Dynamic Protein-protein Interactions Research Articles

Interrogation of RNA-protein interaction dynamics in bacterial growth

Characterising RNA–protein interaction dynamics is fundamental to understand how bacteria respond to their environment. In this study, we have analysed the dynamics of 91% of the Escherichia coli expressed proteome and the RNA-interaction properties of 271 RNA-binding proteins (RBPs) at different growth phases. We find that 68% of RBPs differentially bind RNA across growth phases and characterise 17 previously unannotated proteins as bacterial RBPs including YfiF, a ncRNA-binding protein. While these new RBPs are mostly present in Proteobacteria, two of them are orthologs of human mitochondrial proteins associated with rare metabolic disorders. Moreover, we reveal novel RBP functions for proteins such as the chaperone HtpG, a new stationary phase tRNA-binding protein. For the first time, the dynamics of the bacterial RBPome have been interrogated, showcasing how this approach can reveal the function of uncharacterised proteins and identify critical RNA–protein interactions for cell growth which could inform new antimicrobial therapies.

Disrupted protein interaction dynamics in a genetic neurodevelopmental disorder revealed by structural bioinformatics and genetic code expansion.

Deciphering the structural effects of gene variants is essential for understanding the pathophysiological mechanisms of genetic diseases. Using a neurodevelopmental disorder called Bosch-Boonstra-Schaaf Optic Atrophy Syndrome (BBSOAS) as a genetic disease model, we applied structural bioinformatics and Genetic Code Expansion (GCE) strategies to assess the pathogenic impact of human NR2F1 variants and their binding with known and novel partners. While the computational analyses of the NR2F1 structure delineated the molecular basis of the impact of several variants on the isolated and complexed structures, the GCE enabled covalent and site-specific capture of transient supramolecular interactions in living cells. This revealed the variable quaternary conformations of NR2F1 variants and highlighted the disrupted interplay with dimeric partners and the newly identified co-factor, CRABP2. The disclosed consequence of the pathogenic mutations on the conformation, supramolecular interplay, and alterations in the cell cycle, viability, and sub-cellular localization of the different variants reflect the heterogeneous disease spectrum of BBSOAS and set up novel foundation for unveiling the complexity of neurodevelopmental diseases.

In Vivo Detection of Metabolic Fluctuations in Real Time Using the NanoBiT Technology Based on PII Signalling Protein Interactions.

New protein-fragment complementation assays (PCA) have successfully been developed to characterize protein-protein interactions in vitro and in vivo. Notably, the NanoBiT technology, employing fragment complementation of NanoLuc luciferase, stands out for its high sensitivity, wide dynamic range, and straightforward read out. Previously, we explored the in vitro protein interaction dynamics of the PII signalling protein using NanoBiT, revealing significant modulation of luminescence signals generated by the interaction between PII and its receptor protein NAGK by 2-oxoglutarate levels. In the current work, we investigated this technology in vivo, to find out whether recombinantly expressed NanoBiT constructs using the NanoLuc large fragment fused to PII and PII-interaction partners NAGK or PipX-fused to the NanoLuc Small BiT are capable of detecting the metabolic fluctuations in Escherichia coli. Therefore, we devised an assay capable of capturing the metabolic responses of E. coli cells, demonstrating real-time metabolic perturbation upon nitrogen upshift or depletion treatments. In particular, the PII-NAGK NanoBitT sensor pair reported these changes in a highly sensitive manner.

A transient in planta editing assay identifies specific binding of the splicing regulator PTB as a prerequisite for cassette exon inclusion

