has become common practice to prune the full PPI network to the subset of expressed proteins. If RNAseq data is available, one can further resolve the specific protein isoforms present in a cell or tissue. Here, we review various approaches, software tools and webservices that enable users to construct context-specific or tissue-specific PPI networks and how these are rewired between two cellular conditions. We illustrate their different functionalities on the example of the interactions involving the human TNR6 protein. In an outlook, we describe how PPI networks may be integrated with epigenetic data or with data on the activity of splicing factors.

Proteo3Dnet: a web server for the integration of structural information with interactomics data

Proteo3Dnet is a web server dedicated to the analysis of mass spectrometry interactomics experiments. Given a flat list of proteins, its aim is to organize it in terms of structural interactions to provide a clearer overview of the data. This is achieved using three means: (i) the search for interologs with resolved structure available in the protein data bank, including cross-species remote homology search, (ii) the search for possibly weaker interactions mediated through Short Linear Motifs as predicted by ELM-a unique feature of Proteo3Dnet, (iii) the search for protein-protein interactions physically validated in the BioGRID database. The server then compiles this information and returns a graph of the identified interactions and details about the different searches. The graph can be interactively explored to understand the way the core complexes identified could interact. It can also suggest undetected partners to the experimentalists, or specific cases of conditionally exclusive binding. The interest of Proteo3Dnet, previously demonstrated for the difficult cases of the proteasome and pragmin complexes data is, here, illustrated in the context of yeast precursors to the small ribosomal subunits and the smaller interactome of 14-3-3zeta frequent interactors. The Proteo3Dnet web server is accessible at http://bioserv.rpbs.univ-paris-diderot.fr/services/Proteo3Dnet/.

KEA3: improved kinase enrichment analysis via data integration

Phosphoproteomics and proteomics experiments capture a global snapshot of the cellular signaling network, but these methods do not directly measure kinase state. Kinase Enrichment Analysis 3 (KEA3) is a webserver application that infers overrepresentation of upstream kinases whose putative substrates are in a user-inputted list of proteins. KEA3 can be applied to analyze data from phosphoproteomics and proteomics studies to predict the upstream kinases responsible for observed differential phosphorylations. The KEA3 background database contains measured and predicted kinase-substrate interactions (KSI), kinase-protein interactions (KPI), and interactions supported by co-expression and co-occurrence data. To benchmark the performance of KEA3, we examined whether KEA3 can predict the perturbed kinase from single-kinase perturbation followed by gene expression experiments, and phosphoproteomics data collected from kinase-targeting small molecules. We show that integrating KSIs and KPIs across data sources to produce a composite ranking improves the recovery of the expected kinase. The KEA3 webserver is available at https://maayanlab.cloud/kea3.

Mapping the multiscale structure of biological systems

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.

Entamoeba histolytica: Proteomics Bioinformatics Reveal Predictive Functions and Protein-Protein Interactions of Differentially Abundant Membrane and Cytosolic Proteins

Amoebiasis is caused by Entamoeba histolytica and ranked second for parasitic diseases causing death after malaria. E. histolytica membrane and cytosolic proteins play important roles in the pathogenesis. Our previous study had shown several cytosolic proteins were found in the membrane fraction. Therefore, this study aimed to quantify the differential abundance of membrane and cytosolic proteins in membrane versus cytosolic fractions and analyze their predicted functions and interaction. Previous LC-ESI-MS/MS data were analyzed by PERSEUS software for the differentially abundant proteins, then they were classified into their functional annotations and the protein networks were summarized using PantherDB and STRiNG, respectively. The results showed 24 (44.4%) out of the 54 proteins that increased in abundance were membrane proteins and 30 were cytosolic proteins. Meanwhile, 45 cytosolic proteins were found to decrease in abundance. Functional analysis showed differential abundance proteins involved in the molecular function, biological process, and cellular component with 18.88%, 33.04% and, 48.07%, respectively. The STRiNG server predicted that the decreased abundance proteins had more protein-protein network interactions compared to increased abundance proteins. Overall, this study has confirmed the presence of the differentially abundant membrane and cytosolic proteins and provided the predictive functions and interactions between them.

