Comprehensive comparative assessment of the Arabidopsis thaliana MLO2-CALMODULIN2 interaction by various in vitro and in vivo protein-protein interaction assays.

Mildew resistance locus o (MLO) proteins are heptahelical integral membrane proteins of which some isoforms act as susceptibility factors for the powdery mildew pathogen. In many angiosperm plant species, loss-of-function mlo mutants confer durable broad-spectrum resistance against the fungal disease. Barley Mlo is known to interact via a cytosolic carboxyl-terminal domain with the intracellular calcium sensor calmodulin (CAM) in a calcium-dependent manner. Site-directed mutagenesis has revealed key amino acid residues in the barley Mlo calmodulin-binding domain (CAMBD) that, when mutated, affect the MLO-CAM association. We here tested the respective interaction between Arabidopsis thaliana MLO2 and CAM2 using seven different types of in vitro and in vivo protein-protein interaction assays. In each assay, we deployed a wild-type version of either the MLO2 carboxyl terminus (MLO2CT), harboring the CAMBD, or the MLO2 full-length protein and corresponding mutant variants in which two key residues within the CAMBD were substituted by non-functional amino acids. We focused in particular on the substitution of two hydrophobic amino acids (LW/RR mutant) and found in most protein-protein interaction experiments reduced binding of CAM2 to the corresponding MLO2/MLO2CT-LW/RR mutant variants in comparison with the respective wild-type versions. However, the Ura3-based yeast split-ubiquitin system and in planta bimolecular fluorescence complementation (BiFC) assays failed to indicate reduced CAM2 binding to the mutated CAMBD. Our data shed further light on the interaction of MLO and CAM proteins and provide a comprehensive comparative assessment of different types of protein-protein interaction assays with wild-type and mutant versions of an integral membrane protein.

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks.

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein-protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

Assessment of community efforts to advance network-based prediction of protein-protein interactions.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Importance of Three-Body Problems and Protein-Protein Interactions in Proteolysis-Targeting Chimera Modeling: Insights from Molecular Dynamics Simulations.

Proteolysis-targeting chimeras (PROTACs) are a class of bifunctional molecules that can induce the ubiquitin degradation of its target protein by hijacking the E3 ligase to form a target protein-PROTAC-E3 ligase ternary complex. Its underlying principle has inspired the development of a wide range of protein degraders that are similar to or beyond PROTACs in recent years. The formation of the ternary complexes is the key to the success of PROTAC-induced protein degradation. Nevertheless, the lack of effective ternary complex modeling techniques has limited the application of computer-aided drug discovery tools to this emerging and fast developing new land in drug industry. Thus, in this study, we explored the application of the more physically sound molecular dynamics simulation and the molecular mechanics combined with the generalized Born and surface area continuum solvation (MM/GBSA) method to solve the underlying three-body problem in PROTAC modeling. We first verified the accuracy of our approach using a series of known Brd4 BD2 degraders. The calculated binding energy showed a good correlation with the experimental Kd values. The modeling of a unique property, namely, the α value, for PROTACs was also first and accurately performed to our best knowledge. The results also demonstrated the importance of PROTAC-induced protein-protein interactions in its modeling, either qualitatively or quantitatively. Finally, by standing on the success of earlier docking-based approaches, our protocol was also applied as a rescoring function in pose prediction. The results showed a notable improvement in reranking the initial poses generated from a modified Rosetta method, which was reportedly one of the best among a handful of PROTAC modeling approaches available in this field. We hope this work could provide a practical protocol and more insights to study the binding and the design of PROTACs and other protein degraders.

A rapid, efficient, and low-cost BiFC protocol and its application in studying in vivo interaction of seed-specific transcription factors, RISBZ and RPBF.