The dynamic interaction of RNA-binding proteins (RBPs) with their target RNAs contributes to the diversity of ribonucleoprotein (RNP) complexes that are involved in a myriad of biological processes. Identifying the RNP components at high resolution and defining their interactions are key to understanding their regulation and function. Expressing fusions between an RBP of interest and an RNA editing enzyme can result in nucleobase changes in target RNAs, representing a recent addition to experimental approaches for profiling RBP/RNA interactions. Here, we have used the MS2 protein/RNA interaction to test four RNA editing proteins for their suitability to detect target RNAs of RBPs in planta. We have established a transient test system for fast and simple quantification of editing events and identified the hyperactive version of the catalytic domain of an adenosine deaminase (hADARcd) as the most suitable editing enzyme. Examining fusions between homologs of polypyrimidine tract binding proteins (PTBs) from Arabidopsis thaliana and hADARcd allowed determining target RNAs with high sensitivity and specificity. Moreover, almost complete editing of a splicing intermediate provided insight into the order of splicing reactions and PTB dependency of this particular splicing event. Addition of sequences for nuclear localisation of the fusion protein increased the editing efficiency, highlighting this approach’s potential to identify RBP targets in a compartment-specific manner. Our studies have established the editing-based analysis of interactions between RBPs and their RNA targets in a fast and straightforward assay, offering a new system to study the intricate composition and functions of plant RNPs in vivo.

SRC family kinase inhibition rescues molecular and behavioral phenotypes, but not protein interaction network dynamics, in a mouse model of Fragile X syndrome.

Glutamatergic synapses encode information from extracellular inputs using dynamic protein interaction networks (PINs) that undergo widespread reorganization following synaptic activity, allowing cells to distinguish between signaling inputs and generate coordinated cellular responses. Here, we investigate how Fragile X Messenger Ribonucleoprotein (FMRP) deficiency disrupts signal transduction through a glutamatergic synapse PIN downstream of NMDA receptor or metabotropic glutamate receptor (mGluR) stimulation. In cultured cortical neurons or acute cortical slices from P7, P17 and P60 FMR1-/y mice, the unstimulated protein interaction network state resembled that of wildtype littermates stimulated with mGluR agonists, demonstrating resting state pre-activation of mGluR signaling networks. In contrast, interactions downstream of NMDAR stimulation were similar to WT. We identified the Src family kinase (SFK) Fyn as a network hub, because many interactions involving Fyn were pre-activated in FMR1-/y animals. We tested whether targeting SFKs in FMR1-/y mice could modify disease phenotypes, and found that Saracatinib (SCB), an SFK inhibitor, normalized elevated basal protein synthesis, novel object recognition memory and social behavior in FMR1-/y mice. However, SCB treatment did not normalize the PIN to a wild-type-like state in vitro or in vivo, but rather induced extensive changes to protein complexes containing Shank3, NMDARs and Fyn. We conclude that targeting abnormal nodes of a PIN can identify potential disease-modifying drugs, but behavioral rescue does not correlate with PIN normalization.

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 Z\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {Z}$$\\end{document}. 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.

Single-Molecule Measurement of Protein Interaction Dynamics within Biomolecular Condensates.

Biomolecular condensates formed via liquid-liquid phase separation (LLPS) have been considered critical in cellular organization and an increasing number of cellular functions. Characterizing LLPS in live cells is also important because aberrant condensation has been linked to numerous diseases, including cancers and neurodegenerative disorders. LLPS is often driven by selective, transient, and multivalent interactions between intrinsically disordered proteins. Of great interest are the interaction dynamics of proteins participating in LLPS, which are well-summarized by measurements of their binding residence time (RT), that is, the amount of time they spend bound within condensates. Here, we present a method based on live-cell single-molecule imaging that allows us to measure the mean RT of a specific protein within condensates. We simultaneously visualize individual protein molecules and the condensates with which they associate, use single-particle tracking (SPT) to plot single-molecule trajectories, and then fit the trajectories to a model of protein-droplet binding to extract the mean RT of the protein. Finally, we show representative results where this single-molecule imaging method was applied to compare the mean RTs of a protein at its LLPS condensates when fused and unfused to an oligomerizing domain. This protocol is broadly applicable to measuring the interaction dynamics of any protein that participates in LLPS.

Mammalian circadian clock proteins form dynamic interacting microbodies distinct from phase separation.

