Essential proteins identification method based on four-order distances and subcellular localization information

Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.

Small molecules targeting protein-protein interactions for cancer therapy

Protein-protein interactions(PPIs)are fundamental to many biological processes that play an important role in the occurrence and development of a variety of diseases.Targeting the interaction between tumour-related proteins with emerging small molecule drugs has become an attractive approach for treatment of human diseases,especially tumours.Encouragingly,selective PPI-based therapeutic agents have been rapidly advancing over the past decade,providing promising perspectives for novel therapies for patients with cancer.In this review we comprehensively clarify the discovery and development of small molecule modulators of PPIs from multiple aspects,focusing on PPIs in disease,drug design and discovery strategies,structure-activity relationships,inherent dilemmas,and future directions.

Drug discovery by targeting the protein–protein interactions involved in autophagy

Autophagy is a cellular process in which proteins and organelles are engulfed in autophagosomal vesicles and transported to the lysosome/vacuole for degradation.Protein–protein interactions(PPIs)play a crucial role at many stages of autophagy,which present formidable but attainable targets for autophagy regulation.Moreover,selective regulation of PPIs tends to have a lower risk in causing undesired off-target effects in the context of a complicated biological network.Thus,small-molecule regulators,including peptides and peptidomimetics,targeting the critical PPIs involved in autophagy provide a new opportunity for innovative drug discovery.This article provides general background knowledge of the critical PPIs involved in autophagy and reviews a range of successful attempts on discovering regulators targeting those PPIs.Successful strategies and existing limitations in this field are also discussed.

Identification of anti-Mycobacterium tuberculosis agents targeting the interaction of bacterial division proteins FtsZ and SepFe

Tuberculosis(TB)is one of the deadly diseases caused by Mycobacterium tuberculosis(Mtb),which presents a significant public health challenge.Treatment of TB relies on the combination of several anti-TB drugs to create shorter and safer regimens.Therefore,new anti-TB agents working by different mechanisms are urgently needed.FtsZ,a tubulin-like protein with GTPase activity,forms a dynamic Z-ring in cell division.Most of FtsZ inhibitors are designed to inhibit GTPase activity.In Mtb,the function of Z-ring is modulated by SepF,a FtsZ binding protein.The FtsZ/SepF interaction is essential for FtsZ bundling and localization at the site of division.Here,we established a yeast twohybrid based screening system to identify inhibitors of FtsZ/SepF interaction in M.tuberculosis.Using this system,we found compound T0349 showing strong anti-Mtb activity but with low toxicity to other bacteria strains and mice.Moreover,we have demonstrated that T0349 binds specifically to SepF to block FtsZ/SepF interaction by GST pull-down,fluorescence polarization(FP),surface plasmon resonance(SPR)and CRISPRi knockdown assays.Furthermore,T0349 can inhibit bacterial cell division by inducing filamentation and abnormal septum.Our data demonstrated that FtsZ/SepF interaction is a promising anti-TB drug target for identifying agents with novel mechanisms.

Discovery of direct inhibitors of Keap1–Nrf2 protein–protein interaction as potential therapeutic and preventive agents

The Keap1–Nrf2–ARE pathway is an important antioxidant defense mechanism that protects cells from oxidative stress and the Keap1–Nrf2 protein–protein interaction(PPI) has become an important drug target to upregulate the expression of ARE-controlled cytoprotective oxidative stress response enzymes in the development of therapeutic and preventive agents for a number of diseases and conditions. However, most known Nrf2 activators/ARE inducers are indirect inhibitors of Keap1–Nrf2PPI and they are electrophilic species that act by modifying the sulfhydryl groups of Keap1's cysteine residues. The electrophilicity of these indirect inhibitors may cause "off-target" side effects by reacting with cysteine residues of other important cellular proteins. Efforts have recently been focused on the development of direct inhibitors of Keap1–Nrf2 PPI. This article reviews these recent research efforts including the development of high throughput screening assays, the discovery of peptide and small molecule direct inhibitors, and the biophysical characterization of the binding of these inhibitors to the target Keap1 Kelch domain protein. These non-covalent direct inhibitors of Keap1–Nrf2 PPI could potentially be developed into effective therapeutic or preventive agents for a variety of diseases and conditions.

