Fundamentals of protein interaction network mapping

Oxidative protein damage negatively affects protein-protein interaction: The case of KRAS-cRAF

Protein-protein interactions (PPIs) play crucial roles in cellular signaling, transmitting signals from the cell surface to its interior. One of the most important signaling cascades is the RAS-RAF-MEK-ERK pathway. This pathway is initiated by various upstream signaling reactions, including receptor tyrosine kinase (RTK) activation, and it controls many biological functions like cell proliferation, differentiation, and survival. Once RAS is activated, it binds RAF and relays the signal to downstream proteins. The RAS-binding domain (RBD) in RAF protein plays a crucial role in this process, facilitating the RAS-ERK pathway signaling. In this study, we explored the effect of oxidative stress induced by UV radiation on the KRAS-RBD interaction. Using the Split Intein-Mediated Protein Ligation (SIMPL) method, we assessed the impact of different UV doses on KRAS-RBD interactions and observed a disruption of this interaction at higher doses. UV-treated samples exhibited high levels of protein carbonylation, as detected by Oxime Blot and mass spectrometry (MS) analysis, indicating oxidative damage. The MS results provided detailed insights into specific carbonylation modifications on the KRAS protein. Our study demonstrates that protein oxidation and carbonylation can disrupt protein-protein interactions, specifically the KRAS/c-RAF interaction. These findings highlight the impact of oxidative stress on signaling pathways, such as those triggered by UV irradiation. A deeper understanding of these molecular changes may aid in developing therapies targeting diseases linked to oxidative stress, including cancer.

Sensitive and reliable lab-on-paper biosensor for label-free detection of exosomes by electrochemical impedance spectroscopy

A new, sensitive, and cost-effective lab-on-paper-based immunosensor was designed based on electrochemical impedance spectroscopy (EIS) for the detection of exosomes. EIS was selected as the determination method since there was a surface blockage in electron transfer by binding the exosomes to the transducer. Briefly, the carbon working electrode (WE) on the paper electrode (PE) was modified with gold particles (AuPs@PE) and then conjugated with anti-CD9 (Anti-CD9/AuPs@PE) for the detection of exosomes. Variables involved in the biosensor design were optimized with the univariate mode. The developed method presents the limit of detection of  8.7 × 102 exosomes mL-1, which is lower than that of many other available methods under the best conditions. The biosensor was also tested with urine samples from cancer patients with high recoveries. Due to this  a unique, low-cost, biodegradable technology is presented that can directly measure exosomes without labeling them for early cancer or metastasis detection.

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

BACKGROUND

Protein-protein interactions are the primary means through which proteins carry out their functions. These interactions thus have crucial roles in life activities. The wide availability of fully sequenced animal and plant genomes has facilitated establishment of relatively complete global protein interaction networks for some model species. The genomes of cultivated and wild peanut (Arachis hypogaea L.) have also been sequenced, but the functions of most of the encoded proteins remain unclear.

RESULTS

We here used homologous mapping of validated protein interaction data from model species to generate complete peanut protein interaction networks for A. hypogaea cv. 'Tifrunner' (282,619 pairs), A. hypogaea cv. 'Shitouqi' (256,441 pairs), A. monticola (440,470 pairs), A. duranensis (136,363 pairs), and A. ipaensis (172,813 pairs). A detailed analysis was conducted for a putative disease-resistance subnetwork in the Tifrunner network to identify candidate genes and validate functional interactions. The network suggested that DX2UEH and its interacting partners may participate in peanut resistance to bacterial wilt; this was preliminarily validated with overexpression experiments in peanut.

CONCLUSION

Our results provide valuable new information for future analyses of gene and protein functions and regulatory networks in peanut.

In silico approaches supporting drug repurposing for Leishmaniasis: a scoping review

The shortage of treatment options for leishmaniasis, especially those easy to administer and viable for deployment in the world's poorest regions, highlights the importance of employing these strategies to cost-effectively investigate repurposing candidates. This scoping review aims to map the studies using in silico methodologies for drug repurposing against leishmaniasis. This study followed JBI recommendations for scoping reviews. Articles were searched on PubMed, Scopus, and Web of Science databases using keywords related to leishmaniasis and in silico methods for drug discovery, without publication date restrictions. The selection was based on primary studies involving computational methods for antileishmanial drug repurposing. Information about methodologies, obtained data, and outcomes were extracted. After the full-text appraisal, 34 studies were included in this review. Molecular docking was the preferred method for evaluating repurposing candidates (n=25). Studies reported 154 unique ligands and 72 different targets, sterol 14-alpha demethylase and trypanothione reductase being the most frequently reported. In silico screening was able to correctly pinpoint some known active pharmaceutical classes and propose previously untested drugs. Fifteen drugs investigated in silico exhibited low micromolar inhibition (IC50 < 10 µM) of Leishmania spp. in vitro. In conclusion, several in silico repurposing candidates are yet to be investigated in vitro and in vivo. Future research could expand the number of targets screened and employ advanced methods to optimize drug selection, offering new starting points for treatment development. See also the graphical abstract(Fig. 1).

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

Pupylation-Based Proximity-Tagging of FERONIA-Interacting Proteins in Arabidopsis

The plasma membrane-localized receptor kinase FERONIA (FER) plays critical roles in a remarkable variety of biological processes throughout the life cycle of Arabidopsis thaliana. Revealing the molecular connections of FER that underlie these processes starts with identifying the proteins that interact with FER. We applied pupylation-based interaction tagging (PUP-IT) to survey cellular proteins in proximity to FER, encompassing weak and transient interactions that can be difficult to capture for membrane proteins. We reproducibly identified 581, 115, and 736 specific FER-interacting protein candidates in protoplasts, seedlings, and flowers, respectively. We also confirmed 14 previously characterized FER-interacting proteins. Protoplast transient gene expression expedited the testing of new gene constructs for PUP-IT analyses and the validation of candidate proteins. We verified the proximity labeling of five selected candidates that were not previously characterized as FER-interacting proteins. The PUP-IT method could be a valuable tool to survey and validate protein-protein interactions for targets of interest in diverse subcellular compartments in plants.