Proteins regulate cellular and biological processes in all living organisms. More than 80% of the proteins interact with one another to perform their respective functions; therefore, studying the protein-protein-interaction has gained attention in functional characterization studies. Bimolecular fluorescence complement (BiFC) assay is widely adopted to determine the physical interaction of two proteins in vivo. Here, we developed a simple, yet effective BiFC assay for protein-protein-interaction using transient Agrobacterium-mediated-transformation of onion epidermal cells by taking case study of Rice-P-box-Binding-Factor (RPBF) and rice-seed-specific-bZIP (RISBZ) in vivo interaction. Our result revealed that both the proteins, i.e., RISBZ and RPBF, interacted in the nucleus and cytosol. These two transcription factors are known for their coordinate/synergistic regulation of seed-protein content via concurrent binding to the promoter region of the seed storage protein (SSP) encoding genes. We further validated our results with BiFC assay in Nicotiana by agroinfiltration method, which exhibited similar results as Agrobacterium-mediated-transformation of onion epidermal cells. We also examined the subcellular localization of RISBZ and RPBF to assess the efficacy of the protocol. The subcellular localization and BiFC assay presented here is quite easy-to-follow, reliable, and reproducible, which can be completed within 2-3 days without using costly instruments and technologies that demand a high skill set.

Assessment of protein-protein interfaces in cryo-EM derived assemblies.

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein-protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.

On the interactions of the receptor-binding domain of SARS-CoV-1 and SARS-CoV-2 spike proteins with monoclonal antibodies and the receptor ACE2.

A new betacoronavirus named SARS-CoV-2 has emerged as a new threat to global health and economy. A promising target for both diagnosis and therapeutics treatments of the new disease named COVID-19 is the coronavirus (CoV) spike (S) glycoprotein. By constant-pH Monte Carlo simulations and the PROCEEDpKa method, we have mapped the electrostatic epitopes for four monoclonal antibodies and the angiotensin-converting enzyme 2 (ACE2) on both SARS-CoV-1 and the new SARS-CoV-2 S receptor binding domain (RBD) proteins. We also calculated free energy of interactions and shown that the S RBD proteins from both SARS viruses binds to ACE2 with similar affinities. However, the affinity between the S RBD protein from the new SARS-CoV-2 and ACE2 is higher than for any studied antibody previously found complexed with SARS-CoV-1. Based on physical chemical analysis and free energies estimates, we can shed some light on the involved molecular recognition processes, their clinical aspects, the implications for drug developments, and suggest structural modifications on the CR3022 antibody that would improve its binding affinities for SARS-CoV-2 and contribute to address the ongoing international health crisis.

Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network.

English Wikipedia, containing more than five millions articles, has approximately eleven thousands web pages devoted to proteins or genes most of which were generated by the Gene Wiki project. These pages contain information about interactions between proteins and their functional relationships. At the same time, they are interconnected with other Wikipedia pages describing biological functions, diseases, drugs and other topics curated by independent, not coordinated collective efforts. Therefore, Wikipedia contains a directed network of protein functional relations or physical interactions embedded into the global network of the encyclopedia terms, which defines hidden (indirect) functional proximity between proteins. We applied the recently developed reduced Google Matrix (REGOMAX) algorithm in order to extract the network of hidden functional connections between proteins in Wikipedia. In this network we discovered tight communities which reflect areas of interest in molecular biology or medicine and can be considered as definitions of biological functions shaped by collective intelligence. Moreover, by comparing two snapshots of Wikipedia graph (from years 2013 and 2017), we studied the evolution of the network of direct and hidden protein connections. We concluded that the hidden connections are more dynamic compared to the direct ones and that the size of the hidden interaction communities grows with time. We recapitulate the results of Wikipedia protein community analysis and annotation in the form of an interactive online map, which can serve as a portal to the Gene Wiki project.

Docking proteins and peptides under evolutionary constraints in Critical Assessment of PRediction of Interactions rounds 38 to 45.

Computational structural prediction of macromolecular interactions is a fundamental tool toward the global understanding of cellular processes. The Critical Assessment of PRediction of Interactions (CAPRI) community-wide experiment provides excellent opportunities for blind testing computational docking methods and includes original targets, thus widening the range of docking applications. Our participation in CAPRI rounds 38 to 45 enabled us to expand the way we include evolutionary information in structural predictions beyond our standard free docking InterEvDock pipeline. InterEvDock integrates a coarse-grained potential that accounts for interface coevolution based on joint multiple sequence alignments of two protein partners (co-alignments). However, even though such co-alignments could be built for none of the CAPRI targets in rounds 38 to 45, including host-pathogen and protein-oligosaccharide complexes and a redesigned interface, we identified multiple strategies that can be used to incorporate evolutionary constraints, which helped us to identify the most likely macromolecular binding modes. These strategies include template-based modeling where only local adjustments should be applied when query-template sequence identity is above 30% and larger perturbations are needed below this threshold; covariation-based structure prediction for individual protein partners; and the identification of evolutionarily conserved and structurally recurrent anchoring interface motifs. Overall, we submitted correct predictions among the top 5 models for 12 out of 19 interface challenges, including four High- and five Medium-quality predictions. Our top 20 models included correct predictions for three out of the five targets we missed in the top 5, including two targets for which misleading biological data led us to downgrade correct free docking models.