Newfound Coding Potential of Transcripts Unveils Missing Members of Human Protein Communities

Recent proteogenomic approaches have led to the discovery that regions of the transcriptome previously annotated as non-coding regions[i.e.,untranslated regions(UTRs),open reading frames overlapping annotated coding sequences in a different reading frame,and non-coding RNAs]frequently encode proteins,termed alternative proteins(altProts).This suggests that previously identified protein–protein interaction(PPI)networks are partially incomplete because altProts are not present in conventional protein databases.Here,we used the proteogenomic resource OpenProt and a combined spectrum-and peptide-centric analysis for the re-analysis of a highthroughput human network proteomics dataset,thereby revealing the presence of 261 altProts in the network.We found 19 genes encoding both an annotated(reference)and an alternative protein interacting with each other.Of the 117 altProts encoded by pseudogenes,38 are direct interactors of reference proteins encoded by their respective parental genes.Finally,we experimentally validate several interactions involving altProts.These data improve the blueprints of the human PPI network and suggest functional roles for hundreds of altProts.

Systems understanding of plant-pathogen interactions through genome-wide protein-protein interaction networks

Plants are frequently affected by pathogen infections.To effectively defend against such infections,two major modes of innate immunity have evolved in plants;pathogen-associated molecular pattern-triggered immunity and effector-triggered immunity.Although the molecular components as well as the corresponding pathways involved in these two processes have been identified,many aspects of the molecular mechanisms of the plant immune system remain elusive.Recently,the rapid development of omics techniques(e.g.,genomics,proteomics and transcriptomics) has provided a great opportunity to explore plant–pathogen interactions from a systems perspective and studies on protein–protein interactions(PPIs) between plants and pathogens have been carried out and characterized at the network level.In this review,we introduce experimental and computational identification methods of PPIs,popular PPI network analysis approaches,and existing bioinformatics resources/tools related to PPIs.Then,we focus on reviewing the progress in genome-wide PPI networks related to plant–pathogen interactions,including pathogen-centric PPI networks,plant-centric PPI networks and interspecies PPI networks between plants and pathogens.We anticipate genome-wide PPI network analysis will provide a clearer understanding of plant–pathogen interactions and will offer some new opportunities for crop protection and improvement.

Molecular glues modulate protein functions by inducing protein aggregation:A promising therapeutic strategy of small molecules for disease treatment

Molecular glues can specifically induce aggregation between two or more proteins to modulate biological functions.In recent years,molecular glues have been widely used as protein degraders.In addition,however,molecular glues play a variety of vital roles,such as complex stabilization,interactome modulation and transporter inhibition,enabling challenging therapeutic targets to be druggable and offering an exciting novel approach for drug discovery.Since most molecular glues are identified serendipitously,exploration of their systematic discovery and rational design are important.In this review,representative examples of molecular glues with various physiological functions are divided into those mediating homo-dimerization,homo-polymerization and hetero-dimerization according to their aggregation modes,and we attempt to elucidate their mechanisms of action.In particular,we aim to highlight some biochemical techniques typically exploited within these representative studies and classify them in terms of three stages of molecular glue development:starting point,optimization and identification.

Tumor Necrosis Factor-alpha Induced Protein 3 Interacting Protein I Gene Polymorphisms and Pustular Psoriasis in Chinese Han Population