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.

Photoproximity labeling of endogenous receptors in the live mouse brain in minutes

Understanding how protein-protein interaction networks in the brain give rise to cognitive functions necessitates their characterization in live animals. However, tools available for this purpose require potentially disruptive genetic modifications and lack the temporal resolution necessary to track rapid changes in vivo. Here we leverage affinity-based targeting and photocatalyzed singlet oxygen generation to identify neurotransmitter receptor-proximal proteins in the live mouse brain using only small-molecule reagents and minutes of photoirradiation. Our photooxidation-driven proximity labeling for proteome identification (named PhoxID) method not only recapitulated the known interactomes of three endogenous neurotransmitter receptors (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR), inhibitory γ-aminobutyric acid type A receptor and ionotropic glutamate receptor delta-2) but also uncovered age-dependent shifts, identifying NECTIN3 and IGSF3 as developmentally regulated AMPAR-proximal proteins in the cerebellum. Overall, this work establishes a flexible and generalizable platform to study receptor microenvironments in genetically intact specimens with an unprecedented temporal resolution.

Molecular glues and induced proximity: An evolution of tools and discovery

Small molecule molecular glues can nucleate protein complexes and rewire interactomes. Molecular glues are widely used as probes for understanding functional proximity at a systems level, and the potential to instigate event-driven pharmacology has motivated their application as therapeutics. Despite advantages such as cell permeability and the potential for low off-target activity, glues are still rare when compared to canonical inhibitors in therapeutic development. Their often simple structure and specific ability to reshape protein-protein interactions pose several challenges for widespread, designer applications. Molecular glue discovery and design campaigns can find inspiration from the fields of synthetic biology and biophysics to mine chemical libraries for glue-like molecules.

Stem-cell states converge in multistage cutaneous squamous cell carcinoma development

Stem cells play a critical role in cancer development by contributing to cell heterogeneity, lineage plasticity, and drug resistance. We created gene expression networks from hundreds of mouse tissue samples (both normal and tumor) and integrated these with lineage tracing and single-cell RNA-seq, to identify convergence of cell states in premalignant tumor cells expressing markers of lineage plasticity and drug resistance. Two of these cell states representing multilineage plasticity or proliferation were inversely correlated, suggesting a mutually exclusive relationship. Treatment of carcinomas in vivo with chemotherapy repressed the proliferative state and activated multilineage plasticity whereas inhibition of differentiation repressed plasticity and potentiated responses to cell cycle inhibitors. Manipulation of this cell state transition point may provide a source of potential combinatorial targets for cancer therapy.

A Tiny Viral Protein, SARS-CoV-2-ORF7b: Functional Molecular Mechanisms

This study presents the interaction with the human host metabolism of SARS-CoV-2 ORF7b protein (43 aa), using a protein-protein interaction network analysis. After pruning, we selected from BioGRID the 51 most significant proteins among 2753 proven interactions and 1708 interactors specific to ORF7b. We used these proteins as functional seeds, and we obtained a significant network of 551 nodes via STRING. We performed topological analysis and calculated topological distributions by Cytoscape. By following a hub-and-spoke network architectural model, we were able to identify seven proteins that ranked high as hubs and an additional seven as bottlenecks. Through this interaction model, we identified significant GO-processes (5057 terms in 15 categories) induced in human metabolism by ORF7b. We discovered high statistical significance processes of dysregulated molecular cell mechanisms caused by acting ORF7b. We detected disease-related human proteins and their involvement in metabolic roles, how they relate in a distorted way to signaling and/or functional systems, in particular intra- and inter-cellular signaling systems, and the molecular mechanisms that supervise programmed cell death, with mechanisms similar to that of cancer metastasis diffusion. A cluster analysis showed 10 compact and significant functional clusters, where two of them overlap in a Giant Connected Component core of 206 total nodes. These two clusters contain most of the high-rank nodes. ORF7b acts through these two clusters, inducing most of the metabolic dysregulation. We conducted a co-regulation and transcriptional analysis by hub and bottleneck proteins. This analysis allowed us to define the transcription factors and miRNAs that control the high-ranking proteins and the dysregulated processes within the limits of the poor knowledge that these sectors still impose.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Trx4, a novel thioredoxin protein, is important for Toxoplasma gondii fitness

BACKGROUND

To successfully replicate within the host cell, Toxoplasma gondii employs several mechanisms to overcome the host cell defenses and mitigate the harmful effects of the free radicals resulting from its own metabolic processes using effectors such as thioredoxin proteins. In this study, we characterize the location and functions of a newly identified thioredoxin in T. gondii, which was named Trx4.

METHODS

We characterized the functional role of Trx4 in T. gondii Type I RH and Type II Pru strains by gene knockout and studied its subcellular localization by endogenous protein HA tagging using CRISPR-Cas9 gene editing. The enzyme-catalyzed proximity labeling technique, the TurboID system, was employed to identify the proteins in proximity to Trx4.

RESULTS

Trx4 was identified as a dense granule protein of T. gondii predominantly expressed in the parasitophorous vacuole (PV) and was partially co-localized with GRA1 and GRA5. Functional analysis showed that deletion of trx4 markedly influenced the parasite lytic cycle, resulting in impaired host cell invasion capacity in both RH and Pru strains. Mutation of Trx domains in Trx4 in RH strain revealed that two Trx domains were important for the parasite invasion. By utilizing the TurboID system to biotinylate proteins in proximity to Trx4, we identified a substantial number of proteins, some of which are novel, and others are previously characterized, predominantly distributed in the dense granules. In addition, we uncovered three novel proteins co-localized with Trx4. Intriguingly, deletion of trx4 did not affect the localization of these three proteins. Finally, a virulence assay demonstrated that knockout of trx4 resulted in a significant attenuation of virulence and a significant reduction in brain cyst loads in mice.