Liquid-liquid phase separation (LLPS) underlies diverse biological processes. Because most LLPS studies were performed in vitro using recombinant proteins or in cells that overexpress protein, the physiological relevance of LLPS for endogenous protein is often unclear. PERIOD, the intrinsically disordered domain-rich proteins, are central mammalian circadian clock components and interact with other clock proteins in the core circadian negative feedback loop. Different core clock proteins were previously shown to form large complexes. Circadian clock studies often rely on experiments that overexpress clock proteins. Here, we show that when Per2 transgene was stably expressed in cells, PER2 protein formed nuclear phosphorylation-dependent slow-moving LLPS condensates that recruited other clock proteins. Super-resolution microscopy of endogenous PER2, however, revealed formation of circadian-controlled, rapidly diffusing nuclear microbodies that were resistant to protein concentration changes, hexanediol treatment, and loss of phosphorylation, indicating that they are distinct from the LLPS condensates caused by protein overexpression. Surprisingly, only a small fraction of endogenous PER2 microbodies transiently interact with endogenous BMAL1 and CRY1, a conclusion that was confirmed in cells and in mice tissues, suggesting an enzyme-like mechanism in the circadian negative feedback process. Together, these results demonstrate that the dynamic interactions of core clock proteins are a key feature of mammalian circadian clock mechanism and the importance of examining endogenous proteins in LLPS and circadian clock studies.

Interaction networks of Escherichia coli replication proteins under different bacterial growth conditions

In this work we analyzed protein-protein interactions (PPIs) formed by E. coli replication proteins under three disparate bacterial growth conditions. The chosen conditions corresponded to fast exponential growth, slow exponential growth and growth cessation at the stationary phase. We performed affinity purification coupled with mass spectrometry (AP-MS) of chromosomally expressed proteins (DnaA, DnaB, Hda, SeqA, DiaA, DnaG, HolD, NrdB), tagged with sequential peptide affinity (SPA) tag. Composition of protein complexes was characterized using MaxQuant software. To filter out unspecific interactions, we employed double negative control system and we proposed qualitative and quantitative data analysis strategies that can facilitate hits identification in other AP-MS datasets. Our motivation to undertake this task was still insufficient understanding of molecular mechanisms coupling DNA replication to cellular growth. Previous works suggested that such control mechanisms could involve physical interactions of replication factors with metabolic or cell envelope proteins. However, the dynamic replication protein interaction network (PIN) obtained in this study can be used to characterize links between DNA replication and various cellular processes in other contexts.

Synthesis, spectroscopic analysis, DFT, docking, MD and antioxidant activity of tetrahydrocurcumin

In recent years, numerous researchers have made local chemical modifications to the structure of curcumin while its basic structure remains unchanged, thus, producing curcumin derivatives. In this article, tetrahydrocurcumin was obtained by hydrogenation of curcumin, DFT calculation and characterization at the theoretical level of B3LYP/6 -311++G(d,p) were carried out. The observed IR and Raman spectra are in good agreement with the theoretical spectra. The FMO and ESP of tetrahydrocurcumin are predicted. The interaction in the system is shown graphically and analyzed by IGMH. Compared with curcumin, tetrahydrocurcumin lacks the unsaturated C = C bond, which makes it more stable and more bioavailable. Molecular docking with antioxidant targets elucidated the ligand–protein interaction and molecular dynamics simulation showed the antioxidant activity of tetrahydrocurcumin. The antioxidant activity of tetrahydrocurcumin was proved by DPPH• and •OH radical scavenging experiments. In essence, these derivatives exhibit enhanced physiological activity in certain aspects compared to the original curcumin. Moreover, the computational pharmacology techniques lay a theoretical groundwork for the development and modification of high-efficiency, low-toxicity drugs that interface with various targets of curcumin in the future. Communicated by Ramaswamy H. Sarma