Background： Psoriasis is a common immune-mediated inflammatory dermatosis. Generalized pustular psoriasis （GPP） is the severe and rare type of psoriasis. The association between tumor necrosis factor-alpha induced protein 3 interacting protein 1 （TNIP1） gene and psoriasis was confirmed in people with multiple ethnicities. This study was to investigate the association between TNIP1 gene polymorphisms and pustular psoriasis in Chinese Hart population. Methods： Seventy-three patients with GPP, 67 patients with palmoplantar pustulosis （PPP）, and 476 healthy controls were collected from Chinese Hart population. Six single nucleotide polymorphisms （SNPs） of the TNIP1 gene, namely rs3805435, rs3792798, rs3792797, rs869976, rs 17728338, and rs999011 were genotyped by using polymerase chain reaction-ligase detection reaction. Statistical analyses were performed using the PLINK 1.07 package. Allele frequencies and genotyping frequencies for six SNPs were compared by using Chi-square test, odd ratio （OR） （including 95% confidence interval） were calculated. The haplotype analysis was conducted by Haploview software. Results： The frequencies of alleles of five SNPs were significantly different between the GPP group and the control group （P ≤ 7.22 × 10^-3）, especially in the GPP patients without psoriasis vulgaris （PsV）. In the haplotype analysis, the most significantly different haplotype was H4： ACGAAC, with 13.1% frequency in the GPP group but only 3.4% in the control group （OR = 4.16, P = 4.459 × 10^-7）. However, no significant difference in the allele frequencies was found between the PPP group and control group for each of the six SNPs （P 〉 0.05）. Conclusions： Polymorphisms in TNIP1 are associated with GPP in Chinese Han population. However, no association with PPP was found. These findings suggest that TNIPI might be a susceptibility gene for GPE

CacyBP/SIP nuclear translocation induced by gastrin promotes gastric cancer cell proliferation

AIM: To investigate the role of nuclear translocation of calcyclin binding protein, also called Siah-1 interacting protein (CacyBP/SIP), in gastric carcinogenesis.

Computational Screening of Phase-separating Proteins

Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells.Proteins that can undergo phase separation in cells share certain typical sequence features,like intrinsically disordered regions(IDRs)and multiple modular domains.Sequencebased analysis tools are commonly used in the screening of these proteins.However,current phase separation predictors are mostly designed for IDR-containing proteins,thus inevitably overlook the phase-separating proteins with relatively low IDR content.Features other than amino acid sequence could provide crucial information for identifying possible phase-separating proteins:protein–protein interaction(PPI)networks show multivalent interactions that underlie phase separation process;post-translational modifications(PTMs)are crucial in the regulation of phase separation behavior;spherical structures revealed in immunofluorescence(IF)images indicate condensed droplets formed by phase-separating proteins,distinguishing these proteins from non-phaseseparating proteins.Here,we summarize the sequence-based tools for predicting phaseseparating proteins and highlight the importance of incorporating PPIs,PTMs,and IF images into phase separation prediction in future studies.

Predicting overlapping protein complexes in weighted interactome networks

Protein complexes play important roles in integrating individual gene products to perform useful cellular functions.The increasing mount of protein–protein interaction(PPI)data has enabled us to predict protein complexes.In spite of the advances in these computational approaches and experimental techniques,it is impossible to construct an absolutely reliable PPI network.Taking into account the reliability of interactions in the PPI network,we have constructed a weighted protein–protein interaction(WPPI)network,in which the reliability of each interaction is represented as a weight using the topology of the PPI network.As overlaps are likely to have biological importance,we proposed a novel method named WN-PC(weighted network-based method for predicting protein complexes)to predict overlapping protein complexes on the WPPI network.The proposed algorithm predicts neighborhood graphs with an aggregation coefficient over a threshold as candidate complexes,and binds attachment proteins to candidate complexes.Finally,we have filtered redundant complexes which overlap other complexes to a very high extent in comparison to their density and size.A comprehensive comparison between competitive algorithms and our WN-PC method has been made in terms of the F-measure,coverage rate,and P-value.We have applied WN-PC to two different yeast PPI data sets,one of which is a huge PPI network consisting of over 6000 proteins and 200000 interactions.Experimental results show that WN-PC outperforms the state-of-the-art methods.We think that our research may be helpful for other applications in PPI networks.

Global Landscape of Native Protein Complexes in Synechocystis sp.PCC 6803

Synechocystis sp.PCC 6803(hereafter:Synechocystis)is a model organism for studying photosynthesis,energy metabolism,and environmental stress.Although known as the first fully sequenced phototrophic organism,Synechocystis still has almost half of its proteome without functional annotations.In this study,by using co-fractionation coupled with liquid chromatographytandem mass spectrometry(LC-MS/MS),we define 291 multi-protein complexes,encompassing24,092 protein±protein interactions(PPIs)among 2062 distinct gene products.This information not only reveals the roles of photosynthesis in metabolism,cell motility,DNA repair,cell division,and other physiological processes,but also shows how protein functions vary from bacteria to higher plants due to changes in interaction partners.It also allows us to uncover the functions of hypothetical proteins,such as Sll0445,Sll0446,and Sll0447 involved in photosynthesis and cell motility,and Sll1334 involved in regulation of fatty acid biogenesis.Here we present the most extensive PPI data for Synechocystis so far,which provide critical insights into fundamental molecular mechanisms in cyanobacteria.