CONCLUSIONS

Trx4 plays an important role in T. gondii invasion and virulence in Type I RH strain and Type II Pru strain. Combining the TurboID system with CRISPR-Cas9 technique revealed many PV-localized proximity proteins associated with Trx4. These findings suggest a versatile role of Trx4 in mediating the processes that occur in this distinctive intracellular membrane-bound vacuolar compartment.

Inferring a directed acyclic graph of phenotypes from GWAS summary statistics

Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available.

PhyloString: A web server designed to identify, visualize, and evaluate functional relationships between orthologous protein groups across different phylogenetic lineages

Proteins are biological units whose essence is defined by their functional relationships with other proteins or biomolecules such as RNA, DNA, lipids, or carbohydrates. These functions encompass enzymatic, structural, regulatory, or physical interaction roles. The STRING database (Nucleic Acids Research, 8 Jan 2021;49(D1): D605-12) provides an index that defines the functional interaction networks between proteins in model organisms. To facilitate the identification, visualization, and evaluation of potential functional networks across organisms from different phylogenetic lineages, we have developed PhyloString (https://biocomputo.ibt.unam.mx/phylostring/), a web server that utilizes the indices of the STRING database. PhyloString decomposes these functional networks into modules, representing cohesive units of proteins grouped based on their similarity of STRING values and the phylogenetic origins of their respective organisms. This study presents and thoroughly discusses examples of such functional networks and their modules identified using PhyloString.

Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation

Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of drug mechanisms of action and the dissection of protein interaction dynamics in different cellular states. One of the barriers to the wide applications of MS-CETSA is that MS-CETSA experiments must be performed on the specific cell lines of interest, which is typically time-consuming and costly in terms of labeling reagents and mass spectrometry time. In this study, we aim to predict CETSA features in various cell lines by introducing a computational framework called CycleDNN based on deep neural network technology. For a given set of n cell lines, CycleDNN comprises n auto-encoders. Each auto-encoder includes an encoder to convert CETSA features from one cell line into latent features in a latent space [Formula: see text]. It also features a decoder that transforms the latent features back into CETSA features for another cell line. In such a way, the proposed CycleDNN creates a cyclic prediction of CETSA features across different cell lines. The prediction loss, cycle-consistency loss, and latent space regularization loss are used to guide the model training. Experimental results on a public CETSA dataset demonstrate the effectiveness of our proposed approach. Furthermore, we confirm the validity of the predicted MS-CETSA data from our proposed CycleDNN through validation in protein-protein interaction prediction.

Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry

We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.

Inferring a directed acyclic graph of phenotypes from GWAS summary statistics

Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available at https://github.com/chunlinli/sumdag.

Improved in situ characterization of protein complex dynamics at scale with thermal proximity co-aggregation

Cellular activities are carried out vastly by protein complexes but large repertoire of protein complexes remains functionally uncharacterized which necessitate new strategies to delineate their roles in various cellular processes and diseases. Thermal proximity co-aggregation (TPCA) is readily deployable to characterize protein complex dynamics in situ and at scale. We develop a version termed Slim-TPCA that uses fewer temperatures increasing throughputs by over 3X, with new scoring metrics and statistical evaluation that result in minimal compromise in coverage and detect more relevant complexes. Less samples are needed, batch effects are minimized while statistical evaluation cost is reduced by two orders of magnitude. We applied Slim-TPCA to profile K562 cells under different duration of glucose deprivation. More protein complexes are found dissociated, in accordance with the expected downregulation of most cellular activities, that include 55S ribosome and respiratory complexes in mitochondria revealing the utility of TPCA to study protein complexes in organelles. Protein complexes in protein transport and degradation are found increasingly assembled unveiling their involvement in metabolic reprogramming during glucose deprivation. In summary, Slim-TPCA is an efficient strategy for characterization of protein complexes at scale across cellular conditions, and is available as Python package at https://pypi.org/project/Slim-TPCA/ .

Mapping the protein-protein interactome in the tumor immune microenvironment

The cell-to-cell communication primarily occurs through cell-surface and secreted proteins, which form a sophisticated network that coordinates systemic immune function. Uncovering these protein-protein interactions (PPIs) is indispensable for understanding the molecular mechanism and elucidating immune system aberrances under diseases. Traditional biological studies typically focus on a limited number of PPI pairs due to the relative low throughput of commonly used techniques. Encouragingly, classical methods have advanced, and many new systems tailored for large-scale protein-protein screening have been developed and successfully utilized. These high-throughput PPI investigation techniques have already made considerable achievements in mapping the immune cell interactome, enriching PPI databases and analysis tools, and discovering therapeutic targets for cancer and other diseases, which will definitely bring unprecedented insight into this field.

An amino acid-resolution interactome for motile cilia illuminates the structure and function of ciliopathy protein complexes

Motile cilia are ancient, evolutionarily conserved organelles whose dysfunction underlies motile ciliopathies, a broad class of human diseases. Motile cilia contain myriad different proteins that assemble into an array of distinct machines, so understanding the interactions and functional hierarchies among them presents an important challenge. Here, we defined the protein interactome of motile axonemes using cross-linking mass spectrometry (XL/MS) in Tetrahymena thermophila. From over 19,000 XLs, we identified 4,757 unique amino acid interactions among 1,143 distinct proteins, providing both macromolecular and atomic-scale insights into diverse ciliary machines, including the Intraflagellar Transport system, axonemal dynein arms, radial spokes, the 96 nm ruler, and microtubule inner proteins, among others. Guided by this dataset, we used vertebrate multiciliated cells to reveal novel functional interactions among several poorly-defined human ciliopathy proteins. The dataset therefore provides a powerful resource for studying the basic biology of an ancient organelle and the molecular etiology of human genetic disease.