Deconvoluting complex protein interaction networks through reductionist strategies in peptide biochemistry: Modern approaches and research questions

Following the decoding of the first human genome, researchers have vastly improved their understanding of cell biology and its regulation. As a result, it has become clear that it is not merely genetic information, but the aberrant changes in the functionality and connectivity of its encoded proteins that drive cell response to periods of stress and external cues. Therefore, proper utilization of refined methods that help to describe protein signalling or regulatory networks (i.e., functional connectivity), can help us understand how change in the signalling landscape effects the cell. However, given the vast complexity in 'how and when' proteins communicate or interact with each other, it is extremely difficult to define, characterize, and understand these interaction networks in a tangible manner. Herein lies the challenge of tackling the functional proteome; its regulation is encoded in multiple layers of interaction, chemical modification and cell compartmentalization. To address and refine simple research questions, modern reductionist strategies in protein biochemistry have successfully used peptide-based experiments; their summation helping to simplify the overall complexity of these protein interaction networks. In this way, peptides are powerful tools used in fundamental research that can be readily applied to comparative biochemical research. Understanding and defining how proteins interact is one of the key aspects towards understanding how the proteome functions. To date, reductionist peptide-based research has helped to address a wide range of proteome-related research questions, including the prediction of enzymes substrates, identification of posttranslational modifications, and the annotation of protein interaction partners. Peptide arrays have been used to identify the binding specificity of reader domains, which are able to recognise the posttranslational modifications; forming dynamic protein interactions that are dependent on modification state. Finally, representing one of the fastest growing classes of inhibitor molecules, peptides are now begin explored as "disruptors" of protein-protein interactions or enzyme activity. Collectively, this review will discuss the use of peptides, peptide arrays, peptide-oriented computational biochemistry as modern reductionist strategies in deconvoluting the functional proteome.

Rare variant analysis of 4241 pulmonary arterial hypertension cases from an international consortium implicates FBLN2, PDGFD, and rare de novo variants in PAH

BACKGROUND

Pulmonary arterial hypertension (PAH) is a lethal vasculopathy characterized by pathogenic remodeling of pulmonary arterioles leading to increased pulmonary pressures, right ventricular hypertrophy, and heart failure. PAH can be associated with other diseases (APAH: connective tissue diseases, congenital heart disease, and others) but often the etiology is idiopathic (IPAH). Mutations in bone morphogenetic protein receptor 2 (BMPR2) are the cause of most heritable cases but the vast majority of other cases are genetically undefined.

METHODS

To identify new risk genes, we utilized an international consortium of 4241 PAH cases with exome or genome sequencing data from the National Biological Sample and Data Repository for PAH, Columbia University Irving Medical Center, and the UK NIHR BioResource - Rare Diseases Study. The strength of this combined cohort is a doubling of the number of IPAH cases compared to either national cohort alone. We identified protein-coding variants and performed rare variant association analyses in unrelated participants of European ancestry, including 1647 IPAH cases and 18,819 controls. We also analyzed de novo variants in 124 pediatric trios enriched for IPAH and APAH-CHD.

RESULTS

Seven genes with rare deleterious variants were associated with IPAH with false discovery rate smaller than 0.1: three known genes (BMPR2, GDF2, and TBX4), two recently identified candidate genes (SOX17, KDR), and two new candidate genes (fibulin 2, FBLN2; platelet-derived growth factor D, PDGFD). The new genes were identified based solely on rare deleterious missense variants, a variant type that could not be adequately assessed in either cohort alone. The candidate genes exhibit expression patterns in lung and heart similar to that of known PAH risk genes, and most variants occur in conserved protein domains. For pediatric PAH, predicted deleterious de novo variants exhibited a significant burden compared to the background mutation rate (2.45×, p = 2.5e-5). At least eight novel pediatric candidate genes carrying de novo variants have plausible roles in lung/heart development.

CONCLUSIONS

Rare variant analysis of a large international consortium identified two new candidate genes-FBLN2 and PDGFD. The new genes have known functions in vasculogenesis and remodeling. Trio analysis predicted that ~ 15% of pediatric IPAH may be explained by de novo variants.

Computational and experimental characterization of the novel ECM glycoprotein SNED1 and prediction of its interactome

The extracellular matrix (ECM) is a complex meshwork of proteins and an essential component of multicellular life. We have recently reported the characterization of a novel ECM protein, SNED1, and showed that it promotes breast cancer metastasis and regulates craniofacial development. However, the mechanisms by which it does so remain unknown. ECM proteins exert their functions by binding to cell surface receptors and interacting with other ECM proteins, actions that we can predict using knowledge of protein's sequence, structure, and post-translational modifications. Here, we combined in-silico and in-vitro approaches to characterize the physico-chemical properties of SNED1 and infer its putative functions. To do so, we established a mammalian cell system to produce and purify SNED1 and its N-terminal fragment, which contains a NIDO domain, and demonstrated experimentally SNED1's potential to be glycosylated, phosphorylated, and incorporated into an insoluble ECM. We also determined the secondary and tertiary structures of SNED1 and its N-terminal fragment and obtained a model for its NIDO domain. Using computational predictions, we identified 114 proteins as putative SNED1 interactors, including the ECM protein fibronectin. Pathway analysis of the predicted SNED1 interactome further revealed that it may contribute to signaling through cell surface receptors, such as integrins, and participate in the regulation of ECM organization and developmental processes. Last, using fluorescence microscopy, we showed that SNED1 forms microfibrils within the ECM and partially colocalizes with fibronectin. Altogether, we provide a wealth of information on an understudied yet important ECM protein with the potential to decipher its pathophysiological functions.