Systems pharmacology-based study of Tanreqing injection in airway mucus hypersecretion.

ETHNOPHARMACOLOGICAL RELEVANCE: Mucus hypersecretion (MH) is recognized as a key pathophysiological and clinical feature of many airway inflammatory diseases. MUC5AC is a major component of airway mucus. Tanreqing injection (TRQ) is a widely used herbal formula for the treatment of respiratory inflammations for years in China. However, a holistic network pharmacology approach to understanding its therapeutic mechanisms against MH has not been pursued. AIM OF THE STUDY: This study aimed to explore the systems-level potential active compounds and therapeutic mechanisms of TRQ in the treatment of MH. MATERIALS AND METHODS: We established systems pharmacology-based strategies comprising compound screenings, target predictions, and pathway identifications to speculate the potential active compounds and therapeutic targets of TRQ. We also applied compound-target and target-disease network analyses to evaluate the possible action mechanisms of TRQ. Then, lipopolysaccharide (LPS)-induced Sprague-Dawley (SD) rat model was constructed to assess the effect of TRQ in the treatment of MH and to validate the possible molecular mechanisms as predicted in systems pharmacology approach. RESULTS: The comprehensive compound collection successfully generated 55 compound candidates from TRQ. Among them, 11 compounds with high relevance to the potential targets were defined as representative and potential active ingredients in TRQ formula. Target identification revealed 172 potential targets, including pro-inflammatory cytokines of tumor necrosis factor α (TNF-α), interleukin (IL)-6, and IL-8. Pathway analyses uncovered the possible action of TRQ in the regulation of IL-17 signaling pathway and its downstream protein MUC5AC. Then in vivo experiment indicated that TRQ could significantly inhibit LPS stimulated MUC5AC over-production as well as the expression of TNF-α, IL-6, IL-8, and IL-17A, in both protein and mRNA levels. CONCLUSIONS: Based on the systems pharmacology method and in vivo experiment, our work provided a general knowledge on the potential active compounds and possible therapeutic targets of TRQ formula in its anti-MH process. This work might suggest directions for further research on TRQ and provide more insight into better understanding the chemical and pharmacological mechanisms of complex herbal prescriptions in a network perspective.

LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules.

BACKGROUND: Cross-species analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved interaction patterns. Identifying such conserved substructures between PPI networks of different species increases our understanding of the principles deriving evolution of cellular organizations and their functions in a system level. In recent years, network alignment techniques have been applied to genome-scale PPI networks to predict evolutionary conserved modules. Although a wide variety of network alignment algorithms have been introduced, developing a scalable local network alignment algorithm with high accuracy is still challenging. RESULTS: We present a novel pairwise local network alignment algorithm, called LePrimAlign, to predict conserved modules between PPI networks of three different species. The proposed algorithm exploits the results of a pairwise global alignment algorithm with many-to-many node mapping. It also applies the concept of graph entropy to detect initial cluster pairs from two networks. Finally, the initial clusters are expanded to increase the local alignment score that is formulated by a combination of intra-network and inter-network scores. The performance comparison with state-of-the-art approaches demonstrates that the proposed algorithm outperforms in terms of accuracy of identified protein complexes and quality of alignments. CONCLUSION: The proposed method produces local network alignment of higher accuracy in predicting conserved modules even with large biological networks at a reduced computational cost.

Detection of dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach.

The accessibility of a huge amount of protein-protein interaction (PPI) data has allowed to do research on biological networks that reveal the structure of a protein complex, pathways and its cellular organization. A key demand in computational biology is to recognize the modular structure of such biological networks. The detection of protein complexes from the PPI network, is one of the most challenging and significant problems in the post-genomic era. In Bioinformatics, the frequently employed approach for clustering the networks is Markov Clustering (MCL). Many of the researches for protein complex detection were done on the static PPI network, which suffers from a few drawbacks. To resolve this problem, this paper proposes an approach to detect the dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach (DMCL-EHO). Initially, the proposed method divides the PPI network into a set of dynamic subnetworks under various time points by combining the gene expression data and secondly, it employs the clustering analysis on every subnetwork using the MCL along with Elephant Herd Optimization approach. The experimental analysis was employed on different PPI network datasets and the proposed method surpasses various existing approaches in terms of accuracy measures. This paper identifies the common protein complexes that are expressively enriched in gold-standard datasets and also the pathway annotations of the detected protein complexes using the KEGG database.