Molecular complex detection in protein interaction networks through reinforcement learning

BACKGROUND

Proteins often assemble into higher-order complexes to perform their biological functions. Such protein-protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein-protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks.

RESULTS

The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling.

CONCLUSIONS

Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.

A comprehensive protein interaction map and druggability investigation prioritized dengue virus NS1 protein as promising therapeutic candidate

Dengue Virus (DENV) is a serious threat to human life worldwide and is one of the most dangerous vector-borne diseases, causing thousands of deaths annually. We constructed a comprehensive PPI map of DENV with its host Homo sapiens and performed various bioinformatics analyses. We found 1195 interactions between 858 human and 10 DENV proteins. Pathway enrichment analysis was performed on the two sets of gene products, and the top 5 human proteins with the maximum number of interactions with dengue viral proteins revealed noticeable results. The non-structural protein NS1 in DENV had the maximum number of interactions with the host protein, followed by NS5 and NS3. Among the human proteins, HBA1 and UBE2I were associated with 7 viral proteins, and 3 human proteins (CSNK2A1, RRP12, and HSP90AB1) were found to interact with 6 viral proteins. Pharmacophore-based virtual screening of millions of compounds in the public databases was performed to identify potential DENV-NS1 inhibitors. The lead compounds were selected based on RMSD values, docking scores, and strong binding affinities. The top ten hit compounds were subjected to ADME profiling which identified compounds C2 (MolPort-044-180-163) and C6 (MolPort-001-742-737) as lead inhibitors against DENV-NS1. Molecular dynamics trajectory analysis and intermolecular interactions between NS1 and the ligands displayed the molecular stability of the complexes in the cellular environment. The in-silico approaches used in this study could pave the way for the development of potential specie-specific drugs and help in eliminating deadly viral infections. Therefore, experimental and clinical assays are required to validate the results of this study.

In vivo cross-linking-based affinity purification and mass spectrometry for targeting intracellular protein-protein interactions

Comprehensive interactome analysis of targeted proteins is important to understand how proteins work together in regulating functions. Commonly, affinity purification followed by mass spectrometry (AP-MS) has been recognized as the most often used technique for studying protein-protein interactions (PPIs). However, some proteins with weak interactions, which are responsible for key roles in regulation, are easily broken during cell lysis and purification through an AP approach. Herein, we have developed an approach termed in vivo cross-linking-based affinity purification and mass spectrometry (ICAP-MS). By this method, in vivo cross-linking was introduced to covalently fix intracellular PPIs in their functional states to assure all PPIs could be integrally maintained during cell disruption. In addition, the chemically cleavable crosslinkers which were employed enabled unbinding of PPIs for in-depth identification of components within the interactome and biological analysis, while allowing binding of PPIs for cross-linking-mass spectrometry (CXMS)-based direct interaction determination. Multi-level information on targeted PPIs network can be obtained by ICAP-MS, including composition of interacting proteins, as well as direct interacting partners and binding sites. As a proof of concept, the interactome of MAPK3 from 293A cells was profiled with 6.15-fold improvement in identification than by conventional AP-MS. Meanwhile, 184 cross-link site pairs of these PPIs were experimentally identified by CXMS. Furthermore, ICAP-MS was applied in the temporal profiling of MAPK3 interactions under activation by cAMP-mediated pathway. The regulatory manner of MAPK pathways was presented through the quantitative changes of MAPK3 and its interacting proteins at different time points after activation. Therefore, all reported results demonstrated that the ICAP-MS approach may provide comprehensive information on interactome of targeted protein for functional exploration.

Nephroprotective mechanisms of Rhizoma Chuanxiong and Radix et Rhizoma Rhei against acute renal injury and renal fibrosis based on network pharmacology and experimental validation

The molecular mechanisms of Rhizoma Chuanxiong (Chuanxiong, CX) and Rhei Radix et Rhizoma (Dahuang, DH) in treating acute kidney injury (AKI) and subsequent renal fibrosis (RF) were investigated in this study by applying network pharmacology and experimental validation. The results showed that aloe-emodin, (-)-catechin, beta-sitosterol, and folic acid were the core active ingredients, and TP53, AKT1, CSF1R, and TGFBR1 were the core target genes. Enrichment analyses showed that the key signaling pathways were the MAPK and IL-17 signaling pathways. In vivo experiments confirmed that Chuanxiong and Dahuang pretreatments significantly inhibited the levels of SCr, BUN, UNAG, and UGGT in contrast media-induced acute kidney injury (CIAKI) rats (p < 0.001). The results of Western blotting showed that compared with the control group, the protein levels of p-p38/p38 MAPK, p53, and Bax in the contrast media-induced acute kidney injury group were significantly increased, and the levels of Bcl-2 were significantly reduced (p < 0.001). Chuanxiong and Dahuang interventions significantly reversed the expression levels of these proteins (p < 0.01). The localization and quantification of p-p53 expression in immunohistochemistry technology also support the aforementioned results. In conclusion, our data also suggest that Chuanxiong and Dahuang may inhibit tubular epithelial cell apoptosis and improve acute kidney injury and renal fibrosis by inhibiting p38 MAPK/p53 signaling.

Analysis of context-specific KRAS-effector (sub)complexes in Caco-2 cells

Ras is a key switch controlling cell behavior. In the GTP-bound form, Ras interacts with numerous effectors in a mutually exclusive manner, where individual Ras-effectors are likely part of larger cellular (sub)complexes. The molecular details of these (sub)complexes and their alteration in specific contexts are not understood. Focusing on KRAS, we performed affinity purification (AP)-mass spectrometry (MS) experiments of exogenously expressed FLAG-KRAS WT and three oncogenic mutants ("genetic contexts") in the human Caco-2 cell line, each exposed to 11 different culture media ("culture contexts") that mimic conditions relevant in the colon and colorectal cancer. We identified four effectors present in complex with KRAS in all genetic and growth contexts ("context-general effectors"). Seven effectors are found in KRAS complexes in only some contexts ("context-specific effectors"). Analyzing all interactors in complex with KRAS per condition, we find that the culture contexts had a larger impact on interaction rewiring than genetic contexts. We investigated how changes in the interactome impact functional outcomes and created a Shiny app for interactive visualization. We validated some of the functional differences in metabolism and proliferation. Finally, we used networks to evaluate how KRAS-effectors are involved in the modulation of functions by random walk analyses of effector-mediated (sub)complexes. Altogether, our work shows the impact of environmental contexts on network rewiring, which provides insights into tissue-specific signaling mechanisms. This may also explain why KRAS oncogenic mutants may be causing cancer only in specific tissues despite KRAS being expressed in most cells and tissues.