Integrating Multimeric Threading With High-throughput Experiments for Structural Interactome of Escherichia coli

Genome-wide protein-protein interaction (PPI) determination remains a significant unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (HTEs) have often a relatively high false-positive rate in assigning PPIs, and PPI quaternary structures are more difficult to solve than tertiary structures using traditional structural biology techniques. We proposed a uniform pipeline, Threpp, to address both problems. Starting from a pair of monomer sequences, Threpp first threads both sequences through a complex structure library, where the alignment score is combined with HTE data using a naïve Bayesian classifier model to predict the likelihood of two chains to interact with each other. Next, quaternary complex structures of the identified PPIs are constructed by reassembling monomeric alignments with dimeric threading frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs which is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks show a scale-free cluster size distribution, consistent with previous studies, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Furthermore, complex structure models were constructed for all predicted E. coli PPIs based on the quaternary threading alignments, where 6771 of them were found to have a high confidence score that corresponds to the correct fold of the complexes with a TM-score >0.5, and 39 showed a close consistency with the later released experimental structures with an average TM-score = 0.73. These results demonstrated the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.

A survey on computational models for predicting protein-protein interactions

Proteins interact with each other to play critical roles in many biological processes in cells. Although promising, laboratory experiments usually suffer from the disadvantages of being time-consuming and labor-intensive. The results obtained are often not robust and considerably uncertain. Due recently to advances in high-throughput technologies, a large amount of proteomics data has been collected and this presents a significant opportunity and also a challenge to develop computational models to predict protein-protein interactions (PPIs) based on these data. In this paper, we present a comprehensive survey of the recent efforts that have been made towards the development of effective computational models for PPI prediction. The survey introduces the algorithms that can be used to learn computational models for predicting PPIs, and it classifies these models into different categories. To understand their relative merits, the paper discusses different validation schemes and metrics to evaluate the prediction performance. Biological databases that are commonly used in different experiments for performance comparison are also described and their use in a series of extensive experiments to compare different prediction models are discussed. Finally, we present some open issues in PPI prediction for future work. We explain how the performance of PPI prediction can be improved if these issues are effectively tackled.

HIR V2: a human interactome resource for the biological interpretation of differentially expressed genes via gene set linkage analysis

To facilitate biomedical studies of disease mechanisms, a high-quality interactome that connects functionally related genes is needed to help investigators formulate pathway hypotheses and to interpret the biological logic of a phenotype at the biological process level. Interactions in the updated version of the human interactome resource (HIR V2) were inferred from 36 mathematical characterizations of six types of data that suggest functional associations between genes. This update of the HIR consists of 88 069 pairs of genes (23.2% functional interactions of HIR V2 are in common with the previous version of HIR), representing functional associations that are of strengths similar to those between well-studied protein interactions. Among these functional interactions, 57% may represent protein interactions, which are expected to cover 32% of the true human protein interactome. The gene set linkage analysis (GSLA) tool is developed based on the high-quality HIR V2 to identify the potential functional impacts of the observed transcriptomic changes, helping to elucidate their biological significance and complementing the currently widely used enrichment-based gene set interpretation tools. A case study shows that the annotations reported by the HIR V2/GSLA system are more comprehensive and concise compared to those obtained by the widely used gene set annotation tools such as PANTHER and DAVID. The HIR V2 and GSLA are available at http://human.biomedtzc.cn.

FGF8 induces epithelial-mesenchymal transition and promotes metastasis in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common cancers worldwide, and with 354 864 new cases each year. Cancer metastasis, recurrence, and drug resistance are the main causes to cripples and deaths of OSCC patients. As potent growth factors, fibroblast growth factors (FGFs) are frequently susceptible to being hijacked by cancer cells. In this study, we show that FGF8 is upregulated in OSCC tissues and high FGF8 expression is related with a set of clinicopathologic parameters, including age, drinking, and survival time. FGF8 treatment enhances the invasive capability of OSCC cells. Lentivirus-based FGF8 expression promotes OSCC metastasis in a mouse lung metastasis model. Further, mechanistic study demonstrates that FGF8 induces epithelial-mesenchymal transition (EMT) in OSCC cells. These results highlight a pro-metastatic function of FGF8, and underscore the role of FGF8 in OSCC development.