Systematic assessment of antibody selectivity in plasma based on a resource of enrichment profiles.

There is a strong need for procedures that enable context and application dependent validation of antibodies. Here, we applied a magnetic bead assisted workflow and immunoprecipitation mass spectrometry (IP-MS/MS) to assess antibody selectivity for the detection of proteins in human plasma. A resource was built on 414 IP experiments using 157 antibodies (targeting 120 unique proteins) in assays with heat-treated or untreated EDTA plasma. For each protein we determined their antibody related degrees of enrichment using z-scores and their frequencies of identification across all IP assays. Out of 1,313 unique endogenous proteins, 426 proteins (33%) were detected in >20% of IPs, and these background components were mainly comprised of proteins from the complement system. For 45% (70/157) of the tested antibodies, the expected target proteins were enriched (z-score ≥ 3). Among these 70 antibodies, 59 (84%) co-enriched other proteins beside the intended target and mainly due to sequence homology or protein abundance. We also detected protein interactions in plasma, and for IGFBP2 confirmed these using several antibodies and sandwich immunoassays. The protein enrichment data with plasma provide a very useful and yet lacking resource for the assessment of antibody selectivity. Our insights will contribute to a more informed use of affinity reagents for plasma proteomics assays.

Protein Interaction Z Score Assessment (PIZSA): an empirical scoring scheme for evaluation of protein-protein interactions.

Our web server, PIZSA (http://cospi.iiserpune.ac.in/pizsa), assesses the likelihood of protein-protein interactions by assigning a Z Score computed from interface residue contacts. Our score takes into account the optimal number of atoms that mediate the interaction between pairs of residues and whether these contacts emanate from the main chain or side chain. We tested the score on 174 native interactions for which 100 decoys each were constructed using ZDOCK. The native structure scored better than any of the decoys in 146 cases and was able to rank within the 95th percentile in 162 cases. This easily outperforms a competing method, CIPS. We also benchmarked our scoring scheme on 15 targets from the CAPRI dataset and found that our method had results comparable to that of CIPS. Further, our method is able to analyse higher order protein complexes without the need to explicitly identify chains as receptors or ligands. The PIZSA server is easy to use and could be used to score any input three-dimensional structure and provide a residue pair-wise break up of the results. Attractively, our server offers a platform for users to upload their own potentials and could serve as an ideal testing ground for this class of scoring schemes.

Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields.

The understanding of subcellular localization (SCL) of proteins and proteome variation in the different tissues and organs of the human body are two crucial aspects for increasing our knowledge of the dynamic rules of proteins, the cell biology, and the mechanism of diseases. Although there have been tremendous contributions to these two fields independently, the lack of knowledge of the variation of spatial distribution of proteins in the different tissues still exists. Here, we proposed an approach that allows predicting protein SCL on tissue specificity through the use of tissue-specific functional associations and physical protein-protein interactions (PPIs). We applied our previously developed Bayesian collective Markov random fields (BCMRFs) on tissue-specific protein-protein interaction network (PPI network) for nine types of tissues focusing on eight high-level SCL. The evaluated results demonstrate the strength of our approach in predicting tissue-specific SCL. We identified 1,314 proteins that their SCL were previously proven cell line dependent. We predicted 549 novel tissue-specific localized candidate proteins while some of them were validated via text-mining.

Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods.