Turning chiral peptides into a racemic supraparticle to induce the self-degradation of MDM2

INTRODUCTION

Chirality is immanent in nature, and chiral molecules can achieve their pharmacological action through chiral matching with biomolecules and molecular conformation recognition.

OBJECTIVES

Clinical translation of chiral therapeutics, particularly chiral peptide molecules, has been hampered by their unsatisfactory pharmaceutical properties.

METHODS

A mild and simple self-assembly strategy was developed here for the construction of peptide-derived chiral supramolecular nanomedicine with suitable pharmaceutical properties. In this proof-of-concept study, we design a D-peptide as MDM2 Self-Degradation catalysts (MSDc) to induce the self-degradation of a carcinogenic E3 Ubiquitin ligase termed MDM2. Exploiting a metal coordination between mercaptan in peptides and trivalent gold ion, chiral MSDc was self-assembled into a racemic supraparticle (MSDNc) that eliminated the consume from the T-lymphocyte/macrophage phagocytose in circulation.

RESULTS

Expectedly, MSDNc down-regulated MDM2 in more action than its L-enantiomer termed ^CtrlMSDNc. More importantly, MSDNc preponderantly suppressed the tumor progression and synergized the tumor immunotherapy in allograft model of melanoma through p53 restoration in comparison to ^CtrlMSDNc.

CONCLUSION

Collectively, this work not only developed a secure and efficient therapeutic agent targeting MDM2 with the potential of clinical translation, but also provided a feasible and biocompatible strategy for the construction of peptide supraparticle and expanded the application of chiral therapeutic and homo-PROTAC to peptide-derived chiral supramolecular nanomedicine.

SENSDeep: An Ensemble Deep Learning Method for Protein-Protein Interaction Sites Prediction

PURPOSE

The determination of which amino acid in a protein interacts with other proteins is important in understanding the functional mechanism of that protein. Although there are experimental methods to detect protein-protein interaction sites (PPISs), these are costly, time-consuming, and require expertise. Therefore, many computational methods have been proposed to accelerate this type of research, but they are generally insufficient to predict PPISs accurately. There is a need for development in this field.

METHODS

In this study, we introduce a new PPISs prediction method. This method is a sequence-based Stacking ENSemble Deep (SENSDeep) learning method that has an ensemble learning model including the models of RNN, CNN, GRU sequence to sequence (GRUs2s), GRU sequence to sequence with an attention layer (GRUs2satt) and a multilayer perceptron. Two embedded features, secondary structure, and protein sequence information are added to the training data set in addition to twelve existing features to improve the prediction performance of the method.

RESULTS

SENSDeep trained on the training data set without two extra features obtains a better performance on some of the independent testing data sets than that of the other methods in the literature, especially on scoring metrics of sensitivity, F1, MCC, and AUPRC, having increments up to 63.5%, 19.3%, 18.5%, 11.4%, respectively. It is shown that the added extra features improve the performance of the method by having almost the same performance with less data as the method trained on the data set without these added features. On the other hand, different sizes of the sliding window are tried on the data sets and an optimal sliding window size for SENSDeep is found. Moreover, SENSDeep has also been compared to structure-based methods. Some of these methods have been found to perform better. Using SENSDeep obtained by training with both training data sets, PPISs prediction examples of various proteins that are not in these training data sets are also presented. Furthermore, execution times for SENSDeep and its submodels are shown.

AVAILABILITY AND IMPLEMENTATION

https://github.com/enginaybey/SENSDeep.

Transient receptor potential (TRP) channels in the Manila clam (Ruditapes philippinarum): Characterization and expression patterns of the TRP gene family under heat stress in Manila clams based on genome-wide identification

In this study, we identified a total of 40 transient receptor potential genes (RpTRP) in Manila clam by genome-wide identification and classified them into four categories (TRPV, TRPA, TRPM, TRPC) based on gene structure and subfamily relationships. The protein length of RpTRP genes ranges from 281 amino acids to 1601 amino acids. Molecular weight and theoretical PI values range from 182.82 kDa to 32.43 kDa, respectively, with PI values between 5.17 and 9.25. By comparing the expression profiles of TRP genes during heat stress in Manila clams at different latitudes, we found that most genes in the TRP gene family were up-regulated in expression during heat challenge. Therefore, we determined that TRP genes have an important role in the heat stress of Manila clams. This work provides a basis for further studies on the molecular mechanisms of TRP-mediated heat tolerance in Manila clam and for explaining differences in heat tolerance in Manila clam at different latitudes through key differential TRP genes at the molecular level.