Oligomerization of drug transporters:Forms,functions,and mechanisms

Drug transporters are essential players in the transmembrane transport of a wide variety of clinical drugs.The broad substrate spectra and versatile distribution pattern of these membrane proteins infer their pharmacological and clinical significance.With our accumulating knowledge on the three-dimensional structure of drug transporters,their oligomerization status has become a topic of intense study due to the possible functional roles carried out by such kind of post-translational modification(PTM).In-depth studies of oligomeric complexes formed among drug transporters as well as their interactions with other regulatory proteins can help us better understand the regulatory mechanisms of these membrane proteins,provide clues for the development of novel drugs,and improve the therapeutic efficacy.In this review,we describe different oligomerization forms as well as their structural basis of major drug transporters in the ATP-binding cassette and solute carrier superfamilies,summarize our current knowledge on the influence of oligomerization for protein expression level and transport function of these membrane proteins,and discuss the regulatory mechanisms of oligomerization.Finally,we highlight the challenges associated with the current oligomerization studies and propose some thoughts on the pharmaceutical application of this important drug transporter PTM.

Dissecting the molecular basis of spike traits by integrating gene regulatory networks and genetic variation in wheat

Spike architecture influences both grain weight and grain number per spike,which are the two major components of grain yield in bread wheat(Triticum aestivum L.).However,the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits.Here,we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat.We identified 170 loci that are responsible for variations in spike length,spikelet number per spike,and grain number per spike through genome-wide association study and meta-QTL analyses.We constructed gene regulatory networks for young inflorescences at the double ridge stage and thefloret primordium stage,in which the spikelet meristem and thefloret meristem are predominant,respec-tively,by integrating transcriptome,histone modification,chromatin accessibility,eQTL,and protein–pro-tein interactome data.From these networks,we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits.The functions of TaZF-B1,VRT-B2,and TaSPL15-A/D in establishment of wheat spike architecture were verified.This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.

NSD3S stabilizes MYC through hindering its interaction with FBXW7

The MYC transcription factor plays a key role in cell growth control. Enhanced MYC protein stability has been found to promote tumorigenesis. Thus, understanding how MYC stability is controlled may have significant implications for revealing MYC-driven growth regulatory mechanisms in physiological and pathological processes. Our previous work identified the histone lysine methyltransferase nuclear receptor binding SET domain protein 3 (NSD3) as a MYC modulator. NSD3S, a noncatalytic isoform of NSD3 with oncogenic activity, appears to bind, stabilize, and activate the transcriptional activity of MYC. However, the mechanism by which NSD3S stabilizes MYC remains to be elucidated. To uncover the nature of the interaction and the underlying mechanism of MYC regulation by NSD3S, we characterized the binding interface between both proteins by narrowing the interface to a 15-amino acid region in NSD3S that is partially required for MYC regulation. Mechanistically, NSD3S binds to MYC and reduces the association of F-box and WD repeat domain containing 7 (FBXW7) with MYC, which results in suppression of FBXW7-mediated proteasomal degradation of MYC and an increase in MYC protein half-life. These results support a critical role for NSD3S in the regulation of MYC function and provide a novel mechanism for NSD3S oncogenic function through inhibition of FBXW7-mediated degradation of MYC.