Experimental traumatic brain injury increases epichaperome formation

Under normal conditions, heat shock proteins work in unison through dynamic protein interactions collectively referred to as the “chaperome.” Recent work revealed that during cellular stress, the functional interactions of the chaperome are modified to form the “epichaperome,” which results in improper protein folding, degradation, aggregation, and transport. This study is the first to investigate this novel mechanism of protein dishomeostasis in traumatic brain injury (TBI). Male and female adult, Sprague-Dawley rats received a lateral controlled cortical impact (CCI) and the ipsilateral hippocampus was collected 24 h 1, 2, and 4 weeks after injury. The epichaperome complex was visualized by measuring HSP90, HSC70 and HOP expression in native-PAGE and normalized to monomeric protein expression. A two-way ANOVA examined the effect of injury and sex at each time-point. Native HSP90, HSC70 and HOP protein expression showed a significant effect of injury effect across all time-points. Additionally, HSC70 and HOP showed significant sex effects at 24 h and 4 weeks. Altogether, controlled cortical impact significantly increased formation of the epichaperome across all proteins measured. Further investigation of this pathological mechanism can lead to a greater understanding of the link between TBI and increased risk of neurodegenerative disease and targeting the epichaperome for therapeutics.

Structural Dynamics of Protein Interactions Using Site-Directed Spin Labeling of Cysteines to Measure Distances and Rotational Dynamics with EPR Spectroscopy.

Here we review applications of site-directed spin labeling (SDSL) with engineered cysteines in proteins, to study the structural dynamics of muscle and non-muscle proteins, using and developing the electron paramagnetic resonance (EPR) spectroscopic techniques of dipolar EPR, double electron electron resonance (DEER), saturation transfer EPR (STEPR), and orientation measured by EPR. The SDSL technology pioneered by Wayne Hubbell and collaborators has greatly expanded the use of EPR, including the measurement of distances between spin labels covalently attached to proteins and peptides. The Thomas lab and collaborators have applied these techniques to elucidate dynamic interactions in the myosin-actin complex, myosin-binding protein C, calmodulin, ryanodine receptor, phospholamban, utrophin, dystrophin, β-III-spectrin, and Aurora kinase. The ability to design and engineer cysteines in proteins for site-directed covalent labeling has enabled the use of these powerful EPR techniques to measure distances, while showing that they are complementary with optical spectroscopy measurements.

Bioinformatics, thermodynamics, and mechanical resistance of the FtsZ-ZipA complex of Escherichia coli supports a highly dynamic protein interaction in the divisome

In most microorganisms, cell division is guided by the divisome, a multiprotein complex that assembles at the equator of the cell and is responsible for the synthesis of new cell wall material. FtsZ, the first protein to assemble into this complex forms protofilaments in the cytosol which are anchored to the inner side of the cytosolic membrane by the proteins ZipA and FtsA. FtsZ protofilaments generate a force that deforms the cytosolic membrane and may contribute to the constriction force that leads to the septation of the cell. It has not been studied yet how the membrane protein anchors respond to this force generated by FtsZ. Here we studied the effect of force in the FtsZ-ZipA interaction. We used SMD and obtained the distance to the transition state of key interacting amino acids and SASA of FtsZ and ZipA through the dissociation. The SMD mechanism was corroborated by ITC, and the thermodynamic parameters ΔG0, ΔH0 and ΔS0 were obtained. Finally, we used force spectroscopy by optical tweezers to determine the lifetime of the interaction and rupture probability and their dependence on force at single molecule level. We also obtained the transition state distance, and free energy of the interaction. With the gathering of structural, thermodynamic, kinetic and force parameters we conclude that interaction between FtsZ and ZipA proteins is consistence with the highly dynamic treadmilling process and at least seven ZipA molecules are required to bind to a FtsZ protofilaments to transduce a significant force.

A real-time fluorescent gp32 probe-based assay for monitoring single-stranded DNA-dependent DNA processing enzymes

Single-stranded DNA (ssDNA) generated during DNA replication, recombination and damage repair reactions is an important intermediate and ssDNA-binding proteins that binds these intermediates coordinate various DNA metabolic processes. Mechanistic details of these ssDNA-dependent processes can be explored by monitoring the generation and consumption of ssDNA in real time. In this work, a fluorescein-labeled gp32-based sensor was employed to continuously monitor various aspects of ssDNA-dependent DNA replication and recombination processes in real time. The gp32 protein probe displayed high sensitivity and specificity to a variety of ssDNA-dependent processes of T4 phage. Several applications of the probe are illustrated here: the solution dynamics of ssDNA-binding protein, protein-protein and protein-DNA interactions involving gp32 protein and its mode of interaction, ssDNA translocation and protein displacement activities of helicases, primer extension activity of DNA polymerase holoenzyme and nucleoprotein filament formation during DNA recombination. The assay has identified new protein-protein interactions of gp32 during T4 replication and recombination. The fluorescent probe described here can thus be used as a universal probe for monitoring in real time various ssDNA-dependent processes, which is based on a well-characterized and easy-to-express bacteriophage T4 gene 32 protein, gp32.