Characterization of huntingtin interactomes and their dynamic responses in living cells by proximity proteomics

Huntingtin (Htt) is a large protein without clearly defined molecular functions. Mutation in this protein causes Huntington's disease (HD), a fatal inherited neurodegenerative disorder. Identification of Htt-interacting proteins by the traditional approaches including yeast two-hybrid systems and affinity purifications has greatly facilitated the understanding of Htt function. However, these methods eliminated the intracellular spatial information of the Htt interactome during sample preparations. Moreover, the temporal changes of the Htt interactome in response to acute cellular stresses cannot be easily resolved with these approaches. Ascorbate peroxidase (APEX2)-based proximity labeling has been used to spatiotemporally investigate protein-protein interactions in living cells. In this study, we generated stable human SH-SY5Y cell lines expressing full-length Htt23Q and Htt145Q with N-terminus tagged Flag-APEX2 to quantitatively map the spatiotemporal changes of Htt interactome to a mild acute proteotoxic stress. Our data revealed that normal and mutant Htt (muHtt) are associated with distinct intracellular microenvironments. Specifically, mutant Htt is preferentially associated with intermediate filaments and myosin complexes. Furthermore, the dynamic changes of Htt interactomes in response to stress are different between normal and mutant Htt. Vimentin is identified as one of the most significant proteins that preferentially interacts with muHtt in situ. Further functional studies demonstrated that mutant Htt affects the vimentin's function of regulating proteostasis in healthy and HD human neural stem cells. Taken together, our data offer important insights into the molecular functions of normal and mutant Htt by providing a list of Htt-interacting proteins in their natural cellular context for further studies in different HD models.

Multi-Dimensional Structure and Dynamics Landscape of Proteins in Mammalian Cells Revealed by In-Cell NMR

Governing function, half-life and subcellular localization, the 3D structure and dynamics of proteins are in nature constantly changing in a tightly regulated manner to fulfill the physiological and adaptive requirements of the cells. To find evidence for this hypothesis, we applied in-cell NMR to three folded model proteins and propose that the splitting of cross peaks constitutes an atomic fingerprint of distinct structural states that arise from multiple target binding co-existing inside mammalian cells. These structural states change upon protein loss of function or subcellular localisation into distinct cell compartments. In addition to peak splitting, we observed NMR signal intensity attenuations indicative of transient interactions with other molecules and dynamics on the microsecond to millisecond time scale.

Studying Cellular Dynamics Using Proximity-Dependent Biotinylation: Somatic Cell Reprogramming

Assessing the reorganization of proteins and organelles following the induction of reprogramming and differentiation programs is crucial to understand the mechanistic underpinning of morphological and fate changes associated with these processes. The advent of proximity-dependent biotinylation (PDB) methods has overcome some of the limitations of biochemical purification methods, enabling proteomic characterization of most subcellular compartments. The first-generation PDB enzyme, the biotin ligase BirA* used in BioID, has now been used in multiple studies determining the cellular context in which proteins reside, typically under standard growth conditions and using long labeling (usually 8-24 h) times. Capitalizing on the generation of more active PDB enzymes such as miniTurbo that can generate strong biotinylation signals in minutes rather than hours, as well as the development of an inducible lentiviral toolkit for BioID, we define here protocols for time-resolved PDB in primary cells. Here, we report the optimization and application of lentivirally delivered miniTurbo constructs to a mouse fibroblast model of somatic cell reprogramming, allowing the study of this dynamic process. This detailed protocol also provides a baseline reference for researchers who wish to adapt these techniques to other dynamic cellular processes.

Centrality Analysis of Protein-Protein Interaction Networks Using R

Proteins are structural and functional components of cells. They interact with each other to drive specific cellular functions. The physical and functional protein interactions are an important feature of cellular organization and regulation. Protein interactions are represented as a network or a graph in which proteins are nodes, and interactions between them are edges. Perturbations in the network affecting essential or central proteins can have pathological consequences. Network or graph theory is a branch of mathematics that provides a conceptual framework to decipher topologically important proteins in the network. These concepts are known as centrality measures. This chapter introduces various centrality metrics and provides a stepwise protocol to quantify protein's strategic positions in the network using an R programming language.

Search, Retrieve, Visualize, and Analyze Protein-Protein Interactions from Multiple Databases: A Guide for Experimental Biologists

Functional annotation is lacking for over half of the proteins encoded in genomes and model or representative organisms are not an exception to this trend. One of the popular ways of assigning putative functions to uncharacterized proteins is based on the functions of well-characterized proteins that physically interact with them, i.e., guilt-by-association or functional context approach. In the last two decades, several powerful experimental and computational techniques have been used to determine protein-protein interactions (PPIs) at genome level and are made available through many public databases. The PPI data are often complex and heterogeneously represented across databases posing unique challenges in retrieving, integrating, and analyzing the data even for trained computational biologists, the end users-experimental biologists often struggle to work around the data for the protein of their interests. This chapter provides stepwise protocols to import interaction network of the protein of interest in Cytoscape using PSICQUIC, stringApp, and IntAct App. These are next-generation applications that import PPI from multiple databases/resources and provide seamless functions to study the protein of interest and its functional context directly in Cytoscape.

Where protein structure and cell diversity meet

Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.

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.

APPINetwork: an R package for building and computational analysis of protein-protein interaction networks

Background

Protein-protein interactions (PPIs) are essential to almost every process in a cell. Analysis of PPI networks gives insights into the functional relationships among proteins and may reveal important hub proteins and sub-networks corresponding to functional modules. Several good tools have been developed for PPI network analysis but they have certain limitations. Most tools are suited for studying PPI in only a small number of model species, and do not allow second-order networks to be built, or offer relevant functions for their analysis. To overcome these limitations, we have developed APPINetwork (Analysis of Protein-protein Interaction Networks). The aim was to produce a generic and user-friendly package for building and analyzing a PPI network involving proteins of interest from any species as long they are stored in a database.

Methods

APPINetwork is an open-source R package. It can be downloaded and installed on the collaborative development platform GitLab (https://forgemia.inra.fr/GNet/appinetwork). A graphical user interface facilitates its use. Graphical windows, buttons, and scroll bars allow the user to select or enter an organism name, choose data files and network parameters or methods dedicated to network analysis. All functions are implemented in R, except for the script identifying all proteins involved in the same biological process (developed in C) and the scripts formatting the BioGRID data file and generating the IDs correspondence file (implemented in Python 3). PPI information comes from private resources or different public databases (such as IntAct, BioGRID, and iRefIndex). The package can be deployed on Linux and macOS operating systems (OS). Deployment on Windows is possible but it requires the prior installation of Rtools and Python 3.