The identification of plant-pathogen protein-protein interactions (PPIs) is an attractive and challenging research topic for deciphering the complex molecular mechanism of plant immunity and pathogen infection. Considering that the experimental identification of plant-pathogen PPIs is time-consuming and labor-intensive, computational methods are emerging as an important strategy to complement the experimental methods. In this work, we first evaluated the performance of traditional computational methods such as interolog, domain-domain interaction and domain-motif interaction in predicting known plant-pathogen PPIs. Owing to the low sensitivity of the traditional methods, we utilized Random Forest to build an inter-species PPI prediction model based on multiple sequence encodings and novel network attributes in the established plant PPI network. Critical assessment of the features demonstrated that the integration of sequence information and network attributes resulted in significant and robust performance improvement. Additionally, we also discussed the influence of Gene Ontology and gene expression information on the prediction performance. The Web server implementing the integrated prediction method, named InterSPPI, has been made freely available at http://systbio.cau.edu.cn/intersppi/index.php. InterSPPI could achieve a reasonably high accuracy with a precision of 73.8% and a recall of 76.6% in the independent test. To examine the applicability of InterSPPI, we also conducted cross-species and proteome-wide plant-pathogen PPI prediction tests. Taken together, we hope this work can provide a comprehensive understanding of the current status of plant-pathogen PPI predictions, and the proposed InterSPPI can become a useful tool to accelerate the exploration of plant-pathogen interactions.

Structure-based assessment and network analysis of targeting 14-3-3 proteins in prostate cancer.

Developing combination therapy for castrate-resistant prostate cancer (CRPC) may require exploiting new drug targets outside androgen receptor and PI3K / AKT / mTOR signal transduction pathways implicated in prostate cancer (PCa) progression. One such possible new target is YWHAZ of the 14-3-3 protein family as this gene has prognostic significance for metastatic CRPC patients. However, there are no small molecules targeting YWHAZ commercially available. Hence, we explored whether the small molecule BV02 targeting another 14-3-3 protein family member SFN also binds to YWHAZ. Using advanced docking algorithms we find that BV02 docks many other 14-3-3 family members. In addition, the amphipathic groove where drug binding occurs also has a high binding affinity for other drugs used to treat PCa such as docetaxel. The proteome of metastatic PCa models (LNCaP clone FGC and PC-3) was perturbed as a result of BV02 treatment. Through data integration of three proteomics data sets we found that BV02 modulates numerous protein-protein interactions involving 14-3-3 proteins in our PCa models.

Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment.

Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein⁻protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein⁻protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.

PrimAlign: PageRank-inspired Markovian alignment for large biological networks.

Motivation: Cross-species analysis of large-scale protein-protein interaction (PPI) networks has played a significant role in understanding the principles deriving evolution of cellular organizations and functions. Recently, network alignment algorithms have been proposed to predict conserved interactions and functions of proteins. These approaches are based on the notion that orthologous proteins across species are sequentially similar and that topology of PPIs between orthologs is often conserved. However, high accuracy and scalability of network alignment are still a challenge. Results: We propose a novel pairwise global network alignment algorithm, called PrimAlign, which is modeled as a Markov chain and iteratively transited until convergence. The proposed algorithm also incorporates the principles of PageRank. This approach is evaluated on tasks with human, yeast and fruit fly PPI networks. The experimental results demonstrate that PrimAlign outperforms several prevalent methods with statistically significant differences in multiple evaluation measures. PrimAlign, which is multi-platform, achieves superior performance in runtime with its linear asymptotic time complexity. Further evaluation is done with synthetic networks and results suggest that popular topological measures do not reflect real precision of alignments. Availability and implementation: The source code is available at http://web.ecs.baylor.edu/faculty/cho/PrimAlign. Supplementary information: Supplementary data are available at Bioinformatics online.

Identification of key genes in non­alcoholic fatty liver disease progression based on bioinformatics analysis.

Due to economic development and lifestyle changes, the incidence of non­alcoholic fatty liver disease (NAFLD) has gradually increased in recent years. However, the pathogenesis of NAFLD is not yet fully understood. To identify candidate genes that contribute to the development and progression of NAFLD, two microarray datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified and functional enrichment analyses were performed. A protein­protein interaction network was constructed and modules were extracted using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. The enriched functions and pathways of the DEGs included 'cellular macromolecule biosynthetic process', 'cellular response to chemical stimulus', 'extracellular matrix organization', 'metabolic pathways', 'insulin resistance' and 'forkhead box protein O1 signaling pathway'. The DEGs, including type­1 angiotensin II receptor, formin­binding protein 1­like, RNA­binding protein with serine­rich domain 1, Ras­related C3 botulinum toxin substrate 1 and polyubiquitin­C, were identified using multiple bioinformatics methods and validated in vitro with reverse transcription­quantitative polymerase chain reaction analysis. In conclusion, five hub genes were identified in the present study, and they may aid in understanding of the molecular mechanisms underlying the development and progression of NAFLD.