Synchronization of fractional-order repressilatory genetic oscillators with time delay

Genetic oscillators have been widely used in modeling key processes of biological systems, especially cell cycles and circadian rhythms. In particular, repressilatory genetic oscillators have been employed in modeling the dynamics of mRNA and protein interactions with transcriptional and translational feedback loops at the molecular level. In addition, synchronization of these oscillators is crucial for understanding the underlying mechanisms of the associated biological processes. In this paper, models of fractional-order genetic oscillators and their coupling are established, where the aspects of time delay, coupling strength, noise, and stability are all taken into consideration. Communication in the proposed coupling model is based on quorum sensing. The synchronization of the fractional-order repressilator model has been examined through simulations which show three main findings. Firstly, the synchronization of the fractional-order repressilator model can be optimized through coupling weight selection. Secondly, the synchronization can be enhanced by increasing the fractional order and decreasing the time delay and the noise intensity. Finally, transitions between the states of the fractional-order repressilatory oscillator can be achieved through varying the fractional order. The simulation results verify the biological relevance of the genetic oscillator models, and their potential for explaining the underlying mechanisms of the associated biological processes.

PLA-GNN: Computational inference of protein subcellular location alterations under drug treatments with deep graph neural networks

The aberrant protein sorting has been observed in many conditions, including complex diseases, drug treatments, and environmental stresses. It is important to systematically identify protein mis-localization events in a given condition. Experimental methods for finding mis-localized proteins are always costly and time consuming. Predicting protein subcellular localizations has been studied for many years. However, only a handful of existing works considered protein subcellular location alterations. We proposed a computational method for identifying alterations of protein subcellular locations under drug treatments. We took three drugs, including TSA (trichostain A), bortezomib and tacrolimus, as instances for this study. By introducing dynamic protein-protein interaction networks, graph neural network algorithms were applied to aggregate topological information under different conditions. We systematically reported potential protein mis-localization events under drug treatments. As far as we know, this is the first attempt to find protein mis-localization events computationally in drug treatment conditions. Literatures validated that a number of proteins, which are highly related to pharmacological mechanisms of these drugs, may undergo protein localization alterations. We name our method as PLA-GNN (Protein Localization Alteration by Graph Neural Networks). It can be extended to other drugs and other conditions. All datasets and codes of this study has been deposited in a GitHub repository (https://github.com/quinlanW/PLA-GNN).

Molecular Dynamics Simulations of the Proteins Regulating Synaptic Vesicle Fusion

Neuronal transmitters are packaged in synaptic vesicles (SVs) and released by the fusion of SVs with the presynaptic membrane (PM). An inflow of Ca2+ into the nerve terminal triggers fusion, and the SV-associated protein Synaptotagmin 1 (Syt1) serves as a Ca2+ sensor. In preparation for fusion, SVs become attached to the PM by the SNARE protein complex, a coiled-coil bundle that exerts the force overcoming SV-PM repulsion. A cytosolic protein Complexin (Cpx) attaches to the SNARE complex and differentially regulates the evoked and spontaneous release components. It is still debated how the dynamic interactions of Syt1, SNARE proteins and Cpx lead to fusion. This problem is confounded by heterogeneity in the conformational states of the prefusion protein-lipid complex and by the lack of tools to experimentally monitor the rapid conformational transitions of the complex, which occur at a sub-millisecond scale. However, these complications can be overcome employing molecular dynamics (MDs), a computational approach that enables simulating interactions and conformational transitions of proteins and lipids. This review discusses the use of molecular dynamics for the investigation of the pre-fusion protein-lipid complex. We discuss the dynamics of the SNARE complex between lipid bilayers, as well as the interactions of Syt1 with lipids and SNARE proteins, and Cpx regulating the assembly of the SNARE complex.