Results

APPINetwork allows the user to build a PPI network from selected public databases and add their own PPI data. In this network, the proteins have unique identifiers resulting from the standardization of the different identifiers specific to each database. In addition to the construction of the first-order network, APPINetwork offers the possibility of building a second-order network centered on the proteins of interest (proteins known for their role in the biological process studied or subunits of a complex protein) and provides the number and type of experiments that have highlighted each PPI, as well as references to articles containing experimental evidence.

Conclusion

More than a tool for PPI network building, APPINetwork enables the analysis of the resultant network, by searching either for the community of proteins involved in the same biological process or for the assembly intermediates of a protein complex. Results of these analyses are provided in easily exportable files. Examples files and a user manual describing each step of the process come with the package.

In silico functional annotation of hypothetical proteins from the Bacillus paralicheniformis strain Bac84 reveals proteins with biotechnological potentials and adaptational functions to extreme environments

Members of the Bacillus genus are industrial cell factories due to their capacity to secrete significant quantities of biomolecules with industrial applications. The Bacillus paralicheniformis strain Bac84 was isolated from the Red Sea and it shares a close evolutionary relationship with Bacillus licheniformis. However, a significant number of proteins in its genome are annotated as functionally uncharacterized hypothetical proteins. Investigating these proteins' functions may help us better understand how bacteria survive extreme environmental conditions and to find novel targets for biotechnological applications. Therefore, the purpose of our research was to functionally annotate the hypothetical proteins from the genome of B. paralicheniformis strain Bac84. We employed a structured in-silico approach incorporating numerous bioinformatics tools and databases for functional annotation, physicochemical characterization, subcellular localization, protein-protein interactions, and three-dimensional structure determination. Sequences of 414 hypothetical proteins were evaluated and we were able to successfully attribute a function to 37 hypothetical proteins. Moreover, we performed receiver operating characteristic analysis to assess the performance of various tools used in this present study. We identified 12 proteins having significant adaptational roles to unfavorable environments such as sporulation, formation of biofilm, motility, regulation of transcription, etc. Additionally, 8 proteins were predicted with biotechnological potentials such as coenzyme A biosynthesis, phenylalanine biosynthesis, rare-sugars biosynthesis, antibiotic biosynthesis, bioremediation, and others. Evaluation of the performance of the tools showed an accuracy of 98% which represented the rationality of the tools used. This work shows that this annotation strategy will make the functional characterization of unknown proteins easier and can find the target for further investigation. The knowledge of these hypothetical proteins' potential functions aids B. paralicheniformis strain Bac84 in effectively creating a new biotechnological target. In addition, the results may also facilitate a better understanding of the survival mechanisms in harsh environmental conditions.

Multi-omics disease module detection with an explainable Greedy Decision Forest

Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific disciplines, such as systems biology, domain knowledge is available in the form of graphs or networks, and its use can improve model performance. We need network-based algorithms that are versatile and applicable in many research areas. In this work, we demonstrate subnetwork detection based on multi-modal node features using a novel Greedy Decision Forest (GDF) with inherent interpretability. The latter will be a crucial factor to retain experts and gain their trust in such algorithms. To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well. Systems biology is a good example of a field in which statistical data-driven machine learning enables the analysis of large amounts of multi-modal biomedical data. This is important to reach the future goal of precision medicine, where the complexity of patients is modeled on a system level to best tailor medical decisions, health practices and therapies to the individual patient. Our proposed explainable approach can help to uncover disease-causing network modules from multi-omics data to better understand complex diseases such as cancer.

Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Identification of Inflammatory Proteomics Networks of Toll-like Receptor 4 through Immunoprecipitation-Based Chemical Cross-Linking Proteomics

Toll-like receptor 4 (TLR4) is a receptor on an immune cell that can recognize the invasion of bacteria through their attachment with bacterial lipopolysaccharides (LPS). Hence, LPS is a pro-immune response stimulus. On the other hand, statins are lipid-lowering drugs and can also lower immune cell responses. We used human embryonic kidney (HEK 293) cells engineered to express HA-tagged TLR-4 upon treatment with LPS, statin, and both statin and LPS to understand the effect of pro- and anti-inflammatory responses. We performed a monoclonal antibody (mAb) directed co-immunoprecipitation (CO-IP) of HA-tagged TLR4 and its interacting proteins in the HEK 293 extracted proteins. We utilized an ETD cleavable chemical cross-linker to capture weak and transient interactions with TLR4 protein. We tryptic digested immunoprecipitated and cross-linked proteins on beads, followed by liquid chromatography–mass spectrometry (LC-MS/MS) analysis of the peptides. Thus, we utilized the label-free quantitation technique to measure the relative expression of proteins between treated and untreated samples. We identified 712 proteins across treated and untreated samples and performed protein network analysis using Ingenuity Pathway Analysis (IPA) software to reveal their protein networks. After filtering and evaluating protein expression, we identified macrophage myristoylated alanine-rich C kinase substrate (MARCKSL1) and creatine kinase proteins as a potential part of the inflammatory networks of TLR4. The results assumed that MARCKSL1 and creatine kinase proteins might be associated with a statin-induced anti-inflammatory response due to possible interaction with the TLR4.

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life's Mechanism

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5-10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

RAPPPID: towards generalizable protein interaction prediction with AWD-LSTM twin networks

MOTIVATION

Computational methods for the prediction of protein-protein interactions (PPIs), while important tools for researchers, are plagued by challenges in generalizing to unseen proteins. Datasets used for modelling protein-protein predictions are particularly predisposed to information leakage and sampling biases.