TICA: Transcriptional Interaction and Coregulation Analyzer

Transcriptional regulation is critical to cellular processes of all organisms. Regulatory mechanisms often involve more than one transcription factor(TF) from different families, binding together and attaching to the DNA as a single complex. However, only a fraction of the regulatory partners of each TF is currently known. In this paper, we present the Transcriptional Interaction and Coregulation Analyzer(TICA), a novel methodology for predicting heterotypic physical interaction of TFs. TICA employs a data-driven approach to infer interaction phenomena from chromatin immunoprecipitation and sequencing(ChIP-seq) data. Its prediction rules are based on the distribution of minimal distance couples of paired binding sites belonging to different TFs which are located closest to each other in promoter regions. Notably, TICA uses only binding site information from input ChIP-seq experiments, bypassing the need to do motif calling on sequencing data. We present our method and test it on ENCODE ChIP-seq datasets, using three cell lines as reference including HepG2, GM12878, and K562. TICA positive predictions on ENCODE ChIP-seq data are strongly enriched when compared to protein complex(CORUM) and functional interaction(BioGRID) databases. We also compare TICA against both motif/ChIP-seq based methods for physical TF–TF interaction prediction and published literature. Based on our results, TICA offers significant specificity(average 0.902) while maintaining a good recall(average 0.284) with respect to CORUM, providing a novel technique for fast analysis of regulatory effect in cell lines. Furthermore, predictions by TICA are complementary to other methods for TF–TF interaction prediction(in particular, TACO and CENTDIST). Thus, combined application of these prediction tools results in much improved sensitivity in detecting TF–TF interactions compared to TICA alone(sensitivity of 0.526 when combining TICA with TACO and 0.585 when combining with CENTDIST)with little compromise in specificity(specificity 0.760 when combining with TACO and 0.643 with CENTDIST). TICA is publicly available at http://geco.deib.polimi.it/tica/.

TRIB3 promotes pulmonary fibrosis through inhibiting SLUG degradation by physically interacting with MDM2

Pulmonary fibrosis(PF)is the pathological structure of incurable fibroproliferative lung diseases that are attributed to the repeated lung injury-caused failure of lung alveolar regeneration(LAR).Here,we report that repetitive lung damage results in a progressive accumulation of the transcriptional repressor SLUG in alveolar epithelial type II cells(AEC2s).The abnormal increased SLUG inhibits AEC2s from self-renewal and differentiation into alveolar epithelial type I cells(AEC1s).We found that the elevated SLUG represses the expression of the phosphate transporter SLC34A2 in AEC2s,which reduces intracellular phosphate and represses the phosphorylation of JNK and P38 MAPK,two critical kinases supporting LAR,leading to LAR failure.TRIB3,a stress sensor,interacts with the E3 ligase MDM2 to suppress SLUG degradation in AEC2s by impeding MDM2-catalyzed SLUG ubiquitination.Targeting SLUG degradation by disturbing the TRIB3/MDM2 interaction using a new synthetic staple peptide restores LAR capacity and exhibits potent therapeutic efficacy against experimental PF.Our study reveals a mechanism of the TRIB3—MDM2—SLUG—SLC34A2 axis causing the LAR failure in PF,which confers a potential strategy for treating patients with fibroproliferative lung diseases.

On the complexity of average path length for biological networks and patterns

Path length calculation is a frequent requirement in studies related to graph theoretic problems such as genetics. Standard method to calculate average path length （APL） of a graph requires traversing all nodes in the graph repeatedly, which is computationally expensive for graphs containing large number of nodes. We propose a novel method to calculate APL for graphs commonly required in the studies of genetics. The proposed method is computationally less expensive and less time-consuming compared to standard method.

Design of new traditional Chinese medicine herbal formulae for treatment of type 2 diabetes mellitus based on network pharmacology

In the present study, 28 Chinese medicinal herbs belonging to traditional Chinese medicine(TCM) for the treatment of type 2 diabetes were selected to explore the application of network pharmacology in developing new Chinese herbal medicine formulae for the treatment of type 2 diabetes mellitus(T2DM). These herbs have the highest appearance rate in the literature, and their compounds are listed. The human protein–protein interaction network and the T2DM disease protein interaction network were constructed. Then, the related algorithm for network topology was used to perform interventions on the interaction network of disease proteins and normal human proteins to test different Chinese herbal medicine compound combinations, according to the information on the interaction of compounds–targets in two databases, namely TarN et and the Medicinal Plants Database. Results of the intervention scores indicate that the method proposed in this study can provide new effective combinations of Chinese herbal medicines for T2DM. Network pharmacology can effectively promote the modernization and development of TCM.