A Novel Temporal Network-Embedding Algorithm for Link Prediction in Dynamic Networks.

Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical implications. This research aims to enhance our understanding of the evolution of networks by formulating and solving the link-prediction problem for temporal networks using graph representation learning as an advanced machine learning approach. Learning useful representations of nodes in these networks provides greater predictive power with less computational complexity and facilitates the use of machine learning methods. Considering that existing models fail to consider the temporal dimensions of the networks, this research proposes a novel temporal network-embedding algorithm for graph representation learning. This algorithm generates low-dimensional features from large, high-dimensional networks to predict temporal patterns in dynamic networks. The proposed algorithm includes a new dynamic node-embedding algorithm that exploits the evolving nature of the networks by considering a simple three-layer graph neural network at each time step and extracting node orientation by using Given's angle method. Our proposed temporal network-embedding algorithm, TempNodeEmb, is validated by comparing it to seven state-of-the-art benchmark network-embedding models. These models are applied to eight dynamic protein-protein interaction networks and three other real-world networks, including dynamic email networks, online college text message networks, and human real contact datasets. To improve our model, we have considered time encoding and proposed another extension to our model, TempNodeEmb++. The results show that our proposed models outperform the state-of-the-art models in most cases based on two evaluation metrics.

Gd3+-Trityl-Nitroxide Triple Labeling and Distance Measurements in the Heterooligomeric Cobalamin Transport Complex in the Native Lipid Bilayers.

Increased efforts are being made for observing proteins in their native environments. Pulsed electron-electron double resonance spectroscopy (PELDOR, also known as DEER) is a powerful tool for this purpose. Conventionally, PELDOR employs an identical spin pair, which limits the output to a single distance for monomeric samples. Here, we show that the Gd3+-trityl-nitroxide (NO) three-spin system is a versatile tool to study heterooligomeric membrane protein complexes, even within their native membrane. This allowed for an independent determination of four different distances (Gd3+-trityl, Gd3+-NO, trityl-NO, and Gd3+-Gd3+) within the same sample. We demonstrate the feasibility of this approach by observing sequential ligand binding and the dynamics of complex formation in the cobalamin transport system involving four components (cobalamin, BtuB, TonB, and BtuF). Our results reveal that TonB binding alone is sufficient to release cobalamin from BtuB in the native asymmetric bilayers. This approach provides a potential tool for the structural and quantitative analysis of dynamic protein-protein interactions in oligomeric complexes, even within their native surroundings.

A Construction Method of Dynamic Protein Interaction Networks by Using Relevant Features of Gene Expression Data.

Essential proteins play an important role in various life activities and are considered to be a vital part of the organism. Gene expression data are an important dataset to construct dynamic protein-protein interaction networks (DPIN). The existing methods for the construction of DPINs generally utilize all features (or the features in a cycle) of the gene expression data. However, the features observed from successive time points tend to be highly correlated, and thus there are some redundant and irrelevant features in the gene expression data, which will influence the quality of the constructed network and the predictive performance of essential proteins. To address this problem, we propose a construction method of DPINs by using selected relevant features rather than continuous and periodic features. We adopt an improved unsupervised feature selection method based on Laplacian algorithm to remove irrelevant and redundant features from gene expression data, then integrate the chosen relevant features into the static protein-protein interaction network (SPIN) to construct a more concise and effective DPIN (FS-DPIN). To evaluate the effectiveness of the FS-DPIN, we apply 15 network-based centrality methods on the FS-DPIN and compare the results with those on the SPIN and the existing DPINs. Then the predictive performance of the 15 centrality methods is validated in terms of sensitivity, specificity, positive predictive value, negative predictive value, F-measure, accuracy, Jackknife and AUPRC. The experimental results show that the FS-DPIN is superior to the existing DPINs in the identification accuracy of essential proteins.