RESULTS

In this study, we introduce RAPPPID, a method for the Regularized Automatic Prediction of Protein-Protein Interactions using Deep Learning. RAPPPID is a twin Averaged Weight-Dropped Long Short-Term memory network which employs multiple regularization methods during training time to learn generalized weights. Testing on stringent interaction datasets composed of proteins not seen during training, RAPPPID outperforms state-of-the-art methods. Further experiments show that RAPPPID's performance holds regardless of the particular proteins in the testing set and its performance is higher for experimentally supported edges. This study serves to demonstrate that appropriate regularization is an important component of overcoming the challenges of creating models for PPI prediction that generalize to unseen proteins. Additionally, as part of this study, we provide datasets corresponding to several data splits of various strictness, in order to facilitate assessment of PPI reconstruction methods by others in the future.

AVAILABILITY AND IMPLEMENTATION

Code and datasets are freely available at https://github.com/jszym/rapppid and Zenodo.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Hotspot Identification and Drug Design of Protein-Protein Interaction Modulators Using the Fragment Molecular Orbital Method

Protein-protein interactions (PPIs) are essential for the function of many proteins. Aberrant PPIs have the potential to lead to disease, making PPIs promising targets for drug discovery. There are over 64,000 PPIs in the human interactome reference database; however, to date, very few PPI modulators have been approved for clinical use. Further development of PPI-specific therapeutics is highly dependent on the availability of structural data and the existence of reliable computational tools to explore the interface between two interacting proteins. The fragment molecular orbital (FMO) quantum mechanics method offers comprehensive and computationally inexpensive means of identifying the strength (in kcal/mol) and the chemical nature (electrostatic or hydrophobic) of the molecular interactions taking place at the protein-protein interface. We have integrated FMO and PPI exploration (FMO-PPI) to identify the residues that are critical for protein-protein binding (hotspots). To validate this approach, we have applied FMO-PPI to a dataset of protein-protein complexes representing several different protein subfamilies and obtained FMO-PPI results that are in agreement with published mutagenesis data. We observed that critical PPIs can be divided into three major categories: interactions between residues of two proteins (intermolecular), interactions between residues within the same protein (intramolecular), and interactions between residues of two proteins that are mediated by water molecules (water bridges). We extended our findings by demonstrating how this information obtained by FMO-PPI can be utilized to support the structure-based drug design of PPI modulators (SBDD-PPI).

A physical wiring diagram for the human immune system

The human immune system is composed of a distributed network of cells circulating throughout the body, which must dynamically form physical associations and communicate using interactions between their cell-surface proteomes1. Despite their therapeutic potential2, our map of these surface interactions remains incomplete3,4. Here, using a high-throughput surface receptor screening method, we systematically mapped the direct protein interactions across a recombinant library that encompasses most of the surface proteins that are detectable on human leukocytes. We independently validated and determined the biophysical parameters of each novel interaction, resulting in a high-confidence and quantitative view of the receptor wiring that connects human immune cells. By integrating our interactome with expression data, we identified trends in the dynamics of immune interactions and constructed a reductionist mathematical model that predicts cellular connectivity from basic principles. We also developed an interactive multi-tissue single-cell atlas that infers immune interactions throughout the body, revealing potential functional contexts for new interactions and hubs in multicellular networks. Finally, we combined targeted protein stimulation of human leukocytes with multiplex high-content microscopy to link our receptor interactions to functional roles, in terms of both modulating immune responses and maintaining normal patterns of intercellular associations. Together, our work provides a systematic perspective on the intercellular wiring of the human immune system that extends from systems-level principles of immune cell connectivity down to mechanistic characterization of individual receptors, which could offer opportunities for therapeutic intervention.

Homogeneous surrogate virus neutralization assay to rapidly assess neutralization activity of anti-SARS-CoV-2 antibodies

The COVID-19 pandemic triggered the development of numerous diagnostic tools to monitor infection and to determine immune response. Although assays to measure binding antibodies against SARS-CoV-2 are widely available, more specific tests measuring neutralization activities of antibodies are immediately needed to quantify the extent and duration of protection that results from infection or vaccination. We previously developed a 'Serological Assay based on a Tri-part split-NanoLuc® (SATiN)' to detect antibodies that bind to the spike (S) protein of SARS-CoV-2. Here, we expand on our previous work and describe a reconfigured version of the SATiN assay, called Neutralization SATiN (Neu-SATiN), which measures neutralization activity of antibodies directly from convalescent or vaccinated sera. The results obtained with our assay and other neutralization assays are comparable but with significantly shorter preparation and run time for Neu-SATiN. As the assay is modular, we further demonstrate that Neu-SATiN enables rapid assessment of the effectiveness of vaccines and level of protection against existing SARS-CoV-2 variants of concern and can therefore be readily adapted for emerging variants.

Unmasking allosteric binding sites: Novel targets for GPCR drug discovery

INTRODUCTION

Unexpected non-apparent and hidden allosteric binding sites are non-classical and non-apparent allosteric centers in 3-D X-ray protein structures until orthosteric or allosteric ligands bind to them. The orthosteric center of one protomer that modulates binding centers of the other protomers within an oligomer is also an unexpected allosteric site. Furthermore, another partner protein can also produce these effects, acting as an unexpected allosteric modulator.

AREAS COVERED

This review summarizes both classical and non-classical allosterism. The authors focus on G protein-coupled receptor (GPCR) oligomers as a paradigm of allosteric molecules. Moreover, they show several examples of unexpected allosteric sites such as hidden allosteric sites in a protomer that appear after the interaction with other molecules and the allosterism exerted between orthosteric sites within GPCR oligomer, emphasizing on the allosteric modulations that can occur between binding sites.

EXPERT OPINION

The study of these new non-classical allosteric sites will expand the diversity of allosteric control on the function of orthosteric sites within proteins, whether GPCRs or other receptors, enzymes or transporters. Moreover, the design of new drugs targeting these hidden allosteric sites or already known orthosteric sites acting as allosteric sites in protein homo- or hetero-oligomers will increase the therapeutic potential of allosterism.